diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json index f99f61ce4..31e207735 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json @@ -31,7 +31,7 @@ "properties": { "title": "USGSHABs1", "description": "All forecasts for the Daily_Chlorophyll_a variable for the USGSHABs1 model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BLWA, TOMB, FLNT.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-12T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json index a391bebc8..f46e76e3a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json @@ -55,7 +55,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Chlorophyll_a variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json index 2b064c2a3..df8e78a3f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -59,7 +59,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BARC, BLWA, FLNT, SUGG, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json index 57160e93f..33a949394 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -59,7 +59,7 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Chlorophyll_a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json index 9fb4cbe7c..591076f93 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procBlanchardMonod", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procBlanchardMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json index 0096a9077..71867b6a4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procCTMIMonod", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procCTMIMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json index 96385b935..93f0903c1 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procEppleyNorbergMonod", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procEppleyNorbergMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json index 674a7c6ac..77e4dba1a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json @@ -47,7 +47,7 @@ "properties": { "title": "procEppleyNorbergSteele", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procEppleyNorbergSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json index 866f073e9..04bc0b75b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procHinshelwoodMonod", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procHinshelwoodMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json index 7b98da524..0a7bf72b3 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json @@ -47,7 +47,7 @@ "properties": { "title": "procHinshelwoodSteele", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procHinshelwoodSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index 167644467..88cdbfbb7 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index 7e0ab949a..64fc68625 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json index 97cc98cf9..ffee94fb4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json index 936fe994a..98451d859 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json index 59d1e266a..b49b4eceb 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json index 3acd64daa..26ae2a601 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json index 39b999578..36d1fc963 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json index 442d6780f..d3b306449 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index d0990f17a..f32f91015 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json index f64b679f4..35347aebc 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json index 287825cee..22d5ea864 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json index ad935e536..b51b53ff4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -11,62 +11,62 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/tg_arima.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index da8f95a86..f91662002 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -31,7 +31,7 @@ "properties": { "title": "AquaticEcosystemsOxygen", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: BARC, WLOU, ARIK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-03T00:00:00Z", "end_datetime": "2024-08-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json index 4cb6fd85e..df3146bef 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json index c5d2ef72f..482a9eb8a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 + ], + [ + -147.504, + 65.1532 + ], + [ + -105.5442, + 40.035 + ], + [ + -89.4737, + 46.2097 + ], + [ + -66.9868, + 18.1135 + ], + [ + -84.4374, + 31.1854 + ], + [ + -66.7987, + 18.1741 + ], + [ + -72.3295, + 42.4719 + ], + [ + -96.6038, + 39.1051 + ], + [ + -83.5038, + 35.6904 + ], + [ + -88.1589, + 31.8534 + ], [ -149.6106, 68.6307 @@ -85,77 +149,13 @@ [ -119.0274, 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 - ], - [ - -66.9868, - 18.1135 - ], - [ - -84.4374, - 31.1854 - ], - [ - -66.7987, - 18.1741 - ], - [ - -72.3295, - 42.4719 - ], - [ - -96.6038, - 39.1051 - ], - [ - -83.5038, - 35.6904 ] ] }, "properties": { "title": "air2waterSat_2", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOMB, TOOK, WALK, WLOU, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -186,6 +186,22 @@ "oxygen", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO", + "TOMB", "TOOK", "WALK", "WLOU", @@ -203,23 +219,7 @@ "REDB", "SUGG", "SYCA", - "TECR", - "TOMB", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO" + "TECR" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json index c241dc06d..f163c9d28 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json @@ -147,7 +147,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 848595d4b..4c9a3ba3e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -15,12 +15,24 @@ "type": "MultiPoint", "coordinates": [ [ - -87.7982, - 32.5415 + -102.4471, + 39.7582 ], [ - -147.504, - 65.1532 + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 ], [ -105.5442, @@ -102,34 +114,22 @@ -105.9154, 39.8914 ], - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], [ -88.1589, 31.8534 ], + [ + -87.7982, + 32.5415 + ], [ -89.4737, 46.2097 ], + [ + -147.504, + 65.1532 + ], [ -89.7048, 45.9983 @@ -154,8 +154,8 @@ }, "properties": { "title": "climatology", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ @@ -186,8 +186,11 @@ "oxygen", "Daily", "P1D", - "BLWA", - "CARI", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "COMO", "CUPE", "FLNT", @@ -208,13 +211,10 @@ "TECR", "WALK", "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", "TOMB", + "BLWA", "CRAM", + "CARI", "LIRO", "PRPO", "PRLA", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index 91569c336..5041dfa37 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -75,7 +75,7 @@ "properties": { "title": "hotdeck", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, BLDE, BIGC, MCRA, REDB, SYCA, CRAM, LIRO, PRIN, POSE, MAYF, LEWI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-05T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index 99857b319..dafff065d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -15,108 +15,88 @@ "type": "MultiPoint", "coordinates": [ [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 + -102.4471, + 39.7582 ], [ - -89.4737, - 46.2097 + -82.0084, + 29.676 ], [ - -66.9868, - 18.1135 + -119.2575, + 37.0597 ], [ - -97.7823, - 33.3785 + -110.5871, + 44.9501 ], [ - -99.1139, - 47.1591 + -96.6242, + 34.4442 ], [ - -99.2531, - 47.1298 + -83.5038, + 35.6904 ], [ - -111.7979, - 40.7839 + -77.9832, + 39.0956 ], [ - -82.0177, - 29.6878 + -89.7048, + 45.9983 ], [ - -111.5081, - 33.751 + -121.9338, + 45.7908 ], [ -87.4077, 32.9604 ], [ - -96.443, - 38.9459 + -111.5081, + 33.751 ], [ - -122.1655, - 44.2596 + -119.0274, + 36.9559 ], [ - -149.143, - 68.6698 + -88.1589, + 31.8534 ], [ - -78.1473, - 38.8943 + -149.6106, + 68.6307 ], [ - -96.6242, - 34.4442 + -84.2793, + 35.9574 ], [ -87.7982, 32.5415 ], [ - -105.9154, - 39.8914 - ], - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 + -147.504, + 65.1532 ], [ - -119.0274, - 36.9559 + -105.5442, + 40.035 ], [ - -88.1589, - 31.8534 + -89.4737, + 46.2097 ], [ - -149.6106, - 68.6307 + -66.9868, + 18.1135 ], [ - -84.2793, - 35.9574 + -105.9154, + 39.8914 ], [ -84.4374, @@ -135,27 +115,47 @@ 39.1051 ], [ - -83.5038, - 35.6904 + -97.7823, + 33.3785 ], [ - -77.9832, - 39.0956 + -99.1139, + 47.1591 ], [ - -89.7048, - 45.9983 + -99.2531, + 47.1298 ], [ - -121.9338, - 45.7908 + -111.7979, + 40.7839 + ], + [ + -82.0177, + 29.6878 + ], + [ + -96.443, + 38.9459 + ], + [ + -122.1655, + 44.2596 + ], + [ + -149.143, + 68.6698 + ], + [ + -78.1473, + 38.8943 ] ] }, "properties": { "title": "persistenceRW", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, LECO, LEWI, LIRO, MART, MAYF, SYCA, TECR, TOMB, TOOK, WALK, BLWA, CARI, COMO, CRAM, CUPE, WLOU, FLNT, GUIL, HOPB, KING, PRIN, PRLA, PRPO, REDB, SUGG, MCDI, MCRA, OKSR, POSE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ @@ -186,40 +186,40 @@ "oxygen", "Daily", "P1D", - "CARI", - "COMO", - "CRAM", - "CUPE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "BLUE", - "BLWA", - "WLOU", "ARIK", "BARC", "BIGC", "BLDE", + "BLUE", + "LECO", + "LEWI", + "LIRO", + "MART", + "MAYF", + "SYCA", "TECR", "TOMB", "TOOK", "WALK", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "WLOU", "FLNT", "GUIL", "HOPB", "KING", - "LECO", - "LEWI", - "LIRO", - "MART" + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "MCDI", + "MCRA", + "OKSR", + "POSE" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index 7065b84e0..c831b8943 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index 99a8628e4..ffdc6bdd4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json index 94bdbd622..2c363ff74 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json index d88c289b1..a741001a4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json index bed0d68be..41eb735b9 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json index 771004717..290c6839f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json index f3902f5dc..37e863573 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json index a1b7273cc..ab43671fe 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index cba2c2bf2..457ab27cb 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json index 85545bc09..3e8059684 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json index 6f06c0ee1..b73196292 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/collection.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/collection.json index 86c7cce0a..c2eb32441 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/collection.json @@ -11,112 +11,112 @@ { "rel": "item", "type": "application/json", - "href": "./models/fTSLM_lag.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM_noDA.json" + "href": "./models/fTSLM_lag.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM_noDA.json" + "href": "./models/flareGLM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat_noDA.json" + "href": "./models/flareGLM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flare_ler.json" + "href": "./models/flareGOTM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flare_ler_baselines.json" + "href": "./models/flareSimstrat_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/flare_ler.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/flare_ler_baselines.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fARIMA_clim_ensemble.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", @@ -126,17 +126,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/GLEON_JRabaey_temp_physics.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/fARIMA_clim_ensemble.json" }, { "rel": "item", @@ -176,17 +176,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/mlp1_wtempforecast_LF.json" + "href": "./models/mkricheldorf_w_lag.json" }, { "rel": "item", "type": "application/json", - "href": "./models/zimmerman_proj1.json" + "href": "./models/mlp1_wtempforecast_LF.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mkricheldorf_w_lag.json" + "href": "./models/zimmerman_proj1.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json index 6bcc94577..27a863c73 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json @@ -47,7 +47,7 @@ "properties": { "title": "GAM_air_wind", "description": "All forecasts for the Daily_Water_temperature variable for the GAM_air_wind model. Information for the model is provided as follows: I used a GAM (mgcv) with a linear relationship to air temperature and smoothing for eastward and northward winds..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index ab30a7058..c2e2bb79e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -155,7 +155,7 @@ "properties": { "title": "GLEON_JRabaey_temp_physics", "description": "All forecasts for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index e250b2840..ba68d5878 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All forecasts for the Daily_Water_temperature variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_physics.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_physics.json index 8974290f1..d7a97e7c4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_physics.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_physics.json @@ -43,7 +43,7 @@ "properties": { "title": "GLEON_physics", "description": "All forecasts for the Daily_Water_temperature variable for the GLEON_physics model. Information for the model is provided as follows: A simple, process-based model was developed to replicate the water temperature dynamics of a\nsurface water layer sensu Chapra (2008). The model focus was only on quantifying the impacts of\natmosphere-water heat flux exchanges on the idealized near-surface water temperature dynamics.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2023-12-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json index 2e22488c7..447448e53 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json @@ -47,7 +47,7 @@ "properties": { "title": "TSLM_seasonal_JM", "description": "All forecasts for the Daily_Water_temperature variable for the TSLM_seasonal_JM model. Information for the model is provided as follows: My model uses the fable package TSLM, and uses built in exogenous regressors to represent the trend and seasonality of the data as well as air temperature to predict water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-06-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/acp_fableLM.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/acp_fableLM.json index 3aad61c27..7a6d1a0b0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/acp_fableLM.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/acp_fableLM.json @@ -47,7 +47,7 @@ "properties": { "title": "acp_fableLM", "description": "All forecasts for the Daily_Water_temperature variable for the acp_fableLM model. Information for the model is provided as follows: Time series linear model with FABLE.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-11T00:00:00Z", "end_datetime": "2024-04-13T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json index 802254507..1f4830701 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json @@ -155,7 +155,7 @@ "properties": { "title": "air2waterSat_2", "description": "All forecasts for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index 37aeb772f..9ce073b07 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -15,52 +15,32 @@ "type": "MultiPoint", "coordinates": [ [ - -78.1473, - 38.8943 - ], - [ - -97.7823, - 33.3785 + -87.7982, + 32.5415 ], [ - -111.7979, - 40.7839 + -105.5442, + 40.035 ], [ - -82.0177, - 29.6878 + -66.9868, + 18.1135 ], [ - -96.443, - 38.9459 + -84.4374, + 31.1854 ], [ - -122.1655, - 44.2596 + -66.7987, + 18.1741 ], [ -72.3295, 42.4719 ], [ - -96.6038, - 39.1051 - ], - [ - -83.5038, - 35.6904 - ], - [ - -77.9832, - 39.0956 - ], - [ - -121.9338, - 45.7908 - ], - [ - -87.4077, - 32.9604 + -82.0177, + 29.6878 ], [ -111.5081, @@ -83,24 +63,44 @@ 39.8914 ], [ - -87.7982, - 32.5415 + -96.6038, + 39.1051 ], [ - -105.5442, - 40.035 + -83.5038, + 35.6904 ], [ - -66.9868, - 18.1135 + -77.9832, + 39.0956 ], [ - -84.4374, - 31.1854 + -121.9338, + 45.7908 ], [ - -66.7987, - 18.1741 + -87.4077, + 32.9604 + ], + [ + -96.443, + 38.9459 + ], + [ + -122.1655, + 44.2596 + ], + [ + -78.1473, + 38.8943 + ], + [ + -97.7823, + 33.3785 + ], + [ + -111.7979, + 40.7839 ], [ -102.4471, @@ -130,10 +130,6 @@ -89.7048, 45.9983 ], - [ - -147.504, - 65.1532 - ], [ -99.1139, 47.1591 @@ -142,6 +138,10 @@ -99.2531, 47.1298 ], + [ + -147.504, + 65.1532 + ], [ -149.143, 68.6698 @@ -154,8 +154,8 @@ }, "properties": { "title": "baseline_ensemble", - "description": "All forecasts for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: POSE, PRIN, REDB, SUGG, MCDI, MCRA, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, CARI, PRLA, PRPO, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,28 +186,28 @@ "temperature", "Daily", "P1D", - "POSE", - "PRIN", - "REDB", - "SUGG", - "MCDI", - "MCRA", + "BLWA", + "COMO", + "CUPE", + "FLNT", + "GUIL", "HOPB", - "KING", - "LECO", - "LEWI", - "MART", - "MAYF", + "SUGG", "SYCA", "TECR", "TOMB", "WALK", "WLOU", - "BLWA", - "COMO", - "CUPE", - "FLNT", - "GUIL", + "KING", + "LECO", + "LEWI", + "MART", + "MAYF", + "MCDI", + "MCRA", + "POSE", + "PRIN", + "REDB", "ARIK", "BARC", "BIGC", @@ -215,9 +215,9 @@ "BLUE", "CRAM", "LIRO", - "CARI", "PRLA", "PRPO", + "CARI", "OKSR", "TOOK" ], diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json index 5319bdcb4..a97ee16a8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json @@ -22,10 +22,6 @@ -99.2531, 47.1298 ], - [ - -82.0084, - 29.676 - ], [ -89.4737, 46.2097 @@ -34,6 +30,10 @@ -99.1139, 47.1591 ], + [ + -82.0084, + 29.676 + ], [ -82.0177, 29.6878 @@ -46,8 +46,8 @@ }, "properties": { "title": "bee_bake_RFModel_2024", - "description": "All forecasts for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: LIRO, PRPO, BARC, CRAM, PRLA, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: LIRO, PRPO, CRAM, PRLA, BARC, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", "providers": [ @@ -80,9 +80,9 @@ "P1D", "LIRO", "PRPO", - "BARC", "CRAM", "PRLA", + "BARC", "SUGG", "TOOK" ], diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/cb_prophet.json index a7a39230a..c73a6dab3 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/cb_prophet.json @@ -147,7 +147,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Water_temperature variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, TECR, TOMB, WALK, WLOU, SYCA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/climatology.json index 56b69b4ad..e448b5742 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/climatology.json @@ -155,7 +155,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, LIRO, PRPO, CRAM, PRLA, CARI, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index 8edc1b899..f0c74eef5 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -155,7 +155,7 @@ "properties": { "title": "fARIMA_clim_ensemble", "description": "All forecasts for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, CRAM, FLNT, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-10T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index 4b585feca..b049936b3 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -155,7 +155,7 @@ "properties": { "title": "fTSLM_lag", "description": "All forecasts for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-08T00:00:00Z", "end_datetime": "2024-09-14T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM.json index 40d8eb4db..292e4dc9e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGLM", "description": "All forecasts for the Daily_Water_temperature variable for the flareGLM model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019) and data assimilation algorithm to generate\nensemble forecasts of lake water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index 1458dd4c2..9baa215ec 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGLM_noDA", "description": "All forecasts for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-03-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json index 0ca5af66e..403c12e97 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGOTM_noDA", "description": "All forecasts for the Daily_Water_temperature variable for the flareGOTM_noDA model. Information for the model is provided as follows: FLARE-GOTM uses the General Ocean Turbulence Model (GOTM) hydrodynamic model. GOTM is a 1-D\nhydrodynamic turbulence model (Umlauf et al., 2005) that estimates water column temperatures.\n The model predicts this variable at the following sites: BARC, CRAM, SUGG, LIRO, PRLA, PRPO, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-20T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json index 6824c7da8..4ba4dac41 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json @@ -43,7 +43,7 @@ "properties": { "title": "flareSimstrat_noDA", "description": "All forecasts for the Daily_Water_temperature variable for the flareSimstrat_noDA model. Information for the model is provided as follows: FLARE-Simstrat uses the same principles and overarching framework as FLARE-GLM with the\nhydrodynamic model replaced with Simstrat. Simstrat is a 1-D hydrodynamic turbulence model\n(Goudsmit et al., 2002) that estimates water column temperatures..\n The model predicts this variable at the following sites: BARC, SUGG, TOOK, CRAM, PRLA, PRPO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler.json index c10d229ca..24de006b2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler.json @@ -43,7 +43,7 @@ "properties": { "title": "flare_ler", "description": "All forecasts for the Daily_Water_temperature variable for the flare_ler model. Information for the model is provided as follows: The LER MME is a multi-model ensemble (MME) derived from the three process models from\nFLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat). To generate the MME, an ensemble\nforecast was generated by sampling from the submitted models\u2019 ensemble members.\n The model predicts this variable at the following sites: SUGG, CRAM, LIRO, PRLA, PRPO, BARC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json index fe905fd25..d194411c0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json @@ -27,7 +27,7 @@ "properties": { "title": "flare_ler_baselines", "description": "All forecasts for the Daily_Water_temperature variable for the flare_ler_baselines model. Information for the model is provided as follows: The LER-baselines model is a multi-model ensemble (MME) comprised of the three process\nmodels from FLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat) and the two baseline\nmodels (day-of-year, persistence), submitted by Challenge organisers. To generate the MME, an\nensemble forecast was generated by sampling from the submitted model\u2019s ensemble members (either\nfrom an ensemble forecast in the case of the FLARE models and persistence, or from the distribution for\nthe day-of-year forecasts).\n The model predicts this variable at the following sites: SUGG, BARC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/hotdeck.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/hotdeck.json index 246c54a6d..bfe489a85 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -139,7 +139,7 @@ "properties": { "title": "hotdeck", "description": "All forecasts for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json index baef4bdab..b8a2fe643 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json @@ -47,7 +47,7 @@ "properties": { "title": "lm_AT_WTL_WS", "description": "All forecasts for the Daily_Water_temperature variable for the lm_AT_WTL_WS model. Information for the model is provided as follows: This forecast of water temperature at NEON Lake sites uses a linear model, incorporating air temperature, wind speed, and the previous day's forecasted water temperature as variables..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json index b5208512d..fdfd54a23 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json @@ -47,7 +47,7 @@ "properties": { "title": "mkricheldorf_w_lag", "description": "All forecasts for the Daily_Water_temperature variable for the mkricheldorf_w_lag model. Information for the model is provided as follows: I used an autoregressive linear model using the lm() function.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-06T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json index ec7a99277..f72c0b59e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json @@ -47,7 +47,7 @@ "properties": { "title": "mlp1_wtempforecast_LF", "description": "All forecasts for the Daily_Water_temperature variable for the mlp1_wtempforecast_LF model. Information for the model is provided as follows: Modelling for water temperature using a single layer neural network (mlp() in tidymodels). Used relative humidity, precipitation flux and air temperature as drivers. Hypertuned parameters for models to be run with 100 epochs and penalty value of 0.01..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/persistenceRW.json index ec6a9b138..ba02dbff8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -155,7 +155,7 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KING, LECO, LEWI, LIRO, MART, MAYF, ARIK, BARC, BIGC, BLDE, BLUE, MCDI, MCRA, OKSR, POSE, PRIN, WLOU, CUPE, FLNT, GUIL, HOPB, PRLA, PRPO, REDB, SUGG, SYCA, BLWA, CARI, COMO, CRAM, TECR, TOMB, TOOK, WALK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/precip_mod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/precip_mod.json index 4d89a48a0..fc853462b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/precip_mod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/precip_mod.json @@ -47,7 +47,7 @@ "properties": { "title": "precip_mod", "description": "All forecasts for the Daily_Water_temperature variable for the precip_mod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-12-21T00:00:00Z", "end_datetime": "2024-01-24T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_arima.json index 69c1e97d1..33045a7de 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -87.4077, + 32.9604 + ], + [ + -96.443, + 38.9459 + ], + [ + -122.1655, + 44.2596 + ], + [ + -149.143, + 68.6698 + ], + [ + -78.1473, + 38.8943 + ], + [ + -97.7823, + 33.3785 + ], + [ + -99.1139, + 47.1591 + ], + [ + -99.2531, + 47.1298 + ], + [ + -111.7979, + 40.7839 + ], + [ + -82.0177, + 29.6878 + ], + [ + -111.5081, + 33.751 + ], + [ + -119.0274, + 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 + ], [ -102.4471, 39.7582 @@ -85,77 +149,13 @@ [ -121.9338, 45.7908 - ], - [ - -87.4077, - 32.9604 - ], - [ - -96.443, - 38.9459 - ], - [ - -122.1655, - 44.2596 - ], - [ - -149.143, - 68.6698 - ], - [ - -78.1473, - 38.8943 - ], - [ - -97.7823, - 33.3785 - ], - [ - -99.1139, - 47.1591 - ], - [ - -99.2531, - 47.1298 - ], - [ - -111.7979, - 40.7839 - ], - [ - -82.0177, - 29.6878 - ], - [ - -111.5081, - 33.751 - ], - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ @@ -186,6 +186,22 @@ "temperature", "Daily", "P1D", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -203,23 +219,7 @@ "LECO", "LEWI", "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "MART" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_ets.json index ca79b54e2..0587a869e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json index faa97739d..e905734e7 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Water_temperature variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json index 71e4db30d..84c921b94 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Water_temperature variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_lasso.json index 935637f5c..7e501db12 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_lasso.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Daily_Water_temperature variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json index d126a07e0..130b7170a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Water_temperature variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json index 97aa23aa5..9a47d4393 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Water_temperature variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_randfor.json index 81776bc47..728a97c4e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_randfor.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Water_temperature variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_tbats.json index 73245530a..9baca15e9 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json index 39de59997..3fa28b9b3 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -102.4471, 39.7582 @@ -149,13 +137,25 @@ [ -88.1589, 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ] ] }, "properties": { "title": "tg_temp_lm", - "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -186,9 +186,6 @@ "temperature", "Daily", "P1D", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -219,7 +216,10 @@ "SUGG", "SYCA", "TECR", - "TOMB" + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json index a719470ba..f7fa89dd1 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json index 9bf19f973..b1ae30a8c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -47,7 +47,7 @@ "properties": { "title": "zimmerman_proj1", "description": "All forecasts for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/collection.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/collection.json index 05e553441..0ea98ca8f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/collection.json @@ -8,6 +8,11 @@ ], "type": "Collection", "links": [ + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_arima.json" + }, { "rel": "item", "type": "application/json", @@ -43,11 +48,6 @@ "type": "application/json", "href": "./models/tg_temp_lm_all_sites.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_arima.json" - }, { "rel": "item", "type": "application/json", @@ -56,12 +56,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index 19ebcbf81..ed50828c2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index 01ea3aa70..cf1d822c8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -14,6 +14,62 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], [ -149.2133, 63.8758 @@ -145,69 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 ] ] }, "properties": { "title": "tg_ets", - "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -238,6 +238,20 @@ "abundance", "Weekly", "P1W", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -270,21 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json index ffd180b3e..5b3375719 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json index 4f0b48443..7d4a833de 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json index dc997e9ce..3800cd61a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json @@ -199,7 +199,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json index 0579a1361..6e26a877b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json index 12a76a21a..fc586f631 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json index 2ced329ad..c901c59b8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index 10d67e4f8..6350fd73e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json index 5b5b34272..9e279a5d6 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json index 201656126..0549522ec 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/collection.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/collection.json index a614eaa78..dbcde56fc 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/collection.json @@ -8,11 +8,6 @@ ], "type": "Collection", "links": [ - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_arima.json" - }, { "rel": "item", "type": "application/json", @@ -21,7 +16,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", @@ -41,12 +36,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_humidity_lm_all_sites.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_temp_lm.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index 9d12f3afd..4c9a98569 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index 0cef13122..8d5c9e2d6 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -14,6 +14,82 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -125,89 +201,13 @@ [ -84.2826, 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_ets", - "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -238,6 +238,25 @@ "richness", "Weekly", "P1W", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -265,26 +284,7 @@ "NOGP", "OAES", "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "ORNL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json index 0bf7305c9..ef3a0266f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json index 0109f027a..cd6c9bd93 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json index a859f7bb4..161157356 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json @@ -199,7 +199,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json index 74c1158e4..8fe893ed0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json index c2de34661..d94e1b98b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json index b1d3cf24a..91a4cdddd 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -106.8425, 32.5907 @@ -137,77 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 ] ] }, "properties": { "title": "tg_randfor", - "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ @@ -238,6 +238,22 @@ "richness", "Weekly", "P1W", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -268,23 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index f233f80f4..9096591ad 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json index 1b272ec29..2019bcb7b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json index e12dd3b6f..992ac8a15 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/collection.json index 27df9df31..a975cb756 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -11,72 +11,72 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index 4547cd564..7b14ae404 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -27,7 +27,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the ChlorophyllCrusaders model. Information for the model is provided as follows: Our project utilizes a historical GCC data to fit a Dynamic Linear Model (DLM). After this DLM is trained, we utilize forecasted temperature data to predict future GCC data..\n The model predicts this variable at the following sites: HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json index c74e5d8d8..e921317a6 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json @@ -207,7 +207,7 @@ "properties": { "title": "PEG", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json index 05bee0329..aeb50f537 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json @@ -207,7 +207,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index 5d1a74874..af90fea9a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -194,20 +194,20 @@ -149.2133, 63.8758 ], - [ - -149.3705, - 68.6611 - ], [ -156.6194, 71.2824 + ], + [ + -149.3705, + 68.6611 ] ] }, "properties": { "title": "climatology", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, BARR, TOOL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -283,8 +283,8 @@ "BONA", "DEJU", "HEAL", - "TOOL", - "BARR" + "BARR", + "TOOL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index f098f54f2..e48d47372 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, SJER, SOAP, SRER, STEI, STER, TALL, NOGP, OAES, ONAQ, ORNL, OSBS, TEAK, TOOL, TREE, UKFS, UNDE, LAJA, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, JERC, JORN, KONA, KONZ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index ceb0069be..09ec39608 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -14,22 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +185,29 @@ [ -95.1921, 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,10 +238,6 @@ "gcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +280,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index db972cb40..05663c526 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -14,6 +14,78 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -129,85 +201,13 @@ [ -81.9934, 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_ets", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,24 @@ "gcc_90", "Daily", "P1D", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -266,25 +284,7 @@ "OAES", "ONAQ", "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "OSBS" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json index df0f2e2db..dc46e7c50 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 30fa5992f..7a9f87da7 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json index 35c2a4629..db07676d8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json index bcda8df8d..e782d7c6b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 6b1adfd22..616f4a038 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json index 88429c80b..9ce0a1329 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index 04af50bc0..54e540bc8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -14,14 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +193,21 @@ [ -99.2413, 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_tbats", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,8 +238,6 @@ "gcc_90", "Daily", "P1D", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +282,9 @@ "TREE", "UKFS", "UNDE", - "WOOD" + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json index 6a6fc1fbc..3cbf54437 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json @@ -14,6 +14,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], [ -156.6194, 71.2824 @@ -197,17 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 ] ] }, "properties": { "title": "tg_temp_lm", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,6 +238,7 @@ "gcc_90", "Daily", "P1D", + "ABBY", "BARR", "BART", "BLAN", @@ -283,8 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json index b263a282d..6c2da7402 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/collection.json index ff66eaf44..bc4e1efa7 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -11,22 +11,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", @@ -36,37 +36,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", @@ -81,7 +81,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json index 67ad557ac..1c54ba054 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json @@ -207,7 +207,7 @@ "properties": { "title": "PEG", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index fb878e71f..40d6dd451 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -194,20 +194,20 @@ -149.2133, 63.8758 ], - [ - -156.6194, - 71.2824 - ], [ -149.3705, 68.6611 + ], + [ + -156.6194, + 71.2824 ] ] }, "properties": { "title": "baseline_ensemble", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, BARR, TOOL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, TOOL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -283,8 +283,8 @@ "BONA", "DEJU", "HEAL", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json index 8fcb40b63..69e2c91bd 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json @@ -207,7 +207,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index 73aa564ef..5ac63567e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -194,20 +194,20 @@ -147.5026, 65.154 ], - [ - -149.3705, - 68.6611 - ], [ -156.6194, 71.2824 + ], + [ + -149.3705, + 68.6611 ] ] }, "properties": { "title": "climatology", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, BARR, TOOL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -283,8 +283,8 @@ "DEJU", "HEAL", "BONA", - "TOOL", - "BARR" + "BARR", + "TOOL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index 38a7a5d41..627232c4f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -14,6 +14,42 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], [ -96.6129, 39.1104 @@ -43,28 +79,24 @@ 44.9535 ], [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 + -110.8355, + 31.9107 ], [ - -71.2874, - 44.0639 + -89.5864, + 45.5089 ], [ - -78.0418, - 39.0337 + -103.0293, + 40.4619 ], [ - -147.5026, - 65.154 + -87.3933, + 32.9505 ], [ - -97.57, - 33.4012 + -119.006, + 37.0058 ], [ -104.7456, @@ -86,6 +118,10 @@ -81.4362, 28.1251 ], + [ + -99.0588, + 35.4106 + ], [ -112.4524, 40.1776 @@ -102,58 +138,6 @@ -155.3173, 19.5531 ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -99.0588, - 35.4106 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], [ -80.5248, 37.3783 @@ -201,13 +185,29 @@ [ -149.2133, 63.8758 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 ] ] }, "properties": { "title": "persistenceRW", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, ONAQ, ORNL, OSBS, PUUM, SRER, STEI, STER, TALL, TEAK, RMNP, SCBI, SERC, SJER, SOAP, OAES, JERC, JORN, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, RMNP, SCBI, SERC, SJER, SOAP, JERC, JORN, KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, SRER, STEI, STER, TALL, TEAK, CPER, DCFS, DEJU, DELA, DSNY, OAES, ONAQ, ORNL, OSBS, PUUM, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL, BART, BLAN, BONA, CLBJ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -238,6 +238,15 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -245,34 +254,21 @@ "WOOD", "WREF", "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", "CPER", "DCFS", "DEJU", "DELA", "DSNY", + "OAES", "ONAQ", "ORNL", "OSBS", "PUUM", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "OAES", - "JERC", - "JORN", "MLBS", "MOAB", "NIWO", @@ -284,7 +280,11 @@ "GRSM", "GUAN", "HARV", - "HEAL" + "HEAL", + "BART", + "BLAN", + "BONA", + "CLBJ" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index 8a613f30d..2b170448c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index 8e33ca712..81b811d98 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -14,74 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], [ -96.6129, 39.1104 @@ -201,13 +133,81 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 ] ] }, "properties": { "title": "tg_ets", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,23 +238,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", "KONA", "KONZ", "LAJA", @@ -284,7 +267,24 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json index dbed35d7c..b68b10f38 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 475561bfb..ec3817734 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json index 14d52c8aa..f13772bb2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json @@ -14,54 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], [ -66.8687, 17.9696 @@ -201,13 +153,61 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 ] ] }, "properties": { "title": "tg_lasso", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,18 +238,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -284,7 +272,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json index 3aba1f48b..8896aee6d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json @@ -14,6 +14,66 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], [ -84.4686, 31.1948 @@ -141,73 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 ] ] }, "properties": { "title": "tg_precip_lm", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,6 +238,21 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -269,22 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 23318850d..31edffec1 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -14,54 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], [ -66.8687, 17.9696 @@ -201,13 +153,61 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 ] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -238,18 +238,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -284,7 +272,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json index 24b60600f..33689a279 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -106.8425, 32.5907 @@ -137,77 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 ] ] }, "properties": { "title": "tg_randfor", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,6 +238,22 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -268,23 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index 60a79640e..9e84837f6 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -14,22 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +185,29 @@ [ -95.1921, 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_tbats", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,10 +238,6 @@ "rcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +280,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json index 99e5ccd38..675dfdc02 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json index f5b66b6ac..dc7dbe6ab 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json index fd07db8c2..12336bed6 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the 30min_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_latent_heat_flux/models/climatology.json index f165bda28..acb8e49a2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_latent_heat_flux/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, STER, TALL, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json index ec1700be3..ff55cc61b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -11,67 +11,67 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json index c1a789ba4..c0bb9ed02 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json @@ -23,7 +23,7 @@ "properties": { "title": "USUNEEDAILY", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the USUNEEDAILY model. Information for the model is provided as follows: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates..\n The model predicts this variable at the following sites: PUUM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-12-12T00:00:00Z", "end_datetime": "2024-01-16T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index b7923b5ac..6474e771a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -27,7 +27,7 @@ "properties": { "title": "bookcast_forest", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: TALL, OSBS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-10T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json index 70cf9d8a7..131a16a0f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json @@ -203,7 +203,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: PUUM, GUAN, OSBS, SCBI, MOAB, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, ONAQ, DSNY, BONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json index 6c82b573c..773692c71 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index 2d1a8f76d..fb444b38d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -15,112 +15,112 @@ "type": "MultiPoint", "coordinates": [ [ - -156.6194, - 71.2824 + -100.9154, + 46.7697 ], [ - -71.2874, - 44.0639 + -99.0588, + 35.4106 ], [ - -78.0418, - 39.0337 + -112.4524, + 40.1776 ], [ - -147.5026, - 65.154 + -84.2826, + 35.9641 ], [ - -119.7323, - 37.1088 + -81.9934, + 29.6893 ], [ - -119.2622, - 37.0334 + -89.5373, + 46.2339 ], [ - -110.8355, - 31.9107 + -99.2413, + 47.1282 ], [ - -89.5864, - 45.5089 + -121.9519, + 45.8205 ], [ - -103.0293, - 40.4619 + -110.5391, + 44.9535 ], [ - -87.3933, - 32.9505 + -147.5026, + 65.154 ], [ - -67.0769, - 18.0213 + -97.57, + 33.4012 ], [ - -88.1612, - 31.8539 + -104.7456, + 40.8155 ], [ - -80.5248, - 37.3783 + -99.1066, + 47.1617 ], [ - -109.3883, - 38.2483 + -145.7514, + 63.8811 ], [ - -105.5824, - 40.0543 + -87.8039, + 32.5417 ], [ - -100.9154, - 46.7697 + -149.2133, + 63.8758 ], [ - -99.0588, - 35.4106 + -84.4686, + 31.1948 ], [ - -112.4524, - 40.1776 + -106.8425, + 32.5907 ], [ - -84.2826, - 35.9641 + -96.6129, + 39.1104 ], [ - -81.9934, - 29.6893 + -96.5631, + 39.1008 ], [ - -122.3303, - 45.7624 + -67.0769, + 18.0213 ], [ - -119.006, - 37.0058 + -119.7323, + 37.1088 ], [ - -149.3705, - 68.6611 + -119.2622, + 37.0334 ], [ - -89.5857, - 45.4937 + -110.8355, + 31.9107 ], [ - -95.1921, - 39.0404 + -89.5864, + 45.5089 ], [ - -89.5373, - 46.2339 + -103.0293, + 40.4619 ], [ - -87.8039, - 32.5417 + -87.3933, + 32.9505 ], [ -81.4362, @@ -139,52 +139,52 @@ 42.5369 ], [ - -149.2133, - 63.8758 + -119.006, + 37.0058 ], [ - -97.57, - 33.4012 + -149.3705, + 68.6611 ], [ - -104.7456, - 40.8155 + -89.5857, + 45.4937 ], [ - -99.1066, - 47.1617 + -95.1921, + 39.0404 ], [ - -145.7514, - 63.8811 + -122.3303, + 45.7624 ], [ - -99.2413, - 47.1282 + -156.6194, + 71.2824 ], [ - -121.9519, - 45.8205 + -71.2874, + 44.0639 ], [ - -110.5391, - 44.9535 + -78.0418, + 39.0337 ], [ - -84.4686, - 31.1948 + -88.1612, + 31.8539 ], [ - -106.8425, - 32.5907 + -80.5248, + 37.3783 ], [ - -96.6129, - 39.1104 + -109.3883, + 38.2483 ], [ - -96.5631, - 39.1008 + -105.5824, + 40.0543 ], [ -155.3173, @@ -206,8 +206,8 @@ }, "properties": { "title": "persistenceRW", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, ABBY, TEAK, TOOL, TREE, UKFS, UNDE, DELA, DSNY, GRSM, GUAN, HARV, HEAL, CLBJ, CPER, DCFS, DEJU, WOOD, WREF, YELL, JERC, JORN, KONA, KONZ, PUUM, RMNP, SCBI, SERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, UNDE, WOOD, WREF, YELL, BONA, CLBJ, CPER, DCFS, DEJU, DELA, HEAL, JERC, JORN, KONA, KONZ, LAJA, SJER, SOAP, SRER, STEI, STER, TALL, DSNY, GRSM, GUAN, HARV, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -238,49 +238,49 @@ "nee", "Daily", "P1D", - "BARR", - "BART", - "BLAN", - "BONA", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", "NOGP", "OAES", "ONAQ", "ORNL", "OSBS", - "ABBY", - "TEAK", - "TOOL", - "TREE", - "UKFS", "UNDE", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", + "WOOD", + "WREF", + "YELL", + "BONA", "CLBJ", "CPER", "DCFS", "DEJU", - "WOOD", - "WREF", - "YELL", + "DELA", + "HEAL", "JERC", "JORN", "KONA", "KONZ", + "LAJA", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "ABBY", + "BARR", + "BART", + "BLAN", + "LENO", + "MLBS", + "MOAB", + "NIWO", "PUUM", "RMNP", "SCBI", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index 6cf9aee05..6dd3f572e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -14,78 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], [ -96.5631, 39.1008 @@ -201,13 +129,85 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,24 +238,6 @@ "nee", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", "KONZ", "LAJA", "LENO", @@ -284,7 +266,25 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index 281863ca4..df5f78242 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json index 5ac00c428..39e3275a8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json index fca1f50ed..ea23c6da0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json index 2d62b6834..d846d8fe9 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json index 7e93f1e9c..cd2b3dd8f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json index 8b4e2d792..90aeb1225 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index e8d45520c..60b66553d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -14,6 +14,94 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], + [ + -96.5631, + 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 + ], + [ + -105.5824, + 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], [ -81.9934, 29.6893 @@ -113,101 +201,13 @@ [ -97.57, 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], - [ - -96.5631, - 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 - ], - [ - -105.5824, - 40.0543 - ], - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 ] ] }, "properties": { "title": "tg_tbats", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,28 @@ "nee", "Daily", "P1D", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", "OSBS", "PUUM", "RMNP", @@ -262,29 +284,7 @@ "BART", "BLAN", "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL" + "CLBJ" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json index 7c425e7b9..72dfb3b3f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -137,77 +201,13 @@ [ -105.546, 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_temp_lm", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,6 +238,22 @@ "nee", "Daily", "P1D", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -268,23 +284,7 @@ "ORNL", "OSBS", "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "RMNP" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json index 78233db93..bbfa2bc31 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/collection.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/collection.json index 9874f0ec5..133d1f294 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/collection.json @@ -11,62 +11,62 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/climatology.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json index 2a24102d8..1a4a59032 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -97.57, - 33.4012 - ], - [ - -119.7323, - 37.1088 - ], - [ - -112.4524, - 40.1776 - ], [ -81.4362, 28.1251 @@ -46,10 +34,6 @@ -66.8687, 17.9696 ], - [ - -81.9934, - 29.6893 - ], [ -71.2874, 44.0639 @@ -194,16 +178,32 @@ -119.006, 37.0058 ], + [ + -97.57, + 33.4012 + ], + [ + -119.7323, + 37.1088 + ], + [ + -81.9934, + 29.6893 + ], [ -147.5026, 65.154 + ], + [ + -112.4524, + 40.1776 ] ] }, "properties": { "title": "cb_prophet", - "description": "All forecasts for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: CLBJ, SJER, ONAQ, DSNY, SCBI, MOAB, PUUM, GUAN, OSBS, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, BONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DSNY, SCBI, MOAB, PUUM, GUAN, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, OSBS, BONA, ONAQ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -234,15 +234,11 @@ "le", "Daily", "P1D", - "CLBJ", - "SJER", - "ONAQ", "DSNY", "SCBI", "MOAB", "PUUM", "GUAN", - "OSBS", "BART", "CPER", "HARV", @@ -279,7 +275,11 @@ "WREF", "LAJA", "TEAK", - "BONA" + "CLBJ", + "SJER", + "OSBS", + "BONA", + "ONAQ" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/climatology.json index e2bceae2e..7b1b5548d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index cd8f67b86..c0f4d3eac 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index 7da5beb42..52b6901c7 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -14,98 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], - [ - -96.5631, - 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 - ], [ -105.5824, 40.0543 @@ -201,13 +109,105 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], + [ + -96.5631, + 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 ] ] }, "properties": { "title": "tg_ets", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,29 +238,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", "NIWO", "NOGP", "OAES", @@ -284,7 +261,30 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json index 8751bab64..394e309f0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], [ -122.3303, 45.7624 @@ -78,6 +66,18 @@ -66.8687, 17.9696 ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -106.8425, 32.5907 @@ -206,8 +206,8 @@ }, "properties": { "title": "tg_humidity_lm", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HARV, HEAL, JERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,9 +238,6 @@ "le", "Daily", "P1D", - "HARV", - "HEAL", - "JERC", "ABBY", "BARR", "BART", @@ -254,6 +251,9 @@ "DSNY", "GRSM", "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json index 7665d12b8..ce3505548 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json index 5dcf81f09..6cee03b03 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json @@ -14,66 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], [ -84.4686, 31.1948 @@ -201,13 +141,73 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 ] ] }, "properties": { "title": "tg_precip_lm", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,21 +238,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", "JERC", "JORN", "KONA", @@ -284,7 +269,22 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json index b46e78deb..29920c0ef 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json @@ -14,6 +14,78 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], [ -96.5631, 39.1008 @@ -129,85 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 ] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -238,6 +238,24 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -266,25 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json index 58263191d..ed63aed39 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index 186ccf33b..241e8f741 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json index dc4a8a75c..c611457de 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json index b00d54041..bf9647842 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/collection.json index 72501a130..7f621db2b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/collection.json @@ -21,17 +21,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", @@ -41,22 +41,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json index 1ce4fa420..da27851b8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -78.0418, - 39.0337 - ], - [ - -96.5631, - 39.1008 - ], - [ - -88.1612, - 31.8539 - ], [ -84.2826, 35.9641 @@ -49,13 +37,25 @@ [ -95.1921, 39.0404 + ], + [ + -78.0418, + 39.0337 + ], + [ + -96.5631, + 39.1008 + ], + [ + -88.1612, + 31.8539 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ORNL, OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ @@ -86,15 +86,15 @@ "amblyomma_americanum", "Weekly", "P1W", - "BLAN", - "KONZ", - "LENO", "ORNL", "OSBS", "SCBI", "SERC", "TALL", - "UKFS" + "UKFS", + "BLAN", + "KONZ", + "LENO" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json index 39567c026..c5be46e8a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json index 5f4b7b88d..9c6f59814 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json index 6ce392c9c..9ef3938ad 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json index 526395366..435f92eef 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json @@ -51,7 +51,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json index cae715425..b8f739abb 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json index 3be61ae21..3ba0afce7 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json index 14f97aa4f..629b38198 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json @@ -51,7 +51,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json index 765df1dd8..14fcb5c5e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json index 27102f209..bd74dbb5c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json index 0546a30eb..3d96300ea 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Pseudo/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Pseudo/collection.json index 4fed43fcf..49fcb6deb 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Pseudo/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Pseudo/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-07T00:00:00Z" + "2024-09-08T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Stage1-stats/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Stage1-stats/collection.json index 26345827c..533105dad 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Stage1-stats/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Stage1-stats/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-07T00:00:00Z" + "2024-09-08T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Stage1/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Stage1/collection.json index 71bb2cb73..d9400471c 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Stage1/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Stage1/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-07T00:00:00Z" + "2024-09-08T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Stage2/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Stage2/collection.json index d2d42c986..a13a5c53c 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Stage2/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Stage2/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-07T00:00:00Z" + "2024-09-08T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Stage3/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Stage3/collection.json index 8895500ab..91d794723 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Stage3/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Stage3/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-07T00:00:00Z" + "2024-09-08T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/collection.json index 5f5dabe0f..47824d994 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/collection.json @@ -86,7 +86,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-07T00:00:00Z" + "2024-09-08T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json index 9da5bf602..2860d1819 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -59,7 +59,7 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRPO, SUGG, TOMB, PRLA, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json index f35a6c75f..631aa25c2 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -59,7 +59,7 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Chlorophyll_a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index c4db6cad9..3d58e5d93 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_arima", "description": "All scores for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index 2133e8697..efb44fa2b 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Chlorophyll_a variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index 5f7a7b49a..524096120 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/collection.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/collection.json index 99eeb51e2..2c6d90166 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -11,22 +11,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/AquaticEcosystemsOxygen.json" }, { "rel": "item", "type": "application/json", - "href": "./models/hotdeck.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/AquaticEcosystemsOxygen.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", @@ -41,7 +41,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/hotdeck.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index d117a6ff7..6958b1368 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -23,7 +23,7 @@ "properties": { "title": "AquaticEcosystemsOxygen", "description": "All scores for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-31T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 231a93241..b3a7385d5 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -14,6 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], [ -96.6242, 34.4442 @@ -90,10 +106,6 @@ -97.7823, 33.3785 ], - [ - -99.1139, - 47.1591 - ], [ -99.2531, 47.1298 @@ -118,10 +130,6 @@ -88.1589, 31.8534 ], - [ - -149.6106, - 68.6307 - ], [ -84.2793, 35.9574 @@ -131,31 +139,23 @@ 39.8914 ], [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 + -99.1139, + 47.1591 ], [ -149.143, 68.6698 + ], + [ + -149.6106, + 68.6307 ] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, OKSR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, PRLA, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -186,6 +186,10 @@ "oxygen", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", "BLUE", "BLWA", "CARI", @@ -205,21 +209,17 @@ "MCRA", "POSE", "PRIN", - "PRLA", "PRPO", "REDB", "SUGG", "SYCA", "TECR", "TOMB", - "TOOK", "WALK", "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "OKSR" + "PRLA", + "OKSR", + "TOOK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index dbbb0b2f5..b60164c0c 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -75,7 +75,7 @@ "properties": { "title": "hotdeck", "description": "All scores for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, BIGC, BLDE, CRAM, KING, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, REDB, SUGG, SYCA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index 7c1823d65..4203f3411 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -14,6 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 + ], [ -102.4471, 39.7582 @@ -133,29 +149,13 @@ [ -119.0274, 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 ] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -186,6 +186,10 @@ "oxygen", "Daily", "P1D", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -215,11 +219,7 @@ "REDB", "SUGG", "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "TECR" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index f1e28ba48..56ef0e7e7 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -14,54 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -78.1473, - 38.8943 - ], - [ - -97.7823, - 33.3785 - ], - [ - -99.1139, - 47.1591 - ], - [ - -99.2531, - 47.1298 - ], - [ - -111.7979, - 40.7839 - ], - [ - -82.0177, - 29.6878 - ], - [ - -111.5081, - 33.751 - ], - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -102.4471, 39.7582 @@ -149,13 +101,61 @@ [ -149.143, 68.6698 + ], + [ + -78.1473, + 38.8943 + ], + [ + -97.7823, + 33.3785 + ], + [ + -99.1139, + 47.1591 + ], + [ + -99.2531, + 47.1298 + ], + [ + -111.7979, + 40.7839 + ], + [ + -82.0177, + 29.6878 + ], + [ + -111.5081, + 33.751 + ], + [ + -119.0274, + 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,18 +186,6 @@ "oxygen", "Daily", "P1D", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -219,7 +207,19 @@ "MAYF", "MCDI", "MCRA", - "OKSR" + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index 91fd2b409..71084d5e2 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index 80eb5f226..2de20e92d 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/collection.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/collection.json index b7ee05b36..07858036a 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/collection.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/collection.json @@ -21,62 +21,62 @@ { "rel": "item", "type": "application/json", - "href": "./models/GLEON_JRabaey_temp_physics.json" + "href": "./models/flareGLM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/flareGLM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/hotdeck.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/lm_AT_WTL_WS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/mkricheldorf_w_lag.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GAM_air_wind.json" + "href": "./models/mlp1_wtempforecast_LF.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM_noDA.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/hotdeck.json" + "href": "./models/GAM_air_wind.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/bee_bake_RFModel_2024.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/zimmerman_proj1.json" + "href": "./models/bee_bake_RFModel_2024.json" }, { "rel": "item", @@ -86,22 +86,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/lm_AT_WTL_WS.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mkricheldorf_w_lag.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mlp1_wtempforecast_LF.json" + "href": "./models/zimmerman_proj1.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json index 67b0f873c..a5ed50b56 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json @@ -47,7 +47,7 @@ "properties": { "title": "GAM_air_wind", "description": "All scores for the Daily_Water_temperature variable for the GAM_air_wind model. Information for the model is provided as follows: I used a GAM (mgcv) with a linear relationship to air temperature and smoothing for eastward and northward winds..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index 067fe6f63..39506f750 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -14,66 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -96.443, - 38.9459 - ], - [ - -122.1655, - 44.2596 - ], - [ - -149.143, - 68.6698 - ], - [ - -78.1473, - 38.8943 - ], - [ - -97.7823, - 33.3785 - ], - [ - -99.1139, - 47.1591 - ], - [ - -99.2531, - 47.1298 - ], - [ - -111.7979, - 40.7839 - ], - [ - -82.0177, - 29.6878 - ], - [ - -111.5081, - 33.751 - ], - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -102.4471, 39.7582 @@ -149,13 +89,73 @@ [ -87.4077, 32.9604 + ], + [ + -96.443, + 38.9459 + ], + [ + -122.1655, + 44.2596 + ], + [ + -149.143, + 68.6698 + ], + [ + -78.1473, + 38.8943 + ], + [ + -97.7823, + 33.3785 + ], + [ + -99.1139, + 47.1591 + ], + [ + -99.2531, + 47.1298 + ], + [ + -111.7979, + 40.7839 + ], + [ + -82.0177, + 29.6878 + ], + [ + -111.5081, + 33.751 + ], + [ + -119.0274, + 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ] ] }, "properties": { "title": "GLEON_JRabaey_temp_physics", - "description": "All scores for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-02T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ @@ -186,21 +186,6 @@ "temperature", "Daily", "P1D", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -219,7 +204,22 @@ "LEWI", "LIRO", "MART", - "MAYF" + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index 8123d42f2..d862a80af 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All scores for the Daily_Water_temperature variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-02T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index 03aad42e1..941165944 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -155,7 +155,7 @@ "properties": { "title": "baseline_ensemble", "description": "All scores for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json index d0d8e3bfa..f0eb21aff 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json @@ -47,7 +47,7 @@ "properties": { "title": "bee_bake_RFModel_2024", "description": "All scores for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/climatology.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/climatology.json index c8dc4aeef..b54721372 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/climatology.json @@ -14,6 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -119.0274, + 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 + ], [ -102.4471, 39.7582 @@ -130,22 +146,6 @@ -111.5081, 33.751 ], - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -149.6106, 68.6307 @@ -154,8 +154,8 @@ }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -186,6 +186,10 @@ "temperature", "Daily", "P1D", + "TECR", + "TOMB", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -215,10 +219,6 @@ "REDB", "SUGG", "SYCA", - "TECR", - "TOMB", - "WALK", - "WLOU", "TOOK" ], "table:columns": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index e0b4e0ad1..fe8bf7256 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -14,34 +14,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -110.5871, - 44.9501 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 - ], - [ - -66.9868, - 18.1135 - ], - [ - -84.4374, - 31.1854 - ], [ -66.7987, 18.1741 ], + [ + -72.3295, + 42.4719 + ], [ -96.6038, 39.1051 @@ -94,10 +74,6 @@ -99.2531, 47.1298 ], - [ - -111.7979, - 40.7839 - ], [ -82.0177, 29.6878 @@ -106,6 +82,10 @@ -111.5081, 33.751 ], + [ + -119.0274, + 36.9559 + ], [ -88.1589, 31.8534 @@ -127,31 +107,51 @@ 29.676 ], [ - -119.2575, - 37.0597 + -110.5871, + 44.9501 ], [ - -72.3295, - 42.4719 + -87.7982, + 32.5415 ], [ - -149.6106, - 68.6307 + -147.504, + 65.1532 ], [ - -119.0274, - 36.9559 + -105.5442, + 40.035 ], [ - -87.7982, - 32.5415 + -89.4737, + 46.2097 + ], + [ + -66.9868, + 18.1135 + ], + [ + -84.4374, + 31.1854 + ], + [ + -111.7979, + 40.7839 + ], + [ + -119.2575, + 37.0597 + ], + [ + -149.6106, + 68.6307 ] ] }, "properties": { "title": "fARIMA_clim_ensemble", - "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: BLDE, CARI, COMO, CRAM, CUPE, FLNT, GUIL, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TOMB, WALK, WLOU, ARIK, BARC, BIGC, HOPB, TOOK, TECR, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, REDB, BIGC, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-01T00:00:00Z", "providers": [ @@ -182,13 +182,8 @@ "temperature", "Daily", "P1D", - "BLDE", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", "GUIL", + "HOPB", "KING", "LECO", "LEWI", @@ -202,19 +197,24 @@ "PRIN", "PRLA", "PRPO", - "REDB", "SUGG", "SYCA", + "TECR", "TOMB", "WALK", "WLOU", "ARIK", "BARC", + "BLDE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "REDB", "BIGC", - "HOPB", - "TOOK", - "TECR", - "BLWA" + "TOOK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index ae727f455..e554c8f23 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -14,42 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 - ], [ -66.9868, 18.1135 @@ -149,13 +113,49 @@ [ -105.9154, 39.8914 + ], + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 + ], + [ + -147.504, + 65.1532 + ], + [ + -105.5442, + 40.035 + ], + [ + -89.4737, + 46.2097 ] ] }, "properties": { "title": "fTSLM_lag", - "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -186,15 +186,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", "CUPE", "FLNT", "GUIL", @@ -219,7 +210,16 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json index 875f69dd6..85da05cbe 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGLM", "description": "All scores for the Daily_Water_temperature variable for the flareGLM model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019) and data assimilation algorithm to generate\nensemble forecasts of lake water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index fda37f6d9..95e7d7129 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -14,6 +14,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -82.0084, + 29.676 + ], + [ + -89.4737, + 46.2097 + ], + [ + -89.7048, + 45.9983 + ], [ -99.1139, 47.1591 @@ -29,25 +41,13 @@ [ -149.6106, 68.6307 - ], - [ - -82.0084, - 29.676 - ], - [ - -89.4737, - 46.2097 - ], - [ - -89.7048, - 45.9983 ] ] }, "properties": { "title": "flareGLM_noDA", - "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOOK, BARC, CRAM, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -78,13 +78,13 @@ "temperature", "Daily", "P1D", + "BARC", + "CRAM", + "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK", - "BARC", - "CRAM", - "LIRO" + "TOOK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json index 135011d30..deeecc030 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -14,62 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 - ], - [ - -66.9868, - 18.1135 - ], - [ - -84.4374, - 31.1854 - ], - [ - -66.7987, - 18.1741 - ], - [ - -72.3295, - 42.4719 - ], - [ - -96.6038, - 39.1051 - ], [ -83.5038, 35.6904 @@ -133,13 +77,69 @@ [ -105.9154, 39.8914 + ], + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 + ], + [ + -147.504, + 65.1532 + ], + [ + -105.5442, + 40.035 + ], + [ + -89.4737, + 46.2097 + ], + [ + -66.9868, + 18.1135 + ], + [ + -84.4374, + 31.1854 + ], + [ + -66.7987, + 18.1741 + ], + [ + -72.3295, + 42.4719 + ], + [ + -96.6038, + 39.1051 ] ] }, "properties": { "title": "hotdeck", - "description": "All scores for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: LECO, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -170,20 +170,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", "LECO", "LEWI", "LIRO", @@ -199,7 +185,21 @@ "TECR", "TOMB", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json index 330787c4c..bcac73547 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json @@ -47,7 +47,7 @@ "properties": { "title": "lm_AT_WTL_WS", "description": "All scores for the Daily_Water_temperature variable for the lm_AT_WTL_WS model. Information for the model is provided as follows: This forecast of water temperature at NEON Lake sites uses a linear model, incorporating air temperature, wind speed, and the previous day's forecasted water temperature as variables..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json index ed545a85e..610bf02b6 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json @@ -47,7 +47,7 @@ "properties": { "title": "mkricheldorf_w_lag", "description": "All scores for the Daily_Water_temperature variable for the mkricheldorf_w_lag model. Information for the model is provided as follows: I used an autoregressive linear model using the lm() function.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-26T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json index ee2d8a6c9..6397c407e 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json @@ -47,7 +47,7 @@ "properties": { "title": "mlp1_wtempforecast_LF", "description": "All scores for the Daily_Water_temperature variable for the mlp1_wtempforecast_LF model. Information for the model is provided as follows: Modelling for water temperature using a single layer neural network (mlp() in tidymodels). Used relative humidity, precipitation flux and air temperature as drivers. Hypertuned parameters for models to be run with 100 epochs and penalty value of 0.01..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json index eb8ed8199..bb61b1010 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -155,7 +155,7 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json index 35fe3cd03..bfaa0c8c7 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_arima", "description": "All scores for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json index 86d9d14eb..fc5413550 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json index 7c04f20b5..ded743cb7 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json index e360133ae..75f5d0b27 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -47,7 +47,7 @@ "properties": { "title": "zimmerman_proj1", "description": "All scores for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index 776ef3d8e..c5b180343 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-07-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index f786ecb93..dde1105bb 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-07-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index 675fce48d..358ac4af9 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-07-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/collection.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/collection.json index 131f5a67a..1ac355122 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/collection.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/collection.json @@ -11,17 +11,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_arima.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index 9b54dc99f..881d739b0 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index 210fdaed0..6547642db 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -14,6 +14,42 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -165,49 +201,13 @@ [ -103.0293, 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ @@ -238,6 +238,15 @@ "richness", "Weekly", "P1W", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -275,16 +284,7 @@ "SOAP", "SRER", "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "STER" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index 67517cf86..9db70e642 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json index b9c7789df..345a5933a 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -11,12 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/ChlorophyllCrusaders.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/climatology.json" }, { "rel": "item", @@ -31,12 +31,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/ChlorophyllCrusaders.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/persistenceRW.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index 247a04985..8fef8c5d3 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -23,7 +23,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the ChlorophyllCrusaders model. Information for the model is provided as follows: Our project utilizes a historical GCC data to fit a Dynamic Linear Model (DLM). After this DLM is trained, we utilize forecasted temperature data to predict future GCC data..\n The model predicts this variable at the following sites: HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index 3b7356c66..862f40d22 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index 090e36023..4a00f4274 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -14,6 +14,66 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], [ -84.4686, 31.1948 @@ -141,73 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 ] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -238,6 +238,21 @@ "gcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -269,22 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index 925e71af3..43c089d00 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -14,6 +14,62 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -145,69 +201,13 @@ [ -76.56, 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -238,6 +238,20 @@ "gcc_90", "Daily", "P1D", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -270,21 +284,7 @@ "PUUM", "RMNP", "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SERC" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index b78371dfc..00d3a9747 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index f45d8ed9f..f59e314f4 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json index ae8477fc7..afd105fa1 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -11,32 +11,32 @@ { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_ets.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index 44b8e86bd..b3b0312fc 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -207,7 +207,7 @@ "properties": { "title": "baseline_ensemble", "description": "All scores for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index 97698e4f8..b09f5f13c 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index eb44765c3..f52d2e055 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index 1e84f62b6..8dceadf15 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -14,6 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], [ -147.5026, 65.154 @@ -185,29 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,10 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", "BONA", "CLBJ", "CPER", @@ -280,11 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index d7da53740..310f8b061 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index 4b4e5d0ad..43bdfc7b7 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -137,77 +201,13 @@ [ -105.546, 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,22 @@ "rcc_90", "Daily", "P1D", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -268,23 +284,7 @@ "ORNL", "OSBS", "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "RMNP" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json index 0d5fe3c56..0bf9573c7 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -16,27 +16,27 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/bookcast_forest.json" }, { "rel": "item", "type": "application/json", - "href": "./models/bookcast_forest.json" + "href": "./models/climatology.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index 722eedca9..eb3323adf 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -23,7 +23,7 @@ "properties": { "title": "bookcast_forest", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: OSBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json index ff4dded2a..de10cba1b 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json @@ -14,6 +14,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], [ -71.2874, 44.0639 @@ -193,21 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 ] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-03T00:00:00Z", "providers": [ @@ -238,6 +238,8 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", "BART", "BLAN", "BONA", @@ -282,9 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index 5aab054c4..3fc90aa41 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -14,42 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], [ -104.7456, 40.8155 @@ -201,13 +165,49 @@ [ -89.5373, 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 ] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-03T00:00:00Z", "providers": [ @@ -238,15 +238,6 @@ "nee", "Daily", "P1D", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", "CPER", "DCFS", "DEJU", @@ -284,7 +275,16 @@ "TOOL", "TREE", "UKFS", - "UNDE" + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index c280c8783..8539425e3 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -106.8425, 32.5907 @@ -137,77 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,22 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -268,23 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index b17a4bc59..cba772112 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index c81b8a062..f1a8c3ea0 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/collection.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/collection.json index b33af43c3..42dc32070 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/collection.json @@ -16,17 +16,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_arima.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json index 74560faf1..6e21035f3 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -14,6 +14,30 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], [ -96.6129, 39.1104 @@ -177,37 +201,13 @@ [ -81.4362, 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 ] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ @@ -238,6 +238,12 @@ "le", "Daily", "P1D", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -278,13 +284,7 @@ "DCFS", "DEJU", "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN" + "DSNY" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index a227317b5..b9e31e47e 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All scores for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index a86323887..92237c2a6 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index 8c9a551fe..4b35bf783 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/sites/collection.json b/data/challenge/neon4cast-stac/sites/collection.json index 8fa4b0760..895e53809 100644 --- a/data/challenge/neon4cast-stac/sites/collection.json +++ b/data/challenge/neon4cast-stac/sites/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/collection.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/collection.json index 47f5900c2..bb0fb9d9d 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/collection.json @@ -11,107 +11,107 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/USGSHABs1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/procBlanchardMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/procCTMIMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/procEppleyNorbergMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/procEppleyNorbergSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/procHinshelwoodMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/USGSHABs1.json" + "href": "./models/procHinshelwoodSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procBlanchardMonod.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procCTMIMonod.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergMonod.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergSteele.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodMonod.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodSteele.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json index 17a078be6..34a247fb8 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json @@ -47,7 +47,7 @@ "properties": { "title": "procEppleyNorbergSteele", "description": "All summaries for the Daily_Chlorophyll_a variable for the procEppleyNorbergSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json index 3a7c6d229..c436e3053 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procHinshelwoodMonod", "description": "All summaries for the Daily_Chlorophyll_a variable for the procHinshelwoodMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json index cbbd10948..3d94aae29 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json @@ -47,7 +47,7 @@ "properties": { "title": "procHinshelwoodSteele", "description": "All summaries for the Daily_Chlorophyll_a variable for the procHinshelwoodSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index fb15d045a..a3db5ef46 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index e90f1dc4f..f99065deb 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json index 461619262..c8c934f18 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json @@ -14,10 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -149.6106, - 68.6307 - ], [ -82.0084, 29.676 @@ -53,13 +49,17 @@ [ -88.1589, 31.8534 + ], + [ + -149.6106, + 68.6307 ] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -90,7 +90,6 @@ "chla", "Daily", "P1D", - "TOOK", "BARC", "BLWA", "CRAM", @@ -99,7 +98,8 @@ "PRLA", "PRPO", "SUGG", - "TOMB" + "TOMB", + "TOOK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json index f9b5f61e9..ca2050ab4 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json index 540cad21b..49db126f0 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json index 5946abd3d..3ecfcdc7a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json index 2641e2d57..0060b0eec 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json index 38ce23b04..fee4ab602 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index 3095c2620..4978cb8a3 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json index 62e31c7e3..7b0453e57 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json index 7e49857b6..1cd4abe26 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json index 3ce54a066..46f268647 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -26,37 +26,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", @@ -71,22 +71,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_arima.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index 7e16c4f67..5ecacbcf8 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -31,7 +31,7 @@ "properties": { "title": "AquaticEcosystemsOxygen", "description": "All summaries for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: BARC, WLOU, ARIK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-03T00:00:00Z", "end_datetime": "2024-08-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json index ad9f90cad..dcdf96a99 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All summaries for the Daily_Dissolved_oxygen variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json index d2196882f..c67a1787d 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json @@ -155,7 +155,7 @@ "properties": { "title": "air2waterSat_2", "description": "All summaries for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOMB, TOOK, WALK, WLOU, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json index b409f8db3..d7bc99a3e 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json @@ -147,7 +147,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Dissolved_oxygen variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 58651e8ba..989c5e07a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -15,12 +15,24 @@ "type": "MultiPoint", "coordinates": [ [ - -87.7982, - 32.5415 + -102.4471, + 39.7582 ], [ - -147.504, - 65.1532 + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 ], [ -105.5442, @@ -102,34 +114,22 @@ -105.9154, 39.8914 ], - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], [ -88.1589, 31.8534 ], + [ + -87.7982, + 32.5415 + ], [ -89.4737, 46.2097 ], + [ + -147.504, + 65.1532 + ], [ -89.7048, 45.9983 @@ -154,8 +154,8 @@ }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ @@ -186,8 +186,11 @@ "oxygen", "Daily", "P1D", - "BLWA", - "CARI", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "COMO", "CUPE", "FLNT", @@ -208,13 +211,10 @@ "TECR", "WALK", "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", "TOMB", + "BLWA", "CRAM", + "CARI", "LIRO", "PRPO", "PRLA", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index cec19f95f..59540b405 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -26,10 +26,6 @@ -96.6038, 39.1051 ], - [ - -111.5081, - 33.751 - ], [ -110.5871, 44.9501 @@ -54,6 +50,10 @@ -89.7048, 45.9983 ], + [ + -111.5081, + 33.751 + ], [ -97.7823, 33.3785 @@ -74,8 +74,8 @@ }, "properties": { "title": "hotdeck", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, SYCA, BLDE, BIGC, MCRA, REDB, CRAM, LIRO, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, BLDE, BIGC, MCRA, REDB, CRAM, LIRO, SYCA, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-05T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ @@ -109,13 +109,13 @@ "BARC", "SUGG", "KING", - "SYCA", "BLDE", "BIGC", "MCRA", "REDB", "CRAM", "LIRO", + "SYCA", "PRIN", "POSE", "MAYF", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index ab7ef9a61..f3035f700 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -14,6 +14,38 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -87.4077, + 32.9604 + ], + [ + -96.443, + 38.9459 + ], + [ + -122.1655, + 44.2596 + ], + [ + -149.143, + 68.6698 + ], + [ + -78.1473, + 38.8943 + ], + [ + -97.7823, + 33.3785 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 + ], [ -147.504, 65.1532 @@ -30,10 +62,6 @@ -66.9868, 18.1135 ], - [ - -97.7823, - 33.3785 - ], [ -99.1139, 47.1591 @@ -54,34 +82,6 @@ -111.5081, 33.751 ], - [ - -87.4077, - 32.9604 - ], - [ - -96.443, - 38.9459 - ], - [ - -122.1655, - 44.2596 - ], - [ - -149.143, - 68.6698 - ], - [ - -78.1473, - 38.8943 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], [ -105.9154, 39.8914 @@ -154,8 +154,8 @@ }, "properties": { "title": "persistenceRW", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, OKSR, POSE, PRIN, BLUE, BLWA, CARI, COMO, CRAM, CUPE, PRLA, PRPO, REDB, SUGG, SYCA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ @@ -186,23 +186,23 @@ "oxygen", "Daily", "P1D", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "BLUE", + "BLWA", "CARI", "COMO", "CRAM", "CUPE", - "PRIN", "PRLA", "PRPO", "REDB", "SUGG", "SYCA", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "BLUE", - "BLWA", "WLOU", "ARIK", "BARC", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index c35ab2f56..bdcd7daff 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index 387aabc37..50b119f0d 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json index b61f0cb1b..8e42c175f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json index 437b517ab..d2bce084a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json index 94eebc34a..fe2057520 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json index c18a7876a..9786643a6 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json index 68eec4146..6784fa7e1 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json index ed9f1f43c..72c97d664 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index ddd9c5ffe..815b6da1a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json index 623e4bab0..0e4d059bc 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json index ea2495fef..a04008b50 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/collection.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/collection.json index b62ef86fb..8182e97ec 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/collection.json @@ -61,87 +61,87 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fARIMA_clim_ensemble.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_JRabaey_temp_physics.json" + "href": "./models/fARIMA_clim_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_physics.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/GLEON_physics.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json index b378eed99..c033d2556 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json @@ -47,7 +47,7 @@ "properties": { "title": "GAM_air_wind", "description": "All summaries for the Daily_Water_temperature variable for the GAM_air_wind model. Information for the model is provided as follows: I used a GAM (mgcv) with a linear relationship to air temperature and smoothing for eastward and northward winds..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index 3165ec933..c12f86a3f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -155,7 +155,7 @@ "properties": { "title": "GLEON_JRabaey_temp_physics", "description": "All summaries for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index 2e270c986..d3b132c36 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All summaries for the Daily_Water_temperature variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json index 45febffef..58dcb572a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json @@ -43,7 +43,7 @@ "properties": { "title": "GLEON_physics", "description": "All summaries for the Daily_Water_temperature variable for the GLEON_physics model. Information for the model is provided as follows: A simple, process-based model was developed to replicate the water temperature dynamics of a\nsurface water layer sensu Chapra (2008). The model focus was only on quantifying the impacts of\natmosphere-water heat flux exchanges on the idealized near-surface water temperature dynamics.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2023-12-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json index 8dc69ae95..3df27267f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json @@ -47,7 +47,7 @@ "properties": { "title": "TSLM_seasonal_JM", "description": "All summaries for the Daily_Water_temperature variable for the TSLM_seasonal_JM model. Information for the model is provided as follows: My model uses the fable package TSLM, and uses built in exogenous regressors to represent the trend and seasonality of the data as well as air temperature to predict water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-06-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json index ee0ecf987..17275270f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json @@ -47,7 +47,7 @@ "properties": { "title": "acp_fableLM", "description": "All summaries for the Daily_Water_temperature variable for the acp_fableLM model. Information for the model is provided as follows: Time series linear model with FABLE.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-11T00:00:00Z", "end_datetime": "2024-04-13T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json index cb8ffd2be..447a27a4d 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json @@ -15,8 +15,16 @@ "type": "MultiPoint", "coordinates": [ [ - -77.9832, - 39.0956 + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ], [ -89.7048, @@ -139,23 +147,15 @@ 35.6904 ], [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 + -77.9832, + 39.0956 ] ] }, "properties": { "title": "air2waterSat_2", - "description": "All summaries for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -186,7 +186,9 @@ "temperature", "Daily", "P1D", - "LEWI", + "TOOK", + "WALK", + "WLOU", "LIRO", "MART", "MAYF", @@ -217,9 +219,7 @@ "HOPB", "KING", "LECO", - "TOOK", - "WALK", - "WLOU" + "LEWI" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index b278e652a..fa5d78cbe 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -14,14 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -96.443, - 38.9459 - ], - [ - -122.1655, - 44.2596 - ], [ -78.1473, 38.8943 @@ -38,6 +30,14 @@ -82.0177, 29.6878 ], + [ + -96.443, + 38.9459 + ], + [ + -122.1655, + 44.2596 + ], [ -72.3295, 42.4719 @@ -154,8 +154,8 @@ }, "properties": { "title": "baseline_ensemble", - "description": "All summaries for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: MCDI, MCRA, POSE, PRIN, REDB, SUGG, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: POSE, PRIN, REDB, SUGG, MCDI, MCRA, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,12 +186,12 @@ "temperature", "Daily", "P1D", - "MCDI", - "MCRA", "POSE", "PRIN", "REDB", "SUGG", + "MCDI", + "MCRA", "HOPB", "KING", "LECO", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json index e2d536411..9c310da11 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json @@ -15,17 +15,13 @@ "type": "MultiPoint", "coordinates": [ [ - -89.4737, - 46.2097 + -89.7048, + 45.9983 ], [ -99.2531, 47.1298 ], - [ - -89.7048, - 45.9983 - ], [ -99.1139, 47.1591 @@ -34,6 +30,10 @@ -82.0084, 29.676 ], + [ + -89.4737, + 46.2097 + ], [ -82.0177, 29.6878 @@ -46,8 +46,8 @@ }, "properties": { "title": "bee_bake_RFModel_2024", - "description": "All summaries for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: CRAM, PRPO, LIRO, PRLA, BARC, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: LIRO, PRPO, PRLA, BARC, CRAM, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", "providers": [ @@ -78,11 +78,11 @@ "temperature", "Daily", "P1D", - "CRAM", - "PRPO", "LIRO", + "PRPO", "PRLA", "BARC", + "CRAM", "SUGG", "TOOK" ], diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json index 9bfae4c96..e222af1d8 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json @@ -147,7 +147,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Water_temperature variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, TECR, TOMB, WALK, WLOU, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/climatology.json index 9c3d94f36..e2bbe31ac 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/climatology.json @@ -14,6 +14,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], [ -119.2575, 37.0597 @@ -30,18 +38,10 @@ -87.7982, 32.5415 ], - [ - -147.504, - 65.1532 - ], [ -105.5442, 40.035 ], - [ - -89.4737, - 46.2097 - ], [ -66.9868, 18.1135 @@ -70,10 +70,6 @@ -77.9832, 39.0956 ], - [ - -89.7048, - 45.9983 - ], [ -121.9338, 45.7908 @@ -98,14 +94,6 @@ -97.7823, 33.3785 ], - [ - -99.1139, - 47.1591 - ], - [ - -99.2531, - 47.1298 - ], [ -111.7979, 40.7839 @@ -122,10 +110,6 @@ -119.0274, 36.9559 ], - [ - -88.1589, - 31.8534 - ], [ -84.2793, 35.9574 @@ -135,12 +119,28 @@ 39.8914 ], [ - -102.4471, - 39.7582 + -88.1589, + 31.8534 ], [ - -82.0084, - 29.676 + -89.7048, + 45.9983 + ], + [ + -99.2531, + 47.1298 + ], + [ + -89.4737, + 46.2097 + ], + [ + -99.1139, + 47.1591 + ], + [ + -147.504, + 65.1532 ], [ -149.143, @@ -154,8 +154,8 @@ }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, LIRO, PRPO, CRAM, PRLA, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ @@ -186,13 +186,13 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", "BIGC", "BLDE", "BLUE", "BLWA", - "CARI", "COMO", - "CRAM", "CUPE", "FLNT", "GUIL", @@ -200,24 +200,24 @@ "KING", "LECO", "LEWI", - "LIRO", "MART", "MAYF", "MCDI", "MCRA", "POSE", "PRIN", - "PRLA", - "PRPO", "REDB", "SUGG", "SYCA", "TECR", - "TOMB", "WALK", "WLOU", - "ARIK", - "BARC", + "TOMB", + "LIRO", + "PRPO", + "CRAM", + "PRLA", + "CARI", "OKSR", "TOOK" ], diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index 9598b2582..d07c5928c 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -114,14 +114,14 @@ -110.5871, 44.9501 ], - [ - -89.4737, - 46.2097 - ], [ -84.4374, 31.1854 ], + [ + -89.4737, + 46.2097 + ], [ -111.5081, 33.751 @@ -154,8 +154,8 @@ }, "properties": { "title": "fARIMA_clim_ensemble", - "description": "All summaries for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, CRAM, FLNT, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, FLNT, CRAM, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-10T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -211,8 +211,8 @@ "TOMB", "BIGC", "BLDE", - "CRAM", "FLNT", + "CRAM", "SYCA", "LIRO", "PRLA", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index acc068a79..1c5125f0e 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -155,7 +155,7 @@ "properties": { "title": "fTSLM_lag", "description": "All summaries for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-08T00:00:00Z", "end_datetime": "2024-09-14T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json index 3a00c303e..9b6376749 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGLM", "description": "All summaries for the Daily_Water_temperature variable for the flareGLM model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019) and data assimilation algorithm to generate\nensemble forecasts of lake water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index e42a0898f..412b08bf0 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGLM_noDA", "description": "All summaries for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-03-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json index 04c4491f9..1e82ab6a9 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGOTM_noDA", "description": "All summaries for the Daily_Water_temperature variable for the flareGOTM_noDA model. Information for the model is provided as follows: FLARE-GOTM uses the General Ocean Turbulence Model (GOTM) hydrodynamic model. GOTM is a 1-D\nhydrodynamic turbulence model (Umlauf et al., 2005) that estimates water column temperatures.\n The model predicts this variable at the following sites: BARC, CRAM, SUGG, LIRO, PRLA, PRPO, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-20T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json index 5a2fb06c3..b42606b9d 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json @@ -43,7 +43,7 @@ "properties": { "title": "flareSimstrat_noDA", "description": "All summaries for the Daily_Water_temperature variable for the flareSimstrat_noDA model. Information for the model is provided as follows: FLARE-Simstrat uses the same principles and overarching framework as FLARE-GLM with the\nhydrodynamic model replaced with Simstrat. Simstrat is a 1-D hydrodynamic turbulence model\n(Goudsmit et al., 2002) that estimates water column temperatures..\n The model predicts this variable at the following sites: BARC, SUGG, TOOK, CRAM, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json index bf08fc220..f7c05af90 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json @@ -43,7 +43,7 @@ "properties": { "title": "flare_ler", "description": "All summaries for the Daily_Water_temperature variable for the flare_ler model. Information for the model is provided as follows: The LER MME is a multi-model ensemble (MME) derived from the three process models from\nFLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat). To generate the MME, an ensemble\nforecast was generated by sampling from the submitted models\u2019 ensemble members.\n The model predicts this variable at the following sites: SUGG, CRAM, LIRO, PRLA, PRPO, BARC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json index db0e9fc86..68fd24f36 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json @@ -27,7 +27,7 @@ "properties": { "title": "flare_ler_baselines", "description": "All summaries for the Daily_Water_temperature variable for the flare_ler_baselines model. Information for the model is provided as follows: The LER-baselines model is a multi-model ensemble (MME) comprised of the three process\nmodels from FLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat) and the two baseline\nmodels (day-of-year, persistence), submitted by Challenge organisers. To generate the MME, an\nensemble forecast was generated by sampling from the submitted model\u2019s ensemble members (either\nfrom an ensemble forecast in the case of the FLARE models and persistence, or from the distribution for\nthe day-of-year forecasts).\n The model predicts this variable at the following sites: SUGG, BARC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json index 7fc04a8a3..0971486ce 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -102,6 +102,18 @@ -147.504, 65.1532 ], + [ + -89.7048, + 45.9983 + ], + [ + -99.1139, + 47.1591 + ], + [ + -99.2531, + 47.1298 + ], [ -119.2575, 37.0597 @@ -121,25 +133,13 @@ [ -84.2793, 35.9574 - ], - [ - -89.7048, - 45.9983 - ], - [ - -99.1139, - 47.1591 - ], - [ - -99.2531, - 47.1298 ] ] }, "properties": { "title": "hotdeck", - "description": "All summaries for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, LIRO, PRLA, PRPO, BIGC, BLUE, CUPE, GUIL, WALK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ @@ -192,14 +192,14 @@ "WLOU", "CRAM", "CARI", + "LIRO", + "PRLA", + "PRPO", "BIGC", "BLUE", "CUPE", "GUIL", - "WALK", - "LIRO", - "PRLA", - "PRPO" + "WALK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json index 572f7db74..c254ea7dc 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json @@ -47,7 +47,7 @@ "properties": { "title": "lm_AT_WTL_WS", "description": "All summaries for the Daily_Water_temperature variable for the lm_AT_WTL_WS model. Information for the model is provided as follows: This forecast of water temperature at NEON Lake sites uses a linear model, incorporating air temperature, wind speed, and the previous day's forecasted water temperature as variables..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json index e027e1f05..b4bfb34ca 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json @@ -47,7 +47,7 @@ "properties": { "title": "mkricheldorf_w_lag", "description": "All summaries for the Daily_Water_temperature variable for the mkricheldorf_w_lag model. Information for the model is provided as follows: I used an autoregressive linear model using the lm() function.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-06T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json index e2a7636de..20e174fac 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json @@ -47,7 +47,7 @@ "properties": { "title": "mlp1_wtempforecast_LF", "description": "All summaries for the Daily_Water_temperature variable for the mlp1_wtempforecast_LF model. Information for the model is provided as follows: Modelling for water temperature using a single layer neural network (mlp() in tidymodels). Used relative humidity, precipitation flux and air temperature as drivers. Hypertuned parameters for models to be run with 100 epochs and penalty value of 0.01..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json index cf6d729b7..2fe2c18e9 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -155,7 +155,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KING, LECO, LEWI, LIRO, MART, MAYF, ARIK, BARC, BIGC, BLDE, BLUE, MCDI, MCRA, OKSR, POSE, PRIN, WLOU, CUPE, FLNT, GUIL, HOPB, PRLA, PRPO, REDB, SUGG, SYCA, BLWA, CARI, COMO, CRAM, TECR, TOMB, TOOK, WALK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json index 346ccef73..22b8af80d 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json @@ -47,7 +47,7 @@ "properties": { "title": "precip_mod", "description": "All summaries for the Daily_Water_temperature variable for the precip_mod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-12-21T00:00:00Z", "end_datetime": "2024-01-24T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json index c987097a6..bebb699e7 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json index d35b33ca9..8f9a07a66 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json index a8f8fa1aa..b453f3283 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Water_temperature variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json index 56cab011b..1c9cb6346 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Water_temperature variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json index c6d739fc1..d569a27c8 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Water_temperature variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json index 3b8b73d67..90f6d0957 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Water_temperature variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json index 0c6884fbf..522c582bb 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Water_temperature variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json index 2994df70c..275632517 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Water_temperature variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json index 541da8a30..ecdb0157b 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json index 7767cc7ae..302c6e278 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json index 0011472d6..236ac3ca9 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Water_temperature variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json index 2122385b8..648a8f672 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -47,7 +47,7 @@ "properties": { "title": "zimmerman_proj1", "description": "All summaries for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/collection.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/collection.json index ac0851a74..b6fa39227 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/collection.json @@ -8,6 +8,11 @@ ], "type": "Collection", "links": [ + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_arima.json" + }, { "rel": "item", "type": "application/json", @@ -43,11 +48,6 @@ "type": "application/json", "href": "./models/tg_temp_lm_all_sites.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_arima.json" - }, { "rel": "item", "type": "application/json", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index 7bfba61a5..aefa9adf1 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index 360457455..9f9832ce3 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -14,6 +14,62 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], [ -149.2133, 63.8758 @@ -145,69 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 ] ] }, "properties": { "title": "tg_ets", - "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -238,6 +238,20 @@ "abundance", "Weekly", "P1W", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -270,21 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json index b061ae542..f01c588ac 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json index 3b62b7f54..219baeccb 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json index bc6863312..d51287587 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json @@ -199,7 +199,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json index eaf4108fa..f058f74ce 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json index 5ad8d2c6e..9f04e93d3 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json index e3fae55de..340ba1aa5 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index f68f3a29c..ee544fbc8 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json index 4a078d847..69e4cd7e0 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json index 7c68a6680..f13974f48 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/collection.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/collection.json index 735757ce5..a7dfeeb7d 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/collection.json @@ -11,42 +11,42 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index 39a6396b8..b31a4d986 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index 3940ffd67..133e07554 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -14,82 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +125,89 @@ [ -84.2826, 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_ets", - "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -238,25 +238,6 @@ "richness", "Weekly", "P1W", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +265,26 @@ "NOGP", "OAES", "ONAQ", - "ORNL" + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json index dfd416063..f71de3680 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json index a6efe1f98..c6c81a53c 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json index fb061b354..e166e60d0 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json @@ -199,7 +199,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json index a52ce562f..96c85599e 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json index b16833838..73dd3bc32 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json index 895c34333..2214f3731 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json @@ -14,70 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], [ -106.8425, 32.5907 @@ -201,13 +137,77 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 ] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ @@ -238,22 +238,6 @@ "richness", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -284,7 +268,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index 8229d6005..f6f10257f 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json index f383a8354..fd2d16217 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json index 2e7bab0b8..8b555774e 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json index ebc21c5e0..97c3b06f7 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -11,42 +11,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_arima.json" }, { "rel": "item", @@ -78,6 +73,11 @@ "type": "application/json", "href": "./models/tg_precip_lm_all_sites.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_randfor.json" + }, { "rel": "item", "type": "application/json", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index 935e0275e..d60f3b07c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -27,7 +27,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the ChlorophyllCrusaders model. Information for the model is provided as follows: Our project utilizes a historical GCC data to fit a Dynamic Linear Model (DLM). After this DLM is trained, we utilize forecasted temperature data to predict future GCC data..\n The model predicts this variable at the following sites: HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json index 50c301f21..89a6decac 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json @@ -207,7 +207,7 @@ "properties": { "title": "PEG", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json index 831ca7fc2..bdb512bc6 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json @@ -207,7 +207,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index 60474536f..d880b54d7 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -194,20 +194,20 @@ -149.2133, 63.8758 ], - [ - -156.6194, - 71.2824 - ], [ -149.3705, 68.6611 + ], + [ + -156.6194, + 71.2824 ] ] }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -283,8 +283,8 @@ "BONA", "DEJU", "HEAL", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index 17c9b3c89..b063c0272 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, SJER, SOAP, SRER, STEI, STER, TALL, NOGP, OAES, ONAQ, ORNL, OSBS, TEAK, TOOL, TREE, UKFS, UNDE, LAJA, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index 7f94e6654..6ee78971c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index 9e6eb9979..3d191f6dd 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json index 01c618847..33c8e3720 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json @@ -114,26 +114,6 @@ -145.7514, 63.8811 ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], [ -149.2133, 63.8758 @@ -201,13 +181,33 @@ [ -105.546, 40.2759 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 ] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, DELA, DSNY, GRSM, GUAN, HARV.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -263,11 +263,6 @@ "CPER", "DCFS", "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", "HEAL", "JERC", "JORN", @@ -284,7 +279,12 @@ "ORNL", "OSBS", "PUUM", - "RMNP" + "RMNP", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 35639e295..7dc5a09d6 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json index 02d79c529..11654585b 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json index d2cca1fd5..f0bde7aa8 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json @@ -14,70 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +137,77 @@ [ -105.546, 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_precip_lm", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,22 +238,6 @@ "gcc_90", "Daily", "P1D", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +268,23 @@ "ORNL", "OSBS", "PUUM", - "RMNP" + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 8b1ee6386..ee3eec641 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -14,58 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +149,65 @@ [ -119.7323, 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -238,19 +238,6 @@ "gcc_90", "Daily", "P1D", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +271,20 @@ "RMNP", "SCBI", "SERC", - "SJER" + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json index 4de9b8630..abfbda52c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json @@ -14,42 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -109.3883, - 38.2483 - ], - [ - -105.5824, - 40.0543 - ], - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], [ -78.1395, 38.8929 @@ -201,13 +165,49 @@ [ -80.5248, 37.3783 + ], + [ + -109.3883, + 38.2483 + ], + [ + -105.5824, + 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 ] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,15 +238,6 @@ "gcc_90", "Daily", "P1D", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", "SCBI", "SERC", "SJER", @@ -284,7 +275,16 @@ "KONZ", "LAJA", "LENO", - "MLBS" + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index b06dd3f1f..802aa0753 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json index 3f89907a2..38ad19a1b 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json index ffb9ba2d3..6c9df4474 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json index d4651ed84..32c2b1502 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -16,67 +16,67 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json index 95e2290c6..14c934e31 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json @@ -207,7 +207,7 @@ "properties": { "title": "PEG", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index b9e1d693c..26fe94b1c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -182,10 +182,6 @@ -100.9154, 46.7697 ], - [ - -156.6194, - 71.2824 - ], [ -147.5026, 65.154 @@ -201,13 +197,17 @@ [ -149.3705, 68.6611 + ], + [ + -156.6194, + 71.2824 ] ] }, "properties": { "title": "baseline_ensemble", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BARR, BONA, DEJU, HEAL, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -280,11 +280,11 @@ "MOAB", "NIWO", "NOGP", - "BARR", "BONA", "DEJU", "HEAL", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json index 8a5e7d3c1..7ea4676bb 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json @@ -207,7 +207,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index 56bb4fa59..3c91a0c89 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -194,20 +194,20 @@ -147.5026, 65.154 ], - [ - -156.6194, - 71.2824 - ], [ -149.3705, 68.6611 + ], + [ + -156.6194, + 71.2824 ] ] }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -283,8 +283,8 @@ "DEJU", "HEAL", "BONA", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index 96784f48e..462025e7f 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, RMNP, SCBI, SERC, SJER, SOAP, JERC, JORN, KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, SRER, STEI, STER, TALL, TEAK, CPER, DCFS, DEJU, DELA, DSNY, OAES, ONAQ, ORNL, OSBS, PUUM, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, BART, BLAN, BONA, CLBJ, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index a272fd141..75c34fd55 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index 0d7e55161..4e88910b2 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json index 695a2cbfb..e9191fd6c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 6312d6670..a2f7b5f9c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json index 412135dcf..8b4080205 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json @@ -14,6 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -106.8425, 32.5907 @@ -185,29 +201,13 @@ [ -83.5019, 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 ] ] }, "properties": { "title": "tg_lasso", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,6 +238,10 @@ "rcc_90", "Daily", "P1D", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -280,11 +284,7 @@ "DEJU", "DELA", "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "GRSM" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json index c08b105b0..e40af446c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json @@ -14,6 +14,66 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], [ -84.4686, 31.1948 @@ -141,73 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 ] ] }, "properties": { "title": "tg_precip_lm", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,6 +238,21 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -269,22 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 4451f0769..62b99362c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -14,54 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], [ -66.8687, 17.9696 @@ -201,13 +153,61 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 ] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -238,18 +238,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -284,7 +272,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json index 91f80b25f..caed27c38 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -106.8425, 32.5907 @@ -137,77 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 ] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,6 +238,22 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -268,23 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index d05e0e997..239248d0f 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -14,6 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -185,29 +201,13 @@ [ -95.1921, 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,10 @@ "rcc_90", "Daily", "P1D", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -280,11 +284,7 @@ "TEAK", "TOOL", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UKFS" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json index 4f594a58e..c130b6629 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 835c4ef61..b8c573bda 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json index 733abf4e8..503de8b18 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All summaries for the 30min_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json index 666437e4f..4fc2e5af2 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json @@ -14,10 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -76.56, - 38.8901 - ], [ -119.7323, 37.1088 @@ -34,14 +30,6 @@ -89.5864, 45.5089 ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], [ -119.006, 37.0058 @@ -86,6 +74,14 @@ -71.2874, 44.0639 ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], [ -78.0418, 39.0337 @@ -154,14 +150,6 @@ -67.0769, 18.0213 ], - [ - -78.1395, - 38.8929 - ], - [ - -105.546, - 40.2759 - ], [ -88.1612, 31.8539 @@ -201,13 +189,25 @@ [ -155.3173, 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 ] ] }, "properties": { "title": "climatology", - "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, SCBI, RMNP, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, STER, TALL, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ @@ -238,13 +238,10 @@ "le", "30min", "PT30M", - "SERC", "SJER", "SOAP", "SRER", "STEI", - "STER", - "TALL", "TEAK", "TOOL", "TREE", @@ -256,6 +253,8 @@ "ABBY", "BARR", "BART", + "STER", + "TALL", "BLAN", "BONA", "CLBJ", @@ -273,8 +272,6 @@ "KONA", "KONZ", "LAJA", - "SCBI", - "RMNP", "LENO", "MLBS", "MOAB", @@ -284,7 +281,10 @@ "ONAQ", "ORNL", "OSBS", - "PUUM" + "PUUM", + "RMNP", + "SCBI", + "SERC" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json index 539548a90..40ea910b4 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -36,42 +36,42 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json index 5253afec6..197aae615 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json @@ -23,7 +23,7 @@ "properties": { "title": "USUNEEDAILY", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the USUNEEDAILY model. Information for the model is provided as follows: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates..\n The model predicts this variable at the following sites: PUUM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-12-12T00:00:00Z", "end_datetime": "2024-01-16T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index a19cd8020..350361930 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -27,7 +27,7 @@ "properties": { "title": "bookcast_forest", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: TALL, OSBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2024-01-10T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json index ebdd33fa4..f7e329e39 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json @@ -203,7 +203,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: PUUM, GUAN, OSBS, SCBI, MOAB, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, ONAQ, DSNY, BONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json index af32183f3..2809abf80 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index fc71ab1a8..135729188 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, UNDE, WOOD, WREF, YELL, BONA, CLBJ, CPER, DCFS, DEJU, DELA, HEAL, JERC, JORN, KONA, KONZ, LAJA, SJER, SOAP, SRER, STEI, STER, TALL, DSNY, GRSM, GUAN, HARV, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index 0acae062c..ce08334f3 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index dda1400e8..fede88760 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json index e543eeae1..64e0ba396 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json index d228a3974..6935c78ae 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json @@ -14,58 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 - ], - [ - -105.5824, - 40.0543 - ], - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], [ -119.2622, 37.0334 @@ -201,13 +149,65 @@ [ -88.1612, 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 + ], + [ + -105.5824, + 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 ] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -238,19 +238,6 @@ "nee", "Daily", "P1D", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", "SOAP", "SRER", "STEI", @@ -284,7 +271,20 @@ "KONA", "KONZ", "LAJA", - "LENO" + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json index baaea6221..72dc7b215 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json index 51164f529..970fcafd6 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json index 27db693ce..60ced225c 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index 3a7d2fc7b..a665977ec 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -14,6 +14,30 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], [ -104.7456, 40.8155 @@ -177,37 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 ] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,12 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", "CPER", "DCFS", "DEJU", @@ -278,13 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json index 41017d0ca..24997237b 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json index 3939fc55f..eaf8bfd41 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/collection.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/collection.json index 2f231e197..e340c0df0 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/collection.json @@ -11,37 +11,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/climatology.json" }, { "rel": "item", @@ -61,12 +61,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_randfor.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json index 2710accbd..fafb4afb7 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json @@ -203,7 +203,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DSNY, SCBI, MOAB, PUUM, GUAN, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, OSBS, BONA, ONAQ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json index af44f2c78..2a481035b 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All summaries for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index 392b174ca..d0b56547a 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -14,66 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 - ], - [ - -105.5824, - 40.0543 - ], - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], [ -119.2622, 37.0334 @@ -201,13 +141,73 @@ [ -96.5631, 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 + ], + [ + -105.5824, + 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 ] ] }, "properties": { "title": "tg_arima", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,21 +238,6 @@ "le", "Daily", "P1D", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", "SOAP", "SRER", "STEI", @@ -284,7 +269,22 @@ "JERC", "JORN", "KONA", - "KONZ" + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index 61fc63b9a..a3d715b64 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -14,6 +14,98 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], + [ + -96.5631, + 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 + ], [ -105.5824, 40.0543 @@ -109,105 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], - [ - -96.5631, - 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 ] ] }, "properties": { "title": "tg_ets", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,29 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", "NIWO", "NOGP", "OAES", @@ -261,30 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json index 240c0f1a0..83ba9acbe 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json index 6a8f907fe..ad887a847 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json index a0d038e23..866bb5c3b 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json index c34cf4289..75bc84519 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json @@ -14,6 +14,78 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], [ -96.5631, 39.1008 @@ -129,85 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 ] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -238,6 +238,24 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -266,25 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json index d44058f60..a7b3fe921 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index 847b9592f..d1026530f 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json index db8d10fd7..11ed732ff 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json index 7804b0770..91ac7a3ca 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json index 253c426d4..e74d53c9f 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json @@ -21,17 +21,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", @@ -41,22 +41,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json index bf7b934e7..df22d3957 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -78.0418, - 39.0337 - ], - [ - -96.5631, - 39.1008 - ], - [ - -88.1612, - 31.8539 - ], [ -84.2826, 35.9641 @@ -49,13 +37,25 @@ [ -95.1921, 39.0404 + ], + [ + -78.0418, + 39.0337 + ], + [ + -96.5631, + 39.1008 + ], + [ + -88.1612, + 31.8539 ] ] }, "properties": { "title": "tg_arima", - "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ORNL, OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ @@ -86,15 +86,15 @@ "amblyomma_americanum", "Weekly", "P1W", - "BLAN", - "KONZ", - "LENO", "ORNL", "OSBS", "SCBI", "SERC", "TALL", - "UKFS" + "UKFS", + "BLAN", + "KONZ", + "LENO" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json index d7b6cedb0..a6ce0cec4 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json index 4985e42b8..ef3a4144d 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json index 6b7c098e3..50b35fb3e 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json index ebfbd67f3..47c88b3c2 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json @@ -51,7 +51,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json index 3c5d47184..bdab70404 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json index d0751b227..f672f4d52 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json index b4f0b476f..650aa9800 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json @@ -51,7 +51,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json index 50039e242..f870e4fe1 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json index 2ccf39f1e..c7709e519 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json index 83b24b167..d069254bd 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-07T00:00:00Z", + "datetime": "2024-09-08T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/targets/collection.json b/data/challenge/neon4cast-stac/targets/collection.json index 6e8c2dc2f..9b57b5361 100644 --- a/data/challenge/neon4cast-stac/targets/collection.json +++ b/data/challenge/neon4cast-stac/targets/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/collection.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/collection.json index c9c7b0957..e392fd950 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/collection.json @@ -33,11 +33,6 @@ "type": "application/json", "href": "./models/asl.met.lm.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/asl.persistence.json" - }, { "rel": "item", "type": "application/json", @@ -83,6 +78,11 @@ "type": "application/json", "href": "./models/monthly_mean.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.persistence.json" + }, { "rel": "item", "type": "application/json", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json index 8fe474f58..eba60bd83 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.climate.window.json index 9f5224b02..0a6484060 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json index 9d1696506..b597ee09f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json index ed4daf5c3..e485d4a0a 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json index 672259274..b7564b330 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.persistence.json index cc31ead6b..2dc3b044e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json index ff038a71f..0c48028c5 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json index 8f4d8b229..bfc8deb46 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/climatology.json index dff44524f..9a33668a5 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Bloom_binary variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json index a2ae7ba66..cf124a214 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "All forecasts for the Daily_Bloom_binary variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json index df1918f3a..e11f0c01b 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "All forecasts for the Daily_Bloom_binary variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json index 20506b64b..ced4f05c4 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "All forecasts for the Daily_Bloom_binary variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json index cd9678da3..e922d783e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_Bloom_binary variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-20T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json index a41ddf6d7..037f85e10 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_Bloom_binary variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json index c9ff50ab5..367394d7c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_Bloom_binary variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json index b40b4a33e..fe34d3212 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Bloom_binary variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/collection.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/collection.json index 3cc06a966..2edf03825 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/collection.json @@ -11,82 +11,82 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/asl.climate.window.json" }, { "rel": "parent", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json index bf74ef46f..f382f939c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json index 053051cc2..cc3563328 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json index f8661571c..cf2f843f8 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "asl.ets", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json index d47865abd..8ebe13f8a 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json index ac5644a36..f65252eab 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.persistence.json index 128735115..b1f989f01 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json index d8be40bdf..8817d285c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "asl.tbats", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json index b3b316cb7..6f27d1f42 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json index d14b81a8f..3a8b8430e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Chlorophyll-a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-02T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json index ff0422c3e..54e5d1a55 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "fableARIMA", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json index fbdddfee2..0543565a8 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableETS", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json index 545c716a6..8fe69d72c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json index f3b844e73..d638562b9 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_Chlorophyll-a variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json index da4bc9496..8ac1c4811 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_Chlorophyll-a variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json index 84bac336e..c132968cc 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_Chlorophyll-a variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json index e9961e00f..834d464c0 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Chlorophyll-a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/collection.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/collection.json index 007c2b181..f9cff9738 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/collection.json @@ -131,7 +131,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json index ef4e81a98..7c822d480 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json index a67f5266e..3ec84a77e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json index 39387659b..d915fdc1f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json index a4f29a3df..4127dc2b4 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json index 71a7f5643..e6f4d5c11 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.persistence.json index 0b80a933c..b982ad7be 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json index 485ff1d30..1815e78da 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json index e1ff1291c..91fc14bc7 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json index c4ac68729..e2df454c1 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_oxygen_concentration variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json index de71c6264..3c188eeba 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json index 669f42c7f..56e1b6e52 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json index 360818754..c9c336268 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_oxygen_concentration variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json index 8f40efe24..5e4e4d43d 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_oxygen_concentration variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json index c8f4890e9..38b0ef7cd 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_oxygen_concentration variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json index 53944e128..b39fc6ec1 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_oxygen_concentration variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/collection.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/collection.json index 62173fe6f..766abfe79 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/collection.json @@ -81,12 +81,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/asl.persistence.json" }, { "rel": "parent", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json index 425d45a35..1c107a648 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_Secchi variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.climate.window.json index 3fba50a85..eaeffc842 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_Secchi variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.ets.json index fdf1e5a6b..05778e778 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_Secchi variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json index c87062f96..40e5e5cac 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_Secchi variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json index bcac8d5aa..58e8cde19 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_Secchi variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.persistence.json index df8e5069d..0926c3697 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_Secchi variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.tbats.json index ca34cc91c..7c46c750d 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All forecasts for the Daily_Secchi variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json index 668bd39db..2fe99045e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_Secchi variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/climatology.json index bcec3798a..843415ecb 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json index bf7a37f6c..3c05ddbe2 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json index 204817146..4584a7fe8 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "fableNNETAR_focal", - "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", @@ -58,8 +58,8 @@ "Secchi_m_sample", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json index 22de58dee..ac38a89c5 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_Secchi variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/historic_mean.json index 6fd37313c..489d9f133 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_Secchi variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/monthly_mean.json index 54e454eac..b1b70ccf2 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_Secchi variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/persistenceRW.json index 0033f8166..74fa87f8f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Secchi variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json index d3b8b738b..41821cec4 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json @@ -23,7 +23,7 @@ "properties": { "title": "secchi_last3obs_mean", "description": "All forecasts for the Daily_Secchi variable for the secchi_last3obs_mean model. Information for the model is provided as follows: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-02T00:00:00Z", "end_datetime": "2024-09-20T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/collection.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/collection.json index ab865ea72..f1fb3d9d8 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/collection.json @@ -11,97 +11,97 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/inflow_gefsClimAED.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/flareGOTM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/flareSimstrat.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/inflow_gefsClimAED.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/persistenceFO.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceFO.json" + "href": "./models/asl.persistence.json" }, { "rel": "parent", @@ -151,7 +151,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json index 51b7646e4..a756adcca 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_Water_temperature variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.climate.window.json index 0b1217fd1..37921a8cb 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_Water_temperature variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json index d47f2958c..862561177 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_Water_temperature variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json index 32b860590..fe762dd53 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_Water_temperature variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json index 8ddfaa08f..1d713cb83 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_Water_temperature variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.persistence.json index 77bf4e9e8..97d120324 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_Water_temperature variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json index 6c58475ae..e473e7dfd 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All forecasts for the Daily_Water_temperature variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json index 1188f0016..77501dd97 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_Water_temperature variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/climatology.json index 3174de185..f6aa55658 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/climatology.json @@ -14,10 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8357, - 37.3078 - ], [ -79.8159, 37.3129 @@ -25,15 +21,19 @@ [ -79.8372, 37.3032 + ], + [ + -79.8357, + 37.3078 ] ] }, "properties": { "title": "climatology", - "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: tubr, bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", @@ -62,9 +62,9 @@ "Temp_C_mean", "Daily", "P1D", - "tubr", "bvre", "fcre", + "tubr", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json index 725615f86..e1a8bf195 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json index 0907ebeba..9f5865e4c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "fableNNETAR_focal", - "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", @@ -58,8 +58,8 @@ "Temp_C_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json index f02811817..9c6488081 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json @@ -23,9 +23,9 @@ "properties": { "title": "flareGOTM", "description": "All forecasts for the Daily_Water_temperature variable for the flareGOTM model. Information for the model is provided as follows: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-10-05T00:00:00Z", + "end_datetime": "2024-10-06T00:00:00Z", "providers": [ { "url": null, diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json index f41992d68..d17171b01 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json @@ -23,9 +23,9 @@ "properties": { "title": "flareSimstrat", "description": "All forecasts for the Daily_Water_temperature variable for the flareSimstrat model. Information for the model is provided as follows: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-10-05T00:00:00Z", + "end_datetime": "2024-10-06T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/ler/combined_workflow_Simstrat.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json index 2d0f0390c..02b3a4c99 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_Water_temperature variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json index b03c0cdc3..5952f8062 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_Water_temperature variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: tubr, fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json index f12be0d99..724f3af97 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json @@ -23,9 +23,9 @@ "properties": { "title": "inflow_gefsClimAED", "description": "All forecasts for the Daily_Water_temperature variable for the inflow_gefsClimAED model. Information for the model is provided as follows: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples..\n The model predicts this variable at the following sites: tubr.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-13T00:00:00Z", - "end_datetime": "2024-10-07T00:00:00Z", + "end_datetime": "2024-10-08T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast_models/blob/main/inflow_aed.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json index ba5d1c8de..215a734f0 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_Water_temperature variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: tubr, fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json index 3f0f33d85..caa041ceb 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json @@ -23,7 +23,7 @@ "properties": { "title": "persistenceFO", "description": "All forecasts for the Daily_Water_temperature variable for the persistenceFO model. Information for the model is provided as follows: another persistence forecast.\n The model predicts this variable at the following sites: bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-27T00:00:00Z", "end_datetime": "2023-10-30T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json index 8569aab6a..a8d3dc791 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json @@ -31,9 +31,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/inventory/collection.json b/data/challenge/vera4cast-stac/inventory/collection.json index 93a816d03..db9885abf 100644 --- a/data/challenge/vera4cast-stac/inventory/collection.json +++ b/data/challenge/vera4cast-stac/inventory/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Pseudo/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Pseudo/collection.json index 55824be5c..b62608113 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Pseudo/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Pseudo/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Stage1-stats/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Stage1-stats/collection.json index 568165566..270355ec6 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Stage1-stats/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Stage1-stats/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Stage1/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Stage1/collection.json index bb230fdbb..bd8549329 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Stage1/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Stage1/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Stage2/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Stage2/collection.json index e8db2a3ab..f4936ea49 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Stage2/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Stage2/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Stage3/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Stage3/collection.json index 9ce548819..e2199422b 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Stage3/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Stage3/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/collection.json index 26b9b472c..05664232e 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/collection.json @@ -86,7 +86,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/collection.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/collection.json index 68c8c18ab..1d115c610 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/collection.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/collection.json @@ -11,78 +11,83 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", "href": "./models/asl.climate.window.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.persistence.json" + }, { "rel": "parent", "type": "application/json", @@ -131,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.auto.arima.json index d608e2f7e..e45c483e0 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.auto.arima.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_Bloom_binary variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", "end_datetime": "2024-06-17T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json index 2137f6fdc..3f28ad296 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All scores for the Daily_Bloom_binary variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.ets.json index e421d1c90..c55eae4cf 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.ets.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_Bloom_binary variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", "end_datetime": "2024-06-17T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.json index be43181ef..e1fffb030 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_Bloom_binary variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-16T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json index 45ab5c601..3938f0059 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_Bloom_binary variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-16T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.persistence.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.persistence.json new file mode 100644 index 000000000..f5b578ef4 --- /dev/null +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.persistence.json @@ -0,0 +1,233 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.persistence_Bloom_binary_mean_P1D_scores", + "bbox": [ + -79.8372, + 37.3032, + -79.8159, + 37.3129 + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [ + -79.8159, + 37.3129 + ], + [ + -79.8372, + 37.3032 + ] + ] + }, + "properties": { + "title": "asl.persistence", + "description": "All scores for the Daily_Bloom_binary variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", + "start_datetime": "2024-09-05T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", + "providers": [ + { + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "http://ecoforecast.centers.vt.edu/", + "name": "Virginia Tech Center for Ecosystem Forecasting", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Scores", + "vera4cast", + "Biological", + "asl.persistence", + "Bloom_binary", + "Bloom_binary_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "table:columns": [ + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "name": "datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "observation", + "type": "double", + "description": "observed value for variable" + }, + { + "name": "crps", + "type": "double", + "description": "crps forecast score" + }, + { + "name": "logs", + "type": "double", + "description": "logs forecast score" + }, + { + "name": "mean", + "type": "double", + "description": "mean forecast prediction" + }, + { + "name": "median", + "type": "double", + "description": "median forecast prediction" + }, + { + "name": "sd", + "type": "double", + "description": "standard deviation forecasts" + }, + { + "name": "quantile97.5", + "type": "double", + "description": "upper 97.5 percentile value of forecast" + }, + { + "name": "quantile02.5", + "type": "double", + "description": "upper 2.5 percentile value of forecast" + }, + { + "name": "quantile90", + "type": "double", + "description": "upper 90 percentile value of forecast" + }, + { + "name": "quantile10", + "type": "double", + "description": "upper 10 percentile value of forecast" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + }, + { + "name": "project_id", + "type": "string", + "description": "unique project identifier" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + } + ] + }, + "collection": "scores", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.persistence" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.persistence" + }, + { + "rel": "self", + "href": "asl.persistence.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/radiantearth", + "type": "text/html", + "title": "Link for Model Code" + }, + { + "rel": "item", + "href": "https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Biological/Daily_Bloom_binary/models/asl.persistence.json", + "type": "text/html", + "title": "Link for rendered STAC item" + }, + { + "rel": "item", + "href": "https://raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Biological/Daily_Bloom_binary/models/asl.persistence.json", + "type": "text/html", + "title": "Link for raw JSON file" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/radiantearth", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Bloom_binary", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} \ No newline at end of file diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.tbats.json index 18789deb5..f5994e2a5 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.tbats.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_Bloom_binary variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", "end_datetime": "2024-06-16T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.temp.lm.json index 4673e62ad..4692bc9b9 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_Bloom_binary variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-16T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/climatology.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/climatology.json index 2618efdef..ca8722e3c 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Bloom_binary variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableARIMA.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableARIMA.json index 6da6aa714..8ab6f4b20 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "All scores for the Daily_Bloom_binary variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableETS.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableETS.json index ab9ebafb8..fe41f42d9 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableETS.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "All scores for the Daily_Bloom_binary variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableNNETAR.json index 6f5fad720..aa73d133f 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_Bloom_binary variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/glm_aed_v1.json index 9bad35e24..84aab0f77 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_Bloom_binary variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-20T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/historic_mean.json index 626159fed..596a408cf 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_Bloom_binary variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/monthly_mean.json index 1a0be3781..bcc3faf61 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_Bloom_binary variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/persistenceRW.json index 64ec9b7a3..afb13cf9d 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Bloom_binary variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/collection.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/collection.json index 38d862b62..9182589fb 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/collection.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/collection.json @@ -13,6 +13,11 @@ "type": "application/json", "href": "./models/asl.auto.arima.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.climate.window.json" + }, { "rel": "item", "type": "application/json", @@ -26,52 +31,52 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", @@ -131,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json index b61017bbe..cec6c56c7 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json index 289a0ac5a..603c603cc 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.ets.json index 8db2346bc..d5ea4d6a9 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json index a8bb3b178..9e364ef45 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json index cf98e60d9..2bbaa4c28 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.persistence.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.persistence.json new file mode 100644 index 000000000..0c1106196 --- /dev/null +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.persistence.json @@ -0,0 +1,233 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.persistence_Chla_ugL_mean_P1D_scores", + "bbox": [ + -79.8372, + 37.3032, + -79.8159, + 37.3129 + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [ + -79.8159, + 37.3129 + ], + [ + -79.8372, + 37.3032 + ] + ] + }, + "properties": { + "title": "asl.persistence", + "description": "All scores for the Daily_Chlorophyll-a variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", + "start_datetime": "2024-09-05T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", + "providers": [ + { + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "http://ecoforecast.centers.vt.edu/", + "name": "Virginia Tech Center for Ecosystem Forecasting", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Scores", + "vera4cast", + "Biological", + "asl.persistence", + "Chlorophyll-a", + "Chla_ugL_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "table:columns": [ + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "name": "datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "observation", + "type": "double", + "description": "observed value for variable" + }, + { + "name": "crps", + "type": "double", + "description": "crps forecast score" + }, + { + "name": "logs", + "type": "double", + "description": "logs forecast score" + }, + { + "name": "mean", + "type": "double", + "description": "mean forecast prediction" + }, + { + "name": "median", + "type": "double", + "description": "median forecast prediction" + }, + { + "name": "sd", + "type": "double", + "description": "standard deviation forecasts" + }, + { + "name": "quantile97.5", + "type": "double", + "description": "upper 97.5 percentile value of forecast" + }, + { + "name": "quantile02.5", + "type": "double", + "description": "upper 2.5 percentile value of forecast" + }, + { + "name": "quantile90", + "type": "double", + "description": "upper 90 percentile value of forecast" + }, + { + "name": "quantile10", + "type": "double", + "description": "upper 10 percentile value of forecast" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + }, + { + "name": "project_id", + "type": "string", + "description": "unique project identifier" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + } + ] + }, + "collection": "scores", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.persistence" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.persistence" + }, + { + "rel": "self", + "href": "asl.persistence.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/radiantearth", + "type": "text/html", + "title": "Link for Model Code" + }, + { + "rel": "item", + "href": "https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Biological/Daily_Chlorophyll-a/models/asl.persistence.json", + "type": "text/html", + "title": "Link for rendered STAC item" + }, + { + "rel": "item", + "href": "https://raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Biological/Daily_Chlorophyll-a/models/asl.persistence.json", + "type": "text/html", + "title": "Link for raw JSON file" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/radiantearth", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Chlorophyll-a", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} \ No newline at end of file diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.tbats.json index c6ad45367..60179e6be 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json index a438351d3..2c482fb26 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/climatology.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/climatology.json index aa059ef4d..88dc31c1c 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Chlorophyll-a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-02T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableARIMA.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableARIMA.json index 394e9c3e1..e6588132a 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "All scores for the Daily_Chlorophyll-a variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableETS.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableETS.json index 52d9beca2..66a63a3e0 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableETS.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "All scores for the Daily_Chlorophyll-a variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json index 435ceac9c..40d0ad581 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_Chlorophyll-a variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json index e891b17a0..a4f4f05ed 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_Chlorophyll-a variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/historic_mean.json index 6c6fecb81..0808a55c5 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_Chlorophyll-a variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/monthly_mean.json index 2f8fd6a43..d5acc0adb 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_Chlorophyll-a variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/persistenceRW.json index a9bfc9cd0..89bf94f3c 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Chlorophyll-a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/collection.json b/data/challenge/vera4cast-stac/scores/Biological/collection.json index 258d31a90..d757c78b2 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/collection.json +++ b/data/challenge/vera4cast-stac/scores/Biological/collection.json @@ -71,7 +71,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/collection.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/collection.json index 666e7b16c..ac1fbe882 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/collection.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/collection.json @@ -11,22 +11,27 @@ { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/asl.met.lm.step.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.persistence.json" }, { "rel": "item", @@ -36,7 +41,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", @@ -46,37 +51,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/climatology.json" }, { "rel": "parent", @@ -126,7 +131,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json index d3d85869d..cfded3d5d 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_oxygen_concentration variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json index 514ed0bab..c39cf2878 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All scores for the Daily_oxygen_concentration variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.ets.json index e4bca7f05..5ec75b8ec 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_oxygen_concentration variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json index 566b6a721..f62802a1c 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_oxygen_concentration variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json index f0e8e1179..de8baf7d3 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_oxygen_concentration variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.persistence.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.persistence.json new file mode 100644 index 000000000..f7c6db014 --- /dev/null +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.persistence.json @@ -0,0 +1,233 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.persistence_DO_mgL_mean_P1D_scores", + "bbox": [ + -79.8372, + 37.3032, + -79.8159, + 37.3129 + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [ + -79.8159, + 37.3129 + ], + [ + -79.8372, + 37.3032 + ] + ] + }, + "properties": { + "title": "asl.persistence", + "description": "All scores for the Daily_oxygen_concentration variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", + "start_datetime": "2024-09-05T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", + "providers": [ + { + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "http://ecoforecast.centers.vt.edu/", + "name": "Virginia Tech Center for Ecosystem Forecasting", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Scores", + "vera4cast", + "Chemical", + "asl.persistence", + "oxygen_concentration", + "DO_mgL_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "table:columns": [ + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "name": "datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "observation", + "type": "double", + "description": "observed value for variable" + }, + { + "name": "crps", + "type": "double", + "description": "crps forecast score" + }, + { + "name": "logs", + "type": "double", + "description": "logs forecast score" + }, + { + "name": "mean", + "type": "double", + "description": "mean forecast prediction" + }, + { + "name": "median", + "type": "double", + "description": "median forecast prediction" + }, + { + "name": "sd", + "type": "double", + "description": "standard deviation forecasts" + }, + { + "name": "quantile97.5", + "type": "double", + "description": "upper 97.5 percentile value of forecast" + }, + { + "name": "quantile02.5", + "type": "double", + "description": "upper 2.5 percentile value of forecast" + }, + { + "name": "quantile90", + "type": "double", + "description": "upper 90 percentile value of forecast" + }, + { + "name": "quantile10", + "type": "double", + "description": "upper 10 percentile value of forecast" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + }, + { + "name": "project_id", + "type": "string", + "description": "unique project identifier" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + } + ] + }, + "collection": "scores", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.persistence" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.persistence" + }, + { + "rel": "self", + "href": "asl.persistence.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/radiantearth", + "type": "text/html", + "title": "Link for Model Code" + }, + { + "rel": "item", + "href": "https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Chemical/Daily_oxygen_concentration/models/asl.persistence.json", + "type": "text/html", + "title": "Link for rendered STAC item" + }, + { + "rel": "item", + "href": "https://raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Chemical/Daily_oxygen_concentration/models/asl.persistence.json", + "type": "text/html", + "title": "Link for raw JSON file" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/radiantearth", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} \ No newline at end of file diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.tbats.json index 3cde9f1ed..f215e3feb 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_oxygen_concentration variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json index 7b78a0132..5276e4752 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_oxygen_concentration variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/climatology.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/climatology.json index 5db4ec7f9..bc05cb159 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_oxygen_concentration variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json index 999ba7f86..273db4d46 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_oxygen_concentration variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json index 79ec68178..df4690b2a 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "All scores for the Daily_oxygen_concentration variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json index 784a8cf28..92d2d1e1d 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_oxygen_concentration variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/historic_mean.json index 560b7ee85..8b714968c 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_oxygen_concentration variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/monthly_mean.json index e92cb0549..ec3ba0cdb 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_oxygen_concentration variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/persistenceRW.json index 671b00978..1a5edae36 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_oxygen_concentration variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/collection.json b/data/challenge/vera4cast-stac/scores/Chemical/collection.json index 37162061f..36795dd23 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/collection.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/collection.json @@ -73,7 +73,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/collection.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/collection.json index d95f3205b..59765f6c4 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/collection.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/collection.json @@ -16,22 +16,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", @@ -41,22 +41,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/secchi_last3obs_mean.json" }, { "rel": "item", @@ -66,17 +66,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/secchi_last3obs_mean.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "parent", @@ -126,7 +126,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-08-26T00:00:00Z" + "2024-09-02T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.auto.arima.json index caac406e7..80971afba 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_Secchi variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.ets.json index 3fd66c9bc..ab81022ab 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_Secchi variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.json index b6810e59e..22b135ce0 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_Secchi variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.step.json index 19ae67959..897b1d756 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_Secchi variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.tbats.json index 640c8bc74..660f8fa2c 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_Secchi variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.temp.lm.json index 1be851649..c0d8bc396 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_Secchi variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/climatology.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/climatology.json index 39b4c130c..fa87672eb 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/climatology.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All scores for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", @@ -58,8 +58,8 @@ "Secchi_m_sample", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR.json index f9c9c9c56..b83aa9e1d 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_Secchi variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR_focal.json index 976c32ab9..3e28b6833 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "All scores for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/glm_aed_v1.json index cf478690e..4116bcda8 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_Secchi variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/historic_mean.json index 84b86f2e7..38fa6435d 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_Secchi variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/monthly_mean.json index e38f4450a..bec079347 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_Secchi variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/persistenceRW.json index ab7e159c6..ccc325deb 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Secchi variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/secchi_last3obs_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/secchi_last3obs_mean.json index f372f817b..60b557674 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/secchi_last3obs_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/secchi_last3obs_mean.json @@ -23,9 +23,9 @@ "properties": { "title": "secchi_last3obs_mean", "description": "All scores for the Daily_Secchi variable for the secchi_last3obs_mean model. Information for the model is provided as follows: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-02T00:00:00Z", - "end_datetime": "2024-08-26T00:00:00Z", + "end_datetime": "2024-09-02T00:00:00Z", "providers": [ { "url": "https://github.com/kjkhoffman/vera4casts/blob/main/forecast_code/run_secchi_forecast.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/collection.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/collection.json index 157b4aba0..07a3b718e 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/collection.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/collection.json @@ -21,42 +21,47 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/flareGOTM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/flareSimstrat.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat.json" + "href": "./models/inflow_gefsClimAED.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/persistenceFO.json" }, { "rel": "item", @@ -66,37 +71,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceFO.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/inflow_gefsClimAED.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/asl.persistence.json" }, { "rel": "parent", @@ -146,7 +151,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.auto.arima.json index 478bb6c82..7b9867760 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_Water_temperature variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json index bc3f5c281..87efce93a 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All scores for the Daily_Water_temperature variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.ets.json index 679e0c819..4685a20bb 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_Water_temperature variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.json index 78aad5607..a98a4b7e6 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_Water_temperature variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.step.json index d71326c5f..223df4502 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_Water_temperature variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.persistence.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.persistence.json new file mode 100644 index 000000000..59f954658 --- /dev/null +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.persistence.json @@ -0,0 +1,233 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.persistence_Temp_C_mean_P1D_scores", + "bbox": [ + -79.8372, + 37.3032, + -79.8159, + 37.3129 + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [ + -79.8159, + 37.3129 + ], + [ + -79.8372, + 37.3032 + ] + ] + }, + "properties": { + "title": "asl.persistence", + "description": "All scores for the Daily_Water_temperature variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-07T00:00:00Z", + "start_datetime": "2024-09-05T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", + "providers": [ + { + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "http://ecoforecast.centers.vt.edu/", + "name": "Virginia Tech Center for Ecosystem Forecasting", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Scores", + "vera4cast", + "Physical", + "asl.persistence", + "Water_temperature", + "Temp_C_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "table:columns": [ + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "name": "datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "observation", + "type": "double", + "description": "observed value for variable" + }, + { + "name": "crps", + "type": "double", + "description": "crps forecast score" + }, + { + "name": "logs", + "type": "double", + "description": "logs forecast score" + }, + { + "name": "mean", + "type": "double", + "description": "mean forecast prediction" + }, + { + "name": "median", + "type": "double", + "description": "median forecast prediction" + }, + { + "name": "sd", + "type": "double", + "description": "standard deviation forecasts" + }, + { + "name": "quantile97.5", + "type": "double", + "description": "upper 97.5 percentile value of forecast" + }, + { + "name": "quantile02.5", + "type": "double", + "description": "upper 2.5 percentile value of forecast" + }, + { + "name": "quantile90", + "type": "double", + "description": "upper 90 percentile value of forecast" + }, + { + "name": "quantile10", + "type": "double", + "description": "upper 10 percentile value of forecast" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + }, + { + "name": "project_id", + "type": "string", + "description": "unique project identifier" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + } + ] + }, + "collection": "scores", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.persistence" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.persistence" + }, + { + "rel": "self", + "href": "asl.persistence.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/radiantearth", + "type": "text/html", + "title": "Link for Model Code" + }, + { + "rel": "item", + "href": "https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Physical/Daily_Water_temperature/models/asl.persistence.json", + "type": "text/html", + "title": "Link for rendered STAC item" + }, + { + "rel": "item", + "href": "https://raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Physical/Daily_Water_temperature/models/asl.persistence.json", + "type": "text/html", + "title": "Link for raw JSON file" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/radiantearth", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} \ No newline at end of file diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.tbats.json index 145543a83..9829a5736 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_Water_temperature variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.temp.lm.json index 453d19db3..867110349 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_Water_temperature variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/climatology.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/climatology.json index e0a128380..d385db9ec 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/climatology.json @@ -31,9 +31,9 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR.json index e435b4639..ab761220b 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_Water_temperature variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json index 016aa0160..e7467217a 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "All scores for the Daily_Water_temperature variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareGOTM.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareGOTM.json index 077986f5e..00e638e97 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareGOTM.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareGOTM.json @@ -23,9 +23,9 @@ "properties": { "title": "flareGOTM", "description": "All scores for the Daily_Water_temperature variable for the flareGOTM model. Information for the model is provided as follows: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": null, diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareSimstrat.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareSimstrat.json index 015db6667..8727e525b 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareSimstrat.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareSimstrat.json @@ -23,9 +23,9 @@ "properties": { "title": "flareSimstrat", "description": "All scores for the Daily_Water_temperature variable for the flareSimstrat model. Information for the model is provided as follows: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/ler/combined_workflow_Simstrat.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/glm_aed_v1.json index 0d26bfe26..4d0877742 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_Water_temperature variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/historic_mean.json index 0aafd66ba..f8764f747 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/historic_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_Water_temperature variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json index a2d0e8cff..0ae3c331e 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json @@ -23,9 +23,9 @@ "properties": { "title": "inflow_gefsClimAED", "description": "All scores for the Daily_Water_temperature variable for the inflow_gefsClimAED model. Information for the model is provided as follows: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples..\n The model predicts this variable at the following sites: tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-13T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast_models/blob/main/inflow_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/monthly_mean.json index a0d571360..b9f1c8155 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/monthly_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_Water_temperature variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceFO.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceFO.json index b8436cd03..1dd712323 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceFO.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceFO.json @@ -23,7 +23,7 @@ "properties": { "title": "persistenceFO", "description": "All scores for the Daily_Water_temperature variable for the persistenceFO model. Information for the model is provided as follows: another persistence forecast.\n The model predicts this variable at the following sites: bvre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-27T00:00:00Z", "end_datetime": "2023-10-30T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceRW.json index 6be6e93d5..84764cefa 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceRW.json @@ -31,9 +31,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-05T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/collection.json b/data/challenge/vera4cast-stac/scores/Physical/collection.json index 258285e35..2f0ad8ee7 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/collection.json +++ b/data/challenge/vera4cast-stac/scores/Physical/collection.json @@ -76,7 +76,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/collection.json b/data/challenge/vera4cast-stac/scores/collection.json index 94b11a791..9898f7b97 100644 --- a/data/challenge/vera4cast-stac/scores/collection.json +++ b/data/challenge/vera4cast-stac/scores/collection.json @@ -74,7 +74,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/sites/collection.json b/data/challenge/vera4cast-stac/sites/collection.json index 26a026f50..6faa97e93 100644 --- a/data/challenge/vera4cast-stac/sites/collection.json +++ b/data/challenge/vera4cast-stac/sites/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/collection.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/collection.json index 754eee3a6..7f9be1c82 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/collection.json @@ -33,11 +33,6 @@ "type": "application/json", "href": "./models/asl.met.lm.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/asl.persistence.json" - }, { "rel": "item", "type": "application/json", @@ -83,6 +78,11 @@ "type": "application/json", "href": "./models/monthly_mean.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.persistence.json" + }, { "rel": "item", "type": "application/json", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.auto.arima.json index a444fd46a..43d2cc18a 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.climate.window.json index a5be1761e..fd7483e44 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.ets.json index 305094051..bd2eaaa1d 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.json index 7a8d4639f..af71fc22d 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json index 015409c6e..1e493a6cb 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.persistence.json index dab8d7841..8f0dd67d2 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.tbats.json index aa0f3d133..09a35d040 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.temp.lm.json index cf0e09665..c7ecbe5ef 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/climatology.json index 62f6c5059..f74c9f9c5 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableARIMA.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableARIMA.json index dd404e28e..d523324ac 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "ARIMA fit using the ARIMA() function in the fable R package", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableETS.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableETS.json index a0c004e3b..56d281b4e 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableETS.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "fable package exponential smoothing model fable::ETS()", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableNNETAR.json index 8edc57257..f0bdd0cc6 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/glm_aed_v1.json index 4154e25a1..1afc04404 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-20T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/historic_mean.json index 69fbc9215..59c1015ca 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/monthly_mean.json index 5c4bdf462..8336ca823 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/persistenceRW.json index a0c8cd1ec..fb53126ef 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/collection.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/collection.json index 7773d8636..4164e4c72 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/collection.json @@ -11,82 +11,82 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/asl.climate.window.json" }, { "rel": "parent", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json index 9533b271b..b42a620cd 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json index 1ab9041cc..4c66ff84c 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.ets.json index 172f6bf68..2027434b3 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.ets.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json index d57b281e5..0d21bd68d 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json index 4b513b1bb..0e419288a 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.persistence.json index 64388f192..c9da04c2e 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.tbats.json index 5a9a58b7a..2b906d85f 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.tbats.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json index 74645f27b..a0b51ee6f 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/climatology.json index ac703839a..78f6c954d 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-02T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableARIMA.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableARIMA.json index 56c333b7f..97d713d80 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableARIMA.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "fableARIMA", "description": "ARIMA fit using the ARIMA() function in the fable R package", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableETS.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableETS.json index 4646263d7..bb0e1978a 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableETS.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableETS.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableETS", "description": "fable package exponential smoothing model fable::ETS()", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json index a120541e2..dd2f43de8 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json index 31d0f9ded..7daec9938 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/historic_mean.json index 0db3a092a..19042ff97 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/monthly_mean.json index d530d229e..3f4fd7582 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/persistenceRW.json index 073cd4cba..56f2c0416 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/collection.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/collection.json index 0afb0ebc2..bd5ad7728 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/collection.json @@ -131,7 +131,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json index dd39f7015..acda73b41 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json index c3a7a3408..7743ed90a 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.ets.json index b17c70033..ac654f9f7 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json index 3f30a29c5..491376cff 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json index 41a3a7728..b43101ef4 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.persistence.json index 971275e34..4cc2a4ebc 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.tbats.json index 9489d459f..bed2049d0 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json index 849014c97..97e8f282d 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/climatology.json index e8a638c81..cddbfa25f 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json index 77512dbd2..8e50f3856 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json index 9cb3a2cd7..b04267767 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json index 3449ea0cf..a96697cf6 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/historic_mean.json index 555f873be..6d89241db 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/monthly_mean.json index 61eb0c028..fa93b6888 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/persistenceRW.json index 2c38a0056..541fdcf87 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/collection.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/collection.json index 181eac598..cf2d126b5 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/collection.json @@ -81,12 +81,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/asl.persistence.json" }, { "rel": "parent", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.auto.arima.json index dac3f44bf..32f37afba 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.climate.window.json index 0d1ec39d5..1f8a8f90e 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.ets.json index e959d9633..9ba7d42b4 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.json index ebbc33814..c93152d1c 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.step.json index 3212dd2f0..1dc83760e 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.persistence.json index 763d31d61..95260b13c 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.tbats.json index 5a7452837..ef5c5bc4a 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.temp.lm.json index eb3bbd600..0883b67d1 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/climatology.json index e599f13b0..764b936eb 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR.json index 8c6897f68..b75f4d410 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR_focal.json index 27ca63428..5e1eda267 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR_focal.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "fableNNETAR_focal", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", @@ -58,8 +58,8 @@ "Secchi_m_sample", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/glm_aed_v1.json index de8be43e3..60afda589 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/historic_mean.json index 113128178..de4d90fbd 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/monthly_mean.json index e2b406528..9b69d3bfd 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/persistenceRW.json index ff7e36bea..b678b7c00 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/secchi_last3obs_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/secchi_last3obs_mean.json index 01c88374d..b74c4c1d4 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/secchi_last3obs_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/secchi_last3obs_mean.json @@ -23,7 +23,7 @@ "properties": { "title": "secchi_last3obs_mean", "description": "This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-02T00:00:00Z", "end_datetime": "2024-09-20T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/collection.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/collection.json index d625f93cd..535d011a2 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/collection.json @@ -11,97 +11,97 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/inflow_gefsClimAED.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/flareGOTM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/flareSimstrat.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/inflow_gefsClimAED.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/persistenceFO.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceFO.json" + "href": "./models/asl.persistence.json" }, { "rel": "parent", @@ -151,7 +151,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-10-11T00:00:00Z" + "2024-10-12T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.auto.arima.json index 580ec8cd8..483ac44b1 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.climate.window.json index 545802821..b32b1df48 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.ets.json index 2429b04f7..ec82746d5 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.json index 13a470469..bde5a360e 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.step.json index d491e4d78..9a5f4bceb 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.persistence.json index 1f76b417f..5e4674097 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.tbats.json index d82d2fdd7..6c456e010 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.temp.lm.json index caa7dec77..c3576392f 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/climatology.json index 79f1087af..7368b65ab 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/climatology.json @@ -14,10 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8357, - 37.3078 - ], [ -79.8159, 37.3129 @@ -25,15 +21,19 @@ [ -79.8372, 37.3032 + ], + [ + -79.8357, + 37.3078 ] ] }, "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", @@ -62,9 +62,9 @@ "Temp_C_mean", "Daily", "P1D", - "tubr", "bvre", "fcre", + "tubr", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR.json index 354c128cd..15a2e21cd 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json index 61e5a24db..9721ffaea 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "fableNNETAR_focal", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", @@ -58,8 +58,8 @@ "Temp_C_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareGOTM.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareGOTM.json index 7b72a7036..073355590 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareGOTM.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareGOTM.json @@ -23,9 +23,9 @@ "properties": { "title": "flareGOTM", "description": "FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-10-05T00:00:00Z", + "end_datetime": "2024-10-06T00:00:00Z", "providers": [ { "url": null, diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareSimstrat.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareSimstrat.json index f0616639e..c117980b6 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareSimstrat.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareSimstrat.json @@ -23,9 +23,9 @@ "properties": { "title": "flareSimstrat", "description": "FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-10-05T00:00:00Z", + "end_datetime": "2024-10-06T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/ler/combined_workflow_Simstrat.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/glm_aed_v1.json index 33d9ca42d..2f5ff5a85 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/historic_mean.json index 969c4f109..3c2539fab 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/historic_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json index 3ab7ff93b..bb5162acd 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json @@ -23,9 +23,9 @@ "properties": { "title": "inflow_gefsClimAED", "description": "flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-10-13T00:00:00Z", - "end_datetime": "2024-10-07T00:00:00Z", + "end_datetime": "2024-10-08T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast_models/blob/main/inflow_aed.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/monthly_mean.json index 12babb5be..72f007302 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/monthly_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-11T00:00:00Z", + "end_datetime": "2024-10-12T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceFO.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceFO.json index ce12f5a17..b2a605d10 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceFO.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceFO.json @@ -23,7 +23,7 @@ "properties": { "title": "persistenceFO", "description": "another persistence forecast", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-27T00:00:00Z", "end_datetime": "2023-10-30T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceRW.json index 3fade3133..123978dda 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceRW.json @@ -31,9 +31,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/targets/collection.json b/data/challenge/vera4cast-stac/targets/collection.json index 8c6b806e3..d8726695c 100644 --- a/data/challenge/vera4cast-stac/targets/collection.json +++ b/data/challenge/vera4cast-stac/targets/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/output/neon4cast-stac/df3646c13b.json b/data/output/neon4cast-stac/02fc080a6a.json similarity index 99% rename from data/output/neon4cast-stac/df3646c13b.json rename to data/output/neon4cast-stac/02fc080a6a.json index 5103929aa..d2331f422 100644 --- a/data/output/neon4cast-stac/df3646c13b.json +++ b/data/output/neon4cast-stac/02fc080a6a.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:745c0ba93c", "name": "climatology_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -61,8 +61,8 @@ "BONA", "DEJU", "HEAL", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/a459683eec.json b/data/output/neon4cast-stac/0a4cfa4a49.json similarity index 97% rename from data/output/neon4cast-stac/a459683eec.json rename to data/output/neon4cast-stac/0a4cfa4a49.json index 09664c9ff..745500a6b 100644 --- a/data/output/neon4cast-stac/a459683eec.json +++ b/data/output/neon4cast-stac/0a4cfa4a49.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:059ff9054e", "name": "persistenceRW_nee_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, ABBY, TEAK, TOOL, TREE, UKFS, UNDE, DELA, DSNY, GRSM, GUAN, HARV, HEAL, CLBJ, CPER, DCFS, DEJU, WOOD, WREF, YELL, JERC, JORN, KONA, KONZ, PUUM, RMNP, SCBI, SERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, UNDE, WOOD, WREF, YELL, BONA, CLBJ, CPER, DCFS, DEJU, DELA, HEAL, JERC, JORN, KONA, KONZ, LAJA, SJER, SOAP, SRER, STEI, STER, TALL, DSNY, GRSM, GUAN, HARV, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,49 +16,49 @@ "nee", "Daily", "P1D", - "BARR", - "BART", - "BLAN", - "BONA", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", "NOGP", "OAES", "ONAQ", "ORNL", "OSBS", - "ABBY", - "TEAK", - "TOOL", - "TREE", - "UKFS", "UNDE", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", + "WOOD", + "WREF", + "YELL", + "BONA", "CLBJ", "CPER", "DCFS", "DEJU", - "WOOD", - "WREF", - "YELL", + "DELA", + "HEAL", "JERC", "JORN", "KONA", "KONZ", + "LAJA", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "ABBY", + "BARR", + "BART", + "BLAN", + "LENO", + "MLBS", + "MOAB", + "NIWO", "PUUM", "RMNP", "SCBI", diff --git a/data/output/neon4cast-stac/0abadd4838.json b/data/output/neon4cast-stac/0caeb23965.json similarity index 99% rename from data/output/neon4cast-stac/0abadd4838.json rename to data/output/neon4cast-stac/0caeb23965.json index 4798f1bf6..b0df9c7e7 100644 --- a/data/output/neon4cast-stac/0abadd4838.json +++ b/data/output/neon4cast-stac/0caeb23965.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:c1cd9534fc", "name": "climatology_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, BARR, TOOL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -61,8 +61,8 @@ "BONA", "DEJU", "HEAL", - "TOOL", - "BARR" + "BARR", + "TOOL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/8d87364236.json b/data/output/neon4cast-stac/0d9d92507c.json similarity index 96% rename from data/output/neon4cast-stac/8d87364236.json rename to data/output/neon4cast-stac/0d9d92507c.json index 0d350af51..b9ec6803c 100644 --- a/data/output/neon4cast-stac/8d87364236.json +++ b/data/output/neon4cast-stac/0d9d92507c.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:296eb005fb", "name": "tg_arima_gcc_90_P1D_scores scores", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,20 @@ "gcc_90", "Daily", "P1D", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -48,21 +62,7 @@ "PUUM", "RMNP", "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SERC" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/3819d4b0f3.json b/data/output/neon4cast-stac/0fd3712ccd.json similarity index 96% rename from data/output/neon4cast-stac/3819d4b0f3.json rename to data/output/neon4cast-stac/0fd3712ccd.json index 668a4cd75..dad8659ac 100644 --- a/data/output/neon4cast-stac/3819d4b0f3.json +++ b/data/output/neon4cast-stac/0fd3712ccd.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:93c4907742", "name": "tg_randfor_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,22 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -46,23 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f4ca151278.json b/data/output/neon4cast-stac/1b13fa76cf.json similarity index 96% rename from data/output/neon4cast-stac/f4ca151278.json rename to data/output/neon4cast-stac/1b13fa76cf.json index d1fa4cc20..35a5f474f 100644 --- a/data/output/neon4cast-stac/f4ca151278.json +++ b/data/output/neon4cast-stac/1b13fa76cf.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:09955825b1", "name": "tg_lasso_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,18 +16,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -62,7 +50,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/404ed9404a.json b/data/output/neon4cast-stac/1c2d2a7888.json similarity index 99% rename from data/output/neon4cast-stac/404ed9404a.json rename to data/output/neon4cast-stac/1c2d2a7888.json index 8a47d1b6f..dd7326ab9 100644 --- a/data/output/neon4cast-stac/404ed9404a.json +++ b/data/output/neon4cast-stac/1c2d2a7888.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:1a3e66d93f", "name": "baseline_ensemble_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BARR, BONA, DEJU, HEAL, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -58,11 +58,11 @@ "MOAB", "NIWO", "NOGP", - "BARR", "BONA", "DEJU", "HEAL", - "TOOL" + "TOOL", + "BARR" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/e7b97b6872.json b/data/output/neon4cast-stac/1cba9bab7f.json similarity index 96% rename from data/output/neon4cast-stac/e7b97b6872.json rename to data/output/neon4cast-stac/1cba9bab7f.json index 1014fb87d..5cbd6e1f8 100644 --- a/data/output/neon4cast-stac/e7b97b6872.json +++ b/data/output/neon4cast-stac/1cba9bab7f.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:42c52a193d", "name": "tg_tbats_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,6 @@ "gcc_90", "Daily", "P1D", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +60,9 @@ "TREE", "UKFS", "UNDE", - "WOOD" + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f2b2ceebd7.json b/data/output/neon4cast-stac/2274041530.json similarity index 97% rename from data/output/neon4cast-stac/f2b2ceebd7.json rename to data/output/neon4cast-stac/2274041530.json index 2a92ca1ab..c1746ed5f 100644 --- a/data/output/neon4cast-stac/f2b2ceebd7.json +++ b/data/output/neon4cast-stac/2274041530.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9e56bc1104", "name": "tg_arima_oxygen_P1D_scores scores", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,18 +16,6 @@ "oxygen", "Daily", "P1D", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -49,7 +37,19 @@ "MAYF", "MCDI", "MCRA", - "OKSR" + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/4d8a71c555.json b/data/output/neon4cast-stac/26b9ced9ef.json similarity index 97% rename from data/output/neon4cast-stac/4d8a71c555.json rename to data/output/neon4cast-stac/26b9ced9ef.json index ac8fe691c..77e5cb0be 100644 --- a/data/output/neon4cast-stac/4d8a71c555.json +++ b/data/output/neon4cast-stac/26b9ced9ef.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:7ffe793c7b", "name": "tg_tbats_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,10 @@ "rcc_90", "Daily", "P1D", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -58,11 +62,7 @@ "TEAK", "TOOL", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UKFS" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/e21a512e58.json b/data/output/neon4cast-stac/286dd591cc.json similarity index 97% rename from data/output/neon4cast-stac/e21a512e58.json rename to data/output/neon4cast-stac/286dd591cc.json index 17d17c9ca..cb39cf394 100644 --- a/data/output/neon4cast-stac/e21a512e58.json +++ b/data/output/neon4cast-stac/286dd591cc.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f909c21fda", "name": "persistenceRW_nee_P1D_scores scores", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,15 +16,6 @@ "nee", "Daily", "P1D", - "WOOD", - "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", "CPER", "DCFS", "DEJU", @@ -62,7 +53,16 @@ "TOOL", "TREE", "UKFS", - "UNDE" + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f61f0a1c35.json b/data/output/neon4cast-stac/28df136ea6.json similarity index 97% rename from data/output/neon4cast-stac/f61f0a1c35.json rename to data/output/neon4cast-stac/28df136ea6.json index 4e5fd96c3..179dcd144 100644 --- a/data/output/neon4cast-stac/f61f0a1c35.json +++ b/data/output/neon4cast-stac/28df136ea6.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:c8f696974b", "name": "GLEON_JRabaey_temp_physics_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,21 +16,6 @@ "temperature", "Daily", "P1D", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -49,7 +34,22 @@ "LEWI", "LIRO", "MART", - "MAYF" + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/fec91bb8f9.json b/data/output/neon4cast-stac/2ec1a1b38d.json similarity index 97% rename from data/output/neon4cast-stac/fec91bb8f9.json rename to data/output/neon4cast-stac/2ec1a1b38d.json index fe8df415d..1ab86952f 100644 --- a/data/output/neon4cast-stac/fec91bb8f9.json +++ b/data/output/neon4cast-stac/2ec1a1b38d.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:e46a01488a", "name": "fTSLM_lag_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,15 +16,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", "CUPE", "FLNT", "GUIL", @@ -49,7 +40,16 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/e8c3fe1387.json b/data/output/neon4cast-stac/30177f7d40.json similarity index 96% rename from data/output/neon4cast-stac/e8c3fe1387.json rename to data/output/neon4cast-stac/30177f7d40.json index c859486b1..cc9819116 100644 --- a/data/output/neon4cast-stac/e8c3fe1387.json +++ b/data/output/neon4cast-stac/30177f7d40.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:7a2df61c88", "name": "climatology_nee_P1D_scores scores", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,8 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", "BART", "BLAN", "BONA", @@ -60,9 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/5b863dc9b5.json b/data/output/neon4cast-stac/3339b872b3.json similarity index 96% rename from data/output/neon4cast-stac/5b863dc9b5.json rename to data/output/neon4cast-stac/3339b872b3.json index 504b70231..0ab7311b5 100644 --- a/data/output/neon4cast-stac/5b863dc9b5.json +++ b/data/output/neon4cast-stac/3339b872b3.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:812c245286", "name": "tg_precip_lm_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,21 +16,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", "JERC", "JORN", "KONA", @@ -62,7 +47,22 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c122e806c8.json b/data/output/neon4cast-stac/358999fc06.json similarity index 98% rename from data/output/neon4cast-stac/c122e806c8.json rename to data/output/neon4cast-stac/358999fc06.json index 7cd241a7a..86221953d 100644 --- a/data/output/neon4cast-stac/c122e806c8.json +++ b/data/output/neon4cast-stac/358999fc06.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:23867334a8", "name": "persistenceRW_oxygen_P1D_summaries summaries", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, OKSR, POSE, PRIN, BLUE, BLWA, CARI, COMO, CRAM, CUPE, PRLA, PRPO, REDB, SUGG, SYCA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,23 +16,23 @@ "oxygen", "Daily", "P1D", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "BLUE", + "BLWA", "CARI", "COMO", "CRAM", "CUPE", - "PRIN", "PRLA", "PRPO", "REDB", "SUGG", "SYCA", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "BLUE", - "BLWA", "WLOU", "ARIK", "BARC", diff --git a/data/output/neon4cast-stac/2f0d29ccea.json b/data/output/neon4cast-stac/36123a2f6d.json similarity index 97% rename from data/output/neon4cast-stac/2f0d29ccea.json rename to data/output/neon4cast-stac/36123a2f6d.json index 32c400580..2d80586d6 100644 --- a/data/output/neon4cast-stac/2f0d29ccea.json +++ b/data/output/neon4cast-stac/36123a2f6d.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:b392c65b4d", "name": "climatology_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,10 @@ "temperature", "Daily", "P1D", + "TECR", + "TOMB", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -45,10 +49,6 @@ "REDB", "SUGG", "SYCA", - "TECR", - "TOMB", - "WALK", - "WLOU", "TOOK" ], "citation": { diff --git a/data/output/neon4cast-stac/df109a347a.json b/data/output/neon4cast-stac/384d6288d3.json similarity index 97% rename from data/output/neon4cast-stac/df109a347a.json rename to data/output/neon4cast-stac/384d6288d3.json index 4c55c936f..2130cc9a9 100644 --- a/data/output/neon4cast-stac/df109a347a.json +++ b/data/output/neon4cast-stac/384d6288d3.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:481488ef29", "name": "tg_arima_temperature_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,22 @@ "temperature", "Daily", "P1D", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -33,23 +49,7 @@ "LECO", "LEWI", "LIRO", - "MART", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "MART" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/47ccfd8ef7.json b/data/output/neon4cast-stac/47c27ff979.json similarity index 96% rename from data/output/neon4cast-stac/47ccfd8ef7.json rename to data/output/neon4cast-stac/47c27ff979.json index 0c380ddf2..9b660ebb9 100644 --- a/data/output/neon4cast-stac/47ccfd8ef7.json +++ b/data/output/neon4cast-stac/47c27ff979.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:fbde1ff371", "name": "tg_lasso_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,10 @@ "rcc_90", "Daily", "P1D", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -58,11 +62,7 @@ "DEJU", "DELA", "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "GRSM" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/a3a156a3d9.json b/data/output/neon4cast-stac/4afa628c4f.json similarity index 98% rename from data/output/neon4cast-stac/a3a156a3d9.json rename to data/output/neon4cast-stac/4afa628c4f.json index 06f400ae2..1b027951d 100644 --- a/data/output/neon4cast-stac/a3a156a3d9.json +++ b/data/output/neon4cast-stac/4afa628c4f.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:231f50d8d2", "name": "tg_humidity_lm_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, DELA, DSNY, GRSM, GUAN, HARV.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -41,11 +41,6 @@ "CPER", "DCFS", "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", "HEAL", "JERC", "JORN", @@ -62,7 +57,12 @@ "ORNL", "OSBS", "PUUM", - "RMNP" + "RMNP", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f0e0422543.json b/data/output/neon4cast-stac/4de4f6cdf3.json similarity index 96% rename from data/output/neon4cast-stac/f0e0422543.json rename to data/output/neon4cast-stac/4de4f6cdf3.json index 4621f364a..d250fec6a 100644 --- a/data/output/neon4cast-stac/f0e0422543.json +++ b/data/output/neon4cast-stac/4de4f6cdf3.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:1773e1b92e", "name": "tg_randfor_richness_P1W_summaries summaries", - "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,22 +16,6 @@ "richness", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -62,7 +46,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/52987ded66.json b/data/output/neon4cast-stac/52877770cb.json similarity index 97% rename from data/output/neon4cast-stac/52987ded66.json rename to data/output/neon4cast-stac/52877770cb.json index df77dcfd9..12a32947b 100644 --- a/data/output/neon4cast-stac/52987ded66.json +++ b/data/output/neon4cast-stac/52877770cb.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:a528091491", "name": "climatology_oxygen_P1D_summaries summaries", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,8 +16,11 @@ "oxygen", "Daily", "P1D", - "BLWA", - "CARI", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "COMO", "CUPE", "FLNT", @@ -38,13 +41,10 @@ "TECR", "WALK", "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", "TOMB", + "BLWA", "CRAM", + "CARI", "LIRO", "PRPO", "PRLA", diff --git a/data/output/neon4cast-stac/60a3c0bc9f.json b/data/output/neon4cast-stac/5b824d9568.json similarity index 98% rename from data/output/neon4cast-stac/60a3c0bc9f.json rename to data/output/neon4cast-stac/5b824d9568.json index 7631129f6..f14336697 100644 --- a/data/output/neon4cast-stac/60a3c0bc9f.json +++ b/data/output/neon4cast-stac/5b824d9568.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:2f2dfcd3a5", "name": "tg_humidity_lm_chla_P1D_summaries summaries", - "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,7 +16,6 @@ "chla", "Daily", "P1D", - "TOOK", "BARC", "BLWA", "CRAM", @@ -25,7 +24,8 @@ "PRLA", "PRPO", "SUGG", - "TOMB" + "TOMB", + "TOOK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/4bc72f7388.json b/data/output/neon4cast-stac/5d46bfea09.json similarity index 96% rename from data/output/neon4cast-stac/4bc72f7388.json rename to data/output/neon4cast-stac/5d46bfea09.json index c9a379c53..f16fc4480 100644 --- a/data/output/neon4cast-stac/4bc72f7388.json +++ b/data/output/neon4cast-stac/5d46bfea09.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:ef4a9d268c", "name": "tg_precip_lm_all_sites_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,18 +16,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -62,7 +50,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/5206ef36c9.json b/data/output/neon4cast-stac/5d6b407bc6.json similarity index 96% rename from data/output/neon4cast-stac/5206ef36c9.json rename to data/output/neon4cast-stac/5d6b407bc6.json index 9718b1506..26cbf91d3 100644 --- a/data/output/neon4cast-stac/5206ef36c9.json +++ b/data/output/neon4cast-stac/5d6b407bc6.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:0ce2955e76", "name": "tg_precip_lm_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,21 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -47,22 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/2857aaa6ee.json b/data/output/neon4cast-stac/5eca35a608.json similarity index 96% rename from data/output/neon4cast-stac/2857aaa6ee.json rename to data/output/neon4cast-stac/5eca35a608.json index 96bfb4085..f862ea075 100644 --- a/data/output/neon4cast-stac/2857aaa6ee.json +++ b/data/output/neon4cast-stac/5eca35a608.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:0097b9ae35", "name": "tg_ets_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,24 @@ "gcc_90", "Daily", "P1D", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -44,25 +62,7 @@ "OAES", "ONAQ", "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "OSBS" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c61e6663ac.json b/data/output/neon4cast-stac/6139dd1d99.json similarity index 99% rename from data/output/neon4cast-stac/c61e6663ac.json rename to data/output/neon4cast-stac/6139dd1d99.json index 7090bccbc..8e781494e 100644 --- a/data/output/neon4cast-stac/c61e6663ac.json +++ b/data/output/neon4cast-stac/6139dd1d99.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:cec89fd292", "name": "bee_bake_RFModel_2024_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: CRAM, PRPO, LIRO, PRLA, BARC, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: LIRO, PRPO, PRLA, BARC, CRAM, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,11 +16,11 @@ "temperature", "Daily", "P1D", - "CRAM", - "PRPO", "LIRO", + "PRPO", "PRLA", "BARC", + "CRAM", "SUGG", "TOOK" ], diff --git a/data/output/neon4cast-stac/ddd70c9a70.json b/data/output/neon4cast-stac/6360cf8ae6.json similarity index 97% rename from data/output/neon4cast-stac/ddd70c9a70.json rename to data/output/neon4cast-stac/6360cf8ae6.json index 5000154d6..8be1e6714 100644 --- a/data/output/neon4cast-stac/ddd70c9a70.json +++ b/data/output/neon4cast-stac/6360cf8ae6.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:b8e2fc553f", "name": "tg_precip_lm_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,21 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -47,22 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/987815bffb.json b/data/output/neon4cast-stac/638ffd38ff.json similarity index 97% rename from data/output/neon4cast-stac/987815bffb.json rename to data/output/neon4cast-stac/638ffd38ff.json index 82a8fd823..4e0f3fe8c 100644 --- a/data/output/neon4cast-stac/987815bffb.json +++ b/data/output/neon4cast-stac/638ffd38ff.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:4a19ff7506", "name": "hotdeck_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: LECO, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,20 +16,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", "LECO", "LEWI", "LIRO", @@ -45,7 +31,21 @@ "TECR", "TOMB", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/0201d176bf.json b/data/output/neon4cast-stac/63bff6b0a8.json similarity index 97% rename from data/output/neon4cast-stac/0201d176bf.json rename to data/output/neon4cast-stac/63bff6b0a8.json index 33be81353..bf0429bbd 100644 --- a/data/output/neon4cast-stac/0201d176bf.json +++ b/data/output/neon4cast-stac/63bff6b0a8.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:bf5c14415a", "name": "tg_precip_lm_all_sites_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,18 +16,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -62,7 +50,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/62676da375.json b/data/output/neon4cast-stac/6672db05f1.json similarity index 99% rename from data/output/neon4cast-stac/62676da375.json rename to data/output/neon4cast-stac/6672db05f1.json index 22cd71a44..8cc1422e9 100644 --- a/data/output/neon4cast-stac/62676da375.json +++ b/data/output/neon4cast-stac/6672db05f1.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f4e222c2eb", "name": "climatology_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, BARR, TOOL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -61,8 +61,8 @@ "DEJU", "HEAL", "BONA", - "TOOL", - "BARR" + "BARR", + "TOOL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/13227c9a0a.json b/data/output/neon4cast-stac/67a15ebc21.json similarity index 97% rename from data/output/neon4cast-stac/13227c9a0a.json rename to data/output/neon4cast-stac/67a15ebc21.json index 6820e19ed..ae941aa78 100644 --- a/data/output/neon4cast-stac/13227c9a0a.json +++ b/data/output/neon4cast-stac/67a15ebc21.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:38a85021ba", "name": "baseline_ensemble_temperature_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: POSE, PRIN, REDB, SUGG, MCDI, MCRA, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, CARI, PRLA, PRPO, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,28 +16,28 @@ "temperature", "Daily", "P1D", - "POSE", - "PRIN", - "REDB", - "SUGG", - "MCDI", - "MCRA", + "BLWA", + "COMO", + "CUPE", + "FLNT", + "GUIL", "HOPB", - "KING", - "LECO", - "LEWI", - "MART", - "MAYF", + "SUGG", "SYCA", "TECR", "TOMB", "WALK", "WLOU", - "BLWA", - "COMO", - "CUPE", - "FLNT", - "GUIL", + "KING", + "LECO", + "LEWI", + "MART", + "MAYF", + "MCDI", + "MCRA", + "POSE", + "PRIN", + "REDB", "ARIK", "BARC", "BIGC", @@ -45,9 +45,9 @@ "BLUE", "CRAM", "LIRO", - "CARI", "PRLA", "PRPO", + "CARI", "OKSR", "TOOK" ], diff --git a/data/output/neon4cast-stac/36dc70b107.json b/data/output/neon4cast-stac/6847631ec2.json similarity index 99% rename from data/output/neon4cast-stac/36dc70b107.json rename to data/output/neon4cast-stac/6847631ec2.json index 8fdf0a590..dba1becdd 100644 --- a/data/output/neon4cast-stac/36dc70b107.json +++ b/data/output/neon4cast-stac/6847631ec2.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f6a8f8b75e", "name": "baseline_ensemble_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, BARR, TOOL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, TOOL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -61,8 +61,8 @@ "BONA", "DEJU", "HEAL", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/ed90359530.json b/data/output/neon4cast-stac/6d7b887a03.json similarity index 96% rename from data/output/neon4cast-stac/ed90359530.json rename to data/output/neon4cast-stac/6d7b887a03.json index 955f26c9f..fda0c465f 100644 --- a/data/output/neon4cast-stac/ed90359530.json +++ b/data/output/neon4cast-stac/6d7b887a03.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:e1d19fb287", "name": "tg_randfor_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,22 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -46,23 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/af559992ea.json b/data/output/neon4cast-stac/7003c19953.json similarity index 97% rename from data/output/neon4cast-stac/af559992ea.json rename to data/output/neon4cast-stac/7003c19953.json index ae9ce93fc..b2e947e51 100644 --- a/data/output/neon4cast-stac/af559992ea.json +++ b/data/output/neon4cast-stac/7003c19953.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:5c07774407", "name": "tg_tbats_nee_P1D_summaries summaries", - "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,12 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", "CPER", "DCFS", "DEJU", @@ -56,13 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/573b1a20f8.json b/data/output/neon4cast-stac/7233a9d37d.json similarity index 97% rename from data/output/neon4cast-stac/573b1a20f8.json rename to data/output/neon4cast-stac/7233a9d37d.json index 925b9d880..3dad693ca 100644 --- a/data/output/neon4cast-stac/573b1a20f8.json +++ b/data/output/neon4cast-stac/7233a9d37d.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:6a10d4165e", "name": "air2waterSat_2_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,7 +16,9 @@ "temperature", "Daily", "P1D", - "LEWI", + "TOOK", + "WALK", + "WLOU", "LIRO", "MART", "MAYF", @@ -47,9 +49,7 @@ "HOPB", "KING", "LECO", - "TOOK", - "WALK", - "WLOU" + "LEWI" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/d751f6b7be.json b/data/output/neon4cast-stac/7628393150.json similarity index 97% rename from data/output/neon4cast-stac/d751f6b7be.json rename to data/output/neon4cast-stac/7628393150.json index 458c8bacd..9c4f5196d 100644 --- a/data/output/neon4cast-stac/d751f6b7be.json +++ b/data/output/neon4cast-stac/7628393150.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:8c6fb800fc", "name": "tg_ets_abundance_P1W_summaries summaries", - "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,20 @@ "abundance", "Weekly", "P1W", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -48,21 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/e86fe823c1.json b/data/output/neon4cast-stac/7886584881.json similarity index 99% rename from data/output/neon4cast-stac/e86fe823c1.json rename to data/output/neon4cast-stac/7886584881.json index 6d47a237c..d35f78d34 100644 --- a/data/output/neon4cast-stac/e86fe823c1.json +++ b/data/output/neon4cast-stac/7886584881.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:6e71ba850b", "name": "hotdeck_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, LIRO, PRLA, PRPO, BIGC, BLUE, CUPE, GUIL, WALK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -38,14 +38,14 @@ "WLOU", "CRAM", "CARI", + "LIRO", + "PRLA", + "PRPO", "BIGC", "BLUE", "CUPE", "GUIL", - "WALK", - "LIRO", - "PRLA", - "PRPO" + "WALK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/5df0b520a2.json b/data/output/neon4cast-stac/7d41fcf814.json similarity index 97% rename from data/output/neon4cast-stac/5df0b520a2.json rename to data/output/neon4cast-stac/7d41fcf814.json index 3fa725754..55e4d260b 100644 --- a/data/output/neon4cast-stac/5df0b520a2.json +++ b/data/output/neon4cast-stac/7d41fcf814.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f2a39857ea", "name": "climatology_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,11 @@ "oxygen", "Daily", "P1D", - "BLWA", - "CARI", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "COMO", "CUPE", "FLNT", @@ -38,13 +41,10 @@ "TECR", "WALK", "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", "TOMB", + "BLWA", "CRAM", + "CARI", "LIRO", "PRPO", "PRLA", diff --git a/data/output/neon4cast-stac/7eaaeb9504.json b/data/output/neon4cast-stac/848026454e.json similarity index 99% rename from data/output/neon4cast-stac/7eaaeb9504.json rename to data/output/neon4cast-stac/848026454e.json index 57011e868..176065160 100644 --- a/data/output/neon4cast-stac/7eaaeb9504.json +++ b/data/output/neon4cast-stac/848026454e.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:64e8cd2032", "name": "climatology_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -61,8 +61,8 @@ "DEJU", "HEAL", "BONA", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/cafc6149d2.json b/data/output/neon4cast-stac/89978ee180.json similarity index 99% rename from data/output/neon4cast-stac/cafc6149d2.json rename to data/output/neon4cast-stac/89978ee180.json index 8489b26eb..8ed5b7c38 100644 --- a/data/output/neon4cast-stac/cafc6149d2.json +++ b/data/output/neon4cast-stac/89978ee180.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:b9d1d8bf4e", "name": "baseline_ensemble_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: MCDI, MCRA, POSE, PRIN, REDB, SUGG, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: POSE, PRIN, REDB, SUGG, MCDI, MCRA, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,12 +16,12 @@ "temperature", "Daily", "P1D", - "MCDI", - "MCRA", "POSE", "PRIN", "REDB", "SUGG", + "MCDI", + "MCRA", "HOPB", "KING", "LECO", diff --git a/data/output/neon4cast-stac/47f945ead0.json b/data/output/neon4cast-stac/89aca1bd6a.json similarity index 96% rename from data/output/neon4cast-stac/47f945ead0.json rename to data/output/neon4cast-stac/89aca1bd6a.json index a84ead6d1..8fe3bec98 100644 --- a/data/output/neon4cast-stac/47f945ead0.json +++ b/data/output/neon4cast-stac/89aca1bd6a.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:615ebe648a", "name": "tg_tbats_nee_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,28 @@ "nee", "Daily", "P1D", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", "OSBS", "PUUM", "RMNP", @@ -40,29 +62,7 @@ "BART", "BLAN", "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL" + "CLBJ" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/352bdfc869.json b/data/output/neon4cast-stac/9280060140.json similarity index 96% rename from data/output/neon4cast-stac/352bdfc869.json rename to data/output/neon4cast-stac/9280060140.json index 702e30706..516c4910e 100644 --- a/data/output/neon4cast-stac/352bdfc869.json +++ b/data/output/neon4cast-stac/9280060140.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:2fa9911235", "name": "tg_temp_lm_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,7 @@ "gcc_90", "Daily", "P1D", + "ABBY", "BARR", "BART", "BLAN", @@ -61,8 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c97da26853.json b/data/output/neon4cast-stac/9ca719ed8f.json similarity index 96% rename from data/output/neon4cast-stac/c97da26853.json rename to data/output/neon4cast-stac/9ca719ed8f.json index f41c5d503..76ef58106 100644 --- a/data/output/neon4cast-stac/c97da26853.json +++ b/data/output/neon4cast-stac/9ca719ed8f.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:af99cbbcef", "name": "tg_arima_rcc_90_P1D_scores scores", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,10 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", "BONA", "CLBJ", "CPER", @@ -58,11 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/142d58d780.json b/data/output/neon4cast-stac/9d54460c8c.json similarity index 97% rename from data/output/neon4cast-stac/142d58d780.json rename to data/output/neon4cast-stac/9d54460c8c.json index 58e823016..999315a17 100644 --- a/data/output/neon4cast-stac/142d58d780.json +++ b/data/output/neon4cast-stac/9d54460c8c.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:17fff87a82", "name": "tg_precip_lm_all_sites_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,19 +16,6 @@ "gcc_90", "Daily", "P1D", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +49,20 @@ "RMNP", "SCBI", "SERC", - "SJER" + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/3a8a98270a.json b/data/output/neon4cast-stac/a1ccea9b23.json similarity index 99% rename from data/output/neon4cast-stac/3a8a98270a.json rename to data/output/neon4cast-stac/a1ccea9b23.json index fc4c83c9d..50f2ea46d 100644 --- a/data/output/neon4cast-stac/3a8a98270a.json +++ b/data/output/neon4cast-stac/a1ccea9b23.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:4e6a37a3d8", "name": "fARIMA_clim_ensemble_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, CRAM, FLNT, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, FLNT, CRAM, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -41,8 +41,8 @@ "TOMB", "BIGC", "BLDE", - "CRAM", "FLNT", + "CRAM", "SYCA", "LIRO", "PRLA", diff --git a/data/output/neon4cast-stac/519c76c185.json b/data/output/neon4cast-stac/a4570ff348.json similarity index 97% rename from data/output/neon4cast-stac/519c76c185.json rename to data/output/neon4cast-stac/a4570ff348.json index 13b03ad35..f1935f856 100644 --- a/data/output/neon4cast-stac/519c76c185.json +++ b/data/output/neon4cast-stac/a4570ff348.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:fa6bec4585", "name": "tg_ets_richness_P1W_summaries summaries", - "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,25 +16,6 @@ "richness", "Weekly", "P1W", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +43,26 @@ "NOGP", "OAES", "ONAQ", - "ORNL" + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/9644305abd.json b/data/output/neon4cast-stac/a96735a20f.json similarity index 96% rename from data/output/neon4cast-stac/9644305abd.json rename to data/output/neon4cast-stac/a96735a20f.json index c321e443a..bba7f3b40 100644 --- a/data/output/neon4cast-stac/9644305abd.json +++ b/data/output/neon4cast-stac/a96735a20f.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:2b16aa0907", "name": "cb_prophet_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: CLBJ, SJER, ONAQ, DSNY, SCBI, MOAB, PUUM, GUAN, OSBS, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, BONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DSNY, SCBI, MOAB, PUUM, GUAN, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, OSBS, BONA, ONAQ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,15 +16,11 @@ "le", "Daily", "P1D", - "CLBJ", - "SJER", - "ONAQ", "DSNY", "SCBI", "MOAB", "PUUM", "GUAN", - "OSBS", "BART", "CPER", "HARV", @@ -61,7 +57,11 @@ "WREF", "LAJA", "TEAK", - "BONA" + "CLBJ", + "SJER", + "OSBS", + "BONA", + "ONAQ" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/8cf133c119.json b/data/output/neon4cast-stac/ae85d7402b.json similarity index 97% rename from data/output/neon4cast-stac/8cf133c119.json rename to data/output/neon4cast-stac/ae85d7402b.json index b620d46b6..e2b32589c 100644 --- a/data/output/neon4cast-stac/8cf133c119.json +++ b/data/output/neon4cast-stac/ae85d7402b.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:bfefa24d1b", "name": "climatology_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, LIRO, PRPO, CRAM, PRLA, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,13 +16,13 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", "BIGC", "BLDE", "BLUE", "BLWA", - "CARI", "COMO", - "CRAM", "CUPE", "FLNT", "GUIL", @@ -30,24 +30,24 @@ "KING", "LECO", "LEWI", - "LIRO", "MART", "MAYF", "MCDI", "MCRA", "POSE", "PRIN", - "PRLA", - "PRPO", "REDB", "SUGG", "SYCA", "TECR", - "TOMB", "WALK", "WLOU", - "ARIK", - "BARC", + "TOMB", + "LIRO", + "PRPO", + "CRAM", + "PRLA", + "CARI", "OKSR", "TOOK" ], diff --git a/data/output/neon4cast-stac/58c28b07db.json b/data/output/neon4cast-stac/ae8b02090a.json similarity index 97% rename from data/output/neon4cast-stac/58c28b07db.json rename to data/output/neon4cast-stac/ae8b02090a.json index 300dbec73..d6cc33007 100644 --- a/data/output/neon4cast-stac/58c28b07db.json +++ b/data/output/neon4cast-stac/ae8b02090a.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:d76708245c", "name": "persistenceRW_gcc_90_P1D_scores scores", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,21 @@ "gcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -47,22 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/a0e20bffcd.json b/data/output/neon4cast-stac/b0a3a2c44b.json similarity index 96% rename from data/output/neon4cast-stac/a0e20bffcd.json rename to data/output/neon4cast-stac/b0a3a2c44b.json index ff7895497..1f39c9215 100644 --- a/data/output/neon4cast-stac/a0e20bffcd.json +++ b/data/output/neon4cast-stac/b0a3a2c44b.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:14a26d8fef", "name": "tg_tbats_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,10 +16,6 @@ "rcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +58,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c47ba8a319.json b/data/output/neon4cast-stac/b1fd26b4bf.json similarity index 97% rename from data/output/neon4cast-stac/c47ba8a319.json rename to data/output/neon4cast-stac/b1fd26b4bf.json index 706c1c9df..5e9306927 100644 --- a/data/output/neon4cast-stac/c47ba8a319.json +++ b/data/output/neon4cast-stac/b1fd26b4bf.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:535dca9ae8", "name": "climatology_oxygen_P1D_scores scores", - "description": "All scores for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, OKSR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, PRLA, OKSR, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,10 @@ "oxygen", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", "BLUE", "BLWA", "CARI", @@ -35,21 +39,17 @@ "MCRA", "POSE", "PRIN", - "PRLA", "PRPO", "REDB", "SUGG", "SYCA", "TECR", "TOMB", - "TOOK", "WALK", "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "OKSR" + "PRLA", + "OKSR", + "TOOK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/17ff2cf74e.json b/data/output/neon4cast-stac/b2b689d95d.json similarity index 98% rename from data/output/neon4cast-stac/17ff2cf74e.json rename to data/output/neon4cast-stac/b2b689d95d.json index c91e16643..03c6bcb50 100644 --- a/data/output/neon4cast-stac/17ff2cf74e.json +++ b/data/output/neon4cast-stac/b2b689d95d.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:5458e6b959", "name": "tg_arima_amblyomma_americanum_P1W_forecast forecasts", - "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ORNL, OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,15 +16,15 @@ "amblyomma_americanum", "Weekly", "P1W", - "BLAN", - "KONZ", - "LENO", "ORNL", "OSBS", "SCBI", "SERC", "TALL", - "UKFS" + "UKFS", + "BLAN", + "KONZ", + "LENO" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/045872c4ee.json b/data/output/neon4cast-stac/b31090ce38.json similarity index 98% rename from data/output/neon4cast-stac/045872c4ee.json rename to data/output/neon4cast-stac/b31090ce38.json index e9c9dd29f..28d8099eb 100644 --- a/data/output/neon4cast-stac/045872c4ee.json +++ b/data/output/neon4cast-stac/b31090ce38.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:4042f047eb", "name": "tg_arima_amblyomma_americanum_P1W_summaries summaries", - "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ORNL, OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,15 +16,15 @@ "amblyomma_americanum", "Weekly", "P1W", - "BLAN", - "KONZ", - "LENO", "ORNL", "OSBS", "SCBI", "SERC", "TALL", - "UKFS" + "UKFS", + "BLAN", + "KONZ", + "LENO" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/3983b013ac.json b/data/output/neon4cast-stac/b495c34f2d.json similarity index 97% rename from data/output/neon4cast-stac/3983b013ac.json rename to data/output/neon4cast-stac/b495c34f2d.json index 3fc40e2f3..ff83cec76 100644 --- a/data/output/neon4cast-stac/3983b013ac.json +++ b/data/output/neon4cast-stac/b495c34f2d.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9ba672453b", "name": "persistenceRW_oxygen_P1D_scores scores", - "description": "All scores for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,10 @@ "oxygen", "Daily", "P1D", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -45,11 +49,7 @@ "REDB", "SUGG", "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "TECR" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/1c8d6c38f0.json b/data/output/neon4cast-stac/bc10520c4b.json similarity index 99% rename from data/output/neon4cast-stac/1c8d6c38f0.json rename to data/output/neon4cast-stac/bc10520c4b.json index 2329aaf1b..f979c316f 100644 --- a/data/output/neon4cast-stac/1c8d6c38f0.json +++ b/data/output/neon4cast-stac/bc10520c4b.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:65f97f5ad2", "name": "bee_bake_RFModel_2024_temperature_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: LIRO, PRPO, BARC, CRAM, PRLA, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: LIRO, PRPO, CRAM, PRLA, BARC, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -18,9 +18,9 @@ "P1D", "LIRO", "PRPO", - "BARC", "CRAM", "PRLA", + "BARC", "SUGG", "TOOK" ], diff --git a/data/output/neon4cast-stac/e323663d39.json b/data/output/neon4cast-stac/bd769a7076.json similarity index 98% rename from data/output/neon4cast-stac/e323663d39.json rename to data/output/neon4cast-stac/bd769a7076.json index eeb8917c9..962fb32b5 100644 --- a/data/output/neon4cast-stac/e323663d39.json +++ b/data/output/neon4cast-stac/bd769a7076.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:2576630982", "name": "tg_humidity_lm_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HARV, HEAL, JERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,9 +16,6 @@ "le", "Daily", "P1D", - "HARV", - "HEAL", - "JERC", "ABBY", "BARR", "BART", @@ -32,6 +29,9 @@ "DSNY", "GRSM", "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", diff --git a/data/output/neon4cast-stac/02ef56e31d.json b/data/output/neon4cast-stac/be23170df1.json similarity index 96% rename from data/output/neon4cast-stac/02ef56e31d.json rename to data/output/neon4cast-stac/be23170df1.json index da1fd75c4..8a91947bd 100644 --- a/data/output/neon4cast-stac/02ef56e31d.json +++ b/data/output/neon4cast-stac/be23170df1.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:4d8a66c898", "name": "tg_temp_lm_nee_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,22 @@ "nee", "Daily", "P1D", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -46,23 +62,7 @@ "ORNL", "OSBS", "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "RMNP" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/002c9b32e5.json b/data/output/neon4cast-stac/c0e44bd418.json similarity index 97% rename from data/output/neon4cast-stac/002c9b32e5.json rename to data/output/neon4cast-stac/c0e44bd418.json index 9ead26f60..8c7476471 100644 --- a/data/output/neon4cast-stac/002c9b32e5.json +++ b/data/output/neon4cast-stac/c0e44bd418.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:66e0c4ba3c", "name": "tg_arima_le_P1D_summaries summaries", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,21 +16,6 @@ "le", "Daily", "P1D", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", "SOAP", "SRER", "STEI", @@ -62,7 +47,22 @@ "JERC", "JORN", "KONA", - "KONZ" + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/ef06ecc0d2.json b/data/output/neon4cast-stac/c4629f9667.json similarity index 97% rename from data/output/neon4cast-stac/ef06ecc0d2.json rename to data/output/neon4cast-stac/c4629f9667.json index e187ffc53..56904cc8f 100644 --- a/data/output/neon4cast-stac/ef06ecc0d2.json +++ b/data/output/neon4cast-stac/c4629f9667.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:009f6fb0c1", "name": "fARIMA_clim_ensemble_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: BLDE, CARI, COMO, CRAM, CUPE, FLNT, GUIL, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TOMB, WALK, WLOU, ARIK, BARC, BIGC, HOPB, TOOK, TECR, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, REDB, BIGC, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,13 +16,8 @@ "temperature", "Daily", "P1D", - "BLDE", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", "GUIL", + "HOPB", "KING", "LECO", "LEWI", @@ -36,19 +31,24 @@ "PRIN", "PRLA", "PRPO", - "REDB", "SUGG", "SYCA", + "TECR", "TOMB", "WALK", "WLOU", "ARIK", "BARC", + "BLDE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "REDB", "BIGC", - "HOPB", - "TOOK", - "TECR", - "BLWA" + "TOOK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/cde022ce66.json b/data/output/neon4cast-stac/c464e4f62d.json similarity index 96% rename from data/output/neon4cast-stac/cde022ce66.json rename to data/output/neon4cast-stac/c464e4f62d.json index cecdc23f7..e0f4f8aab 100644 --- a/data/output/neon4cast-stac/cde022ce66.json +++ b/data/output/neon4cast-stac/c464e4f62d.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:85ed7edcc2", "name": "tg_randfor_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,15 +16,6 @@ "gcc_90", "Daily", "P1D", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", "SCBI", "SERC", "SJER", @@ -62,7 +53,16 @@ "KONZ", "LAJA", "LENO", - "MLBS" + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/2bee071192.json b/data/output/neon4cast-stac/c5208604bc.json similarity index 96% rename from data/output/neon4cast-stac/2bee071192.json rename to data/output/neon4cast-stac/c5208604bc.json index f3e9a694f..7eb30f3a9 100644 --- a/data/output/neon4cast-stac/2bee071192.json +++ b/data/output/neon4cast-stac/c5208604bc.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:4fa58bec92", "name": "persistenceRW_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, ONAQ, ORNL, OSBS, PUUM, SRER, STEI, STER, TALL, TEAK, RMNP, SCBI, SERC, SJER, SOAP, OAES, JERC, JORN, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, RMNP, SCBI, SERC, SJER, SOAP, JERC, JORN, KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, SRER, STEI, STER, TALL, TEAK, CPER, DCFS, DEJU, DELA, DSNY, OAES, ONAQ, ORNL, OSBS, PUUM, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL, BART, BLAN, BONA, CLBJ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,15 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -23,34 +32,21 @@ "WOOD", "WREF", "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", "CPER", "DCFS", "DEJU", "DELA", "DSNY", + "OAES", "ONAQ", "ORNL", "OSBS", "PUUM", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "OAES", - "JERC", - "JORN", "MLBS", "MOAB", "NIWO", @@ -62,7 +58,11 @@ "GRSM", "GUAN", "HARV", - "HEAL" + "HEAL", + "BART", + "BLAN", + "BONA", + "CLBJ" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/105bf242a4.json b/data/output/neon4cast-stac/c52f06509f.json similarity index 96% rename from data/output/neon4cast-stac/105bf242a4.json rename to data/output/neon4cast-stac/c52f06509f.json index e4743c319..b401f70fc 100644 --- a/data/output/neon4cast-stac/105bf242a4.json +++ b/data/output/neon4cast-stac/c52f06509f.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:936ecdc096", "name": "tg_precip_lm_all_sites_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,24 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -44,25 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/10b4400ef2.json b/data/output/neon4cast-stac/c7c49d0a51.json similarity index 97% rename from data/output/neon4cast-stac/10b4400ef2.json rename to data/output/neon4cast-stac/c7c49d0a51.json index 44a6b114f..d8c8429bf 100644 --- a/data/output/neon4cast-stac/10b4400ef2.json +++ b/data/output/neon4cast-stac/c7c49d0a51.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:1fa224d8c5", "name": "tg_precip_lm_all_sites_le_P1D_summaries summaries", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,24 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -44,25 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/89ad857c65.json b/data/output/neon4cast-stac/c8b265a2b5.json similarity index 97% rename from data/output/neon4cast-stac/89ad857c65.json rename to data/output/neon4cast-stac/c8b265a2b5.json index 2aea60ac0..d115edb74 100644 --- a/data/output/neon4cast-stac/89ad857c65.json +++ b/data/output/neon4cast-stac/c8b265a2b5.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:c99c01e7b5", "name": "tg_ets_richness_P1W_scores scores", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,15 @@ "richness", "Weekly", "P1W", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -53,16 +62,7 @@ "SOAP", "SRER", "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "STER" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c26f3dacb6.json b/data/output/neon4cast-stac/cb719f2302.json similarity index 97% rename from data/output/neon4cast-stac/c26f3dacb6.json rename to data/output/neon4cast-stac/cb719f2302.json index d92b57a06..673852d75 100644 --- a/data/output/neon4cast-stac/c26f3dacb6.json +++ b/data/output/neon4cast-stac/cb719f2302.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9c831fe601", "name": "persistenceRW_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, LECO, LEWI, LIRO, MART, MAYF, SYCA, TECR, TOMB, TOOK, WALK, BLWA, CARI, COMO, CRAM, CUPE, WLOU, FLNT, GUIL, HOPB, KING, PRIN, PRLA, PRPO, REDB, SUGG, MCDI, MCRA, OKSR, POSE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,40 +16,40 @@ "oxygen", "Daily", "P1D", - "CARI", - "COMO", - "CRAM", - "CUPE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "BLUE", - "BLWA", - "WLOU", "ARIK", "BARC", "BIGC", "BLDE", + "BLUE", + "LECO", + "LEWI", + "LIRO", + "MART", + "MAYF", + "SYCA", "TECR", "TOMB", "TOOK", "WALK", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "WLOU", "FLNT", "GUIL", "HOPB", "KING", - "LECO", - "LEWI", - "LIRO", - "MART" + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "MCDI", + "MCRA", + "OKSR", + "POSE" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/d4ee14ee52.json b/data/output/neon4cast-stac/d6bca11a77.json similarity index 97% rename from data/output/neon4cast-stac/d4ee14ee52.json rename to data/output/neon4cast-stac/d6bca11a77.json index dfa3fdfe2..937772494 100644 --- a/data/output/neon4cast-stac/d4ee14ee52.json +++ b/data/output/neon4cast-stac/d6bca11a77.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:0d3f01f255", "name": "tg_temp_lm_temperature_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,9 +16,6 @@ "temperature", "Daily", "P1D", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -49,7 +46,10 @@ "SUGG", "SYCA", "TECR", - "TOMB" + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/599c7721f5.json b/data/output/neon4cast-stac/d8176111f8.json similarity index 96% rename from data/output/neon4cast-stac/599c7721f5.json rename to data/output/neon4cast-stac/d8176111f8.json index f59538c72..9bae7a4b7 100644 --- a/data/output/neon4cast-stac/599c7721f5.json +++ b/data/output/neon4cast-stac/d8176111f8.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:c78386b201", "name": "tg_arima_nee_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,24 +16,6 @@ "nee", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", "KONZ", "LAJA", "LENO", @@ -62,7 +44,25 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/30faf9501d.json b/data/output/neon4cast-stac/ddbdd8e934.json similarity index 96% rename from data/output/neon4cast-stac/30faf9501d.json rename to data/output/neon4cast-stac/ddbdd8e934.json index 6e7ab4ef5..b86d1a628 100644 --- a/data/output/neon4cast-stac/30faf9501d.json +++ b/data/output/neon4cast-stac/ddbdd8e934.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9fae5a1d6a", "name": "tg_arima_nee_P1D_scores scores", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,22 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -46,23 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/65ceceb537.json b/data/output/neon4cast-stac/df58511eed.json similarity index 96% rename from data/output/neon4cast-stac/65ceceb537.json rename to data/output/neon4cast-stac/df58511eed.json index 448d34a3d..ca54afeab 100644 --- a/data/output/neon4cast-stac/65ceceb537.json +++ b/data/output/neon4cast-stac/df58511eed.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:ba11e250f0", "name": "tg_ets_abundance_P1W_forecast forecasts", - "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,20 @@ "abundance", "Weekly", "P1W", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -48,21 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/9bba26e5e4.json b/data/output/neon4cast-stac/dfd185dec0.json similarity index 96% rename from data/output/neon4cast-stac/9bba26e5e4.json rename to data/output/neon4cast-stac/dfd185dec0.json index bd751d8d6..6f49987df 100644 --- a/data/output/neon4cast-stac/9bba26e5e4.json +++ b/data/output/neon4cast-stac/dfd185dec0.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:2d55c03470", "name": "tg_arima_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,10 +16,6 @@ "gcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +58,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/1eeaa151df.json b/data/output/neon4cast-stac/e0a9e7ac68.json similarity index 97% rename from data/output/neon4cast-stac/1eeaa151df.json rename to data/output/neon4cast-stac/e0a9e7ac68.json index 28df45639..30d88f4d6 100644 --- a/data/output/neon4cast-stac/1eeaa151df.json +++ b/data/output/neon4cast-stac/e0a9e7ac68.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:4c1bd3b1c1", "name": "tg_precip_lm_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,22 +16,6 @@ "gcc_90", "Daily", "P1D", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +46,23 @@ "ORNL", "OSBS", "PUUM", - "RMNP" + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/0ddb9eaa22.json b/data/output/neon4cast-stac/eb12c65609.json similarity index 96% rename from data/output/neon4cast-stac/0ddb9eaa22.json rename to data/output/neon4cast-stac/eb12c65609.json index 5830d4540..8130123d6 100644 --- a/data/output/neon4cast-stac/0ddb9eaa22.json +++ b/data/output/neon4cast-stac/eb12c65609.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:b66262dba9", "name": "climatology_le_PT30M_summaries summaries", - "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, SCBI, RMNP, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, STER, TALL, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,13 +16,10 @@ "le", "30min", "PT30M", - "SERC", "SJER", "SOAP", "SRER", "STEI", - "STER", - "TALL", "TEAK", "TOOL", "TREE", @@ -34,6 +31,8 @@ "ABBY", "BARR", "BART", + "STER", + "TALL", "BLAN", "BONA", "CLBJ", @@ -51,8 +50,6 @@ "KONA", "KONZ", "LAJA", - "SCBI", - "RMNP", "LENO", "MLBS", "MOAB", @@ -62,7 +59,10 @@ "ONAQ", "ORNL", "OSBS", - "PUUM" + "PUUM", + "RMNP", + "SCBI", + "SERC" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f195a43d1f.json b/data/output/neon4cast-stac/eb25550af8.json similarity index 97% rename from data/output/neon4cast-stac/f195a43d1f.json rename to data/output/neon4cast-stac/eb25550af8.json index 495693bff..9b367d8ad 100644 --- a/data/output/neon4cast-stac/f195a43d1f.json +++ b/data/output/neon4cast-stac/eb25550af8.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:6b53089167", "name": "tg_humidity_lm_all_sites_nee_P1D_summaries summaries", - "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,19 +16,6 @@ "nee", "Daily", "P1D", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", "SOAP", "SRER", "STEI", @@ -62,7 +49,20 @@ "KONA", "KONZ", "LAJA", - "LENO" + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/0de889ff04.json b/data/output/neon4cast-stac/f05befad20.json similarity index 98% rename from data/output/neon4cast-stac/0de889ff04.json rename to data/output/neon4cast-stac/f05befad20.json index a07c10f09..2c202f9e4 100644 --- a/data/output/neon4cast-stac/0de889ff04.json +++ b/data/output/neon4cast-stac/f05befad20.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:bcec4bf7bf", "name": "flareGLM_noDA_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOOK, BARC, CRAM, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,13 +16,13 @@ "temperature", "Daily", "P1D", + "BARC", + "CRAM", + "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK", - "BARC", - "CRAM", - "LIRO" + "TOOK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/875165a198.json b/data/output/neon4cast-stac/f0745f818f.json similarity index 97% rename from data/output/neon4cast-stac/875165a198.json rename to data/output/neon4cast-stac/f0745f818f.json index 826ef8211..6c0ce40e3 100644 --- a/data/output/neon4cast-stac/875165a198.json +++ b/data/output/neon4cast-stac/f0745f818f.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:190e4ee776", "name": "tg_ets_le_P1D_summaries summaries", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,29 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", "NIWO", "NOGP", "OAES", @@ -39,30 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/898116acc5.json b/data/output/neon4cast-stac/f4149fa12b.json similarity index 96% rename from data/output/neon4cast-stac/898116acc5.json rename to data/output/neon4cast-stac/f4149fa12b.json index 77789ef40..201c30a38 100644 --- a/data/output/neon4cast-stac/898116acc5.json +++ b/data/output/neon4cast-stac/f4149fa12b.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:12319ac0bb", "name": "tg_ets_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,29 +16,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", "NIWO", "NOGP", "OAES", @@ -62,7 +39,30 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/7fcabd1dc3.json b/data/output/neon4cast-stac/f46739d0e0.json similarity index 96% rename from data/output/neon4cast-stac/7fcabd1dc3.json rename to data/output/neon4cast-stac/f46739d0e0.json index 61d9dc894..528ca031c 100644 --- a/data/output/neon4cast-stac/7fcabd1dc3.json +++ b/data/output/neon4cast-stac/f46739d0e0.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9a97ede338", "name": "climatology_le_P1D_scores scores", - "description": "All scores for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,12 @@ "le", "Daily", "P1D", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -56,13 +62,7 @@ "DCFS", "DEJU", "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN" + "DSNY" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/1f30f1b732.json b/data/output/neon4cast-stac/f9b30c203b.json similarity index 97% rename from data/output/neon4cast-stac/1f30f1b732.json rename to data/output/neon4cast-stac/f9b30c203b.json index 78abf0c01..c085362f2 100644 --- a/data/output/neon4cast-stac/1f30f1b732.json +++ b/data/output/neon4cast-stac/f9b30c203b.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:8f80238d58", "name": "tg_tbats_rcc_90_P1D_scores scores", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,22 @@ "rcc_90", "Daily", "P1D", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -46,23 +62,7 @@ "ORNL", "OSBS", "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "RMNP" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/432e9e9a6a.json b/data/output/neon4cast-stac/f9d1dca977.json similarity index 96% rename from data/output/neon4cast-stac/432e9e9a6a.json rename to data/output/neon4cast-stac/f9d1dca977.json index 33a130983..6b66dd692 100644 --- a/data/output/neon4cast-stac/432e9e9a6a.json +++ b/data/output/neon4cast-stac/f9d1dca977.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:cd0e2b13d1", "name": "tg_ets_richness_P1W_forecast forecasts", - "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,25 @@ "richness", "Weekly", "P1W", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -43,26 +62,7 @@ "NOGP", "OAES", "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "ORNL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f87e6e2260.json b/data/output/neon4cast-stac/f9dd53a834.json similarity index 96% rename from data/output/neon4cast-stac/f87e6e2260.json rename to data/output/neon4cast-stac/f9dd53a834.json index ffb41c92a..b07985366 100644 --- a/data/output/neon4cast-stac/f87e6e2260.json +++ b/data/output/neon4cast-stac/f9dd53a834.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:12ef1ad854", "name": "tg_ets_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,23 +16,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", "KONA", "KONZ", "LAJA", @@ -62,7 +45,24 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/e5a6cb5f91.json b/data/output/neon4cast-stac/fac727aa67.json similarity index 97% rename from data/output/neon4cast-stac/e5a6cb5f91.json rename to data/output/neon4cast-stac/fac727aa67.json index 456c76644..0e10043e0 100644 --- a/data/output/neon4cast-stac/e5a6cb5f91.json +++ b/data/output/neon4cast-stac/fac727aa67.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:39e87a715c", "name": "air2waterSat_2_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOMB, TOOK, WALK, WLOU, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,22 @@ "oxygen", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO", + "TOMB", "TOOK", "WALK", "WLOU", @@ -33,23 +49,7 @@ "REDB", "SUGG", "SYCA", - "TECR", - "TOMB", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO" + "TECR" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/1b3917171e.json b/data/output/neon4cast-stac/fcf8629dac.json similarity index 96% rename from data/output/neon4cast-stac/1b3917171e.json rename to data/output/neon4cast-stac/fcf8629dac.json index 3df8b89ba..0a68f67f4 100644 --- a/data/output/neon4cast-stac/1b3917171e.json +++ b/data/output/neon4cast-stac/fcf8629dac.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:a8d2068a7b", "name": "tg_randfor_richness_P1W_forecast forecasts", - "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,22 @@ "richness", "Weekly", "P1W", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -46,23 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f48fc9d331.json b/data/output/neon4cast-stac/fda5824678.json similarity index 99% rename from data/output/neon4cast-stac/f48fc9d331.json rename to data/output/neon4cast-stac/fda5824678.json index 52abd6bcb..e4ed34407 100644 --- a/data/output/neon4cast-stac/f48fc9d331.json +++ b/data/output/neon4cast-stac/fda5824678.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:6a6382a4b9", "name": "hotdeck_oxygen_P1D_summaries summaries", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, SYCA, BLDE, BIGC, MCRA, REDB, CRAM, LIRO, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, BLDE, BIGC, MCRA, REDB, CRAM, LIRO, SYCA, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -19,13 +19,13 @@ "BARC", "SUGG", "KING", - "SYCA", "BLDE", "BIGC", "MCRA", "REDB", "CRAM", "LIRO", + "SYCA", "PRIN", "POSE", "MAYF", diff --git a/data/output/sitemap/sitemap_neon4cast.xml b/data/output/sitemap/sitemap_neon4cast.xml index b9ecb041c..1e3c56198 100644 --- a/data/output/sitemap/sitemap_neon4cast.xml +++ b/data/output/sitemap/sitemap_neon4cast.xml @@ -1,21 +1,22 @@ https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/31d91c5392.json + https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/5d46bfea09.json https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/2dad7629c7.json + https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/c4629f9667.json https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/cb0c5f756c.json + https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/6672db05f1.json + https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/0fd3712ccd.json https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/be8e12e597.json https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/bc8e9ff3a0.json https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/4db2bf5259.json - 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"fcre", "bvre", + "fcre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/97d8e4aee4.json b/data/output/vera4cast-stac/0c6e5729d0.json similarity index 100% rename from data/output/vera4cast-stac/97d8e4aee4.json rename to data/output/vera4cast-stac/0c6e5729d0.json index 6ac189aab..44c7c6605 100644 --- a/data/output/vera4cast-stac/97d8e4aee4.json +++ b/data/output/vera4cast-stac/0c6e5729d0.json @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/a83ff3510a.json b/data/output/vera4cast-stac/14d7766f40.json similarity index 100% rename from data/output/vera4cast-stac/a83ff3510a.json rename to data/output/vera4cast-stac/14d7766f40.json index 01b4b37ee..edf77ccf7 100644 --- a/data/output/vera4cast-stac/a83ff3510a.json +++ b/data/output/vera4cast-stac/14d7766f40.json @@ -16,8 +16,8 @@ "Temp_C_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/5d8e0b5934.json b/data/output/vera4cast-stac/19e005c7ab.json similarity index 99% rename from data/output/vera4cast-stac/5d8e0b5934.json rename to data/output/vera4cast-stac/19e005c7ab.json index 75e9c0d34..f7c3880ef 100644 --- a/data/output/vera4cast-stac/5d8e0b5934.json +++ b/data/output/vera4cast-stac/19e005c7ab.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:851ebc8711", "name": "asl.tbats_Chla_ugL_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/6f98ed03e7.json b/data/output/vera4cast-stac/22a9776c17.json similarity index 99% rename from data/output/vera4cast-stac/6f98ed03e7.json rename to data/output/vera4cast-stac/22a9776c17.json index 9fb59c0ce..1c1e9e437 100644 --- a/data/output/vera4cast-stac/6f98ed03e7.json +++ b/data/output/vera4cast-stac/22a9776c17.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:3f3769e511", "name": "fableARIMA_Chla_ugL_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/ffbd6af5b2.json b/data/output/vera4cast-stac/32e4576453.json similarity index 100% rename from data/output/vera4cast-stac/ffbd6af5b2.json rename to data/output/vera4cast-stac/32e4576453.json index 7e6642eaa..507c5f7a4 100644 --- a/data/output/vera4cast-stac/ffbd6af5b2.json +++ b/data/output/vera4cast-stac/32e4576453.json @@ -16,8 +16,8 @@ "Secchi_m_sample", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/e69fa6fa11.json b/data/output/vera4cast-stac/571f9186af.json similarity index 99% rename from data/output/vera4cast-stac/e69fa6fa11.json rename to data/output/vera4cast-stac/571f9186af.json index 6254de6d6..0cbd5d148 100644 --- a/data/output/vera4cast-stac/e69fa6fa11.json +++ b/data/output/vera4cast-stac/571f9186af.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:369fac2514", "name": "climatology_Temp_C_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: tubr, bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,9 +16,9 @@ "Temp_C_mean", "Daily", "P1D", - "tubr", "bvre", "fcre", + "tubr", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/d045f62c2c.json b/data/output/vera4cast-stac/573c158ece.json similarity index 99% rename from data/output/vera4cast-stac/d045f62c2c.json rename to data/output/vera4cast-stac/573c158ece.json index 591767d8b..fce628517 100644 --- a/data/output/vera4cast-stac/d045f62c2c.json +++ b/data/output/vera4cast-stac/573c158ece.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:36e13f6944", "name": "fableNNETAR_focal_Secchi_m_sample_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Secchi_m_sample", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/6d750726a3.json b/data/output/vera4cast-stac/6d750726a3.json new file mode 100644 index 000000000..65cef5650 --- /dev/null +++ b/data/output/vera4cast-stac/6d750726a3.json @@ -0,0 +1,266 @@ +{ + "@context": { + "@vocab": "https://schema.org/" + }, + "@type": "Dataset", + "@id": "urn:vera4cast-stac:0529568386", + "name": "asl.persistence_DO_mgL_mean_P1D_scores scores", + "description": "All scores for the Daily_oxygen_concentration variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datePublished": "2022-01-01", + "keywords": [ + "Scores", + "vera4cast", + "Chemical", + "asl.persistence", + "oxygen_concentration", + "DO_mgL_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "citation": { + "@type": "CreativeWork", + "@id": "https://doi.org/10.1002/ecs2.4686", + "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686", + "name": "A community convention for ecological forecasting: Output files and metadata version 1.0", + "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ", + "identifier": { + "@type": "PropertyValue", + "propertyID": "https://registry.identifiers.org/registry/doi", + "value": "doi:10.1002/ecs2.4686", + "url": "https://doi.org/10.1002/ecs2.4686" + } + }, + "variableMeasured": [ + { + "@type": "PropertyValue", + "name": "reference_datetime", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "@type": "PropertyValue", + "name": "site_id", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "@type": "PropertyValue", + "name": "datetime", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "@type": "PropertyValue", + "name": "family", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "@type": "PropertyValue", + "name": "pub_datetime", + "description": "datetime that forecast was submitted" + }, + { + "@type": "PropertyValue", + "name": "depth_m", + "description": "depth (meters) in water column of prediction" + }, + { + "@type": "PropertyValue", + "name": "observation", + "description": "observed value for variable" + }, + { + "@type": "PropertyValue", + "name": "crps", + "description": "crps forecast score" + }, + { + "@type": "PropertyValue", + "name": "logs", + "description": "logs forecast score" + }, + { + "@type": "PropertyValue", + "name": "mean", + "description": "mean forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "median", + "description": "median forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "sd", + "description": "standard deviation forecasts" + }, + { + "@type": "PropertyValue", + "name": "quantile97.5", + "description": "upper 97.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile02.5", + "description": "upper 2.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile90", + "description": "upper 90 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile10", + "description": "upper 10 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "duration", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "@type": "PropertyValue", + "name": "model_id", + "description": "unique model identifier" + }, + { + "@type": "PropertyValue", + "name": "project_id", + "description": "unique project identifier" + }, + { + "@type": "PropertyValue", + "name": "variable", + "description": "name of forecasted variable" + } + ], + "distribution": [ + { + "@type": "DataDownload", + "contentUrl": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json", + "encodingFormat": "application/json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json\")\n\n", + "name": "Model Metadata" + }, + { + "@type": "DataDownload", + "contentUrl": "https://github.com/radiantearth", + "encodingFormat": "text/html", + "description": "The link to the model code provided by the model submission team", + "name": "Link for Model Code" + }, + { + "@type": "DataDownload", + "contentUrl": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org", + "encodingFormat": "application/x-parquet", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n", + "name": "Database Access for Daily oxygen_concentration" + } + ], + "spatialCoverage": { + "@type": "Place", + "geo": { + "@type": "GeoShape", + "box": "-79.8372 37.3032 -79.8159 37.3129" + }, + "additionalProperty": [ + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291041754864156672 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291043953887412224 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291044503643226112 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291045878032760832 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047252422295552 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047802178109440 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291051650468806656 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291052200224620544 + } + ] + } +} \ No newline at end of file diff --git a/data/output/vera4cast-stac/886961c611.json b/data/output/vera4cast-stac/886961c611.json new file mode 100644 index 000000000..dbe2aa4d0 --- /dev/null +++ b/data/output/vera4cast-stac/886961c611.json @@ -0,0 +1,266 @@ +{ + "@context": { + "@vocab": "https://schema.org/" + }, + "@type": "Dataset", + "@id": "urn:vera4cast-stac:d1150e3d41", + "name": "asl.persistence_Chla_ugL_mean_P1D_scores scores", + "description": "All scores for the Daily_Chlorophyll-a variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datePublished": "2022-01-01", + "keywords": [ + "Scores", + "vera4cast", + "Biological", + "asl.persistence", + "Chlorophyll-a", + "Chla_ugL_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "citation": { + "@type": "CreativeWork", + "@id": "https://doi.org/10.1002/ecs2.4686", + "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686", + "name": "A community convention for ecological forecasting: Output files and metadata version 1.0", + "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ", + "identifier": { + "@type": "PropertyValue", + "propertyID": "https://registry.identifiers.org/registry/doi", + "value": "doi:10.1002/ecs2.4686", + "url": "https://doi.org/10.1002/ecs2.4686" + } + }, + "variableMeasured": [ + { + "@type": "PropertyValue", + "name": "reference_datetime", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "@type": "PropertyValue", + "name": "site_id", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "@type": "PropertyValue", + "name": "datetime", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "@type": "PropertyValue", + "name": "family", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "@type": "PropertyValue", + "name": "pub_datetime", + "description": "datetime that forecast was submitted" + }, + { + "@type": "PropertyValue", + "name": "depth_m", + "description": "depth (meters) in water column of prediction" + }, + { + "@type": "PropertyValue", + "name": "observation", + "description": "observed value for variable" + }, + { + "@type": "PropertyValue", + "name": "crps", + "description": "crps forecast score" + }, + { + "@type": "PropertyValue", + "name": "logs", + "description": "logs forecast score" + }, + { + "@type": "PropertyValue", + "name": "mean", + "description": "mean forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "median", + "description": "median forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "sd", + "description": "standard deviation forecasts" + }, + { + "@type": "PropertyValue", + "name": "quantile97.5", + "description": "upper 97.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile02.5", + "description": "upper 2.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile90", + "description": "upper 90 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile10", + "description": "upper 10 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "duration", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "@type": "PropertyValue", + "name": "model_id", + "description": "unique model identifier" + }, + { + "@type": "PropertyValue", + "name": "project_id", + "description": "unique project identifier" + }, + { + "@type": "PropertyValue", + "name": "variable", + "description": "name of forecasted variable" + } + ], + "distribution": [ + { + "@type": "DataDownload", + "contentUrl": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json", + "encodingFormat": "application/json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json\")\n\n", + "name": "Model Metadata" + }, + { + "@type": "DataDownload", + "contentUrl": "https://github.com/radiantearth", + "encodingFormat": "text/html", + "description": "The link to the model code provided by the model submission team", + "name": "Link for Model Code" + }, + { + "@type": "DataDownload", + "contentUrl": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org", + "encodingFormat": "application/x-parquet", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n", + "name": "Database Access for Daily Chlorophyll-a" + } + ], + "spatialCoverage": { + "@type": "Place", + "geo": { + "@type": "GeoShape", + "box": "-79.8372 37.3032 -79.8159 37.3129" + }, + "additionalProperty": [ + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291041754864156672 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291043953887412224 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291044503643226112 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291045878032760832 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047252422295552 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047802178109440 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291051650468806656 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291052200224620544 + } + ] + } +} \ No newline at end of file diff --git a/data/output/vera4cast-stac/16a8522736.json b/data/output/vera4cast-stac/91e53342fa.json similarity index 100% rename from data/output/vera4cast-stac/16a8522736.json rename to data/output/vera4cast-stac/91e53342fa.json index 47116326a..086e95100 100644 --- a/data/output/vera4cast-stac/16a8522736.json +++ b/data/output/vera4cast-stac/91e53342fa.json @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/9cc490a9c8.json b/data/output/vera4cast-stac/9cc490a9c8.json new file mode 100644 index 000000000..5f72867a2 --- /dev/null +++ b/data/output/vera4cast-stac/9cc490a9c8.json @@ -0,0 +1,266 @@ +{ + "@context": { + "@vocab": "https://schema.org/" + }, + "@type": "Dataset", + "@id": "urn:vera4cast-stac:8bd376b324", + "name": "asl.persistence_Temp_C_mean_P1D_scores scores", + "description": "All scores for the Daily_Water_temperature variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datePublished": "2022-01-01", + "keywords": [ + "Scores", + "vera4cast", + "Physical", + "asl.persistence", + "Water_temperature", + "Temp_C_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "citation": { + "@type": "CreativeWork", + "@id": "https://doi.org/10.1002/ecs2.4686", + "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686", + "name": "A community convention for ecological forecasting: Output files and metadata version 1.0", + "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ", + "identifier": { + "@type": "PropertyValue", + "propertyID": "https://registry.identifiers.org/registry/doi", + "value": "doi:10.1002/ecs2.4686", + "url": "https://doi.org/10.1002/ecs2.4686" + } + }, + "variableMeasured": [ + { + "@type": "PropertyValue", + "name": "reference_datetime", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "@type": "PropertyValue", + "name": "site_id", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "@type": "PropertyValue", + "name": "datetime", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "@type": "PropertyValue", + "name": "family", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "@type": "PropertyValue", + "name": "pub_datetime", + "description": "datetime that forecast was submitted" + }, + { + "@type": "PropertyValue", + "name": "depth_m", + "description": "depth (meters) in water column of prediction" + }, + { + "@type": "PropertyValue", + "name": "observation", + "description": "observed value for variable" + }, + { + "@type": "PropertyValue", + "name": "crps", + "description": "crps forecast score" + }, + { + "@type": "PropertyValue", + "name": "logs", + "description": "logs forecast score" + }, + { + "@type": "PropertyValue", + "name": "mean", + "description": "mean forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "median", + "description": "median forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "sd", + "description": "standard deviation forecasts" + }, + { + "@type": "PropertyValue", + "name": "quantile97.5", + "description": "upper 97.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile02.5", + "description": "upper 2.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile90", + "description": "upper 90 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile10", + "description": "upper 10 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "duration", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "@type": "PropertyValue", + "name": "model_id", + "description": "unique model identifier" + }, + { + "@type": "PropertyValue", + "name": "project_id", + "description": "unique project identifier" + }, + { + "@type": "PropertyValue", + "name": "variable", + "description": "name of forecasted variable" + } + ], + "distribution": [ + { + "@type": "DataDownload", + "contentUrl": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json", + "encodingFormat": "application/json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json\")\n\n", + "name": "Model Metadata" + }, + { + "@type": "DataDownload", + "contentUrl": "https://github.com/radiantearth", + "encodingFormat": "text/html", + "description": "The link to the model code provided by the model submission team", + "name": "Link for Model Code" + }, + { + "@type": "DataDownload", + "contentUrl": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org", + "encodingFormat": "application/x-parquet", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n", + "name": "Database Access for Daily Water_temperature" + } + ], + "spatialCoverage": { + "@type": "Place", + "geo": { + "@type": "GeoShape", + "box": "-79.8372 37.3032 -79.8159 37.3129" + }, + "additionalProperty": [ + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291041754864156672 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291043953887412224 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291044503643226112 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291045878032760832 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047252422295552 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047802178109440 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291051650468806656 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291052200224620544 + } + ] + } +} \ No newline at end of file diff --git a/data/output/vera4cast-stac/7872d24d71.json b/data/output/vera4cast-stac/a949cc82bd.json similarity index 99% rename from data/output/vera4cast-stac/7872d24d71.json rename to data/output/vera4cast-stac/a949cc82bd.json index 796ad88ab..46197466f 100644 --- a/data/output/vera4cast-stac/7872d24d71.json +++ b/data/output/vera4cast-stac/a949cc82bd.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:4f45824853", "name": "asl.ets_Chla_ugL_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/7987bf6594.json b/data/output/vera4cast-stac/b1fc98b45b.json similarity index 100% rename from data/output/vera4cast-stac/7987bf6594.json rename to data/output/vera4cast-stac/b1fc98b45b.json index 1bb126600..0e4111974 100644 --- a/data/output/vera4cast-stac/7987bf6594.json +++ b/data/output/vera4cast-stac/b1fc98b45b.json @@ -16,9 +16,9 @@ "Temp_C_mean", "Daily", "P1D", - "tubr", "bvre", "fcre", + "tubr", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/b6f99ce773.json b/data/output/vera4cast-stac/b6f99ce773.json new file mode 100644 index 000000000..00a61e1ea --- /dev/null +++ b/data/output/vera4cast-stac/b6f99ce773.json @@ -0,0 +1,266 @@ +{ + "@context": { + "@vocab": "https://schema.org/" + }, + "@type": "Dataset", + "@id": "urn:vera4cast-stac:83ec0a2bdf", + "name": "asl.persistence_Bloom_binary_mean_P1D_scores scores", + "description": "All scores for the Daily_Bloom_binary variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datePublished": "2022-01-01", + "keywords": [ + "Scores", + "vera4cast", + "Biological", + "asl.persistence", + "Bloom_binary", + "Bloom_binary_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "citation": { + "@type": "CreativeWork", + "@id": "https://doi.org/10.1002/ecs2.4686", + "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686", + "name": "A community convention for ecological forecasting: Output files and metadata version 1.0", + "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ", + "identifier": { + "@type": "PropertyValue", + "propertyID": "https://registry.identifiers.org/registry/doi", + "value": "doi:10.1002/ecs2.4686", + "url": "https://doi.org/10.1002/ecs2.4686" + } + }, + "variableMeasured": [ + { + "@type": "PropertyValue", + "name": "reference_datetime", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "@type": "PropertyValue", + "name": "site_id", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "@type": "PropertyValue", + "name": "datetime", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "@type": "PropertyValue", + "name": "family", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "@type": "PropertyValue", + "name": "pub_datetime", + "description": "datetime that forecast was submitted" + }, + { + "@type": "PropertyValue", + "name": "depth_m", + "description": "depth (meters) in water column of prediction" + }, + { + "@type": "PropertyValue", + "name": "observation", + "description": "observed value for variable" + }, + { + "@type": "PropertyValue", + "name": "crps", + "description": "crps forecast score" + }, + { + "@type": "PropertyValue", + "name": "logs", + "description": "logs forecast score" + }, + { + "@type": "PropertyValue", + "name": "mean", + "description": "mean forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "median", + "description": "median forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "sd", + "description": "standard deviation forecasts" + }, + { + "@type": "PropertyValue", + "name": "quantile97.5", + "description": "upper 97.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile02.5", + "description": "upper 2.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile90", + "description": "upper 90 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile10", + "description": "upper 10 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "duration", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "@type": "PropertyValue", + "name": "model_id", + "description": "unique model identifier" + }, + { + "@type": "PropertyValue", + "name": "project_id", + "description": "unique project identifier" + }, + { + "@type": "PropertyValue", + "name": "variable", + "description": "name of forecasted variable" + } + ], + "distribution": [ + { + "@type": "DataDownload", + "contentUrl": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json", + "encodingFormat": "application/json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.persistence.json\")\n\n", + "name": "Model Metadata" + }, + { + "@type": "DataDownload", + "contentUrl": "https://github.com/radiantearth", + "encodingFormat": "text/html", + "description": "The link to the model code provided by the model submission team", + "name": "Link for Model Code" + }, + { + "@type": "DataDownload", + "contentUrl": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org", + "encodingFormat": "application/x-parquet", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.persistence?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n", + "name": "Database Access for Daily Bloom_binary" + } + ], + "spatialCoverage": { + "@type": "Place", + "geo": { + "@type": "GeoShape", + "box": "-79.8372 37.3032 -79.8159 37.3129" + }, + "additionalProperty": [ + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291041754864156672 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291043953887412224 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291044503643226112 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291045878032760832 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047252422295552 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047802178109440 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291051650468806656 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291052200224620544 + } + ] + } +} \ No newline at end of file diff --git a/data/output/vera4cast-stac/59565cb0dd.json b/data/output/vera4cast-stac/b9ca57ec4d.json similarity index 99% rename from data/output/vera4cast-stac/59565cb0dd.json rename to data/output/vera4cast-stac/b9ca57ec4d.json index 4db6bcbf1..cbba2b042 100644 --- a/data/output/vera4cast-stac/59565cb0dd.json +++ b/data/output/vera4cast-stac/b9ca57ec4d.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:c403cb797b", "name": "fableNNETAR_focal_Temp_C_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Temp_C_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/6dc951dd86.json b/data/output/vera4cast-stac/c5a54b1c2e.json similarity index 100% rename from data/output/vera4cast-stac/6dc951dd86.json rename to data/output/vera4cast-stac/c5a54b1c2e.json index 85c787855..4423113b9 100644 --- a/data/output/vera4cast-stac/6dc951dd86.json +++ b/data/output/vera4cast-stac/c5a54b1c2e.json @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/8163ce2928.json b/data/output/vera4cast-stac/c7e275d5a5.json similarity index 99% rename from data/output/vera4cast-stac/8163ce2928.json rename to data/output/vera4cast-stac/c7e275d5a5.json index edf6c217f..508c34504 100644 --- a/data/output/vera4cast-stac/8163ce2928.json +++ b/data/output/vera4cast-stac/c7e275d5a5.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:f4256a1ce2", "name": "climatology_Secchi_m_sample_P1D_scores scores", - "description": "All scores for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,8 +16,8 @@ "Secchi_m_sample", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/4f893d5734.json b/data/output/vera4cast-stac/fe7d6a4a05.json similarity index 99% rename from data/output/vera4cast-stac/4f893d5734.json rename to data/output/vera4cast-stac/fe7d6a4a05.json index 2a24861ea..c04b3a66f 100644 --- a/data/output/vera4cast-stac/4f893d5734.json +++ b/data/output/vera4cast-stac/fe7d6a4a05.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:62b9434d19", "name": "fableETS_Chla_ugL_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "empirical" ], "citation": {