如何在xarray中绘制不规则的纬度和经度？

我对XArray不太满意，因此建议使用模块netCDF4解决方案：

#!/usr/bin/env ipython
import xarray as xr
import matplotlib as mpl
mpl.use('tkagg')
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
# =======================================================
from netCDF4 import Dataset
ncin=Dataset('day1.nc');
tempin=ncin.variables['Temperature'][0,:,:]- 273.15;
lonin=ncin.variables['Lon'][:];
latin=ncin.variables['Lat'][:];
ncin.close()
# -------------------------------------------------------
from scipy.interpolate import griddata
import numpy as np
kk=np.where(np.isnan(np.array(tempin).flatten())==False)
lonm,latm=np.meshgrid(lonin,latin);
tinterp=griddata((lonm.flatten()[kk],latm.flatten()[kk]),tempin.flatten()[kk],(lonm,latm));

ax = plt.subplot(121,projection=ccrs.PlateCarree())
ax.pcolormesh(lonin,latin,tempin);
ax = plt.subplot(122,projection=ccrs.PlateCarree())
ax.pcolormesh(lonin,latin,tinterp);
plt.show()

最终结果如下所示：左边是原始图像，右边是插值（nan掉落的数字）。

我可以提出一个答案，在这里我结合XArray和SciPy的的GridData因为interpolate_na不工作很不错（看一部分，并与结果filled_a，filled_b）对我来说：

#!/usr/bin/env ipython
import xarray as xr
import matplotlib as mpl
mpl.use('tkagg')
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
# =======================================================
month_daily1 = xr.open_dataset('day1.nc')

# convert kelvin to celsius
data_nonnull = month_daily1.dropna(dim ='time', how='all')
air = data_nonnull.Temperature - 273.15
air2d = air.isel(time= 0)
# =======================================================
ax = plt.subplot(121,projection=ccrs.PlateCarree())
air2d.plot.pcolormesh('Lon', 'Lat');
ax = plt.subplot(122,projection=ccrs.PlateCarree())
filled_a=air2d.interpolate_na(dim='Lat');
filled_b=filled_a.interpolate_na(dim='Lon');
filled_c=filled_b.interpolate_na(dim='Lat');
filled_c.plot.pcolormesh('Lon', 'Lat');
plt.show()
# =======================================================
tempin=air2d.values[:];
lonin=air2d.Lon
latin=air2d.Lat
# -------------------------------------------------------
from scipy.interpolate import griddata
import numpy as np
kk=np.where(np.isnan(np.array(tempin).flatten())==False)
lonm,latm=np.meshgrid(lonin,latin);
tinterp=griddata((lonm.flatten()[kk],latm.flatten()[kk]),tempin.flatten()[kk],(lonm,latm));

ax = plt.subplot(121,projection=ccrs.PlateCarree())
ax.pcolormesh(lonin,latin,tempin);
ax = plt.subplot(122,projection=ccrs.PlateCarree())
ax.pcolormesh(lonin,latin,tinterp);
plt.show()