Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 3
Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 3
Model description paper
 | 
27 Mar 2020
Model description paper |  | 27 Mar 2020

DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

Alexander Barth, Aida Alvera-Azcárate, Matjaz Licer, and Jean-Marie Beckers

Viewed

Total article views: 8,296 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
6,075 2,095 126 8,296 141 165
  • HTML: 6,075
  • PDF: 2,095
  • XML: 126
  • Total: 8,296
  • BibTeX: 141
  • EndNote: 165
Views and downloads (calculated since 25 Jun 2019)
Cumulative views and downloads (calculated since 25 Jun 2019)

Viewed (geographical distribution)

Total article views: 8,296 (including HTML, PDF, and XML) Thereof 7,579 with geography defined and 717 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Nov 2025
Download
Short summary
DINCAE is a method for reconstructing missing data in satellite datasets using a neural network. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images.
Read more
Share