Swift：一種用於高效天氣預報的自回歸一致性模型

摘要

擴散模型為概率天氣預報提供了一個基於物理的框架，但其在推斷過程中通常依賴於緩慢的迭代求解器，這使得它們在次季節至季節（S2S）應用中顯得不太實用，因為這些應用需要長時間的預報領先期和基於領域的校準。為解決這一問題，我們引入了Swift，這是一種單步一致性模型，首次實現了以連續排名概率得分（CRPS）為目標的概率流模型的自回歸微調。這消除了對多模型集成或參數擾動的需求。結果顯示，Swift能夠生成具有技巧性的6小時預報，並在長達75天的時間內保持穩定，其運行速度比最先進的擴散基準模型快39倍，同時達到了與基於數值運算的業務化IFS ENS相媲美的預報技巧。這標誌著從中期到季節尺度上，向高效可靠的集成預報邁進了一步。

English

Diffusion models offer a physically grounded framework for probabilistic weather forecasting, but their typical reliance on slow, iterative solvers during inference makes them impractical for subseasonal-to-seasonal (S2S) applications where long lead-times and domain-driven calibration are essential. To address this, we introduce Swift, a single-step consistency model that, for the first time, enables autoregressive finetuning of a probability flow model with a continuous ranked probability score (CRPS) objective. This eliminates the need for multi-model ensembling or parameter perturbations. Results show that Swift produces skillful 6-hourly forecasts that remain stable for up to 75 days, running 39times faster than state-of-the-art diffusion baselines while achieving forecast skill competitive with the numerical-based, operational IFS ENS. This marks a step toward efficient and reliable ensemble forecasting from medium-range to seasonal-scales.

Swift：一種用於高效天氣預報的自回歸一致性模型

Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting

摘要

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