Editorial Type:
Article
Article Type:
Research Article

Assessment of Aerosol Modes Used in the MODIS Ocean Aerosol Retrieval

Jiacheng Wang College of Physics and Electronic Information, Fuyang University, Fuyang, China

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Qiang Zhao Key Laboratory of General Optical Calibration and Characterization Techniques, and Remote Sensing Laboratory, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, HeFei, China

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Shengcheng Cui Key Laboratory of General Optical Calibration and Characterization Techniques, and Remote Sensing Laboratory, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, HeFei, China

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Chengjie Zhu Key Laboratory of General Optical Calibration and Characterization Techniques, and Remote Sensing Laboratory, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, HeFei, China

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Print Publication:
01 Dec 2012
DOI:
https://doi.org/10.1175/JAS-D-12-051.1
Page(s):
3595–3605
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Abstract

Coastal and island Aerosol Robotic Network (AERONET) sites are used to determine characteristic aerosol modes over marine environments. They are compared with the assumed modes used in the operational Moderate Resolution Imaging Spectroradiometer (MODIS) ocean aerosol algorithm, and the results show that 1) the standard deviation values of three fine aerosol modes (0.6) and one dustlike aerosol mode (0.8) are much higher than the corresponding statistical AERONET modal values (0.45 and 0.6, respectively). The values of three sea salt aerosol modes (0.6) are somewhat lower than the corresponding statistical AERONET modal value (0.675). 2) The number median radius of the current fine and dustlike aerosol modes cannot span the dynamic range of corresponding aerosol distribution properly. 3) AERONET products show that the standard deviation and the number median radius exhibit an obvious negative correlation, especially for sea salt and dustlike aerosol modes. According to this, a refinement of the current aerosol modes is made. These revised modes are used in a version of the MODIS retrieval over ocean. Compared with the current aerosol modes: 1) more retrieved aerosol optical depths (AODs) from the revised aerosol modes lie within the expected error bars and 2) the linear regression lines of the retrievals from the revised aerosol modes and AERONET are closer to the 1:1 line.

Corresponding author address: Jiacheng Wang, College of Physics and Electronic Information, Fuyang University, Fuyang 236037, China. E-mail: shanqiangw@yahoo.com.cn

Abstract

Coastal and island Aerosol Robotic Network (AERONET) sites are used to determine characteristic aerosol modes over marine environments. They are compared with the assumed modes used in the operational Moderate Resolution Imaging Spectroradiometer (MODIS) ocean aerosol algorithm, and the results show that 1) the standard deviation values of three fine aerosol modes (0.6) and one dustlike aerosol mode (0.8) are much higher than the corresponding statistical AERONET modal values (0.45 and 0.6, respectively). The values of three sea salt aerosol modes (0.6) are somewhat lower than the corresponding statistical AERONET modal value (0.675). 2) The number median radius of the current fine and dustlike aerosol modes cannot span the dynamic range of corresponding aerosol distribution properly. 3) AERONET products show that the standard deviation and the number median radius exhibit an obvious negative correlation, especially for sea salt and dustlike aerosol modes. According to this, a refinement of the current aerosol modes is made. These revised modes are used in a version of the MODIS retrieval over ocean. Compared with the current aerosol modes: 1) more retrieved aerosol optical depths (AODs) from the revised aerosol modes lie within the expected error bars and 2) the linear regression lines of the retrievals from the revised aerosol modes and AERONET are closer to the 1:1 line.

Corresponding author address: Jiacheng Wang, College of Physics and Electronic Information, Fuyang University, Fuyang 236037, China. E-mail: shanqiangw@yahoo.com.cn
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