Towards a Dynamic Data Driven Application System for wildfire simulation

Jan Mandel; Lynn S. Bennethum; Mingshi Chen; Janice L. Coen; Craig C. Douglas; Leopoldo P. Franca; Craig J. Johns; Minjeong Kim; Andrew V. Knyazev; Robert Kremens; Vaibhav Kulkarni; Guan Qin; Anthony Vodacek; Jianjia Wu; Wei Zhao; Adam Zornes

doi:10.1007/11428848_82

Towards a Dynamic Data Driven Application System for wildfire simulation

Jan Mandel, Lynn S. Bennethum, Mingshi Chen, Janice L. Coen, Craig C. Douglas, Leopoldo P. Franca, Craig J. Johns, Minjeong Kim, Andrew V. Knyazev, Robert Kremens, Vaibhav Kulkarni, Guan Qin, Anthony Vodacek, Jianjia Wu, Wei Zhao, Adam Zornes

Mesoscale & Microscale Meteorology Lab

Research output: Contribution to journal › Conference article › peer-review

34 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	632-639
Number of pages	8
Journal	Lecture Notes in Computer Science
Volume	3515
Issue number	II
DOIs	https://doi.org/10.1007/11428848_82
State	Published - 2005
Event	5th International Conference on Computational Science - ICCS 2005 - Atlanta, GA, United States Duration: May 22 2005 → May 25 2005

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Mandel, J., Bennethum, L. S., Chen, M., Coen, J. L., Douglas, C. C., Franca, L. P., Johns, C. J., Kim, M., Knyazev, A. V., Kremens, R., Kulkarni, V., Qin, G., Vodacek, A., Wu, J., Zhao, W., & Zornes, A. (2005). Towards a Dynamic Data Driven Application System for wildfire simulation. Lecture Notes in Computer Science, 3515(II), 632-639. https://doi.org/10.1007/11428848_82

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title = "Towards a Dynamic Data Driven Application System for wildfire simulation",

abstract = "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.",

author = "Jan Mandel and Bennethum, \{Lynn S.\} and Mingshi Chen and Coen, \{Janice L.\} and Douglas, \{Craig C.\} and Franca, \{Leopoldo P.\} and Johns, \{Craig J.\} and Minjeong Kim and Knyazev, \{Andrew V.\} and Robert Kremens and Vaibhav Kulkarni and Guan Qin and Anthony Vodacek and Jianjia Wu and Wei Zhao and Adam Zornes",

year = "2005",

doi = "10.1007/11428848\_82",

language = "English",

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pages = "632--639",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

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note = "5th International Conference on Computational Science - ICCS 2005 ; Conference date: 22-05-2005 Through 25-05-2005",

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AU - Mandel, Jan

AU - Bennethum, Lynn S.

AU - Chen, Mingshi

AU - Coen, Janice L.

AU - Douglas, Craig C.

AU - Franca, Leopoldo P.

AU - Johns, Craig J.

AU - Kim, Minjeong

AU - Knyazev, Andrew V.

AU - Kremens, Robert

AU - Kulkarni, Vaibhav

AU - Qin, Guan

AU - Vodacek, Anthony

AU - Wu, Jianjia

AU - Zhao, Wei

AU - Zornes, Adam

PY - 2005

Y1 - 2005

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AB - 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.

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DO - 10.1007/11428848_82

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AN - SCOPUS:25144444359

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IS - II

T2 - 5th International Conference on Computational Science - ICCS 2005

Y2 - 22 May 2005 through 25 May 2005

ER -

Towards a Dynamic Data Driven Application System for wildfire simulation

Abstract

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