State-parameter estimation approach for data-driven wildland fire spread modeling: Application to the 2012 RxCADRE S5 field-scale experiment

Abstract

Data assimilation is an emerging and powerful tool towards real-time flame front monitoring for wildland fire applications. The key idea is to regularly update the state and/or parameters of a fire spread model using observed firelines in order to improve a forecast on future fire locations. The merits of combining state estimation and parameter estimation through a hybrid state-parameter estimation algorithm are demonstrated through the 2012 RxCADRE S5 field-scale controlled burn experiment. For state estimation, we adopt a cost-effective Luenberger observer formulation to reconstruct a complete view of the burning state at a given time. For parameter estimation, we use an ensemble transform Kalman filter to solve the inverse modeling problem consisting of inferring more realistic wind conditions given observations of the actual burning state. The data-driven model relies on a front shape similarity measure derived from image segmentation theory to quantify position errors. We show that the hybrid approach provides an efficient framework to address all sources of model uncertainties and to select burning scenarios that are most likely to occur. Parameter estimation is a key component of the data-driven model by reducing model bias. Using the fire spread model in forecast mode is then an asset to accurately track the flame front dynamics at future lead times.