A constrained neural differential equation trained on multi-year observations predicts snow albedo with median error under 7.5% and 10-30% improvement over prior models while generalizing to unseen sites.
Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States , volume=
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A Next-Generation Snow Albedo Parameterization for Climate Modeling using Constrained Machine Learning
A constrained neural differential equation trained on multi-year observations predicts snow albedo with median error under 7.5% and 10-30% improvement over prior models while generalizing to unseen sites.