The Influence of Improved Land Surface and Soil Data on Mesoscale Model Predictions

Land surface characteristics play a critical role in the evolution of the planetary boundary layer of the atmosphere. Several key components of the land surface that significantly affect surface sensible heat and moisture fluxes include soil temperature and moisture, fractional vegetation coverage (σf ), and green leaf area index (LAI). The lack of observational data for accurate specification of these components in model initial conditions is one of the most difficult aspects in the evaluation of land surface models. Soil temperature and moisture measurements are unavailable in most areas and routine observations of σf and LAI are not available at high resolution, i.e., with pixel widths on the order of 1 km and daily updates. This gap in our observational capabilities seriously hampers the evaluation and improvement of land surface model parameterizations, since it is very likely that model errors are related to improper initial conditions as much as to inaccuracies in the model formulations. The Penn State–National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5) version 3 (Dudhia 1993; Grell et al. 1994) implements a monthly climatology for fractional vegetation coverage and a constant leaf area index (LAI). Studies have shown that such coarse resolution data based solely on climatology are insufficient to capture the detailed surface characteristics necessary to properly initialize a land surface parameterization (e.g., Chang and Wetzel 1991; Crawford et al. 2001; Kurkowski et al. 2003). By using climatological values for land surface characteristics, the model does not account for short-term or annual variability in vegetation coverage and condition due to daily variations in rainfall, seasonal droughts, flooding, forest fires, irrigation, deforestation, desertification, crop harvesting, land usage, hail or tornado damage, and temporal variations in the growth and senescence of green vegetation. Modeling studies implementing near real-time land surface characteristics from satellite observations have shown great promise for improving forecasts (e.g., Crawford et