Python批量下载ECMWF欧洲中心数据

url: https://cds.climate.copernicus.eu/api/v2key: 60789:10695ca8-b4c6-49ca-b37a-xxxxxxxxxxxxxx

import cdsapic = cdsapi.Client()c.retrieve(    'reanalysis-era5-pressure-levels-monthly-means',    {        'format': 'netcdf',        'product_type': 'monthly_averaged_reanalysis',        'variable': 'temperature',        'pressure_level': '850',        'year': '2020',        'month': [            '01', '02', '03',            '04', '05', '06',            '07', '08', '09',            '10', '11', '12',        ],        'time': '00:00',    },    'download.nc')

# 导入Python库import numpy as npimport matplotlib.pyplot as pltimport matplotlib.ticker as mtickerimport xarray as xrimport cartopy.crs as ccrsimport cartopy.feature as cfeaturefrom cartopy.io.shapereader import Readerfrom cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatterimport maskoutfrom matplotlib import rcParamsconfig = {"font.family":'Times New Roman', "font.size": 12, "mathtext.fontset":'stix'}rcParams.update(config)region=[70, 140, 15, 55]data=xr.open_dataset("F:/Rpython/lp36/download.nc")print(data)lat = data['latitude']lon = data['longitude']


    
temp = data.variables['t'][:]temp2=temp[1,:,:]-273.15print(temp2)print(temp2.shape)# plotfig=plt.figure(dpi=600,figsize=(12,6))proj=ccrs.PlateCarree()ax = plt.axes(projection = proj)ax.set_extent(region, crs = proj)#ax.coastlines(scale, linewidth = 0.5)ax.set_xticks(np.arange(region[0], region[1] + 1, 10), crs = proj)ax.set_yticks(np.arange(region[-2], region[-1] + 1, 10), crs = proj)ax.xaxis.set_major_formatter(LongitudeFormatter(zero_direction_label=False))ax.yaxis.set_major_formatter(LatitudeFormatter())# Add ocean, land, rivers and lakes#ax.add_feature(cfeature.COASTLINE,lw=0.3) ax.add_feature(cfeature.OCEAN.with_scale('50m'))ax.add_feature(cfeature.LAND.with_scale('50m'))ax.add_feature(cfeature.LAKES.with_scale('50m'))ax.add_geometries(Reader(r'F:/Rpython/lp27/data/river1.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='b',linewidth=0.2)ax.add_geometries(Reader(r'F:/Rpython/lp27/data/china1.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.2)ax.add_geometries(Reader(r'F:/Rpython/lp27/data/china2.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.1)ax.add_geometries(Reader(r'F:/Rpython/lp27/data/ne_10m_land.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.2)ax.add_geometries(Reader(r'F:/Rpython/lp27/data/ne_50m_lakes.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.2)c11=ax.contourf(lon,lat,temp2,np.arange(-20,20,5),extend='both',transform=ccrs.PlateCarree(),cmap='gist_rainbow')clip=maskout.shp2clip(c11,ax,'F:/Rpython/lp27/data/china0')cbar=plt.colorbar(c11,shrink=0.99,aspect=30,fraction=.03,pad=0.01)   #aspect控制bar宽度，fraction控制大小比例,pad控制与图的距离cbar.set_ticks(np.arange(-20,20,5))            #设置colorbar范围和刻度标记间隔cbar.ax.tick_params(labelsize=12, direction='in', right=False)font3={'family':'SimHei','size':12,'color':'k'}ax.set_title('2020年2月中国区域ERA5气温分布图',fontdict=font3)# ax.set_title('2006-12 Month Mean Air Temperature (Level 2m)',{'family':'Times New Roman','size':14,'color':'k'}) ax0 = plt.gca()   #获取边框ax0.outline_patch.set_linewidth(0.5)    #修改边框粗细#添加南海，实际上就是新建一个子图覆盖在之前子图的右下角ax2 = fig.add_axes([0.7365, 0.11, 0.2, 0.2],projection=proj)ax2.set_extent([105.8, 122,0,25])ax2.add_feature(cfeature.OCEAN.with_scale('50m'))ax2.add_feature(cfeature.LAND.with_scale('50m'))ax2.add_feature(cfeature.LAKES.with_scale('50m'))ax2.add_geometries(Reader(r'F:/Rpython/lp27/data/river1.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='b',linewidth=0.2)ax2.add_geometries(Reader(r'F:/Rpython/lp27/data/china1.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.2)ax2.add_geometries(Reader(r'F:/Rpython/lp27/data/china2.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.1)ax2.add_geometries(Reader(r'F:/Rpython/lp27/data/ne_10m_land.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.2)ax2.add_geometries(Reader(r'F:/Rpython/lp27/data/ne_50m_lakes.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.2)c21=ax2.contourf(lon,lat,temp2,np.arange(-20,20,5


    
),extend='both',transform=ccrs.PlateCarree(),cmap='gist_rainbow')clip=maskout.shp2clip(c21,ax2,'F:/Rpython/lp27/data/china0')ax0 = plt.gca()   #获取边框ax0.outline_patch.set_linewidth(0.5)    #修改边框粗细plt.savefig('F:/Rpython/lp36/plot90.png',dpi=800,bbox_inches='tight',pad_inches=0)plt.show()

{    "url"   : "https://api.ecmwf.int/v1",    "key"   : "8c6656f87a7df4c2295c2142xxxxxxxx",    "email" : "xxxxxxxxx@hotmail.com"}

#!/usr/bin/env pythonfrom ecmwfapi import ECMWFDataServerserver = ECMWFDataServer()polynya_boundary = "90/-180/80/180"server.retrieve({    "class": "ei",    'area':polynya_boundary,    'format':"netcdf",    "dataset": "interim",    "date": "20190101/20190201/20190301/20190401/20190501/20190601/20190701/20190801",    "expver": "1",    "grid": "0.75/0.75",    "levtype": "sfc",    "param": "205.128",    "step": "0-12",    "stream": "mdfa",    "type": "fc",    "target": "F:/Rpython/lp36/ERAInterim813.nc",})