Computational Modeling of Large Wildfires: A Roadmap

• Published in: 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science pp. 113 - 117
• Main Authors: Coen, Janice L, Douglas, Craig C
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2010
• Subjects: Atmospheric modeling; Computational modeling; Data models; fire behavior; Fires; forest fire; Meteorology; Numerical model; Numerical models; Predictive models; weather prediction; wildfire; wildland fire model
• ISBN: 1424475392; 9781424475391
• DOI: 10.1109/DCABES.2010.29

Wildland fire behavior, particularly that of large, uncontrolled wildfires, has not been well understood or predicted. Our methodology to simulate this phenomenon uses high-resolution dynamic models made of numerical weather prediction (NWP) models coupled to fire behavior models to simulate fire behavior. NWP models are capable of modeling very high resolution (<; 100 m) atmospheric flows. The wildland fire component is based upon semi-empirical formulas for fireline rate of spread, post-frontal heat release, and a canopy fire. The fire behavior is coupled to the atmospheric model such that low level winds drive the spread of the surface fire, which in turn releases sensible heat, latent heat, and smoke fluxes into the lower atmosphere, feeding back to affect the winds directing the fire. These coupled dynamic models capture the rapid spread downwind, flank runs up canyons, bifurcations of the fire into two heads, and rough agreement in area, shape, and direction of spread at periods for which fire location data is available. Yet, intriguing computational science questions arise in applying such models in a predictive manner, including physical processes that span a vast range of scales, processes such as spotting that cannot be modeled deterministically, estimating the consequences of uncertainty, the efforts to steer simulations with field data ("data assimilation"), lingering issues with short term forecasting of weather that may show skill only on the order of a few hours, and the difficulty of gathering pertinent data for verification and initialization in a dangerous environment.