Assessment of ForeFire/Meso-NH for wildland fire/atmosphere coupled simulation of the FireFlux experiment

Filippi, Jean Baptiste; Pialat, Xavier; Clements, Craig B.

doi:10.1016/j.proci.2012.07.022

Cited by 68 publications

(62 citation statements)

References 13 publications

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“…Recently, the coupling of an empirical forest fire spread model with a mesoscale atmospheric model (ForeFire-MesoNH) has been proposed to simulate, at large scale, the propagation and smoke emissions of wildfires [5,6]. In this kind of model, the fire front is represented as a thick line and the depth of the front is calculated from the fire residence time evaluated using the expression proposed by Anderson [2].…”

Section: Introductionmentioning

confidence: 99%

Impact of solid fuel particle size upon the propagation of a surface fire through a homogeneous vegetation layer

Morvan¹,

Lamorlette²

2014

Fire Saf. Sci.

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The aim of this paper is to investigate the role played by the size (thickness) of solid fuel particles upon both the heat transfer and the propagation of a surface fire through a homogeneous vegetation layer. Because all the interactions (mass, momentum, and heat transfer) between the solid fuel layer and the gas phase occur at the interface between these two media, the surface area to volume (SA/V) ratio (inversely proportional to the thickness of the solid fuel particles), which appears in the expression of the specific surface separating these two phases, must affect, more or less significantly, the fire dynamics. This problem has been studied numerically, using a multiphase formulation. Various variables, such as the temperature of the solid fuel, the temperature of the gas, the fire residence time and the heat flux by radiation and convection have been analyzed, in order to understand the role played by the SA/V upon the behaviour of the fire.

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Section: Introductionmentioning

confidence: 99%

Impact of solid fuel particle size upon the propagation of a surface fire through a homogeneous vegetation layer

Morvan¹,

Lamorlette²

2014

Fire Saf. Sci.

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“…Idealized studies have examined fine-scale fire phenomena such as fire whirls [16] and tested sensitivity to fire environment parameters [67]. Some studies attempt to reproduce real prescribed fires [68], often fires conducted during instrumented experiments [19,23,69,70], yet struggle with representing the ambient wind environment [69], which can vary over a small area, and small-scale atmospheric fluctuations [19]. Also, prescribed fires may be ignited with complex ignition patterns and tampered with over time, interfering with the fire's natural evolution.…”

Section: Configuration For Small-scale Firesmentioning

confidence: 99%

Some Requirements for Simulating Wildland Fire Behavior Using Insight from Coupled Weather—Wildland Fire Models

Coen

2018

Fire

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A newer generation of models that interactively couple the atmosphere with fire behavior have shown an increased potential to understand and predict complex, rapidly changing fire behavior. This is possible if they capture intricate, time-varying microscale airflows in mountainous terrain and fire-atmosphere feedbacks. However, this benefit is counterbalanced by additional limitations and requirements, many arising from the atmospheric model upon which they are built. The degree to which their potential is realized depends on how coupled models are built, configured, and applied. Because these are freely available to users with widely ranging backgrounds, I present some limitations and requirements that must be understood and addressed to achieve meaningful fire behavior simulation results. These include how numerical weather prediction models are formulated for specific scales, their solution methods and numerical approximations, optimal model configurations for common scenarios, and how these factors impact reproduction of fire events and phenomena. I discuss methods used to adjust inadequate outcomes and advise on critical interpretation of fire modeling results, such as where errors from model limitations may be misinterpreted as natural unpredictability. I discuss impacts on other weather model-based applications that affect understanding of fire behavior and effects.

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“…Due to the high computational cost of flame-scale CFD and due to the lack of knowledge in environmental conditions, the use of CFDbased detailed modeling approaches such as FIRETEC (Linn et al, 2002), WFDS (Mell et al, 2007) or AVBP-PRISSMA-PYROWO (Rochoux, 2014) is currently restricted to research projects and is not compatible with operational applications. On the other hand, regional-scale fire spread models such as FARSITE (Finney, 1998), FOREFIRE (Filippi et al, , 2013, PROMETHEUS (Tymstra et al, 2010) or PHOENIX RapidFire (Chong et al, 2013) use a semiempirical model that treats the ROS as a parametric function of biomass fuel properties, terrain topography and meteorological conditions; for instance, FARSITE uses a semiempirical model due to Rothermel (1972), while FOREFIRE is based on the quasi-physical model due to Balbi et al (2009); a detailed review of ROS models is provided in Sullivan (2009). One recent strategy to better account for time-varying weather conditions at regional scales consists of coupling a front-tracking simulator for surface fires with a meso-scale CFD atmospheric model for fire-induced atmospheric dynamics, see for instance WRF-Fire (Kochanski et al, 2013) or FOREFIRE-MESONH (Filippi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, regional-scale fire spread models such as FARSITE (Finney, 1998), FOREFIRE (Filippi et al, , 2013, PROMETHEUS (Tymstra et al, 2010) or PHOENIX RapidFire (Chong et al, 2013) use a semiempirical model that treats the ROS as a parametric function of biomass fuel properties, terrain topography and meteorological conditions; for instance, FARSITE uses a semiempirical model due to Rothermel (1972), while FOREFIRE is based on the quasi-physical model due to Balbi et al (2009); a detailed review of ROS models is provided in Sullivan (2009). One recent strategy to better account for time-varying weather conditions at regional scales consists of coupling a front-tracking simulator for surface fires with a meso-scale CFD atmospheric model for fire-induced atmospheric dynamics, see for instance WRF-Fire (Kochanski et al, 2013) or FOREFIRE-MESONH (Filippi et al, 2013). Still, many uncertainties remain due to simplifications in the description of the physics and to knowledge gaps in the description of environmental conditions and yet, errors in the properties of the biomass fuel or in the flame/wind interactions induce strong changes in the heat transfer from the flame to the vegetation and in the biomass fuel pyrolysis for instance.…”

Section: Introductionmentioning

confidence: 99%

Towards predictive data-driven simulations of wildfire spread – Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread

Rochoux

Emery

Ricci

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper is the second part in a series of two articles, which aims at presenting a data-driven modeling strategy for forecasting wildfire spread scenarios based on the assimilation of the observed fire front location and on the sequential correction of model parameters or model state. This model relies on an estimation of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's semi-empirical formulation, in order to propagate the fire front with an Eulerian front-tracking simulator. In Part I, a data assimilation (DA) system based on an ensemble Kalman filter (EnKF) was implemented to provide a spatially uniform correction of biomass fuel and wind parameters and thereby, produce an improved forecast of the wildfire behavior (addressing uncertainties in the input parameters of the ROS model only). In Part II, the objective of the EnKF algorithm is to sequentially update the twodimensional coordinates of the markers along the discretized fire front, in order to provide a spatially distributed correction of the fire front location and thereby, a more reliable initial condition for further model time-integration (addressing all sources of uncertainties in the ROS model). The resulting prototype data-driven wildfire spread simulator is first evaluated in a series of verification tests using synthetically generated observations; tests include representative cases with spatially varying biomass properties and temporally varying wind conditions. In order to properly account for uncertainties during the EnKF update step and to accurately represent error correlations along the fireline, it is shown that members of the EnKF ensemble must be generated through variations in estimates of the fire's initial location as well as through variations in the parameters of the ROS model.The performance of the prototype simulator based on state estimation (SE) or parameter estimation (PE) is then evaluated by comparison with data taken from a reduced-scale controlled grassland fire experiment. Results indicate that data-driven simulations are capable of correcting inaccurate predictions of the fire front location and of subsequently providing an optimized forecast of the wildfire behavior at future lead times. The complementary benefits of both PE and SE approaches, in terms of analysis and forecast performance, are also emphasized. In particular, it is found that the size of the assimilation window must be specified adequately with the persistence of the model initial condition and/or with the temporal and spatial variability of the environmental conditions in order to track sudden changes in wildfire behavior. The present prototype data-driven forecast system is still at an early stage of development. In this regard, this preliminary investigation provides valuable information on how to combine observations with a fire spread model in an efficient way, as well as guidelines to design the future system evolution in order to meet the operational requirements of wildfire spread mo...

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Assessment of ForeFire/Meso-NH for wildland fire/atmosphere coupled simulation of the FireFlux experiment

Cited by 68 publications

References 13 publications

Impact of solid fuel particle size upon the propagation of a surface fire through a homogeneous vegetation layer

Impact of solid fuel particle size upon the propagation of a surface fire through a homogeneous vegetation layer

Some Requirements for Simulating Wildland Fire Behavior Using Insight from Coupled Weather—Wildland Fire Models

Towards predictive data-driven simulations of wildfire spread – Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread

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