UC San Diego

This thesis 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 wildfire perimeter and the measured wildfire perimeter is used to formulate optimal updates for the prediction of the spread of the wildfire. For data assimilation, Wildfire 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 wildfire perimeter and the measured wildfire perimeter. The approach is illustrated on a wildfire simulation representing the 2014 Cocos fire and presents comparison results for hourly data assimilation results. In later chapters we extend FARSITE with the ability to update both fire perimeters and fuel adjustment factors to further improve the accuracy of wildfire spread predictions. To show the effectiveness of fuel adjustment factor updates, a comparison is made using an EnKF with fixed adjustment factor on a wildfire simulation representing the 2014 Cocos fire. The performance of the EnKF technique for tracking time varying fuel adjustment factors based on noisy and limited spatial resolution observations of the fire perimeter is also investigated.