Abstract:
Land surface conditions have a major impact on weather and climate. Soil moisture has long been shown to interact with the atmosphere, soil temperature is a key variable in weather and climate models and snow season characteristics impact climate variability. A number of remote sensing observations directly or indirectly measure these dynamic land conditions and can be used to update large-scale land surface model integrations, which are in turn critical to improving weather and climate forecasts.

Many recent advances in land data assimilation are based on an ensemble-based Kalman filtering approach such as that developed at the NASA Global Modeling and Assimilation Office (GMAO). The presentation will give an overview of the challenges and advantages of assimilating satellite observations into land surface models. Progress on assimilation of surface soil moisture, land surface temperature, snow, and terrestrial water storage at GMAO will be discussed. Special attention will go towards the assimilation of brightness temperatures and retrieved soil moisture as obtained from the current Soil Moisture Ocean Salinity (SMOS) mission, and the plans for soil moisture assimilation from the future Soil Moisture Active Passive (SMAP) mission.