Articles | Volume 17, issue 17
https://doi.org/10.5194/amt-17-5147-2024
https://doi.org/10.5194/amt-17-5147-2024
Research article
 | 
05 Sep 2024
Research article |  | 05 Sep 2024

A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument

Yujin J. Oak, Daniel J. Jacob, Nicholas Balasus, Laura H. Yang, Heesung Chong, Junsung Park, Hanlim Lee, Gitaek T. Lee, Eunjo S. Ha, Rokjin J. Park, Hyeong-Ahn Kwon, and Jhoon Kim

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-393', Anonymous Referee #1, 10 Apr 2024
    • AC1: 'Reply on RC1', Yujin J. Oak, 16 May 2024
  • RC2: 'Comment on egusphere-2024-393', Anonymous Referee #2, 13 May 2024
    • AC2: 'Reply on RC2', Yujin J. Oak, 16 May 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Yujin J. Oak on behalf of the Authors (16 May 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (20 May 2024) by MH AHN
RR by Anonymous Referee #3 (10 Jul 2024)
ED: Publish subject to technical corrections (18 Jul 2024) by MH AHN
AR by Yujin J. Oak on behalf of the Authors (18 Jul 2024)  Author's response   Manuscript 
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Short summary
We present an improved NO2 product from GEMS by calibrating it to TROPOMI using machine learning...
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