python轉(zhuǎn)換wrf輸出的數(shù)據(jù)為網(wǎng)頁可視化json格式

更新時(shí)間：2022年09月26日 14:22:30 作者：oceanography-Rookie

這篇文章主要介紹了python轉(zhuǎn)換wrf輸出的數(shù)據(jù)為網(wǎng)頁可視化json格式，文章圍繞主題展開詳細(xì)的內(nèi)容介紹，具有一定的參考價(jià)值，需要的小伙伴可以參考一下

前言

一般網(wǎng)頁可視化風(fēng)場中的數(shù)據(jù)都是json格式，而如果我們希望將wrf模式模擬輸出的風(fēng)場數(shù)據(jù)在網(wǎng)頁中進(jìn)行展示，這就需要先將wrfoutput數(shù)據(jù)轉(zhuǎn)換為網(wǎng)頁可以識(shí)別的json格式。
這里主要需要用到j(luò)son庫，主要的實(shí)現(xiàn)方式就是將讀取的風(fēng)場風(fēng)量U，V轉(zhuǎn)換為字典并存到j(luò)son文件中
同時(shí)，由于wrf模擬的數(shù)據(jù)一般是非等間距的網(wǎng)格，需要先將數(shù)據(jù)進(jìn)行插值，插值到等間距的網(wǎng)格，這里可以通過NCL的函數(shù)rcm2rgrid_Wrap實(shí)現(xiàn)

舉個(gè)例子，將模式中設(shè)置為蘭伯特投影的網(wǎng)格：

插值為等間距網(wǎng)格：

主要的編程分為兩部分：

第一部分通過NCL腳本將wrfout數(shù)據(jù)轉(zhuǎn)換為等間距網(wǎng)格，并導(dǎo)出為netcdf格式；
第二部分通過python腳本將第一步導(dǎo)出的nc格式進(jìn)行轉(zhuǎn)換，并保存輸出為json格式。

NCL插值腳本1

需要修改的就是路徑和變量，我下面展示腳本不僅有風(fēng)場數(shù)據(jù)u，v還有降水，海表面壓力，氣溫等，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

上述腳本的缺點(diǎn)在于只能基于模式模擬的經(jīng)緯度區(qū)域進(jìn)行插值，意思就是說他的經(jīng)緯度區(qū)域是固定的那么大

NCL插值腳本2

NCL還有一個(gè)函數(shù)可以實(shí)現(xiàn)上述過程，就是ESMF_regrid，該函數(shù)的優(yōu)點(diǎn)在于可以實(shí)現(xiàn)任意經(jīng)緯度范圍的插值，但是不足在于對(duì)于存在高度層的變量，暫時(shí)無法進(jìn)行高度層的數(shù)據(jù)讀取。

（也可能我水平有限不知道。。。。）這里也附上腳本：

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:運(yùn)行該腳本會(huì)生成四個(gè)nc文件，分別為：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，但是其他三個(gè)文件如何在運(yùn)行腳本的過程去掉暫未解決。

python格式轉(zhuǎn)換腳本1

python腳本如下所示：

# -*- coding: utf-8 -*-
"""
Created on %(date)s

@author: %(jixianpu)s

Email : 211311040008@hhu.edu.cn

introduction : keep learning althongh walk slowly
"""
"""
用來讀取用ncl插值后的wrfoutput.nc 數(shù)據(jù),并生成對(duì)應(yīng)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/'#只能是已經(jīng)存在的文件目錄且有數(shù)據(jù)才可以進(jìn)行讀取
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()

上述腳本為Linux系統(tǒng)下運(yùn)行，運(yùn)行方式如下：

python xx.py 起報(bào)時(shí)間時(shí)常間隔

舉個(gè)例子：

我的wrfout數(shù)據(jù)名稱如下：

python  convert_to_json.py 2022071000 12 06

根據(jù)你需要的模式起始時(shí)間，起報(bào)的時(shí)長（小時(shí)）以及預(yù)報(bào)的時(shí)間間隔（小時(shí)）進(jìn)行自動(dòng)化轉(zhuǎn)換。

python 格式轉(zhuǎn)換腳本2

當(dāng)然，這里也準(zhǔn)備了一個(gè)windows下的簡易腳本，轉(zhuǎn)換出的信息也比較簡單，

# -*- coding: utf-8 -*-
"""
Created on %(date)s

@author: %(jixianpu)s

Email : 211311040008@hhu.edu.cn

introduction : keep learning althongh walk slowly
"""
from __future__ import print_function, unicode_literals
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
# parser = argparse.ArgumentParser(description = """
# """, formatter_class = RawDescriptionHelpFormatter)

args = r'J:/wrf自動(dòng)化/wrfout_d01_2022-07-10_01_00_00.nc'

outroot = r'D:/'

uhdr = {"header":{
                  "nx":360,
                  "ny":181,
                  "max":11,
                  }}

data = [uhdr]
newf = Dataset(args)
lat = np.array(newf.variables['lat'])
lon = np.array(newf.variables['lon'])
u10 = np.array(newf.variables['u10'])
v10 = np.array(newf.variables['v10'])

# indx = u10>1000

# u10[indx] = np.nan
# v10[indx] = np.nan

w10 = np.nanmax(np.sqrt(u10*u10+v10*v10))

dys = np.diff(lat, axis = 0).mean(1)
dy =    float(dys.mean())
print('Latitude Error:', np.abs((dy / dys) - 1).max())
print('Latitude Sum Error:', (dy / dys - 1).sum())
dxs = np.diff(lon, axis = 1).mean(0)
dx =    float(dxs.mean())
print('Longitude Error:', np.abs(dx / dxs - 1).max())
print('Longitude Sum Error:', (dx / dxs - 1).sum())

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")

ds= {
                      "nx":360,
                      "ny":181,
                      "max":11,
                      # "lo1":0,
                      # "la1":90,
                      # "lo2":359,
                      # "la2":-90,
                      # "dx":1,
                      # "dy":1,
                      # "parameterUnit":"m.s-1",
                      'data':{}
        }

ds['max']   =    float(w10)
ds['nx']    =    (nx)
ds['ny']    =    (ny)
for ti, time in enumerate(dt):
    #2012/02/07/0100Z/wind/surface/level/orthographic=-74.01,4.38,29184
    datestr = (dt[0][:8])
    timestr = (dt[0][8:10])+'00'
    print('Add "#' + datestr + '/' + timestr + 'Z/wind/surface/level/orthographic" to url to see this time')
    dirpath = os.path.join('D:', *datestr.split('/'))
    os.makedirs(dirpath, exist_ok = True)
    outpath = os.path.join(dirpath, '%s-wind-surface-level-gfs-1.0.json' % (timestr,))
    udata=u10.ravel()
    data[0]['data']=[]
    for i in range(len(udata)):

        data[0]['data'].append([
        u10.ravel().tolist()[i],
        v10.ravel().tolist()[i]])

    ds['data'] = data[0]['data']

outf = open(outpath, 'w')
json.dump(ds,outf)
outf.close()

這個(gè)腳本正常放在編輯器里面運(yùn)行即可。

運(yùn)行完結(jié)束，會(huì)在你的輸出路徑下生成一個(gè)文件夾：

里面有個(gè)json數(shù)據(jù)：

數(shù)據(jù)信息比較簡單，只有nx（經(jīng)度的大小），ny（緯度的大小）以及最大值：

ok，以上就是完整的過程，最終將得到的json數(shù)據(jù)通過.js腳本運(yùn)行就可以部署到網(wǎng)頁上了，簡單試了一下，大概如下圖所示，可以根據(jù)需要自行更改設(shè)置：

到此這篇關(guān)于python轉(zhuǎn)換wrf輸出的數(shù)據(jù)為網(wǎng)頁可視化json格式的文章就介紹到這了,更多相關(guān)python可視化json格式內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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