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
Yale University, Department of Computer Science, P.O. Box 208285, New Haven,
CT 06520-8285, USA
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO
Texas A&M University, Institute for Scientific Computation, 612 Blocker,
3404 TAMU, College Station, TX, 77843-3404, USA
Rochester Institute of Technology, Center for Imaging Science, Rochester, NY
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.