issue_comments

Awesome work @JiaweiZhuang! This could be a great way forward for this important need. I think lots of us would be keen to contribute to your project. I encourage you to add tests and docs...that will help other contributors feel comfortable getting involved.

@godfrey4000: lots of us in the climate community would like xarray-backed regridding. It is a hard problem, however. It wold be great if you wanted to work on it.

At a recent workshop, a group of xarray users developed a draft design document for a regridding package. https://aospy.hackpad.com/Regridding-Design-Document-tENJARIeg83

Your comments on this would be very welcome. An open question is whether the regridding belongs within xarray or in a standalone package (which would of course have xarray as a dependency).

I feel like the biggest application of the multi-dimensional groupby will be with "conservative resampling" and coarse-graining, where you want to make sure to conserve certain integrals (e.g. total heat content) while changing coordinates.

pyresample will be more useful for fine-graining and interpolating missing data.

@forman I am starting to suspect that this might be possible to implement through #818

Pyresample is probably overkill for that case. Aggregating / decimating regular lat-lon grids could probably be done much more simply. For example

python N = 10 fac = 2 x = np.arange(N, dtype=np.float64) np.add.reduceat(x, np.arange(0,N,fac)) / fac

This gives array([ 0.5, 2.5, 4.5, 6.5, 8.5])

This type of resampling has the advantage of preserving certain integral invariants, as opposed to the nearest neighbor resampling in the example above. (Imagine if there had been lots of spatial variance below the 5 degree scale in that example--it would have been aliased horribly. That was only avoided because the original field was very smooth.) It is also very fast.

Pyresample seems best for complicated transformations from one map projection to another. I'm not sure I fully understand how xray handles grids where the coordinates are themselves 2d fields, as in this example.