In this talk, a reduced-cost ensemble Kalman filter (PC-EnKF) is implemented for the estimation of the model input parameters in the context of a front-tracking problem. The forecast step relies on a probabilistic sampling based on a Polynomial Chaos (PC) surrogate model. The performance of the hybrid PC-EnKF strategy is assessed for synthetic front-tracking test cases as well as in the context of wildfire spread, which features a front-like geometry and where the estimation targets are the unknown biomass fuel properties and the surface wind conditions. Results indicate that the hybrid PC-EnKF strategy features similar performance to the standard EnKF algorithm, without loss of accuracy but at a much reduced computational cost.
Reference published in NHESS (2014)
➞ Rochoux, M.C., Ricci, S., Lucor, D., Cuenot, B., and Trouvé, A. (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, Natural Hazards and Earth System Sciences, Special Issue: Numerical Wildland Combustion, from the flame to the atmosphere, vol. 14, pp. 2951-2973, doi: 10.5194/nhess-14-2951-2014, published.