Articles | Volume 21, issue 1
https://doi.org/10.5194/os-21-199-2025
https://doi.org/10.5194/os-21-199-2025
Research article
 | 
27 Jan 2025
Research article |  | 27 Jan 2025

Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms

Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli

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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-2024-1164', Anonymous Referee #1, 15 May 2024
    • AC1: 'Reply on RC1', Daniele Ciani, 14 Oct 2024
  • RC2: 'Comment on egusphere-2024-1164', Anonymous Referee #2, 06 Aug 2024
    • AC2: 'Reply on RC2', Daniele Ciani, 14 Oct 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Daniele Ciani on behalf of the Authors (14 Oct 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (15 Oct 2024) by Alexander Barth
RR by Anonymous Referee #1 (18 Nov 2024)
ED: Publish subject to minor revisions (review by editor) (19 Nov 2024) by Alexander Barth
AR by Daniele Ciani on behalf of the Authors (19 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (20 Nov 2024) by Alexander Barth
AR by Daniele Ciani on behalf of the Authors (24 Nov 2024)  Manuscript 
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Short summary
Ocean surface currents are routinely derived from satellite observations of the sea level, allowing regional- to global-scale synoptic monitoring. In order to overcome the theoretical and instrumental limits of this methodology, we exploit the synergy of multi-sensor satellite observations. We rely on deep learning, physics-informed algorithms to predict ocean currents from sea surface height and sea surface temperature observations. Results are validated by means of in situ measurements.