{"slug": "diffusion-distillation-for-efficient-weather-ensembles", "title": "Diffusion Distillation for Efficient Weather Ensembles", "summary": "Researchers introduced a supervised energy-distance distillation method that compresses a multi-step diffusion teacher into a single-step student for weather ensemble forecasting, matching or surpassing the teacher's skill across key metrics using only one neural function evaluation per autoregressive step. The method outperformed existing distillation approaches on global forecasting and typhoon-track prediction while preserving skill for extreme events, according to a paper submitted to arXiv on 27 Aug 2026.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 27 Aug 2026]\n\n# Title:Diffusion Distillation for Efficient Weather Ensembles\n\n[View PDF](/pdf/2608.27728)\n\n[HTML (experimental)](https://arxiv.org/html/2608.27728v1)\n\nAbstract: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.\n\n### Current browse context:\n\ncs.LG\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/diffusion-distillation-for-efficient-weather-ensembles", "canonical_source": "https://arxiv.org/abs/2608.27728", "published_at": "2026-08-31 04:00:00+00:00", "updated_at": "2026-08-31 04:23:35.847176+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/diffusion-distillation-for-efficient-weather-ensembles", "markdown": "https://wpnews.pro/news/diffusion-distillation-for-efficient-weather-ensembles.md", "text": "https://wpnews.pro/news/diffusion-distillation-for-efficient-weather-ensembles.txt", "jsonld": "https://wpnews.pro/news/diffusion-distillation-for-efficient-weather-ensembles.jsonld"}}