Coupled atmosphere-wildland fire modeling with WRF  3.3 and SFIRE 2011

Mandel, Jan; Beezley, Jonathan D.; Kochanski, Adam K.

doi:10.5194/gmd-4-591-2011

Cited by 230 publications

(270 citation statements)

References 29 publications

(43 reference statements)

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“…WRF-SFIRE system has significantly evolved since its original description was published in Mandel et al (2009Mandel et al ( , 2011. The originally simple fire-modeling framework was advanced by new components, which significantly expanded the original capabilities of WRF-SFIRE.…”

Section: Discussionmentioning

confidence: 99%

“…For the first time, this paper describes new developments in the SFIRE software system during the 2 years since the last reference paper by Mandel et al (2011), and new results demonstrate the relevance of each. Potential fire-severityassessment tools are described in Sect.…”

Section: Introductionmentioning

confidence: 94%

“…8). We do not describe the basic principles, operation, or history of the core of WRF-SFIRE here, and refer to Mandel et al (2011) and the User's Guide (OpenWFM, 2013) instead.…”

Section: Introductionmentioning

confidence: 99%

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Recent advances and applications of WRF–SFIRE

Mandel

Amram

Beezley

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Coupled atmosphere-fire models can now generate forecasts in real time, owing to recent advances in computational capabilities. WRF-SFIRE consists of the Weather Research and Forecasting (WRF) model coupled with the fire-spread model SFIRE. This paper presents new developments, which were introduced as a response to the needs of the community interested in operational testing of WRF-SFIRE. These developments include a fuel-moisture model and a fuel-moisture-data-assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel-moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF-Chem, which allows for a simulation of smoke dispersion and effects of fires on air quality. There is also a data-assimilation method, which provides the capability of starting the fire simulations from an observed fire perimeter, instead of an ignition point. Finally, an example of operational deployment in Israel, utilizing some of the new visualization and datamanagement tools, is presented.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

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Recent advances and applications of WRF–SFIRE

Mandel

Amram

Beezley

et al. 2014

Nat. Hazards Earth Syst. Sci.

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“…However, these are restricted to research use, and small-and particular-scale applications owing to the high computational costs and initialising data required (Viegas 2011). A simplified version of a CFD model that couples weather modelling with Rothermel's fire-spread model (Rothermel 1972) named WRF-SFIRE (Mandel et al 2014(Mandel et al , 2011 was recently reported to have been applied operationally, as a 6-h forecast could be launched with a 30-min run in a multicore processor. Cruz and Alexander (2013) reviewed 49 fire spread models datasets and found that only 3% of the simulations acceptably predicted the observed rate of spread (RoS) and revealed the need to adjust those models if they were to be used operationally.…”

Section: Introductionmentioning

confidence: 99%

Short-term fire front spread prediction using inverse modelling and airborne infrared images

Rios

Pastor

Valero

et al. 2016

Int. J. Wildland Fire

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Abstract.A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.

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“…To better account for nonlinearities in the generalized observation operator, an extension to an EnKF approach was preliminarily explored in Rochoux et al (2012). This ensemble-based DA approach was originally developed for dynamic state estimation (Evensen, 1994) and has already been used in the field of wildfire modeling for correcting the temperature state variable Mandel et al, 2008Mandel et al, , 2011. It was also largely extended to sequential parameter estimation, for instance in the field of hydrology (Durand et al, 2008;Moradkhani et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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

Ricci

Lucor

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. 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 regionalscale 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 PCEnKF 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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Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011

Cited by 230 publications

References 29 publications

Recent advances and applications of WRF–SFIRE

Recent advances and applications of WRF–SFIRE

Short-term fire front spread prediction using inverse modelling and airborne infrared images

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

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