Demonstrating the validity of a wildfire DDDAS

  • Authors:

  • Craig C. Douglas;Jonathan D. Beezley;Janice Coen;Deng Li;Wei Li;Alan K. Mandel;Jan Mandel;Guan Qin;Anthony Vodacek

  • Affiliations:

  • Department of Computer Science, University of Kentucky, Lexington, KY;Department of Mathematical Sciences, University of Colorado at Denver and Health Sciences Center, Denver, CO;National Center for Atmospheric Research, Boulder, CO;Department of Computer Science, University of Kentucky, Lexington, KY;Department of Computer Science, University of Kentucky, Lexington, KY;Department of Computer Science, University of Kentucky, Lexington, KY;Department of Mathematical Sciences, University of Colorado at Denver and Health Sciences Center, Denver, CO;Institute for Scientific Computation, Texas A&M University, College Station, TX;Center for Imaging Science, Rochester Institute of Technology, Rochester, NY

  • Venue:
  • ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
  • Year:
  • 2006

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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 weather and wildfire behavior from real-time weather data, images, and sensor streams. The system changes the forecast as new data is received. We encapsulate the model code and apply an ensemble Kalman filter in time-space with a highly parallel implementation. In this paper, we discuss how we will demonstrate that our system works using a DDDAS testbed approach and data collected from an earlier fire.