A microwave land data assimilation system: Scheme and preliminary evaluation over China

[1] To make use of satellite microwave observations for estimating soil moisture, a dual‐pass land data assimilation system (DLDAS) is developed in this paper by incorporating a dual‐pass assimilation framework into the Community Land Model version 3 (CLM3). In the DLDAS, the model state (volumetric soil moisture content) and model parameters are jointly optimized using the gridded Advanced Microwave Scanning Radiometer–EOS (AMSR‐E) satellite brightness temperature (Tb) data through a radiative transfer model (RTM), which acts as an observation operator to provide a link between the model states and the observational variable (i.e., Tb). The DLDAS embeds a state assimilation pass and a parameter calibration pass. In the assimilation pass, the whole soil moisture profiles are assimilated from the Tb data using an ensemble‐based four‐dimensional variational assimilation method (En4DVar). Simultaneously, several key parameters in the RTM are also optimized using the ensemble Proper Orthogonal Decomposition‐based parameter calibration approach (EnPOD_P) in the parameter optimization pass to account for their high variability or uncertainty. To quantify the impacts of the Tb assimilation on CLM3‐calculated soil moisture, the original CLM3 (Sim) and the DLDAS (Ass) were run separately over China on a 0.5° grid forced with identical, observation‐based atmospheric forcing from 2004 to 2008. Soil moisture data from 226 stations over China are averaged over seven different climate divisions and compared with the soil moisture from the Sim and Ass runs. It is found that the assimilation of the AMSR‐E Tb data through the DLDAS greatly improves the soil moisture content within the top 10 cm with reduced mean biases and enhanced correlations with the station data in all divisions except for southwest China, where the current satellite sensors may have difficulties in measuring soil moisture due to the dense vegetation and complex terrain over this region. The results suggest that the AMSR‐E Tb data can be used to improve soil moisture simulations over many regions and the DLDAS is a promising new tool for estimating soil moisture content from satellite Tb data.