Diffusion Distillation for Efficient Weather Ensembles
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Computer Science > Machine Learning
Title:Diffusion Distillation for Efficient Weather Ensembles
Abstract:Diffusion models generate skillful weather ensembles but require costly iterative sampling. We introduce a supervised energy-distance distillation method that compresses a multi-step diffusion teacher into a single-step student by aligning student forecasts with teacher samples and ground-truth observations. Experiments on global forecasting and typhoon-track prediction show that our student outperforms existing distillation methods and preserves skill for extreme events. It matches or surpasses the teacher across key metrics using only one neural function evaluation per autoregressive step.
| Subjects: | Machine Learning (cs.LG); Applications (stat.AP) |
| Cite as: | arXiv:2608.27728 [cs.LG] |
| (or arXiv:2608.27728v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.27728
arXiv-issued DOI via DataCite (pending registration)
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