Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 18
Articles | Volume 17, issue 18
https://doi.org/10.5194/amt-17-5655-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-5655-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 18
Research article
 | 
26 Sep 2024
Research article |  | 26 Sep 2024

Marine cloud base height retrieval from MODIS cloud properties using machine learning

Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic

Viewed

Total article views: 1,230 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
845 189 196 1,230 32 29
  • HTML: 845
  • PDF: 189
  • XML: 196
  • Total: 1,230
  • BibTeX: 32
  • EndNote: 29
Views and downloads (calculated since 07 Feb 2024)
Cumulative views and downloads (calculated since 07 Feb 2024)

Viewed (geographical distribution)

Total article views: 1,230 (including HTML, PDF, and XML) Thereof 1,221 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Feb 2025
Download
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Read more
Share