SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. While highly efficient for long-range prediction, this formulation can suffer from severe error accumulation when used with shorter time steps and does not explicitly encode the locality and temporal continuity of atmospheric dynamics. To address these limitations, we introduce **SDECast**, a Neural Stochastic Differential Equation (SDE) framework for continuous-time probabilistic weather forecasting. SDECast extends SDE Matching to learn stochastic dynamics directly in physical space, without requiring repeated SDE simulation during training. O...
| Authors | Maria Marchenko, Martin Andrae, Fredrik Lindsten et al. |
|---|---|
| Primary category | stat.ML |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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