Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 23, issue 5
Articles | Volume 23, issue 5
https://doi.org/10.5194/nhess-23-1755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-1755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 5
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

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Cited articles

acse-cz421: DL-WG/Digital-twin-LA-global-wildfire: Reduced-order digital twin and latent data assimilation for global wildfire prediction (v1.1.1), Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.7866704, 2023. 
Amendola, M., Arcucci, R., Mottet, L., Casas, Q. C., Fan, S., Pain, C., Linden, P., and Guo, Y.: Data Assimilation in the Latent Space of a Convolutional Autoencoder, ICCS 2021, Lect. Notes Comput. Sc., 12746, 373–386, https://doi.org/10.1007/978-3-030-77977-1_30, 2021. 
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the green transition, Nat. Clim. Change, 11, 80–83, https://doi.org/10.1038/s41558-021-00986-y, 2021. 
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. 
Bianchi, F. M., De Santis, E., Rizzi, A., and Sadeghian, A.: Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition, IEEE, 3, 1931–1943, https://doi.org/10.1109/ACCESS.2015.2485943, 2015. 
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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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