Assimilation of microwave brightness temperature in a land data assimilation system with multi-observation operators

Authors

  • Binghao Jia,

    1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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  • Xiangjun Tian,

    1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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  • Zhenghui Xie,

    Corresponding author
    1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
    • Corresponding author: Z. Xie, State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, P.O. Box 9804, Beijing 100029, China, http://www.lasg.ac.cn/staff/xie/xie.htm. (zxie@lasg.iap.ac.cn)

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  • Jianguo Liu,

    1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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  • Chunxiang Shi

    1. National Meteorological Information Center, China Meteorological Administration, Beijing, China
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Abstract

[1] A radiative transfer model (RTM) that provides a link between model states and satellite observations (e.g., brightness temperature) can act as an observation operator in land data assimilation to directly assimilate brightness temperatures. In this study, a microwave Land Data Assimilation System (LDAS) was developed with three RTMs (The radiative transfer model for bare field (QH), land emissivity model (LandEM), and Community Microwave Emission Model (CMEM)) as its multi-observation operators (LDAS-MO). Assimilation experiments using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) satellite brightness temperature data from July 2005 to December 2008 were then conducted to investigate the impact of the RTMs on the assimilated results over China. It was found that the assimilated volumetric soil-water content using each of the three observation operators improved the estimation of soil moisture content in the top soil layer (0–10 cm), with reduced root mean square errors (RMSEs), and increased correlation coefficients with field observations (OBS) as compared to a control run with no assimilation for the absence of frozen or snow-covered conditions. 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 while 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 BMA scheme was found to significantly enhance assimilation capability producing the soil moisture analysis, showing the lowest RMSEs and highest correlations with OBS over all areas. It was demonstrated that the BMA scheme with LDAS-MO has the potential to estimate soil moisture with high accuracy.

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