Meteorological Modeling Sensitivity to Parameterizations and Satellite-Derived Surface Datasets During the 2017 Lake Michigan Ozone Study

High-resolution simulations were performed to assess the impact of different parameterization schemes, surface datasets, and analysis nudging on lower-tropospheric conditions near Lake Michigan. Simulations were performed where climatological or coarse-resolution surface datasets were replaced by high-resolution, real-time datasets depicting the lake surface temperatures (SSTs), green vegetation fraction (GVF), and soil moisture and temperature (SOIL). Comparison of two baseline simulations employing different parameterization schemes (referred to as AP-XM and YNT, respectively) showed that the AP-XM simulation produced more accurate analyses on the outermost 12 km resolution domain but that the YNT simulation was superior for higher-resolution nests. The diurnal evolution of the surface energy fluxes was similar in both simulations on the 12 km grid but differed greatly on the 1.3 km grid where the AP-XM simulation had a much smaller sensible heat flux during the daytime and a physically unrealistic ground heat flux. Switching to the YNT configuration led to more accurate 2 m temperature and 2 m water vapor mixing ratio analyses on the 1.3 km grid. Additional improvements occurred when satellite-derived surface datasets were incorporated into the modeling platform, with the SOIL dataset having the largest positive impact on temperature and water vapor. The GVF and SST datasets also produced more accurate temperature and water vapor analyses but had degradations in wind speed, especially when using the GVF dataset. The most accurate simulations were obtained when using the high-resolution SST and SOIL datasets and analysis nudging above 2 km a.g.l. (above ground level). These results demonstrate the value of using high-resolution satellite-derived surface datasets in model simulations.