The diagnosis of mixed-layer characteristics and their relationship to meteorological conditions above eastern U.S. wildland fires

Wind, temperature, moisture, and stability conditions in the surface-based mixed layer can profoundly affect the evolution of a wildland fire. Diagnosing the evolution of mixed-layer characteristics is therefore important for understanding and predicting when atmospheric conditions can influence fire behavior. Since above-ground measurements of meteorological variables are not commonly available in close proximity to the vast majority of wildland fires, the ability to diagnose mixed-layer conditions from mesoscale numerical weather prediction models is an important element of fire-weather forecasting and research.

We will investigate the representation of the surface-based mixed layer using mesoscale model simulations of one or several fire events that occurred in the eastern United States. Since mixed-layer processes are simulated in a mesoscale model using a subgrid-scale parameterization, we will employ a parameterization-independent formulation of the mixed-layer depth and compare the mixed-layer depth against the planetary boundary layer (PBL) depths that appear within the parameterizations. We will then determine the impact of the differences between the mixed-layer depth and the PBL depth on a selection of fire-weather indices to assess the sensitivity of the indices to the difference between the diagnosed mixed-layer depth and parameterized PBL depths.

We will employ the Weather Research and Forecasting (WRF) model to produce the necessary simulations, and use various PBL parameterizations such as the Mellor–Yamada–Janjic (MYJ), the MRF, the Yonsei University (YSU), and the Mellor–Yamada–Nakanishi–Niino (MYNN) schemes to understand the potential for wind, temperature, moisture, and stability conditions within the mixed layer to impact the evolution of a wildland fire.