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

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

doi:https://doi.org/10.5194/essd-16-3001-2024

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

Data sets

CCClim - A machine-learning powered cloud class climatology Arndt Kaps et al. https://doi.org/10.5281/ZENODO.8369202

Model code and software

EyringMLClimateGroup/kaps23ESSD_CCClim: Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology arndtka https://doi.org/10.5281/zenodo.10279992

EyringMLClimateGroup/kaps23ESSD_CCClim: Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology arndtka https://doi.org/10.5281/zenodo.7248773

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.