Accurate initialization of land surface moisture and energy stores is critical in weather and climate prediction because of their regulation of surface water and energy fluxes between the surface and atmosphere over a variety of time scales. Since these are integrated states, errors in land surface forcing and parameterization accumulate in land stores, leading to incorrect surface water and energy partitioning. However, many new land surface observations are becoming available that may provide additional information necessary to constrain the initialization of land surface states critical for weather and climate prediction. These constraints can be imposed in two ways. Firstly, by forcing the land surface primarily by observations (such as precipitation and radiation), the often severe atmospheric numerical weather prediction land surface forcing biases can be avoided. Secondly, by employing land surface data assimilation techniques, observations of land surface storages (soil temperature, soil moisture, and snow depth/cover) can be used to constrain unrealistic simulated storages.

Therefore, a high-resolution, continental and global Land Data Assimilation System that uses relevant remotely-sensed and in-situ observations within a land data assimilation framework has been developed. This development will greatly increase our skill in land surface, weather, and climate prediction, as well as provide high-quality, global land surface assimilated data fields that are useful for subsequent research and applications. Analysis of the constant confrontation of model predictions with observations at various time and space scales provides an opportunity to improve our understanding and assessment of the space-time structure of land-atmosphere interaction, the relationship between model estimates and observations of land surface conditions, and the role of the land surface in regulating hydrologic and climatic variability