【气象常用】等值线填充+九段线+南海小地图+掩膜中国区域数据

效果图：

主要步骤：

1. 数据准备：下载ERA5月平均地表温度数据就好啦（不会的可以看我之前的帖子【数据下载】ERA5地表月平均数据下载-CSDN博客）

2. 数据处理：计算温度距平，掩膜中国区域数据

3. 图像绘制：主要包括等值线填充，经纬网格线绘制，坐标轴标签及南海小图的设置

注：内置地图我国国界线是有问题的，必须换奥，因为我自己的这版地图是好久以前弄到的了，我忘了来源了，就不和大家分享了，大家在vx搜一搜会有很多的，找一版正确的就好了

关于南海小地图的画图：其实就是又画了一张填色图，和绘制组图的道理是一样的，只是图的位置摆放和显示区域的设置问题了

详细代码：着急的直接拖到最后有完整代码

步骤一：导入库包

import numpy as np import cartopy.crs as ccrs import cartopy.feature as cfeature from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter from cartopy.io.shapereader import Reader import matplotlib.pyplot as plt import matplotlib.ticker as mticker import xarray as xr from cnmaps import get_adm_maps, draw_maps # import pygrib as pg # 设置西文字体为新罗马字体，中文为宋体，字号为12 from matplotlib import rcParams config = {             "font.family": 'serif',             "font.size": 12,             "mathtext.fontset": 'stix',             "font.serif": ['SimSun'],           } rcParams.update(config) rcParams['axes.unicode_minus']=False 

步骤二：读入数据

datapath = r'H:/00.csdn/01data/' figpath = r'H:/00.csdn/02fig/' shppath = r'H:/00.csdn/04shp/cn_shp/Province_9/Province_9.shp'  # 读入数据 f = xr.open_dataset(datapath + 't2m_cn_jja_1950-2023.nc') # print(f) t = f['t2m'].loc[:, 55.:0., 70.:140.] time = f['time'] lons = t['longitude'] lats = t['latitude'] lon, lat = np.meshgrid(lons, lats) 

步骤三：计算平均温度及温度距平

# 计算时间维平均，及距平 t_mean = np.mean(t, axis=0) dt = t[-1, :, :] - t_mean # print(np.max(dt)) # print(np.min(dt)) 

步骤四：掩膜数据

#掩膜数据 cn = get_adm_maps(level='国', only_polygon=True, record='first') dt = cn.maskout(lon, lat, dt)

步骤五：定义绘图函数并调用函数（这里所有的东西你都可以选择要与不要，不要注释掉就好了，定义成函数主要是因南海小地图设置那里偷个懒）

# 画图 t_level = np.arange(-6, 7, 1) fig = plt.figure(figsize=(15, 6))  def plot_cont(data, location, level, extent, show):      ax = fig.add_axes(location, projection=ccrs.PlateCarree(central_longitude=120))     c1 = ax.contourf(lon, lat, data, levels=level, extend='both',                       cmap='RdBu_r', transform=ccrs.PlateCarree())  #等值线填充      # 添加地理信息     ax.add_feature(cfeature.OCEAN.with_scale('50m'))  # 海洋填充为蓝色     ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='w')  # 陆地填充为白色     # help(ax.add_feature)     # ax.add_feature(cfeature.RIVERS.with_scale('50m'))  # 添加河流     # ax.add_feature(cfeature.LAKES.with_scale('50m'))  # 添加湖泊     # ax.add_feature(cfeature.BORDERS.with_scale('50m'),lw=0.5)  # 添加国界线     ax.add_feature(cfeature.COASTLINE.with_scale('50m'),lw=0.5)  # 添加海岸线     ax.add_geometries(Reader(shppath).geometries(), ccrs.PlateCarree(),                       facecolor='none', edgecolor='k', linewidth=0.7)  #添加中国国界      # 设置显示范围     ax.set_extent(extent, crs=ccrs.PlateCarree())          # 设置是否显示经纬网格线及坐标刻度     if show == True:         # 画经纬度网格         gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=False, linewidth=1.2,                            color='k', alpha=0.5, linestyle='--')                  gl.top_labels = False  # 关闭顶端的经纬度标签         gl.right_labels = False  # 关闭右侧的经纬度标签         gl.xformatter = LONGITUDE_FORMATTER  # x轴设为经度的格式         gl.yformatter = LATITUDE_FORMATTER  # y轴设为纬度的格式              # 设置经纬度网格的间隔及坐标标签         gl.xlocator = mticker.FixedLocator(np.arange(extent[0], extent[1]+10, 10))         gl.ylocator = mticker.FixedLocator(np.arange(extent[2], extent[3]+10, 10))         ax.set_xticks(list(range(extent[0], extent[1]+10, 10)), crs=ccrs.PlateCarree())         ax.set_yticks(list(range(extent[2], extent[3]+10, 10)), crs=ccrs.PlateCarree())                  lon_formatter = LongitudeFormatter(zero_direction_label=True)  # zero_direction_label用来设置经度的0度加不加E和W         lat_formatter = LatitudeFormatter()         ax.xaxis.set_major_formatter(lon_formatter)         ax.yaxis.set_major_formatter(lat_formatter)              return c1  CN = plot_cont(dt, [0.1, 0.1, 1, 1], t_level, [70, 140, 15, 55], show=True) NH = plot_cont(dt, [0.68, 0.1, 0.4, 0.4], t_level, [105, 123, 2, 23], show=False) 

步骤六：色标设置

# 色标设置 rect = [0.3, 0.015, 0.6, 0.04] cbar_ax = fig.add_axes(rect)  cb = fig.colorbar(CN, drawedges=True, ticks=t_level,                    cax=cbar_ax, orientation='horizontal') cb.set_label('℃', fontsize=12) cb.ax.tick_params(length=0) 

步骤七：保存图像

# 存图 plt.savefig(figpath+'02 南海小地图', dpi=600, bbox_inches = 'tight') plt.show()

完整代码：

import numpy as np import cartopy.crs as ccrs import cartopy.feature as cfeature from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter from cartopy.io.shapereader import Reader import matplotlib.pyplot as plt import matplotlib.ticker as mticker import xarray as xr from cnmaps import get_adm_maps, draw_maps # import pygrib as pg # 设置西文字体为新罗马字体，中文为宋体，字号为12 from matplotlib import rcParams config = {             "font.family": 'serif',             "font.size": 12,             "mathtext.fontset": 'stix',             "font.serif": ['SimSun'],           } rcParams.update(config) rcParams['axes.unicode_minus']=False ########################################################## datapath = r'H:/00.csdn/01data/' figpath = r'H:/00.csdn/02fig/' shppath = r'H:/00.csdn/04shp/cn_shp/Province_9/Province_9.shp'  # 读入数据 f = xr.open_dataset(datapath + 't2m_cn_jja_1950-2023.nc') # print(f) t = f['t2m'].loc[:, 55.:0., 70.:140.] time = f['time'] lons = t['longitude'] lats = t['latitude'] lon, lat = np.meshgrid(lons, lats) # 计算时间维平均，及距平 t_mean = np.mean(t, axis=0) dt = t[-1, :, :] - t_mean # print(np.max(dt)) # print(np.min(dt))  #掩膜数据 cn = get_adm_maps(level='国', only_polygon=True, record='first') dt = cn.maskout(lon, lat, dt) ########################################################## # 画图 t_level = np.arange(-6, 7, 1) fig = plt.figure(figsize=(15, 6)) def plot_cont(data, location, level, extent, show):     ax = fig.add_axes(location, projection=ccrs.PlateCarree(central_longitude=120))     c1 = ax.contourf(lon, lat, data, levels=level,                        extend='both', cmap='RdBu_r', transform=ccrs.PlateCarree())      # 添加地理信息     ax.add_feature(cfeature.OCEAN.with_scale('50m'))  # 海洋填充为蓝色     ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='w')  # 陆地填充为白色     # help(ax.add_feature)     # ax.add_feature(cfeature.RIVERS.with_scale('50m'))  # 添加河流     # ax.add_feature(cfeature.LAKES.with_scale('50m'))  # 添加湖泊     # ax.add_feature(cfeature.BORDERS.with_scale('50m'),lw=0.5)  # 添加国界线     ax.add_feature(cfeature.COASTLINE.with_scale('50m'),lw=0.5)  # 添加海岸线     ax.add_geometries(Reader(shppath).geometries(), ccrs.PlateCarree(),                       facecolor='none', edgecolor='k', linewidth=0.7)  #添加中国国界      # 设置显示范围     ax.set_extent(extent, crs=ccrs.PlateCarree())          # 设置是否显示经纬网格线及坐标刻度     if show == True:         # 画经纬度网格         gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=False, linewidth=1.2,                            color='k', alpha=0.5, linestyle='--')                  gl.top_labels = False  # 关闭顶端的经纬度标签         gl.right_labels = False  # 关闭右侧的经纬度标签         gl.xformatter = LONGITUDE_FORMATTER  # x轴设为经度的格式         gl.yformatter = LATITUDE_FORMATTER  # y轴设为纬度的格式              # 设置经纬度网格的间隔及坐标标签         gl.xlocator = mticker.FixedLocator(np.arange(extent[0], extent[1]+10, 10))         gl.ylocator = mticker.FixedLocator(np.arange(extent[2], extent[3]+10, 10))         ax.set_xticks(list(range(extent[0], extent[1]+10, 10)), crs=ccrs.PlateCarree())         ax.set_yticks(list(range(extent[2], extent[3]+10, 10)), crs=ccrs.PlateCarree())                  lon_formatter = LongitudeFormatter(zero_direction_label=True)  # zero_direction_label用来设置经度的0度加不加E和W         lat_formatter = LatitudeFormatter()         ax.xaxis.set_major_formatter(lon_formatter)         ax.yaxis.set_major_formatter(lat_formatter)              return c1  CN = plot_cont(dt, [0.1, 0.1, 1, 1], t_level, [70, 140, 15, 55], show=True) NH = plot_cont(dt, [0.68, 0.1, 0.4, 0.4], t_level, [105, 123, 2, 23], show=False) ############################################################################### # 色标设置 rect = [0.3, 0.015, 0.6, 0.04] cbar_ax = fig.add_axes(rect)  cb = fig.colorbar(CN, drawedges=True, ticks=t_level,                    cax=cbar_ax, orientation='horizontal') cb.set_label('℃', fontsize=12) cb.ax.tick_params(length=0)  ############################################################################### # 存图 plt.savefig(figpath+'02 南海小地图', dpi=600, bbox_inches = 'tight') plt.show()