Real-Time Data Driven Wildland Fire Modeling

Jonathan D. Beezley1,2, Soham Chakraborty3, Janice L. Coen2, Craig C. Douglas3,5, Jan Mandel1,2, Anthony Vodacek4, and Zhen Wang4

1University of Colorado Denver, Denver, CO 80217-3364, USA
jon.beezley.math@gmail.com
jan.mandel@gmail.com

2National Center for Atmospheric Research, Boulder, CO 80307-3000, USA
janicec@ucar.edu

3University of Kentucky, Lexington, KY 40506-0045, USA
sohaminator@gmail.com
craig.c.douglas@gmail.com

4Rochester Institute of Technology, Rochester, NY 14623-5603, USA
vodacek@cis.rit.edu
zxw7546@cis.rit.edu

5Yale University, New Haven, CT 06520-8285, USA

Abstract. We are developing a wildland fire model based on semi-empirical relations that estimate the rate of spread of a surface fire and post-frontal heat release, coupled with WRF, the Weather Research and Forecasting atmospheric model. A level set method identifies the fire front. Data are assimilated using both amplitude and position corrections using a morphing ensemble Kalman filter. We will use thermal images of a fire for observations that will be compared to synthetic image based on the model state.

Keywords: Dynamic data driven application systems, data assimilation, wildland fire modeling, remote sensing, ensemble Kalman filter, image registration, morphing, level set methods, Weather Research and Forecasting model, WRF