Articles | Volume 18, issue 4
Research article
25 Aug 2022
Research article |  | 25 Aug 2022

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

Etienne Pauthenet, Loïc Bachelot, Kevin Balem, Guillaume Maze, Anne-Marie Tréguier, Fabien Roquet, Ronan Fablet, and Pierre Tandeo


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-25', Anonymous Referee #1, 26 Apr 2022
    • AC1: 'Reply on RC1', Etienne Pauthenet, 22 Jun 2022
  • RC2: 'Comment on egusphere-2022-25', Michel CREPON, 29 May 2022
    • AC2: 'Reply on RC2', Etienne Pauthenet, 22 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Etienne Pauthenet on behalf of the Authors (22 Jun 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (11 Jul 2022) by Anna Rubio
AR by Etienne Pauthenet on behalf of the Authors (12 Jul 2022)
Short summary
Temperature and salinity profiles are essential for studying the ocean’s stratification, but there are not enough of these data. Satellites are able to measure daily maps of the surface ocean. We train a machine to learn the link between the satellite data and the profiles in the Gulf Stream region. We can then use this link to predict profiles at the high resolution of the satellite maps. Our prediction is fast to compute and allows us to get profiles at any locations only from surface data.