Articles | Volume 25, issue 15
https://doi.org/10.5194/acp-25-8507-2025
https://doi.org/10.5194/acp-25-8507-2025
Research article
 | 
06 Aug 2025
Research article |  | 06 Aug 2025

Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning

Kazuyuki Miyazaki, Yuliya Marchetti, James Montgomery, Steven Lu, and Kevin Bowman

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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-2024-3753', Anonymous Referee #1, 11 Feb 2025
    • AC1: 'Reply on RC1', Kazuyuki Miyazaki, 09 May 2025
  • RC2: 'Comment on egusphere-2024-3753', Anonymous Referee #2, 16 Feb 2025
    • AC2: 'Reply on RC2', Kazuyuki Miyazaki, 09 May 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Kazuyuki Miyazaki on behalf of the Authors (09 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (12 May 2025) by Peer Nowack
RR by Anonymous Referee #2 (13 May 2025)
RR by Anonymous Referee #1 (27 May 2025)
ED: Publish as is (28 May 2025) by Peer Nowack
AR by Kazuyuki Miyazaki on behalf of the Authors (28 May 2025)  Manuscript 
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Short summary
This study employs explainable machine learning to analyze the causes of significant biases in surface ozone estimates from chemical reanalysis. By analyzing global observations and chemical reanalysis outputs, key bias drivers, such as meteorological conditions and precursor emissions, were identified. This provides actionable insights to improve chemical transport models, observation systems, and emissions inventories, ultimately enhancing ozone reanalysis for better air pollution management.
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