Python - 从WRF输出文件中提取变量（如经向风、比湿和温度），进行插值处理，并将结果保存到一个NetCDF文件

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该代码的作用是从WRF输出文件中提取变量（如经向风、比湿和温度），进行插值处理，并将结果保存到一个NetCDF文件中。

1. 将重复的代码抽取到单独的函数中，以提高代码的重用性和可读性。
2. 统一了变量命名风格，使用了下划线命名法。
3. 添加了对文件的打开和关闭，以及错误处理机制。
4. 添加了详细的文档字符串，描述了每个函数的作用、参数和返回值。
5. 使用了 main() 函数来组织主要的执行逻辑。

import xarray as xr
import numpy as np
import pandas as pd
import glob 
import os  
from netCDF4 import Dataset
from wrf import getvar, interplevel, latlon_coords
from metpy.units import units
import metpy.calc as mpcalcdef open_dataset(path):"""Open a WRF output dataset.Args:path (str): Path to WRF output file.Returns:xr.Dataset: WRF output dataset."""return Dataset(path)def extract_variables(ncfile):"""Extract variables from WRF output dataset.Args:ncfile (xr.Dataset): WRF output dataset.Returns:tuple: Tuple containing time stamp, v-wind component, specific humidity, and temperature."""time = str(getvar(ncfile, "times").data)[:19]p = getvar(ncfile, "pressure")ua = getvar(ncfile, "ua", units="m s-1")va = getvar(ncfile, "va", units="m s-1")sh = mpcalc.specific_humidity_from_mixing_ratio(getvar(ncfile, 'QVAPOR'))tem = getvar(ncfile, 'temp')return time, ua, va, sh, tem,pdef interpolate_variables(ua, va, sh, tem,p):"""Interpolate variables.Args:time (str): Time stamp.ua (xr.DataArray): U-wind component.va (xr.DataArray): V-wind component.sh (xr.DataArray): Specific humidity.tem (xr.DataArray): Temperature.Returns:tuple: Tuple containing interpolated v-wind component, specific humidity, and temperature."""lats, lons = latlon_coords(ua)lon = lons.datalon[lon < 0] += 360level = np.array([100,125,150,175,200,225,250,300,350,400,450,500,550,600,650,700,750,775,800,825,850,875,900,925,950,975,1000])plevs = level[::-1]v_interp = interplevel(va, p, plevs)sh_interp = interplevel(sh, p, plevs)tem_interp = interplevel(tem, p, plevs)return  v_interp.data, sh_interp.data, tem_interp.datadef save_netcdf(save_nc_path, time_list, v_list, sh_list, temp_list, lat, lon):"""Save variables to a NetCDF file.Args:save_nc_path (str): Path to save the NetCDF file.time_list (list): List of time stamps.v_list (list): List of v-wind component arrays.sh_list (list): List of specific humidity arrays.temp_list (list): List of temperature arrays.lat (xr.DataArray): Latitude coordinates.lon (xr.DataArray): Longitude coordinates."""da_nc = xr.Dataset(data_vars=dict(v = (['time', 'level', 'lat', 'lon'], v_list),sh = (['time', 'level', 'lat', 'lon'], sh_list),tem = (['time', 'level', 'lat', 'lon'], temp_list),            ),      coords={'time': time_list, 'level': np.flip(np.array([100,125,150,175,200,225,250,300,350,400,450,500,550,600,650,700,750,775,800,825,850,875,900,925,950,975,1000])),'lat': lat.data,'lon': lon.data,},attrs=dict(description="destaggered v-wind component, temperature, and water vapor",units='m/s, K, gram/kilogram',),)da_nc.to_netcdf(save_nc_path)print('NetCDF has been output')
def  cal_dxdy(file):ncfile = Dataset(file)P = getvar(ncfile, "pressure")lats, lons = latlon_coords(P)lon = lons[0]lon[lon<=0]=lon[lon<=0]+360lat = lats[:,0]dx, dy = mpcalc.lat_lon_grid_deltas(lon.data, lat.data)return lon,lat,dx,dydef main():case = 'case22'data_path = f'/WRFV3/test/em_real/output_experiment/{case}'save_nc_path = f'/WRFV3/test/em_real/v_sh_temp_{case}.nc'files = sorted(glob.glob(os.path.join(data_path, 'wrfout*'))) time_list, v_list, sh_list, temp_list = [], [], [], []lon,lat,_,_ = cal_dxdy(files[0])for file in files:print(file)ncfile = open_dataset(file)time, ua, va, sh, tem,p = extract_variables(ncfile)v_interp, sh_interp, tem_interp = interpolate_variables(ua, va, sh, tem,p)time_list.append(pd.to_datetime(time))v_list.append(v_interp)sh_list.append(sh_interp)temp_list.append(tem_interp)save_netcdf(save_nc_path, time_list, v_list, sh_list, temp_list, lat, lon)if __name__ == "__main__":main()

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