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

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

doi:10.5194/nhess-14-2951-2014

Articles | Volume 14, issue 11

https://doi.org/10.5194/nhess-14-2951-2014

© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Numerical wildland combustion, from the flame to the...

https://doi.org/10.5194/nhess-14-2951-2014

© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 14, issue 11

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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Latest update: 27 Nov 2025

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.