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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-336
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-336
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Sep 2020

17 Sep 2020

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This preprint is currently under review for the journal AMT.

Integration of GOCI and AHI Yonsei Aerosol Optical Depth Products During the 2016 KORUS-AQ and 2018 EMeRGe Campaigns

Hyunkwang Lim1, Sujung Go1,2, Jhoon Kim1, Myungje Choi2,3, Seoyoung Lee1, Chang-Keun Song4, and Yasuko Kasai5 Hyunkwang Lim et al.
  • 1Department of Atmospheric Sciences, Yonsei University, Seoul 03722, Republic of Korea
  • 2Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA
  • 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 4School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
  • 5Natinal Institute of Information and Communications Technology, Tokyo 184-8759, Japan

Abstract. The Yonsei AErosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 minutes AHI or 1 hour GOCI data at 6 km × 6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), and bias correction based on normalized difference vegetation indexes; and (2) estimation of the fused product using ensemble-mean and maximum-likelihood estimation methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error, median bias error than the retrieved result for each product.

Hyunkwang Lim et al.

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Latest update: 28 Oct 2020
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Short summary
Aerosol property observations by satellites from Geostationary Earth Orbit(GEO) in particular have advantages of frequent sampling better than 1 hour in addition to broader spatial coverage. This study provides data fusion products of aerosol optical properties from four different algorithms for two different GEO Satellites, GOCI and AHI. The fused aerosol products adopted ensemble-mean and maximum-likelihood estimation methods.The data fusion provides improved results with better accuracy.
Aerosol property observations by satellites from Geostationary Earth Orbit(GEO) in particular...
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