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SDECast addresses limitations of autoregressive ML weather models by introducing a Neural SDE framework for continuous-time probabilistic forecasting, learning stochastic dynamics directly in physical space without repeated SDE simulation during training. This approach recovers meaningful drift dynamics and faithfully reproduces continuous-time behavior on simulated geophysical flows. Scaled to global weather forecasting at hourly resolution, SDECast achieves skillful probabilistic forecasts for lead times up to five days.
Continuous-time weather forecasting with Neural SDEs achieves skillful 5-day probabilistic predictions at hourly resolution, overcoming autoregressive error accumulation.
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. On a simulated geophysical flow, we show that SDECast recovers meaningful drift dynamics and faithfully reproduces the underlying continuous-time behavior. We then demonstrate its scalability to global weather forecasting at hourly resolution, where SDECast produces skillful probabilistic forecasts for lead times of up to five days.
