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arXiv Machine Learning By Jannik Wiese, Johannes Schusterbauer, Tommaso Martorella, Bj\"orn Ommer

Just Weather Scoring: Efficient End-to-end Nowcasting with Distributional Diffusion

Read the original on arXiv Machine Learning →

Just Weather Scoring (JWS) is a single‑stage, end‑to‑end diffusion model that forecasts precipitation directly in radar space, eliminating the need for separate compression or deterministic components. By combining Masked Asynchronous Diffusion with a scoring‑rule objective, JWS adapts diffusion training to high‑dimensional spatio‑temporal data and enables few‑step generation. On SEVIR and MeteoNet benchmarks, JWS achieves state‑of‑the‑art probabilistic forecasting while using fewer parameters and delivering over 17× faster inference than existing methods.

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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