An Accurate Fire‐Spread Algorithm in the Weather Research and Forecasting Model Using the Level‐Set Method

Abstract

The level‐set method is typically used to track and propagate the fire perimeter in wildland fire models. Herein, a high‐order level‐set method using fifth‐order WENO scheme for the discretization of spatial derivatives and third‐order explicit Runge‐Kutta temporal integration is implemented within the Weather Research and Forecasting model wildland fire physics package, WRF‐Fire. The algorithm includes solution of an additional partial differential equation for level‐set reinitialization. The accuracy of the fire‐front shape and rate of spread in uncoupled simulations is systematically analyzed. It is demonstrated that the common implementation used by level‐set‐based wildfire models yields to rate‐of‐spread errors in the range 10–35% for typical grid sizes (Δ = 12.5–100 m) and considerably underestimates fire area. Moreover, the amplitude of fire‐front gradients in the presence of explicitly resolved turbulence features is systematically underestimated. In contrast, the new WRF‐Fire algorithm results in rate‐of‐spread errors that are lower than 1% and that become nearly grid independent. Also, the underestimation of fire area at the sharp transition between the fire front and the lateral flanks is found to be reduced by a factor of ≈7. A hybrid‐order level‐set method with locally reduced artificial viscosity is proposed, which substantially alleviates the computational cost associated with high‐order discretizations while preserving accuracy. Simulations of the Last Chance wildfire demonstrate additional benefits of high‐order accurate level‐set algorithms when dealing with complex fuel heterogeneities, enabling propagation across narrow fuel gaps and more accurate fire backing over the lee side of no fuel clusters.

Meteorological Applications Benefiting from an Improved Understanding of Atmospheric Exchange Processes over Mountains

“…On the low ends of those scales, outflow from a pyrocumulonimbus generated by a fire can, in turn, modify that same fire's immediate environment [56,57]. To be most effective, modeling systems for simulating wildfires and the smoke they generate (see Section 8) must account for these interactions [58][59][60]. How well such interactions are represented depends on our understanding of the exchange processes, the surface and boundary layers, and the free atmosphere in and around wildfires.…”

A High Resolution Coupled Fire–Atmosphere Forecasting System to Minimize the Impacts of Wildland Fires: Applications to the Chimney Tops II Wildland Event

“…The fire rate of spread is used to advance the fire perimeter, tracking the interface between the burning/burned regions and the unburned fuel with the level-set method [9]. The original WRF-Fire algorithm has been improved by adding higher-order numerical schemes to solve the level-set and reinitialization equations [26]. Once a grid cell is ignited, the amount of fuel burned is calculated to provide feedback to the atmosphere via sensible and latent heat fluxes.…”

“…To better account for fuel heterogeneities and topography, the fire grid is refined by a factor of four with respect to the atmospheric grid (i.e., 27.75 m). The interested reader is referred to [10,26] for a detailed description of the WRF-Fire model and the level set representation.…”

Experimental Design of a Prescribed Burn Instrumentation

“…After release 3.3, WRF-SFIRE has continued to develop outside of WRF release [1,12]. The version in WRF release was recently selected as a foundation of the operational Colorado Fire Prediction System (CO-FPS) [27] and the level set method was improved [28].…”