A wildland fire model with data assimilation

TL;DR

This paper presents a wildfire model based on energy and fuel balance equations, combined with an ensemble Kalman filter for data assimilation, enabling accurate wildfire simulation even with initial location errors.

Contribution

It introduces a coupled PDE wildfire model with a data assimilation method using ensemble Kalman filter to improve simulation accuracy.

Findings

The assimilation technique accurately tracks wildfire temperature measurements.

The model corrects initial location errors effectively.

Ensemble Kalman filter enhances wildfire simulation reliability.

Abstract

A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients that can be approximated from prior measurements of wildfires. An ensemble Kalman filter technique with regularization is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one.