Recursive updates of wildfire perimeters using barrier points and ensemble kalman filtering

Abstract

This paper shows how the wildfire simulation tool farsite is augmented with data assimilation capabilities that exploit the notion of barrier points and a constraint-point ensemble Kalman filtering to update wildfire perimeter predictions. Based on observations of the actual fire perimeter, stationary points on the fire perimeter are identified as barrier points and combined with a recursive update of the initial fire perimeter. It is shown that the combination of barrier point identification and using the barrier points as constraints in the ensemble Kalman filter gives a significant improvement in the forward prediction of the fire perimeter. The results are illustrated on the use case of the 2016 Sandfire that burned in the Angeles National Forest, east of the Santa Clarita Valley in Los Angeles County, California.