Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.

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Sequential Monte Carlo (SMC) methods have shown their effectiveness in data assimilation for wildfire simulation; however, when errors of wildfire simulation models are extremely large or rare events happen, the current SMC methods have limited impacts on improving the simulation results. The major problem lies in the proposal distribution that is commonly chosen as the system transition prior in order to avoid difficulties in importance weight updating. In this article, we propose a more effective proposal distribution by taking advantage of information contained in sensor data , and also present a method to solve the problem in weight updating. Experimental results demonstrate that a SMC method with this proposal distribution significantly improves wildfire simulation results when the one with a system transition prior proposal fails.

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