11-NCEP.Rmd

---
pagetitle: NCEP
---

# NCEP

## How to use NCEP

1. Navigate to the [NCEP/NCAR Reanalysis site](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html).

2. Navigate to desired variable (e.g. Surface -> Air temperature (4 times daily) -> 2m ->  air.2m.gauss.2017.nc) and download NetCDF file.

3. In Rstudio, install the ncdf4, raster, and AOI packages.

## Code example

***Getting surface temperatures in Nunn, CO (-104.73°, 40.87°) for January 1-31 in 2017***

From [this page](https://psl.noaa.gov/cgi-bin/db_search/DBListFiles.pl?did=195&tid=93660&vid=1251),
download file skt.sfc.gauss.2017.nc


```
library(ncdf4)
library(raster)
library(AOI)

grabNCEP <- function(var, loc, month) {

  # Open variable file and pull out variable
  nc <- nc_open(paste0("skt.sfc.gauss.2017.nc"))
  ncvar <- ncvar_get(nc)
  
  # Find closest data point
  lonInd <- match.closest(mod(-104.73,360), nc$dim$lon$vals)
  lat <- sort(nc$dim$lat$vals)[match.closest(40.87, sort(nc$dim$lat$vals))]
  latInd <- match(lat, nc$dim$lat$vals)
  
  # Pull values from dates of interest
  vals <- c()
  for (i in 1 : (4 * 31)) {
    vals <- c(vals, ncvar[lonInd, latInd, i])
  }
  
  # K to C
  vals <- vals - 273.15
  
  # Create date column 
  days <- c()
  for (i in 1:31) {
    days <- c(days, paste0("2017-0", month, "-", i))
  }
  
  # Create dataframe with Date and Data
  df <- data.frame("Date" = rep(days, each = 4), "Hour" = c(0,6,12,18), "Data" = vals)
  
  # Format date as POSIX
  df$Date <- format(as.POSIXct(paste0(df$Date, " ", df$Hour, ":00")), format = "%Y-%m-%d %H:%M")
  
```