2006 | OriginalPaper | Buchkapitel
Demonstrating the Validity of a Wildfire DDDAS
verfasst von : Craig C. Douglas, Jonathan D. Beezley, Janice Coen, Deng Li, Wei Li, Alan K. Mandel, Jan Mandel, Guan Qin, Anthony Vodacek
Erschienen in: Computational Science – ICCS 2006
Verlag: Springer Berlin Heidelberg
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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.