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 used within the Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) to determine the importance of individual land surface parameters in simulating surface temperatures. Sensitivity tests indicate that soil moisture and the coverage and thickness of green vegetation [as manifested by the values of fractional green vegetation coverage (fVEG) and leaf area index (LAI)] have a large effect on the magnitudes of surface sensible heat fluxes. The combined influence of LAI and fVEG is larger than the influence of soil moisture on the partitioning of the surface energy budget. Values for fVEG, albedo, and LAI, derived from 1-km-resolution Advanced Very High Resolution Radiometer data, are inserted 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 provides 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 more accurate than those with PLACE, even when the land cover data are incorporated into the model. When only the temperature observations above 37 degreesC are analyzed, however, the simulations from the high-resolution land cover dataset with PLACE significantly outperform MM5 without PLACE. Previous land surface models have simply used (at best) climatological values of these crucial land cover parameters. The ability to improve model simulations of surface energy fluxes and the resultant temperatures in a diagnostic sense provides promise for future attempts at ingesting satellite-derived land cover data into numerical models. These model improvements would likely be most helpful in predictions of extreme temperature events (during drought or extremely wet conditions) for which current numerical weather prediction models often perform poorly. The potential value of real-time land cover information for model initialization is substantial.