Articles | Volume 18, issue 14
https://doi.org/10.5194/gmd-18-4667-2025
https://doi.org/10.5194/gmd-18-4667-2025
Development and technical paper
 | 
30 Jul 2025
Development and technical paper |  | 30 Jul 2025

Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data

Meghana Velagar, Christoph Keller, and J. Nathan Kutz

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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 gmd-2024-77', Anonymous Referee #1, 21 Oct 2024
    • AC2: 'Reply on RC1', Nathan Kutz, 07 Feb 2025
  • RC2: 'Comment on gmd-2024-77', Narendra Ojha, 12 Nov 2024
    • AC1: 'Reply on RC2', Nathan Kutz, 07 Feb 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Nathan Kutz on behalf of the Authors (07 Feb 2025)  Author's response   Author's tracked changes 
EF by Katja Gänger (10 Feb 2025)  Manuscript 
ED: Publish subject to minor revisions (review by editor) (13 Mar 2025) by Patrick Jöckel
AR by Nathan Kutz on behalf of the Authors (24 Mar 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (25 Mar 2025) by Patrick Jöckel
AR by Nathan Kutz on behalf of the Authors (22 Apr 2025)
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Short summary
We develop the data-driven method of dynamic mode decomposition for producing a robust and stable surrogate reduced-order model of atmospheric chemistry dynamics. The model is computationally efficient, provides interpretable patterns of activity, and produces uncertainty quantification metrics. It is ideal for the forecasting of atmospheric chemistry in a computationally tractable manner.
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