Towards a dynamic data driven application system for wildfire simulation

Jan Mandel,Lynn S. Bennethum,Mingshi Chen,Janice L. Coen,Craig C. Douglas,Leopoldo P. Franca,Craig J. Johns,Minjeong Kim,Andrew V. Knyazev,Robert Kremens,Vaibhav Kulkarni,Guan Qin,Anthony Vodacek,Jianjia Wu,Wei Zhao,Adam Zornes

International Conference on Computational Science (2)（2005）

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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.

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Key words

dynamic data driven application,wildfire simulation,dynamic data,short-range forecast,real-time weather data,out-of-sequence data assimilation,model code,application system,nonlinear problem,basic approach,hidden model state,new data,ensemble kalman filter,real time,data assimilation

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