Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 23
Articles | Volume 18, issue 23
https://doi.org/10.5194/amt-18-7129-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-18-7129-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 23
Research article
 | 
01 Dec 2025
Research article |  | 01 Dec 2025

A novel machine learning retrieval for the detection of ice crystal icing conditions based on geostationary satellite imagery

Matteo Aricò, Dennis Piontek, Luca Bugliaro, Johanna Mayer, Richard Müller, Frank Kalinka, and Max Butter

Viewed

Total article views: 3,781 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,430 268 83 3,781 81 72
  • HTML: 3,430
  • PDF: 268
  • XML: 83
  • Total: 3,781
  • BibTeX: 81
  • EndNote: 72
Views and downloads (calculated since 19 Aug 2025)
Cumulative views and downloads (calculated since 19 Aug 2025)

Viewed (geographical distribution)

Total article views: 3,781 (including HTML, PDF, and XML) Thereof 3,558 with geography defined and 223 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 10 Mar 2026
Download
Short summary
The goal is to assess the feasibility of an ice crystal icing detection algorithm based exclusively on remote sensing data. Active measurements are used to train and validate a newly developed random forest algorithm that is applied to passive satellite imagery to estimate the ice crystal icing conditions probability. 83 % of ice crystal icing conditions are correctly detected, showing potential for an operational implementation to mitigate its negative effects on the fleet.
Read more
Share
Special issue
The tropopause region in a changing atmosphere (TPChange)...