Data Assimilation for Wildland Fires: Ensemble Kalman filters in coupled atmosphere-surface models

TL;DR

This paper explores data assimilation techniques, specifically ensemble Kalman filters, applied to coupled atmosphere-surface models for wildland fire prediction, integrating reaction-diffusion and level set methods with atmospheric models.

Contribution

It introduces the application of ensemble Kalman filters to coupled fire-atmosphere models, combining reaction-diffusion and level set methods with WRF for improved fire behavior prediction.

Findings

Successful coupling of fire models with WRF for data assimilation

Implementation of regularized and morphing ensemble Kalman filters

Enhanced fire spread prediction accuracy through data assimilation

Abstract

Two wildland fire models are described, one based on reaction-diffusion-convection partial differential equations, and one based on semi-empirical fire spread by the level let method. The level set method model is coupled with the Weather Research and Forecasting (WRF) atmospheric model. The regularized and the morphing ensemble Kalman filter are used for data assimilation.