Assimilation of SMOS soil moisture over the Great Lakes basin

Abstract

Abstract The launch of European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite has opened up the new opportunities for land data assimilation. In this work, the one-dimensional version of the Ensemble Kalman filter (1D-EnKF) is applied to assimilate SMOS soil moisture retrievals (2010–2013) into a land surface-hydrological model, Modelisation Environmentale-Surface et Hydrologie (MESH), over the Great Lakes basin. A priori rescaling on the retrievals is performed by matching their cumulative distribution function (CDF) to the model surface soil moisture's CDF. The SMOS retrievals, the open-loop soil moisture (no assimilation) and the assimilation estimates are validated against point-scale in situ measurements, respectively, in terms of the daily time series correlation coefficient (skill R ). The skill for SMOS retrievals typically decreases with increased canopy density. In contrast, the open-loop model typically provides higher soil moisture skill R for forest surfaces than for crop surfaces. The skill improvement Δ R A-M , defined as the skill for the assimilation soil moisture product minus the skill for the open-loop estimates, for both surface and root-zone soil moisture typically increases as the SMOS observation skill and decreases with increased open-loop skill, showing a strong linear relation to Δ R S-M , defined as the SMOS observation skill minus the open-loop surface soil moisture skill. Every time the SMOS skill is greater than or equal to the open-loop surface soil moisture skill, the assimilation is typically able to significantly improve the model soil moisture skill. The crop-dominated grids typically experience the largest Δ R A-M if the assimilated SMOS retrievals also come from crop surfaces (note that a model grid cell and the SMOS node mapped onto the grid are not exactly matched in space), consistent with a high satellite observation skill and a low open-loop skill, while Δ R A-M is usually weak or even negative for the forest-dominated grids when the SMOS retrievals also from forest surfaces are assimilated, due to the presence of a low observation skill and a high open-loop skill. The dependence of Δ R A-S , referred to as the skill for the surface soil moisture assimilation product minus the SMOS observation skill, upon the open-loop skill and the satellite observation skill is opposite to that for Δ R A-M . Overall our R metric of skill and the anomaly R metric as used in previous studies provide a consistent explanation for the vegetation modulation of the assimilation. This work offers further insight into the impact of the open-loop skill and the satellite observation skill on the assimilation.