Enjoy it!

Javier Fernández-López (2016). rWind: Download, Edit and Transform Wind Data from GFS. R package version 0.1.3. https://CRAN.R-project.org/package=rWind

   
 # Download and install "rWind" package from CRAN:  
 install.packages("rWind")  
 # You should install also "raster" package if you do not have it   
   
 library(rWind)  
 library(raster)  
   
 packageDescription("rWind")  
 help(package="rWind")  
   
 # "rWind" is a package with several tools for downloading, editing and transforming wind data from Global Forecast   
 # System (GFS, see <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs>) of the USA's  
 # National Weather Service (NWS, see <http://www.weather.gov/>).  
   
 citation("rWind")  
   
 # > Javier Fernández-López (2016). rWind: Download, Edit and Transform Wind Data from GFS. R package version 0.1.3.  
 # > https://CRAN.R-project.org/package=rWind  
   
 # First, we can download a wind dataset of a specified date from GFS using wind.dl function  
 # help(wind.dl)  
   
 # Download wind for Spain region at 2015, February 12, 00:00  
 # help(wind.dl)  
   
 wind.dl(2015,2,12,0,-10,5,35,45)  
   
 # By default, this function generates an R object with downloaded data. You can store it...  
   
 wind_data<-wind.dl(2015,2,12,0,-10,5,35,45)  
   
 head(wind_data)  

   
 # or download a CVS file into your work directory with the data using type="csv" argument:  
   
 getwd()  
 wind.dl(2015,2,12,0,-10,5,35,45, type="csv")  
   
 # If you inspect inside wind_data object, you can see that data are organized in a weird way, with  
 # to rows as headers, a column with date and time, longitude data expressed in 0/360 notation and wind  
 # data defined by the two vector components U and V. You can transform these data in a much more nice format
 # using "wind.fit" function:  
 #help(wind.fit)  
   
 wind_data<-wind.fit(wind_data)  
   
 head(wind_data)  
   

 # Now, data are organized by latitude, with -180/180 and U and V vector components are transformed  
 # into direction and speed. You can export the data.frame as an CVS file to be used with a GIS software  
   
 write.csv(wind_data, "wind_data.csv")  
   
 # Once you have data organized by latitude and you have direction and speed information fields,  
 # you can use it to create a raster layer with wind2raster function to be used by GIS software or to be plotted   
 # in R, for example.  
 # As raster layer can only store one information field, you should choose between direction (type="dir")  
 # or speed (type="speed").  
   
 r_dir <- wind2raster(wind_data, type="dir")  
 r_speed <- wind2raster(wind_data, type="speed")   
   
 # Now, you can use rworldmap package to plot countries contours with your direction and speed data!  
   
 #install.packages("rworldmap")  
 library(rworldmap)   
 newmap <- getMap(resolution = "low")  
   
 par(mfrow=c(1,2))  
   
 plot(r_dir, main="direction")  
 lines(newmap, lwd=4)  
   
 plot(r_speed, main="speed")  
 lines(newmap, lwd=4)  
   

   
 # Additionally, you can use arrowDir and Arrowhead (from "shape" package) functions to plot wind direction  
 # over a raster graph:  
   
 #install.packages("shape")  
 library(shape)   
   
 dev.off()  
 alpha<- arrowDir(wind_data)  
 plot(r_speed, main="wind direction (arrows) and speed (colours)")  
 lines(newmap, lwd=4)  
 Arrowhead(wind_data$lon, wind_data$lat, angle=alpha, arr.length = 0.12, arr.type="curved")  

   
 # If you want a time series of wind data, you can download it by using a for-in loop:  
 # First, you should create an empty list where you will store all the data  
   
 wind_serie<- list()  
   
 # Then, you can use a wind.dl inside a for-in loop to download and store wind data of   
 # the first 5 days of February 2015 at 00:00 in Europe region. It could take a while...  
    
 for (d in 1:5){  
  w<-wind.dl(2015,2,d,0,-10,30,35,70)  
  wind_serie[[d]]<-w  
 }  
   
 wind_serie  
   
 # Finally, you can use wind.mean function to calculate wind average   
   
 wind_average<-wind.mean(wind_serie)  
 wind_average<-wind.fit(wind_average)  
 r_average_dir<-wind2raster(wind_average, type="dir")  
 r_average_speed<-wind2raster(wind_average, type="speed")  
    
  par(mfrow=c(1,2))  
   
 plot(r_average_dir, main="direction average")  
 lines(newmap, lwd=1)  
   
 plot(r_average_speed, main="speed average")  
 lines(newmap, lwd=1)