Just about everyone is familiar with weather maps. There are many situations where it is useful to combine the underlying numerical weather data with other types of information. Accessing the weather data is a necessary first step.
The output from the U.S. National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) is freely available. The surface resolution of the model is ≈ 0.3º× 0.3°. The model runs every 6 hours, producing forecasts at 3-hourly intervals extending out to 16 days. As an example of output from GFS, the map (below) shows the predicted average temperature at 2 metres over the entire globe for the next 24 hr (date of this post). The map shows predicted cold conditions in Europe, and the continuing heatwave in Australia.
How the map was made
GFS forecasts are in a format called GRIB2. According to Wikipedia, “GRIB (GRIdded Binary) is a mathematically concise data format commonly used in meteorology to store historical and forecast weather data.” GRIB files contain physical fields such as temperature, humidity etc defined on a spatial grid, as well as boundary conditions such as vegetation type and elevation. The data might be assimilated from observations, or output from a forecast model.
The first step is to translate the GRIB into a raster format such as netcdf which can be read in R. For example, the GRIB2 file gfs.2009121700/gfs.t00z.sfluxgrbf03.grib2 contains the 3-hr forecast surface data on 17 Dec 2009 produced at 00 UTC (midnight universal time). An inventory of the data contained in this file can be seen here. Download this forecast as temp.grb
To read temp.grb a utility called wgrib2 needs to be installed on your system. Then data such as land fraction can extracted into a netcdf file LAND.nc using the R shell command
shell("wgrib2 -s temp03.grb | grep :LAND: | wgrib2 -i temp00.grb -netcdf LAND.nc",intern=T)
The ncdf package can now be used to read the contents of LAND.nc.
landFrac <-open.ncdf("LAND.nc") land <- get.var.ncdf(landFrac,"LAND_surface") x <- get.var.ncdf(landFrac,"longitude") y <- get.var.ncdf(landFrac,"latitude")
The 1152×576 matrix land takes values 1 for land and 0 for water (sea-ice is 1). x and y are the longitude and latitude of the non-uniform GFS grid.
2m temperature data can be read in the same way. The average of the first 8 forecasts was called t2m.mean and plotted using image.plot() from the fields package:
rgb.palette <- colorRampPalette(c("snow1","snow2","snow3","seagreen","orange","firebrick"), space = "rgb")#colors image.plot(x,y,t2m.mean,col=rgb.palette(200),axes=F,main=as.expression(paste("GFS 24hr Average 2M Temperature",day,"00 UTC",sep="")),axes=F,legend.lab="o C") contour(x,y,land,add=TRUE,lwd=1,levels=0.99,drawlabels=FALSE,col="grey30") #add land outline