Articles | Volume 19, issue 16
https://doi.org/10.5194/gmd-19-7835-2026
https://doi.org/10.5194/gmd-19-7835-2026
Development and technical paper
 | 
24 Aug 2026
Development and technical paper |  | 24 Aug 2026

Predicting forecast errors with diffusion model for uncertainty quantification in wind speed nowcasting

Yanwei Zhu, Aitor Atencia, Markus Dabernig, Yong Wang, and Shuyan Zhou

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Cited articles

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Andrae, M., Landelius, T., Oskarsson, J., and Lindsten, F.: Continuous Ensemble Weather Forecasting with Diffusion models, arXiv [preprint], https://doi.org/10.48550/arXiv.2410.05431, 2025. 
Asperti, A., Merizzi, F., Paparella, A., Pedrazzi, G., Angelinelli, M., and Colamonaco, S.: Precipitation nowcasting with generative diffusion models, Appl. Intell., 55, 187, https://doi.org/10.1007/s10489-024-06048-y, 2025. 
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Short summary
The study proposes a diffusion-based framework for uncertainty quantification in wind speed nowcasting by learning forecast error distributions. By randomly generating errors and adding them to a physics-based wind nowcast, multiple forecast scenarios can be produced. The results improve forecast accuracy and provide reliable estimates of forecast uncertainty.
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