Swift让天气预报更快更准，单步生成75天预测
Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting
- 用连续概率得分优化，一步完成天气预报生成
- 6小时预报精度媲美主流数值模型，75天内稳定有效
- 速度比顶尖扩散模型快39倍，适合中长期预报
扩散模型为概率天气预报提供了物理基础，但推理时依赖缓慢的迭代求解器，难以应用于需要长提前期和领域校准的次季节至季节（S2S）预报。为此，我们提出Swift——首个支持概率流模型自回归微调的单步一致性模型，采用连续排名概率分数（CRPS）目标函数，无需多模型集成或参数扰动。结果表明，Swift能生成有效的6小时预报，持续稳定达75天，运行速度比当前最优扩散基线快39倍，预报技能与基于数值的运营型IFS ENS相当。这标志着向中短期到季节尺度高效可靠集合预报迈出了关键一步。
原文摘要 · Abstract (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 $39\times$ 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.
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