HR: 0800h
AN: G11A-1186    [Abstracts]
TI: Wildland Fire Forecasting: Predicting Wildfire Behavior, Growth, and Feedbacks on Weather
AU: * Coen, J L
EM: janicec@ucar.edu
AF: National Center for Atmospheric Research, P. O. Box 3000, Boulder, CO 80307-3000 United States
AB: Recent developments in wildland fire research models have represented more complex of fire behavior. The cost has been to increase the computational requirements. When operational constraints are included, such as the need to produce such forecasts faster than real time, the challenge becomes a balance of how much complexity (with corresponding gains in realism) and accuracy can be achieved in producing the quantities of interest while meeting the specified operational constraints. Current field tools are calculator or Palm-Pilot based algorithms such as BEHAVE and BEHAVE Plus that produce timely estimates of instantaneous fire spread rates, flame length, and fire intensity at a point using readily estimated inputs of fuel model, terrain slope, and atmospheric wind speed at a point. At the cost of requiring a PC and slower calculation, FARSITE represents two-dimensional fire spread and adds capabilities including a parameterized representation of crown fire ignition, This work describes how a coupled atmosphere-fire model previously used as a research tool has been adapted for production of real-time forecasts of fire growth and its interactions with weather over a domain focusing on Colorado during summer 2004. The coupled atmosphere-wildland fire-environment (CAWFE) model composed of a 3-dimensional atmospheric prediction model that has been two-way coupled with an empirical fire spread model. The models are connected in that atmospheric conditions (and fuel conditions influenced by the atmosphere) affect the rate and direction of fire propagation, which releases sensible and latent heat (i.e. thermal and water vapor fluxes) to the atmosphere that in turn alter the winds and atmospheric structure around the fire. Thus, it can represent time and spatially-varying weather and the fire feedbacks on the atmospheric which are at the heart of sudden changes in fire behavior and examples of extreme fire behavior such as blow ups, which are now not predictable with current tools. Thus, although this work shows that is it possible to perform more detailed simulations in real time, fire behavior forecasting remains a challenging problem. This is due to challenges in weather prediction, particularly at fine spatial and temporal scales considered "nowcasting" (0-6 hrs), uncertainties in fire behavior even with known meteorological conditions, limitations in quantitative datasets on fuel properties such as fuel loading, and verification. This work describes efforts to advance these capabilities with input from remote sensing data on fuel characteristics and dynamic steering and object-based verification with remotely sensed fire perimeters.
DE: 3300 ATMOSPHERIC PROCESSES
DE: 3315 Data assimilation
DE: 3329 Mesoscale meteorology
SC: Geodesy [G]
MN: Fall Meeting 2005