Swift：一种用于高效天气预报的自回归一致性模型

Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting

September 30, 2025

作者: Jason Stock, Troy Arcomano, Rao Kotamarthi

cs.AI

摘要

扩散模型为概率天气预报提供了一个基于物理的框架，但其在推理过程中通常依赖缓慢的迭代求解器，这使得它们在次季节至季节（S2S）预测中显得不切实际，因为这类应用需要较长的预报提前期和基于领域的校准。为解决这一问题，我们引入了Swift，一种单步一致性模型，首次实现了以连续排名概率评分（CRPS）为目标的自回归微调概率流模型。这一创新消除了对多模型集成或参数扰动的需求。实验结果表明，Swift能够生成具有技巧的6小时预报，预报稳定性可维持长达75天，运行速度比最先进的扩散基线快39倍，同时其预报技巧与基于数值的运营IFS ENS系统相当。这标志着从中期到季节尺度上，我们向高效可靠的集合预报迈出了重要一步。

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.