Impacts of Assimilating SMAP Soil Moisture Retrievals in the SPoRT Land Information System

The NASA Short-Term Prediction Research and Transition (SPoRT) center at NASA-Marshall Space Flight Center maintains near-real-time simulations of land surface processes within the NASA Land Information System modeling framework over Continental US (CONUS) and Eastern Africa domains. Known as the SPoRT-LIS, it consists of a Noah land surface model run forced by atmospheric analyses and blended radar-rain gauge precipitation. SPoRT-LIS results are used for situational awareness and assessment of risk for floods, drought, and wildfire, and for numerical weather prediction (NWP) initialization. Efforts are underway to improve the accuracy of the SPoRT-LIS by assimilating Level 2 soil moisture retrievals from the Soil Moisture Active-Passive (SMAP) satellite via an Ensemble Kalman Filter. Results are presented validating model output against in situ soil network measurements. The impact of SMAP data is investigated for several bias correction configurations, along with other model configuration settings such as number and depth of model layers. A companion presentation at this meeting (Case et al.) will examine the subsequent impact of SMAP retrieval assimilation within LIS on NWP initialization.