Regional-scale simulations of wildland fire spread informed by real-time flame front observations

This study presents an analysis of the potential benefits of data assimilation techniques for combustion applications through the example of wildland fire propagation. Data assimilation is an efficient strategy, initially developed for meteorological applications, that integrates sensor observations with computational models, accounts for observation and modeling errors, and thereby provides optimal estimates of poorly known variables and improved predictions of complex systems dynamics. In the present study, the effective wildfire rate of spread is calibrated using: measurements of the time-evolving fire front position; a level-set-based fire propagation solver with a description of the local rate of spread based on Rothermel’s model; and a simplified Kalman Filter algorithm. The prototype data-driven wildfire simulation capability is first evaluated in academic tests using synthetically-generated observations; the prototype capability is then evaluated in a more realistic validation test corresponding to a controlled grassland fire experiment. The results indicate that data assimilation has the potential of dramatically increasing fire simulation accuracy.