Demonstrating the Validity of a Wildfire DDDAS

Craig C. Douglas1,2, Jonathan D. Beezley4, Janice Coen3, Deng Li1, Wei Li1, Alan K. Mandel1, Jan Mandel4, Guan&nbs p;Qin5, and Anthony Vodacek6

1 University of Kentucky, Department of Computer Science, 773 Anderson Hall, Lexington, KY 40506-0046, USA
deng.li@uky.edu
wli4@uky.edu

2 Yale University, Department of Computer Science, P.O. Box 208285, New Haven, CT 06520-8285, USA
douglas-craig@cs.yale.edu

3 National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA
janicec@ucar.edu

4 University of Colorado at Denver and Health Sciences Center, Department of Mathematical Sciences, P.O. Box 173364, Denver, CO 80217-3364, USA
jbeezley@math.cudenver.edu
jmandel@math.cudenver.edu

5 Texas A&M University, Institute for Scientific Computation, 612 Blocker, 3404 TAMU, College Station, TX, 77843-3404, USA
guan.qin@tamu.edu

6 Rochester Institute of Technology, Center for Imaging Science, Rochester, NY 14623 USA
vodacek@cis.rit.edu

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.

LNCS 3993, pp. 522-529.

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