ASSIMILATION OF SOIL MOISTURE IN THE STRONGLY COUPLED ATMOSPHERE-LAND SURFACE DATA ASSIMILATION SYSTEM

Soil moisture is essential in numerical weather prediction using a coupled atmosphere-land surface model because it affects the latent and sensible fluxes, emission from the land surface, and eventually the atmospheric variables, especially temperature, water vapor mixing ratio, and precipitation. Therefore, soil moisture observations in a coupled atmosphere-land surface data assimilation system can provide useful information for both land surface and atmosphere. The National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) mission provides space-borne observations of soil moisture and freeze/thaw state: the L-band microwave radiometer aboard SMAP observes soil moisture at the top 5 cm of the land surface, having nearly global coverage every 2-3 days with a 1000 km swath. In this study, we employ the Maximum Likelihood Ensemble Filter (MLEF) to assimilate the SMAP 9-km enhanced soil moisture retrievals into the Noah land surface model (Noah LSM or simply Noah) coupled with the Weather Research and Forecasting (WRF) model. As a strongly coupled atmosphere-land data assimilation system, MLEF simultaneously corrects atmospheric and land surface variables. For the soil moisture assimilation, the observation processing includes quality control, thinning, statistical bias correction, and horizontal and vertical covariance localization. To investigate the soil moisture impacts on the coupled data assimilation, we assimilate both soil moisture and atmospheric observations - the SMAP soil moisture retrievals and the National Centre for Environmental Prediction (NCEP) Prepared Binary Universal Form for the Representation of meteorological data (PrepBUFR), respectively. Our results indicate that the WRF-Noah-MLEF system generates analysis increments of soil moisture that provide additional information to atmospheric variables, especially in the lower atmospheric layers, through cross-covariance between land and atmosphere.