Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911185479139328 |
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| author | Stock, Jason Arcomano, Troy Kotamarthi, Rao |
| author_facet | Stock, Jason Arcomano, Troy Kotamarthi, Rao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_25631 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting Stock, Jason Arcomano, Troy Kotamarthi, Rao Machine Learning 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. |
| title | Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.25631 |