Efficient Generative AI Boosts Probabilistic Forecasting of Sudden Stratospheric Warmings
This work addresses a persistent problem in numerical weather prediction for meteorologists and climate scientists by providing a computationally efficient method for probabilistic forecasting of stratospheric anomalies, though it is incremental as it builds on existing generative AI techniques.
The paper tackles the challenge of accurately and efficiently forecasting Sudden Stratospheric Warmings (SSWs) by developing a Flow Matching-based generative AI model (FM-Cast) for probabilistic forecasting, which skillfully forecasts 10 out of 18 major SSW events up to 20 days in advance with ensemble accuracies above 50% and requires only two minutes for a 50-member, 30-day forecast on a consumer GPU.
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.