Models that simulate wildland fires span a vast range of complexity; the most physically complex present a difficult supercomputing challenge that cannot be solved fast enough to become a forecasting tool. Coupled atmosphere–fire model simulations of the Big Elk Fire, a wildfire that occurred in the Colorado Front Range during 2002, are used to explore whether some factors that make simulations more computationally demanding (such as coupling between the fire and the atmosphere and fine atmospheric model resolution) are needed to capture wildland fire parameters of interest such as fire perimeter growth. In addition to a Control simulation, other simulations remove the feedback to the atmospheric dynamics and use increasingly coarse atmospheric resolution, including some that can be computed in faster than real time on a single processor. These simulations show that, although the feedback between the fire and atmosphere must be included to capture accurately the shape of the fire, the simulations with relatively coarse atmospheric resolution (grid spacing 100–500 m) can qualitatively capture fire growth and behavior such as surface and crown fire spread and smoke transport. A comparison of the computational performance of the model configured at these different spatial resolutions shows that these can be performed faster than real time on a single computer processor. Thus, although this model still requires rigorous testing over a wide range of fire incidents, it is computationally possible to use models that can capture more complex fire behavior (such as rapid changes in intensity, large fire whirls, and interactions between fire, weather, and topography) than those used currently in the field and meet a faster-than-real-time operational constraint.
A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires
