Interpolate wrfout* files from curvilinear gird to rectilinear
wrf模拟的数据一般是非等间距的网格，需要先将数据进行插值，插值到等间距的网格，这里可以通过NCL的函数rcm2rgrid_Wrap实现
举个例子，如果模式中设置为兰伯特投影的网格：
- curvilinear gird
curvilinear gird to rectilinear
- rectilinear gird
method：NCL-1
- 需要修改的就是路径和变量，我下面展示脚本不仅有风场数据u，v还有降水，海表面压力，气温等，可自行修改
begin
a = addfile("/Users/WRF/outdata/2022071000/wrfout_d01_2022-07-10_01:00:00","r")
lat2d = a->XLAT(0,:,:)
lon2d = a->XLONG(0,:,:)
lat1d = lat2d(:,0)
lon1d = lon2d(0,:)
time = wrf_user_getvar(a,"XTIME",-1)
u10 = wrf_user_getvar(a,"U10",0)
v10 = wrf_user_getvar(a,"V10",0)
slp = wrf_user_getvar(a,"slp",0)
t2 = wrf_user_getvar(a,"T2",0)
td = wrf_user_getvar(a,"td",0)
rainc = wrf_user_getvar(a,"RAINC",0)
rainnc = wrf_user_getvar(a,"RAINNC",0)
u10@lat2d = lat2d
u10@lon2d = lon2d
u10_ip = rcm2rgrid_Wrap(lat2d,lon2d,u10,lat1d,lon1d,0)
v10@lat2d = lat2d
v10@lon2d = lon2d
v10_ip = rcm2rgrid_Wrap(lat2d,lon2d,v10,lat1d,lon1d,0)
slp_ip = rcm2rgrid_Wrap(lat2d,lon2d,slp,lat1d,lon1d,0)
t2_ip = rcm2rgrid_Wrap(lat2d,lon2d,t2,lat1d,lon1d,0)
td_ip = rcm2rgrid_Wrap(lat2d,lon2d,td,lat1d,lon1d,0)
rainc_ip = rcm2rgrid_Wrap(lat2d,lon2d,rainc,lat1d,lon1d,0)
rainnc_ip = rcm2rgrid_Wrap(lat2d,lon2d,rainnc,lat1d,lon1d,0)
outf = addfile("/Users/wrfout_d01_2022-07-10_01:00:00.nc","c")
outf->time = time
outf->lat = lat2d
outf->lon = lon2d
outf->u10 = u10_ip
outf->v10 = v10_ip
outf->slp = slp_ip
outf->t2 = t2_ip
outf->td = td_ip
outf->rainc = rainc_ip
outf->rainnc = rainnc_ip
end
- 上述脚本的缺点在于只能基于模式模拟的经纬度区域进行插值，意思就是说他的经纬度区域是固定的那么大
method：NCL-2
NCL还有一个函数可以实现上述过程，就是ESMF_regrid，该函数的优点在于可以实现任意经纬度范围的插值，但是不足在于对于存在高度层的变量，暂时无法进行高度层的数据读取。（也可能我水平有限不知道。。。。）这里也附上脚本：
load "$NCARG_ROOT/lib/ncarg/nclscripts/esmf/ESMF_regridding.ncl"
begin
a = addfile("/Users/WRF/outdata/2022071000/wrfout_d01_2022-07-10_01:00:00","r")
u10 = wrf_user_getvar(a,"U10",0)
v10 = wrf_user_getvar(a,"V10",0)
slp = wrf_user_getvar(a,"slp",0)
t2 = wrf_user_getvar(a,"T2",0)
; td = wrf_user_getvar(a,"td",0)
rainc = wrf_user_getvar(a,"RAINC",0)
rainnc = wrf_user_getvar(a,"RAINNC",0)
u10@lat2d = a->XLAT(0,:,:)
u10@lon2d = a->XLONG(0,:,:)
v10@lat2d = a->XLAT(0,:,:)
v10@lon2d = a->XLONG(0,:,:)
slp@lat2d = a->XLAT(0,:,:)
slp@lon2d = a->XLONG(0,:,:)
t2@lat2d = a->XLAT(0,:,:)
t2@lon2d = a->XLONG(0,:,:)
; td@lat2d = a->XLAT(0,:,:)
; td@lon2d = a->XLONG(0,:,:)
rainc@lat2d = a->XLAT(0,:,:)
rainc@lon2d = a->XLONG(0,:,:)
rainnc@lat2d = a->XLAT(0,:,:)
rainnc@lon2d = a->XLONG(0,:,:)
lat2d = a->XLAT(0,:,:)
lon2d = a->XLONG(0,:,:)
lat1d = lat2d(:,0)
lon1d = lon2d(0,:)
latS = -20
latN = 50
lonW = 95
lonE = 145
Opt = True
Opt@InterpMethod = "bilinear"
Opt@ForceOverwrite = True
Opt@SrcMask2D = where(.not. ismissing(v10),1,0)
Opt@DstGridType = "0.1deg"
Opt@DstLLCorner = (/latS, lonW /)
Opt@DstURCorner = (/latN, lonE /)
u10_regrid = ESMF_regrid(u10,Opt)
v10_regrid = ESMF_regrid(v10,Opt)
slp_regrid = ESMF_regrid(slp,Opt)
t2_regrid = ESMF_regrid(t2,Opt)
; td_regrid = ESMF_regrid(td,Opt)
rainc_regrid = ESMF_regrid(rainc,Opt)
rainnc_regrid = ESMF_regrid(rainnc,Opt)
time = wrf_user_getvar(a,"XTIME",-1)
nlon = dimsizes(v10_regrid&lon)
nlat = dimsizes(v10_regrid&lat)
ofile = "wrfout_d01_2022-07-10_01:00:00.nc"
system("rm -rf "+ofile)
fout = addfile(ofile,"c")
dimNames = (/"lat", "lon"/)
dimSizes = (/nlat, nlon/)
dimUnlim = (/False, False/)
filedimdef(fout,dimNames,dimSizes,dimUnlim) ;-- define dimensions
filevardef(fout,"lat",typeof(v10_regrid&lat),getvardims(v10_regrid&lat))
filevardef(fout,"lon",typeof(v10_regrid&lon),getvardims(v10_regrid&lon))
filevardef(fout,"u10",typeof(u10_regrid),getvardims(u10_regrid))
filevardef(fout,"v10",typeof(v10_regrid),getvardims(v10_regrid))
filevardef(fout,"slp",typeof(slp_regrid),getvardims(slp_regrid))
filevardef(fout,"t2",typeof(t2_regrid),getvardims(t2_regrid))
; filevardef(fout,"td",typeof(td_regrid),getvardims(td_regrid))
filevardef(fout,"rainc",typeof(rainc_regrid),getvardims(rainc_regrid))
filevardef(fout,"rainnc",typeof(rainnc_regrid),getvardims(rainnc_regrid))
filevarattdef(fout,"lat",v10_regrid&lat) ;-- copy lat attributes
filevarattdef(fout,"lon",v10_regrid&lon) ;-- copy lon attributes
filevarattdef(fout,"u10",u10_regrid)
filevarattdef(fout,"v10",v10_regrid)
filevarattdef(fout,"slp",slp_regrid)
filevarattdef(fout,"t2",t2_regrid)
; filevarattdef(fout,"td",td_regrid)
filevarattdef(fout,"rainc",rainc_regrid)
filevarattdef(fout,"rainnc",rainnc_regrid)
setfileoption(fout,"DefineMode",False)
fout->u10 = (/u10_regrid/)
fout->v10 = (/v10_regrid/)
fout->slp = (/slp_regrid/)
fout->t2 = (/t2_regrid/)
; fout->td = (/td_regrid/)
fout->rainc = (/rainc_regrid/)
fout->rainnc = (/rainnc_regrid/)
fout->lat = (/v10_regrid&lat/) ;-- write lat to new netCDF file
fout->lon = (/v10_regrid&lon/) ;-- write lon to new netCDF file
fout->time = time
end
PS:运行该脚本会生成四个nc文件，分别为：destination_grid_file.nc、source_grid_file.nc、weights_file.nc、wrfout_d01_2022-07-10_01:00:00.nc。其中，wrfout_d01_2022-07-10_01:00:00.nc是我需要的文件，但是其他三个文件如何在运行脚本的过程去掉暂未解决。
convert netcdf to json
python code
- 用来读取用ncl插值后的wrfoutput.nc 数据,并生成对应文件名的json格式
python脚本如下所示：
# -*- coding: utf-8 -*-
"""
Created on %(date)s
@author: %(jixianpu)s
Email : xianpuji@hhu.edu.cn
introduction : keep learning althongh walk slowly
"""
"""
用来读取用ncl插值后的wrfoutput.nc 数据,并生成对应文件名的json格式
"""
import pandas as pd
import os
import json
import netCDF4 as nc
import numpy as np
import datetime
from netCDF4 import Dataset
import argparse
from argparse import RawDescriptionHelpFormatter
import xarray as xr
import sys
import glob
date = sys.argv[1]
date = str(date)
frst = sys.argv[2]
step = sys.argv[3]
path = r'/Users/WRF/outdata/2022071000/'#只能是已经存在的文件目录且有数据才可以进行读取
start = datetime.datetime.strptime(date,'%Y%m%d%H').strftime("%Y-%m-%d_%H:%M:%S")
end = (datetime.datetime.strptime(date,'%Y%m%d%H')+datetime.timedelta(hours=int(frst))).strftime("%Y-%m-%d_%H:%M:%S")
intp = (datetime.datetime.strptime(date,'%Y%m%d%H')+datetime.timedelta(hours=int(step))).strftime("%Y-%m-%d_%H:%M:%S")
fstart = path+'/wrfout_d01_'+start+'*'
fintp = path+'/wrfout_d01_'+intp+'*'
fend = path+'/wrfout_d01_'+end+'*'
file = path+'/*'
filestart = glob.glob(fstart)
fileintp = glob.glob(fintp)
fileend = glob.glob(fend)
filelist = glob.glob(file)
filelist.sort()
rstart = np.array(np.where(np.array(filelist)==filestart))[0][0]
rintp = np.array(np.where(np.array(filelist)==fileintp))[0][0]
rend = np.array(np.where(np.array(filelist)==fileend))[0][0]
fn = filelist[rstart:rend:rintp]
outroot = 'Users/'
for i in fn:
uhdr = {"header":{"discipline":0,"disciplineName":"Meteorological products","gribEdition":2,"gribLength":131858,"center":0,"centerName":"WRF OUTPUT","subcenter":0,"refTime":"2014-01-31T00:00:00.000Z","significanceOfRT":1,"significanceOfRTName":"Start of forecast","productStatus":0,"productStatusName":"Operational products","productType":1,"productTypeName":"Forecast products","productDefinitionTemplate":0,"productDefinitionTemplateName":"Analysis/forecast at horizontal level/layer at a point in time","parameterCategory":2,"parameterCategoryName":"Momentum","parameterNumber":2,"parameterNumberName":"U-component_of_wind","parameterUnit":"m.s-1","genProcessType":2,"genProcessTypeName":"Forecast","forecastTime":3,"surface1Type":103,"surface1TypeName":"Specified height level above ground","surface1Value":10,"surface2Type":255,"surface2TypeName":"Missing","surface2Value":0,"gridDefinitionTemplate":0,"gridDefinitionTemplateName":"Latitude_Longitude","numberPoints":65160,"shape":6,"shapeName":"Earth spherical with radius of 6,371,229.0 m","gridUnits":"degrees","resolution":48,"winds":"true","scanMode":0,"nx":360,"ny":181,"basicAngle":0,"subDivisions":0,"lo1":0,"la1":90,"lo2":359,"la2":-90,"dx":1,"dy":1}}
vhdr = {"header":{"discipline":0,"disciplineName":"Meteorological products","gribEdition":2,"gribLength":131858,"center":0,"centerName":"WRF OUTPUT","subcenter":0,"refTime":"2014-01-31T00:00:00.000Z","significanceOfRT":1,"significanceOfRTName":"Start of forecast","productStatus":0,"productStatusName":"Operational products","productType":1,"productTypeName":"Forecast products","productDefinitionTemplate":0,"productDefinitionTemplateName":"Analysis/forecast at horizontal level/layer at a point in time","parameterCategory":2,"parameterCategoryName":"Momentum","parameterNumber":3,"parameterNumberName":"V-component_of_wind","parameterUnit":"m.s-1","genProcessType":2,"genProcessTypeName":"Forecast","forecastTime":3,"surface1Type":103,"surface1TypeName":"Specified height level above ground","surface1Value":10,"surface2Type":255,"surface2TypeName":"Missing","surface2Value":0,"gridDefinitionTemplate":0,"gridDefinitionTemplateName":"Latitude_Longitude","numberPoints":65160,"shape":6,"shapeName":"Earth spherical with radius of 6,371,229.0 m","gridUnits":"degrees","resolution":48,"winds":"true","scanMode":0,"nx":360,"ny":181,"basicAngle":0,"subDivisions":0,"lo1":0,"la1":90,"lo2":359,"la2":-90,"dx":1,"dy":1}}
data = [uhdr, vhdr]
newf = Dataset(i)
lat = np.array(newf.variables['lat'])
# print(fn,lat)
lon = np.array(newf.variables['lon'])
dys = np.diff(lat, axis = 0).mean(1)
dy = float(dys.mean())
dxs = np.diff(lon, axis = 1).mean(0)
dx = float(dxs.mean())
nx = float(lon.shape[1])
ny = float(lat.shape[0])
la1 = float(lat[-1, -1])
la2 = float(lat[0, 0])
lo1 = float(lon[0, 0])
lo2 = float(lon[-1, -1])
time =(newf.variables['time'])
dates = nc.num2date(time[:],units=time.units)
dt = pd.to_datetime(np.array(dates, dtype='datetime64[s]')).strftime("%Y%m%d%H%M%S")
tms =pd.to_datetime(np.array(dates, dtype='datetime64[s]')).strftime("%Y-%m-%d_%H:%M:%S")
for ti, time in enumerate(dt):
datestr = (dt[0][:8])
timestr = (dt[0][8:10])+'00'
dirpath = outroot + date
os.makedirs(dirpath, exist_ok = True)
outpath = os.path.join(dirpath, '%s.json' % (i[-19:]))
for u0_or_v1 in [0, 1]:
h = data[u0_or_v1]['header']
h['la1'] = la1
h['la2'] = la2
h['lo1'] = lo1
h['lo2'] = lo2
h['nx'] = nx
h['ny'] = ny
h['dx'] = dx
h['dy'] = dy
h['forecastTime'] = 0
h['refTime'] = tms[0] + '.000Z'
h['gribLength'] = 1538 + nx * ny * 2
if u0_or_v1 == 0:
data[u0_or_v1]['data'] = np.array(newf.variables['u10']).ravel().tolist()
elif u0_or_v1 == 1:
data[u0_or_v1]['data'] = np.array(newf.variables['v10']).ravel().tolist()
if ti == 0:
outf = open(outpath, 'w')
json.dump(data, outf)
outf.close()
outf = open(outpath, 'w')
json.dump(data, outf)
outf.close()
数据信息比较简单，只有nx（经度的大小），ny（纬度的大小）以及最大值：
那么，得到这个数据可以用来干嘛呢？一般，可以通过js脚本将其放到网页上进行可视化：
