Experimental and numerical study on data-driven prediction for wildfire spread incorporating adaptive observation error adjustment

In recent wildfire prediction research, data assimilation (DA) methods like Ensemble Kalman filtering have gained traction for integrating observation data to enhance prediction accuracy. Most previous studies trusted that the observation data were accurate, and set a small observation error, which causes unreliable predicted results for scenarios with large observation error. To tackle this, our study introduced a method that iteratively adjusted the potential range of observation errors by comparing observation and simulation data over time. We conducted a 30-m experiment and kilometer-scale numerical simulations. Unlike prior research, we adopted larger error ranges (the similarity index with true data ranges from 0.6 to 1) for both real and synthetic observation data. In the experiment, to increase the complexity of fire spread, a heterogeneous fuel arrangement was employed. Irregular flame fronts appeared due to incomplete combustion and were difficult to replicate in simulations. Better accuracy was achieved using real observation data to revise predictions. Furthermore, to improve the applicability of the algorithm, numerical simulations were designed to consider observation error changing over time or not. The Root Mean Square Errors for the fire front prediction using the proposed method remained lower than that of the traditional DA approach.