Articles | Volume 23, issue 5
https://doi.org/10.5194/nhess-23-1755-2023
https://doi.org/10.5194/nhess-23-1755-2023
Research article
 | 
12 May 2023
Research article |  | 12 May 2023

Reduced-order digital twin and latent data assimilation for global wildfire prediction

Caili Zhong, Sibo Cheng, Matthew Kasoar, and Rossella Arcucci

Data sets

DL-WG/Digital-twin-LA-global-wildfire: Reduced-order digital twin and latent data assimilation for global wildfire prediction (v1.1.1) acse-cz421 https://doi.org/10.5281/zenodo.7866704

Model code and software

DL-WG/Digital-twin-LA-global-wildfire: Reduced-order digital twin and latent data assimilation for global wildfire prediction (v1.1.1) acse-cz421 https://doi.org/10.5281/zenodo.7866704

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Short summary
This paper introduces a digital twin fire model using machine learning techniques to improve the efficiency of global wildfire predictions. The proposed model also manages to efficiently adjust the prediction results thanks to data assimilation techniques. The proposed digital twin runs 500 times faster than the current state-of-the-art physics-based model.
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