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Increases in wildfire activity and the resulting impacts have prompted thedevelopment of high-resolution wildfire behavior models for forecasting firespread. Recent progress in using satellites to detect fire locations furtherprovides the opportunity to use measurements to improve fire spread forecastsfrom numerical models through data assimilation. This work develops a methodfor inferring the history of a wildfire from satellite measurements, providingthe necessary information to initialize coupled atmosphere-wildfire models froma measured wildfire state in a physics-informed approach. The fire arrivaltime, which is the time the fire reaches a given spatial location, acts as asuccinct representation of the history of a wildfire. In this work, aconditional Wasserstein Generative Adversarial Network (cWGAN), trained withWRF-SFIRE simulations, is used to infer the fire arrival time from satelliteactive fire data. The cWGAN is used to produce samples of likely fire arrivaltimes from the conditional distribution of arrival times given satellite activefire detections. Samples produced by the cWGAN are further used to assess theuncertainty of predictions. The cWGAN is tested on four California wildfiresoccurring between 2020 and 2022, and predictions for fire extent are comparedagainst high resolution airborne infrared measurements. Further, the predictedignition times are compared with reported ignition times. An average Sorensen'scoefficient of 0.81 for the fire perimeters and an average ignition time errorof 32 minutes suggest that the method is highly accurate.

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