Testing the new land data sets in the NCEP parallel GFS

The land surface interacts with the atmosphere across many time scales including the medium-range NWP. Accurate representation of the land surface is very important because it provides the lower boundary condition for the atmospheric model. During the last two decades, many physics upgrades and the increasing of both horizontal and vertical resolution have been carried out to the NCEP Global Forecast System (GFS). However, some out-of-date and low-resolution land surface data sets such as vegetation/soil types, and surface albedo are still used without any update. In this study, the MODIS-based global 1-km IGBP vegetation classification data, STATSGO soil classification data, monthly snow-free albedo, and the global 0.05o maximum snow albedo data will be used in the current parallel GFS to replace the corresponding old data sets. The impact of the new data sets on the surface variables and the upper atmospheric conditions will be investigated for both summer and winter seasons. The results of this study will provide some guidance for the next land surface upgrades in the next NCEP GFS implementation.