10.24350/CIRM.V.19027403
Rochoux, Mélanie
CECI, CERFACS
Data-driven wildfire behavior modelling: focus on front level-set data assimilation
CIRM
2016
65K10
80A25
Analyse Numérique & Calcul Formel
Physique Mathématique
Hennenfent, Guillaume
2016-08-08T00:00:00
2016-08-02T00:00:00
ENG
video conference
19027403
http://library.cirm-math.fr/Record.htm?record=19281183124910093659
http://videos.cirm-math.fr/2016-08-02_Rochoux.mp4
https://youtu.be/Kp2l-tfrJcE
http://library.cirm-math.fr/19027403.vtt
http://conferences.cirm-math.fr/1430.html
MP4
CC BY NC ND
A front data assimilation system named FIREFLY has been developed at CERFACS in collaboration with the University of Maryland to better estimate the environmental conditions (biomass properties, near-surface wind). We discuss the sequential application of the ensemble Kalman filter (EnKF) in FIREFLY for correcting in a spatially-distributed way, input parameters in order to better track the fire front position. In particular, using a polynomial chaos surrogate to mimic the wildfire spread model in the EnKF algorithm was found in collaboration with LIMSI to be a promising strategy to reduce the computational cost of FIREFLY.
We also discuss the way we represent the distance between simulated and observed fronts. In the CEMRACS project, a new discrepancy operator will be introduced to better represent the match (or mismatch) between simulated fronts and mid-infrared observations in collaboration with INRIA. This front level-set data assimilation derived from image processing and designed for electrophysiology will be extended to wildfire spread monitoring.