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| Titel |
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 |
| VerfasserIn |
M. C. Rochoux, S. Ricci, D. Lucor, B. Cuenot, A. Trouvé |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 14, no. 11 ; Nr. 14, no. 11 (2014-11-10), S.2951-2973 |
| Datensatznummer |
250118747
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| Publikation (Nr.) |
 copernicus.org/nhess-14-2951-2014.pdf |
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| Zusammenfassung |
| This paper is the first part in a series of two articles and presents a
data-driven wildfire simulator for forecasting wildfire spread scenarios, at
a reduced computational cost that is consistent with operational systems. The
prototype simulator features the following components: an Eulerian front
propagation solver FIREFLY that adopts a regional-scale modeling viewpoint,
treats wildfires as surface propagating fronts, and uses a description of the
local rate of fire spread (ROS) as a function of environmental conditions
based on Rothermel's model; a series of airborne-like observations of the
fire front positions; and a data assimilation (DA) algorithm based on an
ensemble Kalman filter (EnKF) for parameter estimation. This stochastic
algorithm partly accounts for the nonlinearities between the input
parameters of the semi-empirical ROS model and the fire front position, and
is sequentially applied to provide a spatially uniform correction to wind and
biomass fuel parameters as observations become available. A wildfire spread
simulator combined with an ensemble-based DA algorithm is therefore a
promising approach to reduce uncertainties in the forecast position of the
fire front and to introduce a paradigm-shift in the wildfire emergency
response. In order to reduce the computational cost of the EnKF algorithm, a
surrogate model based on a polynomial chaos (PC) expansion is used in place
of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The
performance of EnKF and PC-EnKF is assessed on synthetically generated simple
configurations of fire spread to provide valuable information and insight on
the benefits of the PC-EnKF approach, as well as on a controlled grassland
fire experiment. The results indicate that the proposed PC-EnKF algorithm
features similar performance to the standard EnKF algorithm, but at a much
reduced computational cost. In particular, the re-analysis and forecast
skills of DA strongly relate to the spatial and temporal variability of the
errors in the ROS model parameters. |
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