Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 2
Articles | Volume 19, issue 2
https://doi.org/10.5194/gmd-19-1027-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1027-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 2
Model description paper
 | 
30 Jan 2026
Model description paper |  | 30 Jan 2026

A probabilistic approach to wildfire spread prediction using a denoising diffusion surrogate model

Wenbo Yu, Anirbit Ghosh, Tobias Sebastian Finn, Rossella Arcucci, Marc Bocquet, and Sibo Cheng

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Short summary
We introduce the first denoising diffusion model for wildfire spread prediction, a new kind of generative AI model that learns to simulate fires not just as one fixed outcome, but as a range of possible scenarios. This allows us to capture the inherent uncertainty of wildfire dynamics. Our model produces ensembles of forecasts that reflect physically meaningful distributions of where fire might go next.
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