Towards a Dynamic Data Driven Application System for Wildfire Simulation
Jan Mandel1, Lynn S. Bennethum1, Mingshi Chen1, Janice L. Coen2, Craig C. Douglas3, Leopoldo P. Franca1, Craig J. Johns1, Minjeong Kim1, Andrew V. Knyazev1, Robert Kremens4, Vaibhav Kulkarni1, Guan Qin5, Anthony Vodacek4, Jianjia Wu5, Wei Zhao5, and Adam Zornes3
1University of Colorado Denver, Denver, CO 80217-3364, USA
2National Center for Atmospheric Research, Boulder, CO 80307-3000, USA
3University of Kentucky, Lexington, KY 40506-0045, USA
4Rochester Institute of Technology, Rochester, NY 14623-5603, USA
5Texas A&M University, College Station, TX 77843-1112, USA
Abstract. We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space. Several variants of the ensemble Kalman filter are presented, for out-of-sequence data assimilation, hidden model states, and highly nonlinear problems. Parallel implementation and web-based visualization are also discussed.
LNCS 3515, pp. 632-639.