Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting

• URL: http://arxiv.org/abs/2509.25631v1
• Date: Tue, 30 Sep 2025 00:54:24 GMT
• Title: Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting
• Abstract summary: We introduce Swift, a single-step consistency model that enables autoregressive finetuning of a probability flow model.<n>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.
• Score: 0.8503925197267593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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