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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-376', Anonymous Referee #1, 25 Feb 2025
    • AC1: 'Reply on RC1', Sarah Brüning, 23 May 2025
  • RC2: 'Comment on egusphere-2025-376', Anonymous Referee #2, 06 Mar 2025
    • AC2: 'Reply on RC2', Sarah Brüning, 23 May 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Sarah Brüning on behalf of the Authors (23 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (26 May 2025) by Guy Dagan
RR by Anonymous Referee #1 (06 Jun 2025)
ED: Publish subject to minor revisions (review by editor) (21 Jun 2025) by Guy Dagan
AR by Sarah Brüning on behalf of the Authors (02 Jul 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (03 Jul 2025) by Guy Dagan
AR by Sarah Brüning on behalf of the Authors (08 Jul 2025)  Manuscript 
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.
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