Revise get_era5_forecasts.sh for GMD-publication.

Jupyter_Notebooks/get_era5_forecasts.sh

 #!/usr/bin/env bash
 
+# This bash-script reads the 2m temperature from ERA5 short-range forecasts files, crops the data to the largest target domain (see Dataset ID 1-3) and then performs a merging step with the ground truth and the persistence forecast data provided by from the postprocessing of a trained model (see prostprocess-step of workflow.)
+
+# Parse script argument
 yr=$1
 
-indir=/p/fastdata/slmet/slmet111/met_data/ecmwf/era5/grib/${yr}
-indir_ref=/p/project/deepacf/deeprain/video_prediction_shared_folder/results/era5-Y2007-2019M01to12-92x56-3840N0000E-2t_tcc_t_850/savp/20210901T090059_gong1_savp_cv12/
-outdir=/p/scratch/deepacf/deeprain/video_prediction_shared_folder/era5_forecast_ref/${yr}
+# some directory parameters
+indir=<path_to_era5_short_range_forecasts>/${yr}       # directory where the ERA5-short range forecasts are located
+indir_ref=<path_to_postprocessed_model>                # directory where the results of a trained model are located (to get persistence forecast and ground truth data)
+outdir=<path_to_output>                                # output directory
 
 if [[ ! -d "${outdir}" ]]; then
     mkdir ${outdir}
 fi
 
-hh_s=6
-hh_e=12
+# additional parameters
+hh_s=6                        # start of lead time range in hours
+hh_e=12                       # end of lead time range in hours
 
-declare -a hh_list=(18)
+declare -a hh_list=(18)       # initialization hour of ERA5 forecasts to evaluate (either 6 or 18)
 
 for hh in ${hh_list[@]}; do
     hh0=$(printf "%02d" ${hh})
@@ -29,6 +34,7 @@ for hh in ${hh_list[@]}; do
             done
         done
 
+    # merge all ERA5 forecasts in monthly netCDF-files
     date1=`date -d "${yr}0101" '+%Y%m%d'`
     date2=`date -d "${yr}0101 ${hh0}" '+%Y-%m-%d %H:%M:00'`
     date_end=`date -d "{yr}1231 ${hh0}" '+%Y%m%d'`
@@ -44,7 +50,7 @@ for hh in ${hh_list[@]}; do
         ncrename -O -d time,fcst_hour -v time,fcst_hour ${outfile} ${outfile}
         ncap2 -O -s 'fcst_hour=int(fcst_hour)' ${outfile} ${outfile}
 	
-	# add reference data as possible
+	# add ground truth data and persistence forecast from trained model
 	patt=${indir_ref}/vfp_date_${date1}${hh0}_sample_ind*.nc
 	if ls $patt 1>/dev/null 2<&1; then
 	   file_src=`ls $patt`