Python处理全球降水气候数据（GPCP）绘制全球降水四季空间分布图

全球降水气候数据集（GPCP）由NASA戈达德太空飞行中心制作，数据结合了特殊传感器微波成像仪（SSM/I）项目和散射算法、GOES降水指数（GPI）、输出长波降水指数（OPI）、雨量计和NOAA极轨卫星上的TOVS探测仪的降水估计等数据，数据空间分辨率约为150km。

官网下载链接：http://gpcp.umd.edu

Python处理全球降水气候数据（GPCP）：

# -*- coding: utf-8 -*-"""@Features: 空间分布图 季节平均@Date：2022/4/30"""import matplotlib.pyplot as pltimport numpy as npimport netCDF4 as ncimport cartopy.crs as ccrsfrom cartopy.util import add_cyclic_pointimport cartopy.feature as cfeaturefrom cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatterfrom matplotlib import rcParamsconfig = {"font.family":'Times New Roman',"font.size":10,"mathtext.fontset":'stix'}rcParams.update(config)def sea_mean(start):    temp = []    for i in range(start, 468, 12):        temp.append(i)        temp.append(i + 1)        temp.append(i + 2)    mean_season = np.nanmean(pre[temp, :, :], 0)    return mean_seasonif __name__ == '__main__':    dataset = nc.Dataset('F:/RMatlab/data20211219/precip.mon.mean.nc')  # 读取数据    print(dataset.variables.keys())  # 输出所有变量    lon = dataset.variables['lon'][:].data  # 读取经度    lat = dataset.variables['lat'][:].data  # 读取维度    time = dataset.variables['time']  # 读取时间    real_time = nc.num2date(time, time.units).data  # 转成时间格式    pre = dataset.variables['precip'][:].data  # 读取降水    pre[pre < 0] = np.nan    # 绘图    fig = plt.figure()  # 设置一个画板，将其返还给fig    for i in range(4):        mon = [3, 6, 9, 12]        title = ['MAM', 'JAS', 'SON', 'DJS']        proj=ccrs.PlateCarree()        extent = [-180,180,-90,90]        ax = fig.add_subplot(2, 2, i + 1,projection=ccrs.PlateCarree(central_longitude=180),facecolor='gray')        ax.set_extent(extent, crs=ccrs.PlateCarree())        # 改动了居中经线后，横坐标的经纬度维持不变！！！后续的绘图要根据原始的-180~180的横坐标绘制        ax.coastlines()        # 消除白线        cycle_data, cycle_lon = add_cyclic_point(sea_mean(mon[i]), coord=lon)        cycle_LON, cycle_LAT = np.meshgrid(cycle_lon, lat)        cs = ax.contourf(cycle_LON-180, cycle_LAT, cycle_data,cmap='rainbow', extend='both',levels=range(0, 12))        ax.add_feature(cfeature.COASTLINE.with_scale('50m'),linewidth=0.2,zorder=2,color='k')# 添加海岸线        plt.title(title[i],fontsize=8)        cbar=plt.colorbar(cs,shrink=0.63,orientation='vertical'


    
,pad=0.02,aspect=30,extend='both')        ax.set_xticks(np.arange(extent[0], extent[1] + 1, 60), crs = proj)        ax.set_yticks(np.arange(extent[-2], extent[-1] + 1,30), crs = proj)        ax.xaxis.set_major_formatter(LongitudeFormatter(zero_direction_label=False))        ax.yaxis.set_major_formatter(LatitudeFormatter())plt.savefig('F:/Rpython/lp37/plot142.png',dpi=800,bbox_inches='tight',pad_inches=0)plt.show()