Data assimilation for wildland fires

2007/12/31 by Jan Mandel, Jonathan Beezley, Janice L. Coen +1 · 6 citations
Environmental Science · #Atmospheric and Environmental Gas Dynamics #Fire effects on ecosystems #Remote Sensing and LiDAR Applications

paper · doi:10.1109/mcs.2009.932224

openalex publication_date 2009/05/19 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/30

Abstract

Two wildland fire models and methods for assimilating data in those models are presented. The EnKF is implemented ina distributed-memory high-performance computing environment. Data assimilation methods are developed combining EnKF with Tikhonov regularization to avoid nonphysical states and with the ideas of registration and morphing from image processing to allow large position corrections. The data assimilation methods can track the data even in the presence of large corrections, while avoiding divergence. The methods can assimilate gridded data, but the assimilation of station data and steering of data acquisition is left to future developments. A semi-empirical fire spread model is implemented by the level-set method and coupled with the WRF model.

Citations

• Modeling wildland fire propagation with level set methods
• Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
• Level Set Methods and Dynamic Implicit Surfaces
• Description of a coupled atmosphere–fire model

Cited by

• Ignition from a Fire Perimeter in a WRF Wildland Fire Model
• WRF-Fire: Coupled Weather–Wildland Fire Modeling with the Weather Research and Forecasting Model
• Coupled atmosphere-wildland fire modeling with WRF-Fire
• WRF fire simulation coupled with a fuel moisture model and smoke transport by WRF-Chem
• Data assimilation of satellite fire detection in coupled atmosphere-fire simulation by wrf-sfire
• WRF-SFIRE [wikipedia]

Related

• Fire-EnSF: Wildfire Spread Data Assimilation using Ensemble Score Filter
• Assimilation of Satellite Active Fires Data
• Towards Data Assimilation in Level-Set Wildfire Models Using Bayesian Filtering
• Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
• Generative Algorithms for Wildfire Progression Reconstruction from Multi-Modal Satellite Active Fire Measurements and Terrain Height