Towards a dynamic data driven application system for wildfire simulation

  • Authors:

  • 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

  • Affiliations:

  • University of Colorado Denver, Denver, CO;University of Colorado Denver, Denver, CO;University of Colorado Denver, Denver, CO;National Center for Atmospheric Research, Boulder, CO;University of Kentucky, Lexington, KY;University of Colorado Denver, Denver, CO;University of Colorado Denver, Denver, CO;University of Colorado Denver, Denver, CO;University of Colorado Denver, Denver, CO;Rochester Institute of Technology, Rochester, NY;University of Colorado Denver, Denver, CO;Texas A&M University, College Station, TX;Rochester Institute of Technology, Rochester, NY;Texas A&M University, College Station, TX;Texas A&M University, College Station, TX;University of Kentucky, Lexington, KY

  • Venue:
  • ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
  • Year:
  • 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.