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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1167', Anonymous Referee #1, 11 Nov 2022
    • AC2: 'Reply on RC1', Sibo Cheng, 19 Nov 2022
  • RC2: 'Comment on egusphere-2022-1167', Anonymous Referee #2, 17 Nov 2022
    • AC1: 'Reply on RC2', Sibo Cheng, 19 Nov 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish as is (15 Mar 2023) by David Lallemant
ED: Publish as is (23 Mar 2023) by Philip Ward (Executive editor)
AR by Sibo Cheng on behalf of the Authors (29 Mar 2023)  Manuscript 
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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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