Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 6
Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3001-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3001-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 6
Data description paper
 | 
27 Jun 2024
Data description paper |  | 27 Jun 2024

Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology

Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring

Viewed

Total article views: 1,634 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,325 229 80 1,634 93 75
  • HTML: 1,325
  • PDF: 229
  • XML: 80
  • Total: 1,634
  • BibTeX: 93
  • EndNote: 75
Views and downloads (calculated since 09 Nov 2023)
Cumulative views and downloads (calculated since 09 Nov 2023)

Viewed (geographical distribution)

Total article views: 1,634 (including HTML, PDF, and XML) Thereof 1,596 with geography defined and 38 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Feb 2025
Download
Short summary
CCClim displays observations of clouds in terms of cloud classes that have been in use for a long time. CCClim is a machine-learning-powered product based on multiple existing observational products from different satellites. We show that the cloud classes in CCClim are physically meaningful and can be used to study cloud characteristics in more detail. The goal of this is to make real-world clouds more easily understandable to eventually improve the simulation of clouds in climate models.
Read more
Share
Altmetrics
Final-revised paper
Preprint