Preprints
https://doi.org/10.5194/egusphere-2025-376
https://doi.org/10.5194/egusphere-2025-376
05 Feb 2025
 | 05 Feb 2025

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

Sarah Brüning and Holger Tost

Abstract. This sequence of papers examines spatio-temporal patterns of convective cloud activity and organisation. In response to the limitations of current remote sensing sensors, our analysis employs a machine learning (ML)-based contiguous 3D extrapolation of 2D satellite data. In Part 2, we investigate spatio-temporal patterns of convective organisation over West Africa and assess their connection to 3D convective cloud and core properties. We employ three organisation indices (COP, SCAI, ROME) to statistically quantify convective organisation. Our results show that convective organisation increases for long-lasting mesoscale cloud systems with numerous deep convective cores. It is likely connected to the Inter-Tropical Convergence Zone (ITCZ) and its northward shift in summer. In spring (March–May), strong convective organisation appears around the Gulf of Guinea and the remote Atlantic Ocean between 15–30° S. The seasonality of convective cloud development induces an increase of the indices between 5–20 % in summer. For instance, the landmass distribution and the influence of extra-tropical dynamics may cause a considerably higher variability in the southern hemisphere. Over the ocean, the organisation indices (COP, SCAI) are about 5–10 % higher than over land. However, derived statistics may be affected by overlapping effects of isolated and clustered convection occurring in the same region. To disentangle their impact calls for an adaptive index. To summarise, combining the information of multiple remote sensing instruments may deliver more profound insights into convective organisation. For future research, we emphasise a further need for a robust quantification of convective organisation.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Journal article(s) based on this preprint

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
Atmos. Chem. Phys., 25, 10797–10822, https://doi.org/10.5194/acp-25-10797-2025,https://doi.org/10.5194/acp-25-10797-2025, 2025
Short summary
Sarah Brüning and Holger Tost

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

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 

Journal article(s) based on this preprint

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
Atmos. Chem. Phys., 25, 10797–10822, https://doi.org/10.5194/acp-25-10797-2025,https://doi.org/10.5194/acp-25-10797-2025, 2025
Short summary
Sarah Brüning and Holger Tost

Data sets

Convective organisation indices based on 3D radar reflectivities Sarah Brüning https://doi.org/10.5281/zenodo.14724869

Model code and software

Analysing spatial-temporal patterns of convective organisation from 3D data Sarah Brüning https://doi.org/10.5281/zenodo.14710228

Sarah Brüning and Holger Tost

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Latest update: 22 Sep 2025
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Short summary
The connection between convective cloud organisation and severe weather demands a robust characterisation of hazardous clouds. This study sets on to investigate spatio-temporal patterns and regional hotspots of convective organisation using machine learning-based 3D data and combining different organisation indices. While limitations arise due to overlapping effects of isolated and clustered convection, we emphasise the impact of a surface-specific seasonality that depends on the hemisphere.
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