Srivas, T., Artés, T., de Callafon, R., Altintas, I., Wildfire Spread Prediction and Assimilation for FARSITE Using Ensemble Kalman Filtering, In the Data-Driven Computational Sciences Workshop at the 16th International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.328
Abstract
This paper extends FARSITE (a software used for wildfire modeling and simulation) to incorporate data assimilation techniques based on noisy and limited spatial resolution observations of the fire perimeter to improve the accuracy of wildfire spread predictions. To include data assimilation in FARSITE, uncertainty on both the simulated fire perimeter and the measured fire perimeter is used to formulate optimal updates for the prediction of the spread of the wild- fire. For data assimilation, fire perimeter measurements with limited spatial resolution and a known uncertainty are used to formulate an optimal adjustment in the fire perimeter prediction. The adjustment is calculated from the Kalman filter gain in an Ensemble Kalman filter that exploits the uncertainty information on both the simulated fire perimeter and the measured fire perimeter. The approach is illustrated on a wildfire simulation representing the 2014 Cocos fire and presents comparison results for hourly data assimilation results.
