Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models
  with Satellite Data for Initializing Wildfire Forecasts
Bryan Shaddy Deep Ray Angel Farguell Valentina Calaza Jan Mandel James Haley Kyle Hilburn Derek V. Mallia Adam Kochanski Assad Oberai
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 to improve fire spread forecasts
from numerical models through data assimilation. This work develops a method
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 in a physics-informed approach. 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 error
of 32 minutes suggest that the method is highly accurate.