Assimilation of Microwave Brightness Temperature in a Land Data Assimilation System with Multi-Observation Operators

In this study, a microwave Land Data Assimilation System (LDAS) was developed with three RTMs (QH, LandEM and CMEM) as its multi-observation operators (LDAS-MO). Assimilation experiments using AMSR-E satellite brightness temperature data were conducted to investigate the impacts of the RTMs on the assimilated results. It was found that the assimilated soil moisture for the QH model, which was more sensitive to dry soil than the other models, produced closer correlations with OBS in arid and semi-arid regions where smaller RMSEs were observed for LandEM. CMEM agreed most closely with OBS over the middle and lower reaches of the Yangtze River due to its better simulation of the brightness temperature over densely vegetated areas. To improve assimilation accuracy, a Bayesian model averaging (BMA) scheme for the LDAS-MO was developed. The soil moisture estimated using the BMA scheme was found to significantly enhance assimilation capability, showing the smallest RMSEs and highest correlations with OBS over all areas.