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Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach

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Abstract

The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature (T b) simulation. How to reduce the uncertainty in the T b simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging (BMA) ensemble approach. The simulations by the community microwave emission model (CMEM) coupled with the community land model version 4.5 (CLM4.5) over mainland China are conducted by the 24 configurations from four vegetation opacity parameterizations (VOPs), three soil dielectric constant parameterizations (SDCPs), and two soil roughness parameterizations (SRPs). Compared with the simple arithmetical averaging (SAA) method, the BMA reconstructions have a higher spatial correlation coefficient (larger than 0.99) than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system (AMSR-E) at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference (RMSD) of 4K and a temporal correlation coefficient of 0.64.

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Authors and Affiliations

  1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Binghao Jia & Zhenghui Xie

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  1. Binghao Jia
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  2. Zhenghui Xie
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Correspondence to Binghao Jia.

Additional information

Project supported by the China Special Fund for Meteorological Research in the Public Interest (No.GYHY201306045) and the National Natural Science Foundation of China (Nos. 41305066 and 41575096)

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Jia, B., Xie, Z. Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach. Appl. Math. Mech.-Engl. Ed. 37, 1501–1516 (2016). https://doi.org/10.1007/s10483-016-2103-6

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  • DOI: https://doi.org/10.1007/s10483-016-2103-6

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Chinese Library Classification

2010 Mathematics Subject Classification

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