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[2510.26376] Efficient Generative AI Boosts Probabilistic Forecasting of Sudden Stratospheric Warmings
Summary
This article presents a novel generative AI model, FM-Cast, which enhances the probabilistic forecasting of Sudden Stratospheric Warmings (SSWs), crucial for predicting extreme winter weather.
Why It Matters
The ability to accurately forecast SSWs is vital for improving weather predictions and mitigating the impacts of extreme weather events. This research bridges the gap between data-driven approaches and the complex dynamics of atmospheric phenomena, offering a more efficient forecasting method.
Key Takeaways
- FM-Cast forecasts SSW events with skill comparable to leading NWP systems.
- The model operates efficiently, generating a 30-day forecast in just two minutes on a consumer GPU.
- Distinct predictability regimes were identified, enhancing understanding of atmospheric dynamics.
Computer Science > Machine Learning arXiv:2510.26376 (cs) [Submitted on 30 Oct 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Efficient Generative AI Boosts Probabilistic Forecasting of Sudden Stratospheric Warmings Authors:Ningning Tao, Fei Xie, Baoxiang Pan, Hongyu Wang, Han Huang, Zhongpu Qiu, Ke Gui, Jiali Luo, Xiaosong Chen View a PDF of the paper titled Efficient Generative AI Boosts Probabilistic Forecasting of Sudden Stratospheric Warmings, by Ningning Tao and 7 other authors View PDF Abstract:Sudden Stratospheric Warmings (SSWs) are key sources of subseasonal predictability and major drivers of extreme weather in winter. Accurate and efficient probabilistic forecasting of these events remains a persistent challenge for Numerical Weather Prediction (NWP) systems due to computational bottlenecks and limitations in physical representation. While data-driven forecasting is rapidly evolving, its application to the complex, three-dimensional dynamics of SSWs remains underexplored. Here, we bridge this gap by developing a Flow Matching-based generative AI model (FM-Cast) for efficient and skillful probabilistic forecasting of the spatiotemporal evolution of stratospheric circulation in winter. Evaluated across 18 major SSW events (1998-2024), FM-Cast successfully forecasts the onset, intensity, and 3D morphology of the polar vortex up to 15 days in advance for most cases. Notably, it achieves long-range probabilistic forecast skill comparable to or exceedi...
