A Space-Time Transformer for Precipitation Forecasting

• URL: http://arxiv.org/abs/2511.11090v1
• Date: Fri, 14 Nov 2025 09:10:31 GMT
• Title: A Space-Time Transformer for Precipitation Forecasting
• Authors: Levi Harris, Tianlong Chen,
• Abstract summary: SaTformer is a video transformer that skillfully forecasts extreme precipitation from satellite radiances.<n>We reformulate precipitation regression into a classification problem, and employ a class-weighted loss to address label imbalances.<n>Our model scored first place on the NeurIPS Weather4Cast 2025 Cumulative Rainfall challenge.
• Score: 38.87144329787491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meteorological agencies around the world rely on real-time flood guidance to issue live-saving advisories and warnings. For decades traditional numerical weather prediction (NWP) models have been state-of-the-art for precipitation forecasting. However, physically-parameterized models suffer from a few core limitations: first, solving PDEs to resolve atmospheric dynamics is computationally demanding, and second, these methods degrade in performance at nowcasting timescales (i.e., 0-4 hour lead-times). Motivated by these shortcomings, recent work proposes AI-weather prediction (AI-WP) alternatives that learn to emulate analysis data with neural networks. While these data-driven approaches have enjoyed enormous success across diverse spatial and temporal resolutions, applications of video-understanding architectures for weather forecasting remain underexplored. To address these gaps, we propose SaTformer: a video transformer built on full space-time attention that skillfully forecasts extreme precipitation from satellite radiances. Along with our novel architecture, we introduce techniques to tame long-tailed precipitation datasets. Namely, we reformulate precipitation regression into a classification problem, and employ a class-weighted loss to address label imbalances. Our model scored first place on the NeurIPS Weather4Cast 2025 Cumulative Rainfall challenge. Code and model weights are available: https://github.com/leharris3/satformer

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