Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China
Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal vari...
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ftmdpi:oai:mdpi.com:/2072-4292/12/10/1684/ 2023-08-20T03:59:12+02:00 Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China Taixin Zhang Lin Zang Feiyue Mao Youchuan Wan Yannian Zhu agris 2020-05-25 application/pdf https://doi.org/10.3390/rs12101684 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs12101684 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 10; Pages: 1684 AOD CAMS evaluation Himawari-8 MERRA-2 Text 2020 ftmdpi https://doi.org/10.3390/rs12101684 2023-07-31T23:32:43Z Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions. Text Aerosol Robotic Network MDPI Open Access Publishing Merra ENVELOPE(12.615,12.615,65.816,65.816) Remote Sensing 12 10 1684 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
AOD CAMS evaluation Himawari-8 MERRA-2 |
spellingShingle |
AOD CAMS evaluation Himawari-8 MERRA-2 Taixin Zhang Lin Zang Feiyue Mao Youchuan Wan Yannian Zhu Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China |
topic_facet |
AOD CAMS evaluation Himawari-8 MERRA-2 |
description |
Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions. |
format |
Text |
author |
Taixin Zhang Lin Zang Feiyue Mao Youchuan Wan Yannian Zhu |
author_facet |
Taixin Zhang Lin Zang Feiyue Mao Youchuan Wan Yannian Zhu |
author_sort |
Taixin Zhang |
title |
Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China |
title_short |
Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China |
title_full |
Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China |
title_fullStr |
Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China |
title_full_unstemmed |
Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China |
title_sort |
evaluation of himawari-8/ahi, merra-2, and cams aerosol products over china |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12101684 |
op_coverage |
agris |
long_lat |
ENVELOPE(12.615,12.615,65.816,65.816) |
geographic |
Merra |
geographic_facet |
Merra |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 12; Issue 10; Pages: 1684 |
op_relation |
https://dx.doi.org/10.3390/rs12101684 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs12101684 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
10 |
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1684 |
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