Recent advances and applications of WRF–SFIRE

Abstract

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.

Experimental Design of a Prescribed Burn Instrumentation

“…WRF-SFIRE [22,23] evolved from CAWFE [24,25] and it has been a part of WRF since release 3.2 as WRF-Fire [23,26]. 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].…”

“…Therefore, characterizing fire-atmosphere interactions, including wildland fire behavior and plume dynamics, is fundamental for improving estimates of smoke production and dispersion. The proposed observational data to be collected during the Fire and Smoke Model Evaluation Experiment (FASMEE) [9,10] are critical for evaluating and transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM [1,11,12] into operational use.…”

“…As the resolution of weather forecasting models increases, the fire-atmosphere interactions and smoke plumes (that had to be treated as sub-grid-scale processes requiring a simplified treatment through external parameterizations) become explicitly res/olvable. Thanks to improved computational capabilities, operational applications of coupled-fire atmosphere models e.g., Israeli Matash system based on WRF-SFIRE, become increasingly feasible [1,13]. The rapid increase in the resolution of numerical weather prediction products in recent years opens new avenues for development in the near-to-moderate future of integrated systems e.g., WRF-SFIRE-CHEM [12], capable of resolving, in a fully-coupled way, fire progression, plume rise, smoke dispersion and chemical transformations.…”

“…Further, known gaps in scientific understanding, such as relationships between heat release, wind fields, plume dynamics and feedbacks to wildland fire behavior, cannot be addressed. This paper represents an attempt to: (1) identify the most critical observational needs; (2) assist in the planning of actual experimental burns; and (3) help optimize sensor placement during experimental burns. Additional details for the methods employed in this work are explained in [16].…”

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

“…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.…”