One thrust of high spatial resolution land surface modeling is to accurately downscale coarse resolution atmospheric forcing data. Supporting this need for downscaling, we will present the results of our study where we investigate the temporal and spatial characteristics of lapse rate versus temperature using a total of 989 in-situ stations in the western United States. For the first time, we demonstrate a linear relationship between the surface lapse rate and temperature. When used in combination with high-resolution digital elevation map (DEM) data, this regression equation and the resulting lapse rates will support a wide range of applications in downscaling atmospheric forcing data. One such application will be the creation of more accurate temperature forcing data within the North American Land Data Assimilation System (NLDAS).
Additionally, the accurate initialization of land surface and hydrological models is critical for the production of accurate hydrological predictions, because the process of a model adjusting to its forcing can severely bias land surface simulations. We will present a technique that we have developed to generate optimal initial conditions for the 32-year (1979-2010) 4km Noah and SAC-HT retrospective simulations and to provide guidance for real-time simulations.