Assessment Methods

#!/usr/bin/env python
from __future__ import print_function 
from netCDF4 import Dataset 
import numpy as np 
from scipy import stats 
import sys, getopt 
import argparse 
from WRF import getvar, interplevel 
  
parser = argparse.ArgumentParser(description='xxxxx.') 
#parser.add_argument('--key', '-k') 
parser.add_argument('file1') 
parser.add_argument('file2') 
  
args = parser.parse_args() 
  
try: 
    f1 = Dataset(args.file1,'r') 
    f2 = Dataset(args.file2,'r') 
except: 
    print("file open failed") 
    exit(2) 
# Calculate the elements.
elements = ('T2', 'RAINNC') 
indexnumber = len(f1.variables[elements[0]]) 
  
for index in range(indexnumber): 
    print ('%-24s %16s %16s %16s %16s' %('Var', 'Pearson', 'ME', 'MAE', 'RMSE')) 
    for var in elements: 
        var_split = var.split('-') 
        if len(var_split) > 1: 
            if var_split[0]=='wspd_wdir': 
                var0_f1 = getvar(f1, var_split[0])[0,:] 
                var0_f2 = getvar(f2, var_split[0])[0,:] 
  
            else: 
                var0_f1 = getvar(f1, var_split[0]) 
                var0_f2 = getvar(f2, var_split[0]) 
  
            var1_f1 = getvar(f1, var_split[1]) 
            f1_para = interplevel(var0_f1, var1_f1, int(var_split[2]))[index] 
            var1_f2 = getvar(f2, var_split[1]) 
            f2_para = interplevel(var0_f2, var1_f2, int(var_split[2]))[index] 
        else: 
            f1_para = f1.variables[var][index] 
            f2_para = f2.variables[var][index] 
        # Calculate the SCC.
        f1_mean = np.mean(f1_para) 
        f2_mean = np.mean(f2_para) 
  
        divisor = np.sum((f1_para - f1_mean) * (f2_para - f2_mean)) 
        f1_dividend = np.sqrt(np.sum((f1_para - f1_mean) ** 2.)) 
        f2_dividend = np.sqrt(np.sum((f2_para - f2_mean) ** 2.)) 
        
  
        if f1_dividend * f2_dividend == 0: 
            pearson = 'nan' 
        else: 
            pearson = divisor / (f1_dividend * f2_dividend) 
        # Calculate the ME.
        mean = np.mean(f1_para - f2_para) 
        # Calculate the MAE.
        mae = np.mean(abs(f1_para - f2_para)) 
        # Calculate the RMSE.
        rmse = np.sqrt(np.mean((f1_para - f2_para)**2)) 
         
        print ('%-24s %10.6f %10.6f %10.6f %10.6f'  %(var, pearson, mean, mae, rmse))

It is a comparison tool built in the WRF application. After WRF compilation, the tool is generated in the external/io_netcdf directory. It can calculate the root mean square (RMS) and RMSE of each element in two result files and output inconsistent elements. If the values of RMSE in two result files are 0, there is no output. The following figure shows an example:

md5sum is a computer program that hashes file content to generate MD5 verification code, which is used to calculate and verify the 128-bit MD5 hash value described in RFC 1321. The MD5 value can be used as the digital fingerprint of a file. You can run the md5sum command to view the result files of the same computing test case at the same time on different platforms. If the MD5 values are the same, the result files are the same.

NCL is a free visualization software developed by the National Center for Atmospheric Research (NCAR) for scientific data computing. It has very powerful file input and output functions, and can read and write netCDF-3, netCDF-4 classic, HDF4, binary, ASCII and other data. You can use the ncl plugin of WRF to process the ncl file output by WRF for comparison graph generation.

NCL comparison graph generation script:

load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl" 
load "$NCARG_ROOT/lib/ncarg/nclscripts/wrf/WRFUserARW.ncl" 
load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl" 
load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl" 
 
begin 
f=addfile("kunpeng/wrfout_d01_2020-07-08_00:00:00","r") 
f1=addfile("x86/wrfout_d01_2020-07-08_00:00:00","r") 
 
res = True                           ; Set up some basic plot resources 
res@MainTitle = "METGRID FILES" 
res@Footer = False 
res@cnFillOn=True 
res@gsnSpreadColors = True 
opts = res 
pltres=True 
mpres=True 
 
wks = gsn_open_wks("pdf","wrf") 
 
gsn_define_colormap(wks,"NCV_bright") 
 
ter = wrf_user_getvar(f,"RAINNC",0) 
contour = wrf_contour(f,wks,ter,res) 
plot = wrf_map_overlays(f,wks,(/contour/),pltres,mpres) 
ter1 = wrf_user_getvar(f1,"RAINNC",0) 
contour = wrf_contour(f1,wks,ter1,res) 
plot = wrf_map_overlays(f1,wks,(/contour/),pltres,mpres) 
print(ter-ter1) 
 
contour = wrf_contour(f,wks,ter-ter1,res) 
plot = wrf_map_overlays(f,wks,(/contour/),pltres,mpres) 
end

Figure 1 NCL comparison graph example

Grid Analysis and Display System (GrADS) is a data processing and display software system widely used in the meteorological industry. The software system reads, processes, displays and prints meteorological data through its integrated environment.

You can use the grads script to process the grads files output by GrADS for comparison graph generation.

#!/bin/sh 
 
level=500 
 
source ./env-grads.sh 
 
for v in {'h',} 
do 
value=$v 
file=$value'_at_'$level'.png' 
cp draw-all.gs.bak draw.gs 
sed -i "s/FILE/$file/g" draw.gs 
sed -i "s/LEVEL/$level/g" draw.gs 
sed -i "s/VALUE/$value/g" draw.gs 
grads -bpcx draw.gs 
done 

draw.gs script:

'reinit' 
 
infile='../post.ctl_2018110112_192' 
outdir='./out' 
outfile=outdir'/w_at_925.png' 
level=925 
value=w 
 
'open 'infile 
 
'query time' 
res=subwrd(result,3) 
 
'clear' 
'set lev 'level 
'set gxout shade2b' 
'd 'value 
'cbarn' 
'draw title 'value' at 'level'   'res 
'set grid off' 
'set grads off' 
'printim 'outfile' PNG white x800 y800'

Figure 2 GrADS comparison graph example