Preprints
https://doi.org/10.5194/essd-2023-447
https://doi.org/10.5194/essd-2023-447
14 Nov 2023
 | 14 Nov 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

Visibility-derived aerosol optical depth over global land from 1980 to 2021

Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li

Abstract. Long-term and high spatial resolution aerosol optical depth (AOD) data are necessary for climate change detection and attribution. Global ground-based AOD observation stations are sparse, and satellite AOD observations have a low time frequency, as well low accuracy before 2000. In this study, AOD was derived from hourly visibility observations collected at more than 5000 stations of the Automated Surface Observing System (ASOS) over global land from 1980 to 2021. The AOD retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua Earth observation satellite were used to train the machine learning method, and the ERA5 reanalysis boundary layer height was used as input. The predicted result has correlation coefficients of 0.54 and 0.51 with Terra MODIS satellite retrievals and AERONET ground observations. The correlation coefficients are higher at monthly and annual scales, which are 0.81 and 0.61 for the monthly and 0.91 and 0.62 for the annual, when compared with Terra MODIS and AERONET AOD, respectively. The visibility-derived AOD at ASOS stations was gridded into a 0.5-degree resolution by area-weighted ordinary kriging interpolation. Analysis of visibility-derived AOD indicates that the global mean AOD over land is 0.16, which is 0.24, 0.22, 0.11, 0.11, 0.130, and 0.12 for Africa, Asia, Europe, North America, Oceania, and South America, respectively. The mean AOD over global land, the Northern Hemisphere, and the Southern Hemisphere demonstrated decreasing trends of -0.0026/10a, -0.0018/10a, and -0.0059/10a, respectively, from 1980 to 2021. The visibility-derived AOD at station and grid scales over global land from 1980 to 2021 are available at National Tibetan Plateau / Third Pole Environment Data Center (https://doi.org/10.11888/Atmos.tpdc.300822) (Hao et al., 2023).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-447', Anonymous Referee #1, 04 Dec 2023
    • AC1: 'Reply on RC1', Kaicun Wang, 19 Feb 2024
  • RC2: 'Comment on essd-2023-447', Anonymous Referee #2, 22 Jan 2024
    • AC2: 'Reply on RC2', Kaicun Wang, 19 Feb 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-447', Anonymous Referee #1, 04 Dec 2023
    • AC1: 'Reply on RC1', Kaicun Wang, 19 Feb 2024
  • RC2: 'Comment on essd-2023-447', Anonymous Referee #2, 22 Jan 2024
    • AC2: 'Reply on RC2', Kaicun Wang, 19 Feb 2024
Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li

Data sets

Visibility-derived aerosol optical depth over global land from 1980 to 2021 Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, Jing Li https://doi.org/10.11888/Atmos.tpdc.300822

Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li

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Short summary
In this study, we employed a machine learning technique to derive daily AOD from hourly visibility collected at more 5000 airports worldwide from 1980 to 2021 combined with reanalysis meteorological parameters. 
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