Coupled Weather-Fire Modeling of Landscape-Scale Wildland Fires: Understanding & Prediction

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Wednesday, 5 February 2014: 2:00 PM
Room C202 (The Georgia World Congress Center )
Janice L. Coen, NCAR, Boulder, CO; and P. J. Riggan, W. Schroeder, and M. A. Shapiro

Large wildland fires are dynamic weather phenomena that encounter a wide range of fuel, terrain, and changing weather environments even during one event. They can produce extreme behaviors such as fire whirls, blow-ups, bursts of flame along the surface, winds ten times stronger than ambient conditions, and deep pyrocumuli all resulting from the interactions between a fire and its atmospheric environment. Current operational tools use simple relationships to estimate the rate of spread of a fire's leading edge as a function of wind, slope, and wildland fuel properties. In contrast, our coupled weather-wildland fire models tie numerical weather prediction models to wildland fire behavior modules to simulate the impact of a fire on the atmosphere and the subsequent feedback of these fire-induced winds on fire behavior; this coupling allows us to capture many fundamentals of wildland fire behavior. We use them to explain and anticipate numerous fire phenomena; they have application to smoke transport, regional air quality, and land surface impacts.

Case studies of landscape-scale wildland fires will be presented to illustrate our current capability to model the unfolding of large fire events -- foremost for research and understanding, but also to assess their suitability as a predictive tool. Over a wide range of conditions, model results show rough agreement in area, shape, and direction of spread at periods for which fire location data is available; additional events unique to each fire such as locations of sudden acceleration, flank runs up canyons, and bifurcations of a fire into two heads; and locations favorable to formation of phenomena such as fire whirls and horizontal roll vortices, known to impact firefighter safety.

We describe the current challenges in applying such models in a predictive manner, including processes such as spotting that are unlikely to be modeled deterministically, verification, issues of predictability from weather forecasting, and breakthroughs in modeling past these challenges enabled by the assimilation of new satellite fire detection data.