Articles | Volume 11, issue 1
Atmos. Meas. Tech., 11, 385–408, 2018
https://doi.org/10.5194/amt-11-385-2018
Atmos. Meas. Tech., 11, 385–408, 2018
https://doi.org/10.5194/amt-11-385-2018

Research article 17 Jan 2018

Research article | 17 Jan 2018

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

Myungje Choi et al.

Data sets

ECMWF wind speed reanalysis data ECMWF http://apps.ecmwf.int/datasets

AERONET data GSFC https://aeronet.gsfc.nasa.gov

GOCI L1B data KOSC http://kosc.kiost.ac.kr/

MODIS DT and DB aerosol data LAADS DAAC https://ladsweb.modaps.eosdis.nasa.gov

VIIRS EDR aerosol data NOAA https://www.class.ncdc.noaa.gov

SONET data SONET http://www.sonet.ac.cn

Model code and software

libRadtran software package B. Mayer, C. Emde, J. Gasteiger, and A. Kylling http://www.libradtran.org

Short summary
This study is a major version upgrade of the aerosol product from GOCI, the first and unique ocean color imager in geostationary earth orbit. It describes the improvement of version 2 of the GOCI Yonsei aerosol retrieval algorithm for near-real-time processing with improved accuracy from the modification of cloud masking, surface reflectance, etc. The product is validated against AERONET/SONET over East Asia with analyses of various errors features, and a pixel-level uncertainty is calculated.