arXiv Machine Learning
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
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Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting
arXiv:2601.21151v3 Announce Type: replace Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...
arXiv Machine Learning
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SimCast-S2S: An Efficient Generative Model for Subseasonal Precipitation Forecasting via Transfer Learning from Climate Simulations
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Physics-Informed Machine Learning for Short-Term Flood Prediction
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Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling
arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.
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SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting
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arXiv Machine Learning
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High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
arXiv:2602. 13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting.