Articles | Volume 25, issue 18
https://doi.org/10.5194/acp-25-10773-2025
https://doi.org/10.5194/acp-25-10773-2025
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 1: Influence of deep convective cores on the cloud life cycle

Sarah Brüning and Holger Tost

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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
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Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024,https://doi.org/10.5194/amt-17-961-2024, 2024
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Cited articles

Andrews, P. C., Cook, K. H., and Vizy, E. K.: Mesoscale convective systems in the Congo Basin: seasonality, regionality, and diurnal cycles, Clim. Dynam., 62, 609–630, https://doi.org/10.1007/s00382-023-06903-7, 2024. a, b
Atiah, W. A., Amekudzi, L. K., and Danuor, S. K.: Mesoscale convective systems and contributions to flood cases in Southern West Africa (SWA): A systematic review, Weather and Climate Extremes, 39, 100551, https://doi.org/10.1016/j.wace.2023.100551, 2023. a
Bacmeister, J. T. and Stephens, G. L.: Spatial statistics of likely convective clouds in CloudSat data, J. Geophys. Res.-Atmos., 116, D04104, https://doi.org/10.1029/2010JD014444, 2011. a, b
Bony, S., Stevens, B., Frierson, D., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T., Sherwood, S., Siebesma, A., Watanabe, M., and Webb, M.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015. a
Brüning, S.: Detecting ML-based convective clouds using 3D observational data, Zenodo [code], https://doi.org/10.5281/zenodo.15607393, 2025a. a
Short summary
This study analyses the temporal variability and life cycle of 3D convective clouds characteristics in the tropics. We derive the data from a machine-learning-based 3D extrapolation of high-resolution 2D satellite data and an object-based detection algorithm. Cloud properties are not only affected by the surface type. Instead, our findings highlight the impact of convective cores on horizontal and vertical cloud and core properties and a potential prolonging of the cloud life cycle.
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