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Diffusion Distillation for Efficient Weather Ensembles

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Computer Science > Machine Learning

arXiv:2608.27728 (cs)
[Submitted on 27 Aug 2026]

Title:Diffusion Distillation for Efficient Weather Ensembles

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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)

Submission history

From: Yiming Yang [view email]
[v1] Thu, 27 Aug 2026 21:39:50 UTC (14,185 KB)
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