Articles | Volume 21, issue 3
https://doi.org/10.5194/os-21-1065-2025
https://doi.org/10.5194/os-21-1065-2025
Research article
 | Highlight paper
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20 Jun 2025
Research article | Highlight paper |  | 20 Jun 2025

Long-term prediction of the Gulf Stream meander using OceanNet: a principled neural-operator-based digital twin

Michael Gray, Ashesh Chattopadhyay, Tianning Wu, Anna Lowe, and Ruoying He

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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-2024-1238', Anonymous Referee #1, 19 Jun 2024
    • AC1: 'Reply on RC1', Michael Gray, 28 Aug 2024
  • RC2: 'Comment on egusphere-2024-1238', Rachel Furner, 24 Jun 2024
    • AC2: 'Reply on RC2', Michael Gray, 28 Aug 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Michael Gray on behalf of the Authors (28 Aug 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (10 Sep 2024) by Julien Brajard
RR by Rachel Furner (24 Sep 2024)
ED: Reconsider after major revisions (10 Oct 2024) by Julien Brajard
AR by Michael Gray on behalf of the Authors (06 Jan 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (07 Jan 2025) by Julien Brajard
RR by Rachel Furner (18 Jan 2025)
ED: Publish subject to minor revisions (review by editor) (28 Jan 2025) by Julien Brajard
AR by Michael Gray on behalf of the Authors (28 Feb 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (28 Feb 2025) by Julien Brajard
AR by Michael Gray on behalf of the Authors (15 Mar 2025)  Manuscript 
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Co-editor-in-chief
The use of neural networks to emulate the dynamics of the Earth system (ocean, atmosphere) is of critical importance since it enables efficient and operational forecast, data assimilation, and affordable uncertainty quantification. This field has been very productive in numerical weather forecasts but relatively fewer results were demonstrated in the oceans. This paper could be a reference for mesoscale ocean dynamics emulation.
Short summary
The Gulf Stream is a prominent oceanic feature in the northwestern Atlantic Ocean that influences weather patterns in the Northern Hemisphere and is notoriously difficult to predict. We present a machine learning model, OceanNet, to predict the position of the Gulf Stream months in advance. OceanNet is able to perform a 120 d prediction 4 000 000 times faster than traditional methods of ocean modeling with great accuracy.
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