Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns

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 in...

Full description

Bibliographic Details
Published in:Atmospheric Measurement Techniques
Main Authors: H. Lim, S. Go, J. Kim, M. Choi, S. Lee, C.-K. Song, Y. Kasai
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2021
Subjects:
Online Access:https://doi.org/10.5194/amt-14-4575-2021
https://doaj.org/article/c822343808c14642958d7271f8bbcf1b
_version_ 1821557526469017600
author H. Lim
S. Go
J. Kim
M. Choi
S. Lee
C.-K. Song
Y. Kasai
author_facet H. Lim
S. Go
J. Kim
M. Choi
S. Lee
C.-K. Song
Y. Kasai
author_sort H. Lim
collection Directory of Open Access Journals: DOAJ Articles
container_issue 6
container_start_page 4575
container_title Atmospheric Measurement Techniques
container_volume 14
description 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 min AHI or 1 h 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), as well as bias correction based on normalized difference vegetation indexes, and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation (MLE) methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error (RMSE), and median bias error than the retrieved result for each product. If the RMSE and mean AOD bias values used for MLE fusion are correct, the MLE fused products show better accuracy, but the ensemble-mean products can still be useful as MLE.
format Article in Journal/Newspaper
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
id ftdoajarticles:oai:doaj.org/article:c822343808c14642958d7271f8bbcf1b
institution Open Polar
language English
op_collection_id ftdoajarticles
op_container_end_page 4592
op_doi https://doi.org/10.5194/amt-14-4575-2021
op_relation https://amt.copernicus.org/articles/14/4575/2021/amt-14-4575-2021.pdf
https://doaj.org/toc/1867-1381
https://doaj.org/toc/1867-8548
doi:10.5194/amt-14-4575-2021
1867-1381
1867-8548
https://doaj.org/article/c822343808c14642958d7271f8bbcf1b
op_source Atmospheric Measurement Techniques, Vol 14, Pp 4575-4592 (2021)
publishDate 2021
publisher Copernicus Publications
record_format openpolar
spelling ftdoajarticles:oai:doaj.org/article:c822343808c14642958d7271f8bbcf1b 2025-01-16T18:38:30+00:00 Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns H. Lim S. Go J. Kim M. Choi S. Lee C.-K. Song Y. Kasai 2021-06-01T00:00:00Z https://doi.org/10.5194/amt-14-4575-2021 https://doaj.org/article/c822343808c14642958d7271f8bbcf1b EN eng Copernicus Publications https://amt.copernicus.org/articles/14/4575/2021/amt-14-4575-2021.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 doi:10.5194/amt-14-4575-2021 1867-1381 1867-8548 https://doaj.org/article/c822343808c14642958d7271f8bbcf1b Atmospheric Measurement Techniques, Vol 14, Pp 4575-4592 (2021) Environmental engineering TA170-171 Earthwork. Foundations TA715-787 article 2021 ftdoajarticles https://doi.org/10.5194/amt-14-4575-2021 2022-12-31T05:36:59Z 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 min AHI or 1 h 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), as well as bias correction based on normalized difference vegetation indexes, and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation (MLE) methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error (RMSE), and median bias error than the retrieved result for each product. If the RMSE and mean AOD bias values used for MLE fusion are correct, the MLE fused products show better accuracy, but the ensemble-mean products can still be useful as MLE. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Atmospheric Measurement Techniques 14 6 4575 4592
spellingShingle Environmental engineering
TA170-171
Earthwork. Foundations
TA715-787
H. Lim
S. Go
J. Kim
M. Choi
S. Lee
C.-K. Song
Y. Kasai
Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns
title Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns
title_full Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns
title_fullStr Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns
title_full_unstemmed Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns
title_short Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns
title_sort integration of goci and ahi yonsei aerosol optical depth products during the 2016 korus-aq and 2018 emerge campaigns
topic Environmental engineering
TA170-171
Earthwork. Foundations
TA715-787
topic_facet Environmental engineering
TA170-171
Earthwork. Foundations
TA715-787
url https://doi.org/10.5194/amt-14-4575-2021
https://doaj.org/article/c822343808c14642958d7271f8bbcf1b