Diffusion Distillation for Efficient Weather Ensembles
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
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)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both 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.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning
Sep 10
-
When Do Options Help? Policy Necrosis and Redundant Coverage in Option-Critic
Sep 10
-
Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball
Sep 10
-
Capsule Lens: Locating and Tracking Concept Geometry in Model Representations
Sep 10
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.