Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 16
Articles | Volume 19, issue 16
https://doi.org/10.5194/gmd-19-7835-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-7835-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 16
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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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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