Articles | Volume 15, issue 8
Nat. Hazards Earth Syst. Sci., 15, 1721–1739, 2015

Special issue: Numerical wildland combustion, from the flame to the...

Nat. Hazards Earth Syst. Sci., 15, 1721–1739, 2015

Research article 04 Aug 2015

Research article | 04 Aug 2015

Towards predictive data-driven simulations of wildfire spread – Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread

M. C. Rochoux et al.

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Cited articles

Andrews, P. L., Cruz, M. G., and Rothermel, R. C.: Examination of the wind speed limit function in the Rothermel surface fire spread model, Int. J. Wildland Fire, 22, 959–969, 2013.
Balbi, J.-H., Morandini, F., Silvani, X., Filippi, J.-B., and Rinieri, F.: A physical model for wildland fires, Comb. Flame, 156, 2217–2230, 2009.
Buis, S., Piacentini, A., and Declat, D.: PALM: a computational framework for assembling high performance computing applications, Concurr. Comp.-Pract. E., 18, 247–262, 2006.
Burgan, R.: 1988 revisions to the 1978 national fire-danger rating system, Technical Report Research Paper SE-273, US Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, Asheville, NC, USA, 1988.
Burgers, G., van Leeuwen, P., and Evensen, G.: Analysis scheme in the ensemble kalman filter, Mon. Weather Rev., 126, 1719–1724, 1998.
Short summary
This paper, the second part in a series of two articles, aims at presenting a data-driven modeling strategy for forecasting wildfire spread scenarios based on the assimilation of the observed fire front location and on the sequential correction of model parameters or model state. The objective here is to sequentially update the fire front location in order to provide a more reliable initial condition for further model integration and forecast.
Final-revised paper