Efficient Generative AI Boosts Probabilistic Forecasting of Sudden Stratospheric Warmings

• URL: http://arxiv.org/abs/2510.26376v1
• Date: Thu, 30 Oct 2025 11:16:22 GMT
• 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,
• Abstract summary: Sudden Stratospheric Warmings (SSWs) are key sources of subseasonal predictability and major drivers of extreme winter weather.<n>Here, we develop a Flow Matching-based generative AI model (FM-Cast) for efficient and skillful probabilistic forecasting.<n>FM-Cast skillfully forecasts the onset, intensity, and morphology of 10 events up to 20 days in advance, achieving ensemble accuracies above 50%.
• Score: 11.259108118025736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sudden Stratospheric Warmings (SSWs) are key sources of subseasonal predictability and major drivers of extreme winter weather. Yet, their accurate and efficient forecast remains a persistent challenge for numerical weather prediction (NWP) systems due to limitations in physical representation, initialization, and the immense computational demands of ensemble forecasts. While data-driven forecasting is rapidly evolving, its application to the complex, three-dimensional dynamics of SSWs, particularly for probabilistic forecast, 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. Evaluated across 18 major SSW events (1998-2024), FM-Cast skillfully forecasts the onset, intensity, and morphology of 10 events up to 20 days in advance, achieving ensemble accuracies above 50%. Its performance is comparable to or exceeds leading NWP systems while requiring only two minutes for a 50-member, 30-day forecast on a consumer GPU. Furthermore, leveraging FM-Cast as a scientific tool, we demonstrate through idealized experiments that SSW predictability is fundamentally linked to its underlying physical drivers, distinguishing between events forced from the troposphere and those driven by internal stratospheric dynamics. Our work thus establishes a computationally efficient paradigm for probabilistic forecasting stratospheric anomalies and showcases generative AI's potential to deepen the physical understanding of atmosphere-climate dynamics.

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