Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 18, issue 9
Articles | Volume 18, issue 9
https://doi.org/10.5194/acp-18-6157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-18-6157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 9
Research article
 | 
03 May 2018
Research article |  | 03 May 2018

An automated cirrus classification

Edward Gryspeerdt, Johannes Quaas, Tom Goren, Daniel Klocke, and Matthias Brueck

Viewed

Total article views: 3,368 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,344 962 62 3,368 74 71
  • HTML: 2,344
  • PDF: 962
  • XML: 62
  • Total: 3,368
  • BibTeX: 74
  • EndNote: 71
Views and downloads (calculated since 21 Aug 2017)
Cumulative views and downloads (calculated since 21 Aug 2017)

Viewed (geographical distribution)

Total article views: 3,368 (including HTML, PDF, and XML) Thereof 3,379 with geography defined and -11 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (preprint)

Latest update: 14 Dec 2024
Download
Short summary
Cirrus clouds can form by a variety of mechanisms, such as orographic uplift, through convective systems or through large-scale rising motions. In this work, an automated classification of cirrus clouds based on satellite and reanalysis data is presented to separate cirrus by these different formation mechanisms. The classification provides information on the ice origin and cloud-scale updraughts that could not be determined using satellite or reanalysis data alone.
Read more
Altmetrics
Final-revised paper
Preprint