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.
and Sprenger, Michael and Ubbiali, Stefano and Wernli, Heini , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
The explicit-convection km-scale simulation produces fewer and weaker Atlantic hurricanes than parameterized coarser runs because seed vortices fail to amplify after crossing the West African coast due to weaker top-heavy mass flux profiles and underestimated MCS stratiform components.
citing papers explorer
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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.
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Dynamics of East Atlantic seed vortex populations in global km-scale models
The explicit-convection km-scale simulation produces fewer and weaker Atlantic hurricanes than parameterized coarser runs because seed vortices fail to amplify after crossing the West African coast due to weaker top-heavy mass flux profiles and underestimated MCS stratiform components.