I am interested in the coarsen with weights scenario that @dcherian and @mathause described here for a current project of ours.

I solved the issue manually and its not that hard ```python import xarray as xr import numpy as np

example data with weights

data = np.arange(16).reshape(4,4).astype(float)

add some nans

data[2,2] = np.nan data[1,1] = np.nan

create some simple weights

weights = np.repeat(np.array([[1,2,1,3]]).T, 4, axis=1) weights

da = xr.DataArray(data, dims=['x', 'y'], coords={'w':(['x','y'], weights)}) da ```

python masked_weights = da.w.where(~np.isnan(da)) # .weighted() already knows how to do this da_weighted = da * masked_weights da_coarse = da_weighted.coarsen(x=2, y=2).sum() / masked_weights.coarsen(x=2, y=2).sum() da_coarse

but I feel all of this is duplicating existing functionality (e.g. the masking of weights based on nans in the data) and might be sensibly streamlined into something like: python da.weighted(da.w).coarsen(...).mean() at least from a user perspective (there might be unique challenges with the implementation that I am overlooking here).

Happy to help but would definitely need some guidance on this one.

I do believe that this would provide a very useful functionality for many folks who work with curvilinear grids and want to prototype things that depend on some sort of scale reduction (coarsening).

Also cc'ing @TomNicholas who is involved in the same project 🤗