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

Brüning, Sarah; Tost, Holger

doi:https://doi.org/10.5194/acp-25-10797-2025

Articles | Volume 25, issue 18

https://doi.org/10.5194/acp-25-10797-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-25-10797-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 18

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

Data sets

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

Data Products CloudSat Data Processing Center https://www.cloudsat.cira.colostate.edu/data-products

High Rate SEVIRI Level 1.5 Image Data - MSG - 0 degree EUMETSAT Data Services https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:HRSEVIRI

Model code and software

Analysing spatial-temporal patterns of convective organisation from 3D data Sarah Brüning https://doi.org/10.5281/zenodo.15607483

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.