Abstract. Assimilating observations of shallow soil moisture content into land models
is an important step in estimating soil moisture content. In this study,
several modifications of an ensemble Kalman filter (EnKF) are proposed for
improving this assimilation. It was found that a forecast error
inflation-based approach improves the soil moisture content in shallow
layers, but it can increase the analysis error in deep layers. To mitigate
the problem in deep layers while maintaining the improvement in shallow
layers, a vertical localization-based approach was introduced in this study.
During the data assimilation process, although updating the forecast state
using observations can reduce the analysis error, the water balance based on
the physics in the model could be destroyed. To alleviate the imbalance in
the water budget, a weak water balance constrain filter is adopted. The proposed weakly constrained EnKF that includes forecast error inflation
and vertical localization was applied to a synthetic experiment. An
additional bias-aware assimilation for reducing the analysis bias is also
investigated. The results of the assimilation process suggest that the
inflation approach effectively reduces the analysis error from 6.70 % to
2.00 % in shallow layers but increases from 6.38 % to 12.49 % in deep
layers. The vertical localization approach leads to 6.59 % of the analysis
error in deep layers, and the bias-aware assimilation scheme further reduces this
to 6.05 %. The spatial average of the water balance residual is 0.0487 mm
of weakly constrained EnKF scheme, and 0.0737 mm of a weakly constrained EnKF scheme
with inflation and localization, which are much smaller than the 0.1389 mm of the EnKF scheme.