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. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Douglas, C. C., Beezley, J. D., Coen, J., Li, D., Li, W., Mandel, A. K., … Vodacek, A. (2006). Demonstrating the validity of a wildfire DDDAS. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3993 LNCS-III, pp. 522–529). Springer Verlag. https://doi.org/10.1007/11758532_69
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