We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space. Several variants of the ensemble Kalman filter are presented, for out-of-sequence data assimilation, hidden model states, and highly nonlinear problems. Parallel implementation and web-based visualization are also discussed.

Publication Date



"Towards a dynamic driven data application system for wildfire simulation," Proceedings of the 2005 International Conference on Computational Science - ICCS 2005. Held at Emory University: Atlanta, Georgia: 22-25 May 2005. This research has been supported by the National Science Foundation under grants ACI-0325314, 0324989, 0324988, 0324876, and 0324910. ISBN:978-3540-2603-25Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type


Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus