Abstract:
In 2005, with support from AFWA, GMAO's sequential data assimilation system was integrated into the Land Information System (LIS). In addition to ongoing development at GMAO, the LIS-based GMAO data assimilation capabilities are now supporting JCSDA partner land data assimilation efforts at AFWA, NCEP, and NRL. The GMAO land data assimilation system is based on the Ensemble Kalman Filter (EnKF), which is well suited to the non-linear and intermittent character of the land surface. Since that time, this assimilation system has been demonstrated for numerous land surface state variables, including soil moisture, snow cover, snow depth, and land surface temperature. Current work is focused on operational transition of current capabilities, such as assimilating real-time soil moisture retrievals from the Advanced SCATterometer (ASCAT) on board the Metop-A satellite launched in October 2006. Similarly, work is proceeding to assimilate snow covered area products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the AFWA multisensor SNODEP product. In addition to operational transition activities, we are preparing for new sensors, such as the NASA Soil Moisture Active Passive (SMAP) mission scheduled for launch in October 2014. Work preparing for this sensor includes integration and calibration of forward L-band microwave radiative transfer models to support the assimilation of brightness temperatures instead of retrieval products. We will present results showing the impact of assimilating soil moisture, snow cover and snow depth, land surface temperature on evaporation and streamflow. Further, we will demonstrate the potential for brightness temperature assimilation from SMAP.