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Definitely not an easy problem to solve! For what its worth, the decision I came to in my rewrite of the NWP consumer for OCF was to keep the grid as-provided, but standardize variable names, dimension names, and units, as that seemed to suit our ML pipelines the best. |
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One of the ultimate aims is to make it super-easy for users to add a new NWP to their product. Ideally, it'd be great if different NWPs could be as interchangeable as lego bricks.
In practice, this requires standardising:
Horizontal resolution & projection
NWPs from different providers also tend to have different horizontal resolutions and projections.
You could imagine a world in which these are reprojected on-the-fly to a "standard" resolution and projection, but that's computationally expensive, and introduces artefacts.
And, perhaps, it's not necessary (or desirable) to reproject. Attention-based ML models can handle different resolutions and projections (because we "manually" encode the location of each input, so all we have to do is tell the model the lat/long coords of each input pixel). And a lot of use-cases probably just want forecasts that are nearest to a target geospatial location (e.g. a particular wind farm).
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