Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 11, issue 6
Articles | Volume 11, issue 6
https://doi.org/10.5194/amt-11-3373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-3373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 6
Research article
 | 
13 Jun 2018
Research article |  | 13 Jun 2018

The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors

Oliver Sus, Martin Stengel, Stefan Stapelberg, Gregory McGarragh, Caroline Poulsen, Adam C. Povey, Cornelia Schlundt, Gareth Thomas, Matthew Christensen, Simon Proud, Matthias Jerg, Roy Grainger, and Rainer Hollmann

Viewed

Total article views: 6,034 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,593 2,270 171 6,034 158 208
  • HTML: 3,593
  • PDF: 2,270
  • XML: 171
  • Total: 6,034
  • BibTeX: 158
  • EndNote: 208
Views and downloads (calculated since 17 Oct 2017)
Cumulative views and downloads (calculated since 17 Oct 2017)

Viewed (geographical distribution)

Total article views: 6,034 (including HTML, PDF, and XML) Thereof 5,853 with geography defined and 181 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Nov 2025
Download
Short summary
This paper presents a new cloud detection and classification framework, CC4CL. It applies a sophisticated optimal estimation method to derive cloud variables from satellite data of various polar-orbiting platforms and sensors (AVHRR, MODIS, AATSR). CC4CL provides explicit uncertainty quantification and long-term consistency for decadal timeseries at various spatial resolutions. We analysed 5 case studies to show that cloud height estimates are very realistic unless optically thin clouds overlap.
Read more
Share