Coupled Atmosphere‐Wildland Fire Modelling

Abstract

Simulating the interaction between fire and atmosphere is critical to the estimation of the rate of spread of the fire. Wildfire's convection (i.e., entire plume) can modify the local meteorology throughout the atmospheric boundary layer and consequently affect the fire propagation speed and behaviour. In this study, we use for the first time the Méso-NH meso-scale numerical model coupled to the point functional ForeFire simplified physical front-tracking wildfire model to investigate the differences introduced by the atmospheric feedback in propagation speed and behaviour. Both numerical models have been developed as research tools for operational models and are currently used to forecast localized extreme events. These models have been selected because they can be run coupled and support decisions in wildfire management in France and Europe. The main originalities of this combination reside in the fact that Méso-NH is run in a Large Eddy Simulation (LES) configuration and that the rate of spread model used in ForeFire provides a physical formulation to take into account the effect of wind and slope. Simulations of typical experimental configurations show that the numerical atmospheric model is able to reproduce plausible convective effects of the heat produced by the fire. Numerical results are comparable to estimated values for fire-induced winds and present behaviour similar to other existing numerical approaches.

Direct radiative effect of the Russian wildfires and its impact on air temperature and atmospheric dynamics during August 2010

“…Empirical estimation of the fire injection height is commonly used in mesoscale chemistry-transport models and is generally shown to give a satisfactory representation of fire dynamics (Konovalov et al, 2011;Martins et al, 2012;Fu et al, 2012). Note that some alternative modelling approaches have been recently developed, in which the interaction of the atmosphere and the fire is fully resolved at a very fine scale (Filippi et al, 2009;Strada et al, 2012). Furthermore, new retrieval methods are under development with POLDER for retrieval of the aerosol optical thickness in cloudy scenes (Waquet et al, 2013) and of their properties (such as single-scattering albedo and size distribution) over cloud-free land scenes .…”

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

“…However, the accuracy of the current available operational wildfire models such as FARSITE (Finney 1998) or PHOENIX Rapidfire (Tolhurst et al 2008) is limited owing to the scarcity of precise data available to initialise them (Finney et al 2013) and the empirically developed submodels that they contain (Sullivan 2009), which make them unsuitable to be exported to all sorts of different fire events. An alternative approach to wildfire modelling is computational fluid dynamics (CFD)-based models such as FIRETEC (Linn 1997), FOREFIRE (Filippi et al 2009;Filippi et al 2014a) or Wildland-Urban Interface Fire Dynamics Simulator (WFDS) (Mell et al 2007). 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).…”

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

“…In contrast, a regional-scale viewpoint (i.e., a viewpoint that considers scales ranging from a few tens of meters up to several kilometers) is adopted in the following: the fire is described as a two-dimensional front that self-propagates normal to itself into unburnt vegetation; the local propagation speed is called the rate of spread (ROS). This viewpoint is the dominant approach used in current operational wildfire spread simulators, see for instance FARSITE (Finney, 1998), FOREFIRE (Filippi et al, 2009(Filippi et al, , 2013, PROMETHEUS (Tymstra et al, 2010) and PHOENIX RapidFire (Chong et al, 2013). In particular, FARSITE uses a model due to Rothermel (1972) that treats the ROS as a semi-empirical function of biomass fuel properties associated with a pre-defined fuel category (i.e., the vertical thickness of the fuel layer, the fuel moisture content, the fuel particle surface-to-volume ratio, the fuel loading and the fuel particle mass density), topographical properties (i.e., the terrain slope) and meteorological properties (i.e., the wind velocity at mid-flame height).…”