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arXiv Machine Learning By Maria Marchenko, Martin Andrae, Fredrik Lindsten, Christian A. Naesseth

SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs

Read the original on arXiv Machine Learning →

SDECast is a Neural Stochastic Differential Equation framework designed for continuous‑time probabilistic weather forecasting. Unlike traditional autoregressive models that operate at fixed temporal resolutions and can accumulate errors at short time steps, SDECast learns stochastic dynamics directly in physical space without repeated SDE simulation during training. Experiments on a simulated geophysical flow demonstrate that SDECast captures meaningful drift dynamics and reproduces continuous‑time behavior, while large‑scale tests show skillful probabilistic forecasts up to five days ahead at hourly resolution.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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