prec1_sum = prec1.sum(dim='Time') prec2_sum = prec2.sum(dim='Time') #%% ### 累计降水分布 proj = ccrs.PlateCarree() # crange = np.arange(0, 200+10, 10) labels = ['WRF', 'WRF-nolake', 'CMFD', 'Difference'] fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(9, 11), subplot_kw={'projection': proj}) fig.subplots_adjust(hspace=0.2, wspace=0.15) for i in range(2): for j in range(2): set_grid(axes[i, j], lat=[30, 31.5], lon=[90, 91.5], span=.5) add_NamCo(axes[i, j]) for j, prec_sum in enumerate([prec1_sum, prec2_sum]): c = axes[0][j].pcolor(lons, lats, prec_sum, vmin=0, vmax=250, cmap=cmaps.WhiteBlueGreenYellowRed, transform=proj) axes[0][j].set_title(labels[j], fontsize=14, weight='bold') axes[1][0].pcolor(lon, lat, cmfd_sum, vmin=0, vmax=250,
u10_era5 = xr.open_dataset(file_path)['u10'].squeeze() v10_era5 = xr.open_dataset(file_path)['v10'].squeeze() lat, lon = u10_era5.latitude, u10_era5.longitude #%% proj = ccrs.PlateCarree() labels = ['WRF', 'WRF-nolake', 'ERA5', 'Difference'] fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10), dpi=100, subplot_kw={'projection': proj}) for i in range(2): set_grid(axes[0][i], lat=[30, 31.5], lon=[90, 91.5], span=.5) add_NamCo(axes[0][i]) add_terrain(axes[0][i], geo_path) axes[0][i].set_title(labels[i], fontsize=14, weight='bold') q = axes[0][i].quiver(lons.values, lats.values, eval(f'u10{i+1}_mean.values'), eval(f'v10{i+1}_mean.values'), color='b', transform=ccrs.PlateCarree(), scale=35, regrid_shape=15, width=0.0035, headwidth=5) axes[0][i].quiverkey(q,