issue_comments: 497138085

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https://github.com/pydata/xarray/issues/2281#issuecomment-497138085 https://api.github.com/repos/pydata/xarray/issues/2281 497138085 MDEyOklzc3VlQ29tbWVudDQ5NzEzODA4NQ== 539688 2019-05-29T22:56:07Z 2019-05-30T00:14:09Z NONE

@crusaderky what I'm trying to do is what the title of this opened thread says "Does interp() work on curvilinear grids (2D coordinates)?" Working with (x, y, t) all 1D numpy arrays is an easy problem! But that's not the problem I'm dealing with.

Let me state exactly what my problem is. I have two data cubes, cube1 and cube2:

``` cube1 = xr.DataArray(cube1, dims=['t', 'y', 'x'], coords={'time': ('t', t_cube1), # 1D array 'lon': (['y', 'x'], X_cube1), # 2D array!!! 'lat': (['y', 'x'], Y_cube1)}) # 2D array!!!

cube2 = xr.DataArray(cube2, dims=['y', 'x', 't'], coords={'time': ('t', t_cube2), # 1D array 'lon': (['y', 'x'], X_cube2), # 2D array!!! 'lat': (['y', 'x'], Y_cube2)}) # 2D array!!! ```

All I want is to regrid cube1 onto cube2, or in other words, interpolate X_cube2/Y_cube2/t_cube2 onto cube1. Note that I cannot pass X_cube1/Y_cube1 as 1D arrays because they vary for every grid cell (this is a rotated grid). I can, however, pass X_cube2/Y_cube2 as 1D arrays (this is a standard lon/lat grid).

Also note the inverted time dimension between cube1 and cube2 (but that shouldn't be an issue).

So how to perform this operation... or am I missing something?

 
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