Swift: an autoregressive consistency model for efficient weather forecasting

Diffusion models offer a physically grounded framework for probabilistic weather forecasting, but their typical reliance on slow, iterative solvers makes inference expensive, especially for forecasting at long-lead times with a high temporal resolution. To address this, we introduce , a single-step...

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Bibliographic Details

Main Authors:	Jason Stock, Troy Arcomano, Rao Kotamarthi
Format:	Artigo
Language:	Inglês
Published:	IOP Publishing 2026-01-01
Series:	Machine Learning. Earth
Subjects:	diffusion consistency models long-lead stability global weather forecasting
Online Access:	https://doi.org/10.1088/3049-4753/ae802a
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