Scipy 插值网格数据的问题

Question

I have a netcdf file with a spatial resolution of 0.05º and I want to regrid it to a spatial resolution of 0.01º like this other netcdf . I tried using scipy.interpolate.griddata, but I am not really getting there, I think there is something that I am missing.

original_dataset = xr.open_dataset('to_regrid.nc')
target_dataset= xr.open_dataset('SSTA_L4_MED_0_1dg_2022-01-18.nc')

According to scipy.interpolate.griddata documentation , I need to construct my interpolation pipeline as following:

grid = griddata(points, values, (grid_x_new, grid_y_new), method='nearest')

So in my case, I assume it would be as following:

#Saving in variables the old and new grids
grid_x_new = target_dataset['lon']
grid_y_new = target_dataset['lat']
grid_x_old = original_dataset ['lon']
grid_y_old = original_dataset ['lat']

points = (grid_x_old,grid_y_old)
values = original_dataset['analysed_sst'] #My variable in the netcdf is the sea surface temp.

Now, when I run griddata:

from scipy.interpolate import griddata
grid = griddata(points, values, (grid_x_new, grid_y_new),method='nearest')

I am getting the following error:

ValueError: shape mismatch: objects cannot be broadcast to a single shape

I assume it has something to do with the lat/lon array shapes. I am quite new to netcdf field and don't really know what can be the issue here. Any help would be very appreciated!

Answer 1

In your original code the indices in grid_x_old and grid_y_old should correspond to each unique coordinate in the dataset. To get things working correctly something like the following will work:

import xarray as xr
from scipy.interpolate import griddata
original_dataset = xr.open_dataset('to_regrid.nc')
target_dataset= xr.open_dataset('SSTA_L4_MED_0_1dg_2022-01-18.nc')
#Saving in variables the old and new grids
grid_x_old = original_dataset.to_dataframe().reset_index().loc[:,["lat", "lon"]].lon
grid_y_old = original_dataset.to_dataframe().reset_index().loc[:,["lat", "lon"]].lat

grid_x_new = target_dataset.to_dataframe().reset_index().loc[:,["lat", "lon"]].lon
grid_y_new = target_dataset.to_dataframe().reset_index().loc[:,["lat", "lon"]].lat
values = original_dataset.to_dataframe().reset_index().loc[:,["lat", "lon", "analysed_sst"]].analysed_sst
points = (grid_x_old,grid_y_old)
grid = griddata(points, values, (grid_x_new, grid_y_new),method='nearest')

Answer 2

I recommend using xesm for regridding xarray datasets. The code below will regrid your dataset:

import xarray as xr
import xesmf as xe
original_dataset = xr.open_dataset('to_regrid.nc')
target_dataset= xr.open_dataset('SSTA_L4_MED_0_1dg_2022-01-18.nc')
regridder = xe.Regridder(original_dataset, target_dataset, "bilinear")
dr_out = regridder(original_dataset)