Articles | Volume 15, issue 5
Development and technical paper
15 Mar 2022
Development and technical paper |  | 15 Mar 2022

DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations

Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-353', Anonymous Referee #1, 21 Dec 2021
    • AC1: 'Reply on RC1', Alexander Barth, 10 Feb 2022
  • RC2: 'Comment on gmd-2021-353', Anonymous Referee #2, 17 Jan 2022
    • AC2: 'Reply on RC2', Alexander Barth, 10 Feb 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Alexander Barth on behalf of the Authors (10 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (11 Feb 2022) by Le Yu
RR by Anonymous Referee #1 (16 Feb 2022)
ED: Publish as is (17 Feb 2022) by Le Yu
Short summary
Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.