SDECast uses neural SDEs for continuous-time probabilistic weather forecasting, recovering drift dynamics in a simulated geophysical flow and producing skillful global hourly forecasts up to five days
The authors introduce SDECast, a Neural Stochastic Differential Equation (SDE) framework for continuous-time probabilistic weather forecasting that extends SDE Matching to learn stochastic dynamics directly in physical space without repeated SDE simulation during training; on a simulated geophysical flow, SDECast recovers meaningful drift dynamics and faithfully reproduces the underlying continuous-time behavior, and it then scales to global weather forecasting at hourly resolution, producing skillful probabilistic forecasts for lead times up to five days.