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,