Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 17
Articles | Volume 18, issue 17
https://doi.org/10.5194/gmd-18-5549-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-5549-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 17
Model description paper
 | 
04 Sep 2025
Model description paper |  | 04 Sep 2025

CRITER 1.0: a coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data

Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan

Viewed

Total article views: 4,121 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,138 831 152 4,121 139 197
  • HTML: 3,138
  • PDF: 831
  • XML: 152
  • Total: 4,121
  • BibTeX: 139
  • EndNote: 197
Views and downloads (calculated since 06 Feb 2025)
Cumulative views and downloads (calculated since 06 Feb 2025)

Viewed (geographical distribution)

Total article views: 4,121 (including HTML, PDF, and XML) Thereof 3,991 with geography defined and 130 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 22 Sep 2026
Download
Short summary
Accurate sea surface temperature data (SST) are crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic sea data, CRITER outperforms current methods, reducing errors by up to 44 %.
Read more
Share