Articles | Volume 25, issue 18
https://doi.org/10.5194/acp-25-10797-2025
https://doi.org/10.5194/acp-25-10797-2025
Research article
 | 
19 Sep 2025
Research article |  | 19 Sep 2025

A machine-learning-based perspective on deep convective clouds and their organisation in 3D – Part 2: Spatial–temporal patterns of convective organisation

Sarah Brüning and Holger Tost

Viewed

Total article views: 6,410 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,806 1,291 313 6,410 334 312
  • HTML: 4,806
  • PDF: 1,291
  • XML: 313
  • Total: 6,410
  • BibTeX: 334
  • EndNote: 312
Views and downloads (calculated since 05 Feb 2025)
Cumulative views and downloads (calculated since 05 Feb 2025)

Viewed (geographical distribution)

Total article views: 6,410 (including HTML, PDF, and XML) Thereof 6,186 with geography defined and 224 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 22 Sep 2026
Short summary
The connection between convective clouds and severe weather demands a robust characterisation of convective organisation. This study investigates spatio-temporal patterns of convective organisation and their relationship to machine-learning-based 3D cloud properties through a combination of different indices. We analyse how organisation affects cloud and core properties in a tropical domain, revealing overlapping effects of strong and weak organisation that may frequently blur statistics.
Share
Altmetrics
Final-revised paper
Preprint