Articles | Volume 14, issue 11
https://doi.org/10.5194/nhess-14-2951-2014
https://doi.org/10.5194/nhess-14-2951-2014
Research article
 | 
10 Nov 2014
Research article |  | 10 Nov 2014

Towards predictive data-driven simulations of wildfire spread – Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

M. C. Rochoux, S. Ricci, D. Lucor, B. Cuenot, and A. Trouvé

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Cited articles

Beezley, J. D., and Mandel, J.: Morphing Ensemble Kalman filters, Tellus A, 60, 131–140, https://doi.org/10.1111/j.1600-0870.2007.00275.x, 2008.
Birolleau, A., Poëtte, G., and Lucor, D.: Adaptive Bayesian inference for discontinuous inverse problems, application to hyperbolic conservation laws, Commun. Comput. Phys., 16, 1–34, 2014.
Blanchard, E. D., Sandu, A., and Sandu, C.: A polynomial chaos-based Kalman filter approach for parameter estimation of mechanical systems, J. Dyn. Sys., Meas., Control, 132, 061404, https://doi.org/10.1115/1.4002481, 2010.
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Bouttier, F. and Courtier, P.: Data Assimilation Concepts and Methods, ECMWF, Meteorological Training Course Lecture Series, March 1999.
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Short summary
This paper presents a data-driven wildfire simulator for forecasting wildfire spread scenarios at a reduced computational cost that is consistent with operational systems. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is indeed a promising approach to reduce uncertainties in the forecast location of the fire front and to introduce a paradigm shift in the wildfire emergency response.
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