Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts

Bryan Shaddy aDepartment of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California

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Deep Ray aDepartment of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California
bDepartment of Mathematics, University of Maryland, College Park, Maryland

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Angel Farguell cDepartment of Meteorology and Climate Science, San Jose State University, San Jose, California

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Valentina Calaza aDepartment of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California

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Jan Mandel dDepartment of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, Colorado

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James Haley eCooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Kyle Hilburn eCooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Derek V. Mallia fDepartment of Atmospheric Sciences, University of Utah, Salt Lake City, Utah

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Adam Kochanski cDepartment of Meteorology and Climate Science, San Jose State University, San Jose, California

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Assad Oberai aDepartment of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California

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Abstract

Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements towards improving fire spread forecasts from numerical models through data assimilation. This work develops a physics-informed approach for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere-wildfire models from a measured wildfire state. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein Generative Adversarial Network (cWGAN), trained with WRF-SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sorensen’s coefficient of 0.81 for the fire perimeters and an average ignition time difference of 32 minutes suggest that the method is highly accurate.

© 2024 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bryan Shaddy, bshaddy@usc.edu

Abstract

Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements towards improving fire spread forecasts from numerical models through data assimilation. This work develops a physics-informed approach for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere-wildfire models from a measured wildfire state. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein Generative Adversarial Network (cWGAN), trained with WRF-SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sorensen’s coefficient of 0.81 for the fire perimeters and an average ignition time difference of 32 minutes suggest that the method is highly accurate.

© 2024 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bryan Shaddy, bshaddy@usc.edu
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