Value of incorporating satellite-derived land cover data in MM5/PLACE for simulating surface temperatures

The Parameterization for Land-Atmosphere-Cloud Exchange (PLACE) module is u sed within the Fifth-Generation Pennsylvania State University-National Cent er for Atmospheric Research Mesoscale Model (MM5) to determine the importan ce of individual land surface parameters in simulating surface temperatures . Sensitivity tests indicate that soil moisture and the coverage and thickn ess of green vegetation [as manifested by the values of fractional green ve getation coverage (fVEG) and leaf area index (LAI)] have a large effect on the magnitudes of surface sensible heat fluxes. The combined influence of L AI and fVEG is larger than the influence of soil moisture on the partitioni ng of the surface energy budget. Values for fVEG, albedo, and LAI, derived from 1-km-resolution Advanced Very High Resolution Radiometer data, are ins erted into PLACE, and changes in model-simulated 1.5-m air temperatures in Oklahoma during July of 1997 are documented. Use of the land cover data pro vides a clear improvement in afternoon temperature forecasts when compared with model runs with monthly climatological values for each land cover type . However, temperature forecasts from MM5 without PLACE are significantly m ore accurate than those with PLACE, even when the land cover data are incor porated into the model. When only the temperature observations above 37 deg reesC are analyzed, however, the simulations from the high-resolution land cover dataset with PLACE significantly outperform MM5 without PLACE. Previo us land surface models have simply used (at best) climatological values of these crucial land cover parameters. The ability to improve model simulatio ns of surface energy fluxes and the resultant temperatures in a diagnostic sense provides promise for future attempts at ingesting satellite-derived l and cover data into numerical models. These model improvements would likely be most helpful in predictions of extreme temperature events (during droug ht or extremely wet conditions) for which current numerical weather predict ion models often perform poorly. The potential value of real-time land cove r information for model initialization is substantial.