A Dynamic Data Driven Wildland Fire Model

Authors:
Jan Mandel;Jonathan D. Beezley;Lynn S. Bennethum;Soham Chakraborty;Janice L. Coen;Craig C. Douglas;Jay Hatcher;Minjeong Kim;Anthony Vodacek
Affiliations:
University of Colorado at Denver and Health Sciences Center, Denver, CO 80217-3364, USA and National Center for Atmospheric Research, Boulder, CO 80307-3000, USA;University of Colorado at Denver and Health Sciences Center, Denver, CO 80217-3364, USA and National Center for Atmospheric Research, Boulder, CO 80307-3000, USA;University of Colorado at Denver and Health Sciences Center, Denver, CO 80217-3364, USA;University of Kentucky, Lexington, KY 40506-0045, USA;National Center for Atmospheric Research, Boulder, CO 80307-3000, USA;University of Kentucky, Lexington, KY 40506-0045, USA and Yale University, New Haven, CT 06520-8285, USA;University of Kentucky, Lexington, KY 40506-0045, USA;University of Colorado at Denver and Health Sciences Center, Denver, CO 80217-3364, USA;Rochester Institute of Technology, Rochester, NY 14623-5603, USA
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Year:
2007

Citing 0
Cited 7

Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology

Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
A Data Management Framework for Urgent Geoscience Workflows

ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction

ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Real-Time Data Driven Wildland Fire Modeling

ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Computational Steering Strategy to Calibrate Input Variables in a Dynamic Data Driven Genetic Algorithm for Forest Fire Spread Prediction

ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Demonstrating the validity of a wildfire DDDAS

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Half-duplex dynamic data driven application system for forest fire spread prediction

HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an overview of an ongoing project to build DDDAS to use all available data for a short term wildfire prediction. The project involves new data assimilation methods to inject data into a running simulation, a physics based model coupled with weather prediction, on-site data acquisition using sensors that can survive a passing fire, and on-line visualization using Google Earth.