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文章目录
前言
绘制西太和亚洲区域夏季和冬季平均风速风向矢量图，并叠加平均降雨填充底图
一、下载再分析网格数据
1，风速风向数据，主要是在美国的NASA网站下载的NCEP\NCAR数据集，包含了1948-至今平均逐月的数据（u_wnd.nc; v_wnd.nc）.下载链接（https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html），具体步骤可以参考这篇博文（https://blog.csdn.net/qq_58252159/article/details/131946548）
2, 降雨数据也是，下载链接（https://psl.noaa.gov/data/gridded/data.gpcc.html#detail）
二、具体步骤
1.引入库
代码如下：
import xarray as xr import numpy as np import datetime as dt import cartopy.crs as ccrs import cartopy.feature as cfeature import cartopy.mpl.ticker as cticker import matplotlib.pyplot as plt 2.读入数据并筛选数据
代码如下：
import xarray as xr import numpy as np import datetime as dt import cartopy.crs as ccrs import cartopy.feature as cfeature import cartopy.mpl.ticker as cticker import matplotlib.pyplot as plt # 导入降雨数据 f_p = xr.open_dataset('C:\\Users\\huain\\Desktop\\BY\\precip.mon.mean.nc') #筛选降雨数据冬季的月份 p = f_p.precip.loc[f_p.time.dt.month.isin([6,7,8])].loc['1979-01-01':'2023-12-01'] p_cli = np.array(p).reshape(45,3,72,144).mean((0)) lats = f_p.lat lons = f_p.lon #导入纬向风的数据 f_u = xr.open_dataset('C:\\Users\\huain\\Desktop\\BY\\uwnd.mon.mean.nc') #筛选冬季纬向风的数据 u = f_u.uwnd.loc[f_u.time.dt.month.isin([6,7,8])].loc['1979-01-01':'2023-12-01'] u_cli = np.array(u).reshape(45,3,73,144).mean((0)) lat = f_u.lat lon = f_u.lon #导入经向风的数据 f_v = xr.open_dataset('C:\\Users\\huain\\Desktop\\BY\\vwnd.mon.mean.nc') #筛选冬季经向风的数据 v = f_v.vwnd.loc[f_v.time.dt.month.isin([6,7,8])].loc['1979-01-01':'2023-12-01'] v_cli = np.array(v).reshape(45,3,73,144).mean((0)) #计算风速 windspeed = np.sqrt(u_cli**2 + v_cli**2) # ax.quiver(lon[::2],lat[::2],u_cli[0,::2,::2],v_cli[0,::2,::2],transform=ccrs.PlateCarree(),scale=150,color='r') #画图 fig = plt.figure(figsize=(12,8)) proj = ccrs.PlateCarree(central_longitude=180) leftlon, rightlon, lowerlat, upperlat = (80,200,-30,60) img_extent = [leftlon, rightlon, lowerlat, upperlat] ax = fig.add_axes([0.1, 0.1, 0.8, 0.6],projection = proj) ax.set_extent(img_extent, crs=ccrs.PlateCarree()) ax.add_feature(cfeature.COASTLINE) ax.set_xticks(np.arange(leftlon,rightlon,10), crs=ccrs.PlateCarree()) ax.set_yticks(np.arange(lowerlat,upperlat,10), crs=ccrs.PlateCarree()) lon_formatter = cticker.LongitudeFormatter() lat_formatter = cticker.LatitudeFormatter() ax.xaxis.set_major_formatter(lon_formatter) ax.yaxis.set_major_formatter(lat_formatter) q = ax.quiver(lon[::2],lat[::2],u_cli[0,::2,::2],v_cli[0,::2,::2],transform=ccrs.PlateCarree(),scale=150,color='g',width=0.002,headwidth=4,headlength=7) #添加风速比例尺 ax.quiverkey(q, 1, 1.02, 10, '10 m/s', labelpos='E',coordinates='axes') #添加图名 ax.set_title('995Pa Average wind and percpitation 1979-2023 Summer-JJA') #绘制降雨填充图 c = ax.contourf(lons,lats,p_cli[0], cmap='Blues', levels=30, extend='both', add_colorbar=True, transform=ccrs.PlateCarree(),zorder=0) # 添加颜色条 cbar = fig.colorbar(c, drawedges=True, ax=ax, location='right', shrink=0.95, pad=0.01, spacing='uniform', label='Average precipitation (mm)') cbar.ax.tick_params(labelsize=10) # 设置色标尺标签大小 # 添加一个采样点位（129°E，17°N） sample_point = ax.plot(129, 17, 'ro', transform=ccrs.PlateCarree(), markersize=8) # 为采样点添加标签 ax.annotate('WPS-1/2', xy=(129, 17), xytext=(20, 20), textcoords='offset points', ha='left', va='bottom', bbox=dict(boxstyle='round', fc='w', ec='0.5', lw=2), arrowprops=dict(facecolor='black', arrowstyle='->'), transform=ccrs.PlateCarree()) 三、效果图
