Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA

A three dimensional Ensemble Kalman filter (3-D EnKF) and a one dimensional EnKF (1-D EnKF) are used in this study to assimilate Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) coarse grid (25 km) soil moisture retrievals into the Noah land surface model for fine-scale (1 km) surface soil moisture estimation over the Little River Experimental Watershed (LREW), Georgia, USA. For the 1-D EnKF integration, the satellite observations are a priori partitioned to the model fine scale resolution, whereas in the 3-D EnKF integration, the original coarse grid satellite observations are directly used and downscaling is accomplished within the 3-D EnKF update step. In both cases, a first order a priori forecast bias correction is applied. Validation against in situ observations shows that both EnKF algorithms improve the soil moisture estimates, but the 3-D EnKF algorithm better preserves the spatial coherence. It is illustrated how surface soil moisture assimilation affects the deeper layer soil moisture and other water budget variables. Through sensitivity experiments, it is shown that data assimilation accelerates the moisture redistribution compared to the model integrations without assimilation, as surface soil moisture updates are effectively propagated over the entire profile. In the absence of data assimilation, the atmospheric conditions (especially the ratio of evapotranspiration to precipitation) control the model state balancing.

► In this study we analyze the performance of a 3-D EnKF technique.
► Both 1-D and 3-D algorithms improve model forecast soil moisture over our study area.
► Surface moisture assimilation impacts deep layer moisture and water cycle variables.
► EnKF speeds up the state balancing relative to the open loop integration.