Abstract:
Land surface conditions impact 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 affect weather and 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 were achieved with ensemble-based Kalman filtering. The presentation will give an overview of the challenges and advantages of assimilating satellite observations into land surface models. Progress in the assimilation of surface soil moisture, land surface temperature, snow, and terrestrial water storage at the NASA Global Modeling and Assimilation Office (GMAO) will be discussed. Special attention will go towards the preparation for 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.