Abstract
We are developing a wildland fire model based on semi-empirical relations that estimate the rate of spread of a surface fire and post-frontal heat release, coupled with WRF, the Weather Research and Forecasting atmospheric model. A level set method identifies the fire front. Data are assimilated using both amplitude and position corrections using a morphing ensemble Kalman filter. We will use thermal images of a fire for observations that will be compared to synthetic image based on the model state.
Chapter PDF
Similar content being viewed by others
Keywords
References
Mandel, J., Beezley, J.D., Bennethum, L.S., Chakraborty, S., Coen, J.L., Douglas, C.C., Hatcher, J., Kim, M., Vodacek, A.: A dynamic data driven wildland fire model. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4487, pp. 1042–1049. Springer, Heidelberg (2007)
Johns, C.J., Mandel, J.: A two-stage ensemble Kalman filter for smooth data assimilation. Environ. Ecological Stat. 15, 101–110 (2008)
Mandel, J., Bennethum, L.S., Beezley, J.D., Coen, J.L., Douglas, C.C., Franca, L.P., Kim, M., Vodacek, A.: A wildfire model with data assimilation, arXiv:0709.0086 (2006)
Mallet, V., Keyes, D.E., Fendell, F.E.: Modeling wildland fire propagation with level set methodsm arXiv:0710.2694 (2007)
Beezley, J.D., Mandel, J.: Morphing ensemble Kalman filters. Tellus 60A, 131–140 (2008)
Ravela, S., Emanuel, K.A., McLaughlin, D.: Data assimilation by field alignment. Physica D 230, 127–145 (2007)
Mandel, J., Beezley, J.D., Coen, J.L., Kim, M.: Data assimilation for wildland fires: Ensemble Kalman filters in coupled atmosphere-surface models, arXiv:0712.3965 (2007)
Clark, T.L., Coen, J., Latham, D.: Description of a coupled atmosphere-fire model. Int. J. Wildland Fire 13, 49–64 (2004)
Osher, S., Fedkiw, R.: Level set methods and dynamic implicit surfaces. Springer, New York (2003)
Coen, J.L.: Simulation of the Big Elk Fire using coupled atmosphere-fire modeling. Int. J. Wildland Fire 14, 49–59 (2005)
Rothermel, R.C.: A mathematical model for predicting fire spread in wildland fires. USDA Forest Service Research Paper INT-115 (1972)
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics 53, 343–367 (2003)
Chakraborty, S.: Data assimilation and visualization for ensemble wildland fire models. Master’s thesis, University of Kentucky, Department of Computer Science, Lexington, KY (2008)
Wang, Z.: Modeling Wildland Fire Radiance in Synthetic Remote Sensing Scenes. PhD thesis, Rochester Institute of Technology, Center for Imaging Science (2008)
Kremens, R., Faulring, J., Hardy, C.C.: Measurement of the time-temperature and emissivity history of the burn scar for remote sensing applications. In: Paper J1G.5, Proceedings of the 2nd Fire Ecology Congress, Orlando FL, American Meteorological Society (2003)
Digital Imaging and Remote Sensing Laboratory: DIRSIG users manual. Rochester Institute of Technology (2006), http://www.dirsig.org/docs/manual-2006-11.pdf
Schott, J., Brown, S.D., Raqueño, R.V., Gross, H.N., Robinson, G.: An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development. Canadian J. Remote Sensing 25, 99–111 (1999)
Wooster, M.J., Zhukov, B., Oertel, D.: Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products. Remote Sensing Environ. 86, 83–107 (2003)
Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, Cambridge (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Beezley, J.D. et al. (2008). Real-Time Data Driven Wildland Fire Modeling. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2008. ICCS 2008. Lecture Notes in Computer Science, vol 5103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69389-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-69389-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69388-8
Online ISBN: 978-3-540-69389-5
eBook Packages: Computer ScienceComputer Science (R0)