{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts","author":[{"@type":"Person","name":"Bryan Shaddy"},{"@type":"Person","name":"Deep Ray"},{"@type":"Person","name":"Angel Farguell"},{"@type":"Person","name":"Valentina Calaza"},{"@type":"Person","name":"Jan Mandel"},{"@type":"Person","name":"James Haley"},{"@type":"Person","name":"Kyle Hilburn"},{"@type":"Person","name":"Derek V. Mallia"},{"@type":"Person","name":"Adam Kochanski"},{"@type":"Person","name":"Assad Oberai"}],"datePublished":"2024","isPartOf":{"@type":"Periodical","name":"Artificial Intelligence for the Earth Systems"},"identifier":"https://doi.org/10.1175/aies-d-23-0087.1","description":"Abstract\nIncreases 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 toward 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 Sørensen’s coefficient of 0.81 for the fire perimeters and an average ignition time difference of 32 min suggest that the method is highly accurate.\n\nSignificance Statement\nTo initialize coupled atmosphere–wildfire simulations in a physically consistent way based on satellite measurements of active fire locations, it is critical to ensure the state of the fire and atmosphere aligns at the start of the forecast. If known, the history of a wildfire may be used to develop an atmospheric state matching the wildfire state determined from satellite data in a process known as spinup. In this paper, we present a novel method for inferring the early stage history of a wildfire based on satellite active fire measurements. Here, inference of the fire history is performed in a probabilistic sense and physics is further incorporated through the use of training data derived from a coupled atmosphere–wildfire model.","url":"https://innscience.ca/es/community/literature/6de8d854-88ac-4672-9d36-fa20a4f79b5c"}