Towards a Real-Time Data Driven Wildland Fire Model

TL;DR

This paper presents a real-time wildland fire model that integrates semi-empirical fire spread relations with atmospheric modeling, using data assimilation techniques to improve accuracy based on thermal imagery.

Contribution

It introduces a coupled fire-atmosphere model with a level set method and a morphing ensemble Kalman filter for real-time data assimilation.

Findings

Effective fire spread simulation using semi-empirical relations

Successful integration of atmospheric and fire models

Improved fire front prediction with data assimilation

Abstract

A wildland fire model based on semi-empirical relations for the spread rate of a surface fire and post-frontal heat release is coupled with the Weather Research and Forecasting atmospheric model (WRF). The propagation of the fire front is implemented by a level set method. Data is assimilated by a morphing ensemble Kalman filter, which provides amplitude as well as position corrections. Thermal images of a fire will provide the observations and will be compared to a synthetic image from the model state.