Assimilation of SMOS soil moisture in the MESH model with the ensemble Kalman filter

Over the past decade, satellite soil moisture retrievals have showed great potential to improve land surface and hydrologic modeling, especially through an advanced data assimilation system. Data assimilation can be viewed as a process to optimally merge the model estimate and the observed information based upon some estimate of their error characteristics. This paper presents a case study of assimilating the Soil Moisture and Ocean Salinity (SMOS) satellite soil moisture retrievals (2010-2013) into a coupled land-surface and hydrological model MESH with an ensemble Kalman filter (EnKF). The assimilation experiment is conducted over the Great Lakes basin. The assimilation is validated against in situ soil moisture measurements (53 sites) from the Michigan Automated Weather Network, the Soil Climate Analysis Network, and the Fluxnet-Canada, in terms of the daily-spaced anomaly time series correlation coefficient (soil moisture skill). Results indicate that the assimilation of SMOS retrievals enhances the MESH model's soil moisture skill.