A while back I wrote a blog post on the R package I wrote for reading and manipulating grads data. The library is quite basic, and does not support a number of grads data file options:

• Using one ctl file, and multiple binary files
• Some variables have only one vertical level

When I faced data that had these properties, I had a choice to either expand my readGrads library, or convert the data to some other format which was easier to read into R. I chose to convert the data to NetCDF and then reading it using the excellent ncdf library. Luckily I did not have to write a grads to NetCDF conversion tool myself, but I used the cdo tool. This program is “a collection of command line Operators to manipulate and analyse Climate and NWP model Data” (quote from the website). Converting a grads file to NetCDF can be done using the following command:

cdo -f nc import_binary in_grads.ctl out_ncdf.nc

As I wanted to perform this conversion from within R, I created a function which uses cdo through a system call. The grads2nc function looks like:

grads2nc = function(ctl_input, nc_output, verbose = TRUE) {  
  if(Sys.which("cdo")[[1]] == "") stop("cdo executable not found. Check PATH or install cdo.") 
  if(verbose) ext = "" else ext = "> /dev/null"
  cmd = sprintf("cdo -f nc import_binary %s %s %s", ctl_input, nc_output, ext)
  t = system.time(ret_code <- system(cmd))
  if(verbose & (ret_code != 127)) {
    message(sprintf("Processing \"%s\" to \"%s\" is done, time spent:", ctl_input, nc_output))
    print(t)
  } else {
    stop("Conversion of grads file to nc failed")
  }
  return(invisible(ret_code))
}

With this function loaded into the R session, the conversion can be performed using:

> grads2nc("output/attm400.ctl", "output/test.nc")
cdo import_binary: Processed 41 variables. ( 2.50s )
Processing "output/attm400.ctl" to "output/test.nc" is done, time spent:
   user  system elapsed 
  1.765   0.760   3.642