Accurate assessment of the spatial and temporal variation of land surface energy and water balance is essential for understanding climate variability. A Land Data Assimilation System (LDAS) is under development which uses various satellite and ground based observations within a land surface modeling and data assimilation framework to produce optimal output fields of land surface states and fluxes. The goal is to provide retrospective and real time estimates of surface energy, water and carbon fluxes for a range of water resources, ecosystem and climate modeling and management practices. Enhanced understanding using model predictions and observations at various spatial and temporal scales will help assess the role of the land surface in influencing seasonal to annual hydrologic, ecosystem and climate variability. The primary of this study is to assess improvements to LDAS water and energy balance estimates validated using primarily field energy balance data from sites in the "Ameriflux" network.

LDAS currently includes drivers that in a modular mode include several land surface models (LSM's), including Common Land Model (CLM) and "Mosaic". We used the validation data to assess changes between land surface models. In addition, regional global meteorological predictions from NAOA National Center for Environmental Prediction (NCEP) and NASA/GSFC EOS Data Assimilation System (GEOS) and also remote sensing derived data sets were compared to provide LDAS forcing (e.g., radiation and precipitation). We also assessed the use of NOAA AVHRR and TERRA MODIS satellite derived inputs of Leaf Area Index (LAI) for land surface parameter improvements. Last, soil parameter schemes based on vegetation maps, "Reynolds" and "Yun" classifications were each input to LDAS and output results were compared versus surface flux data. Results from the analyses will be presented.