Articles | Volume 25, issue 18
https://doi.org/10.5194/acp-25-10773-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-10773-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2025
Research article |
| 19 Sep 2025
| 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
CORRESPONDING AUTHOR
Institute for Physics of the Atmosphere, Johannes Gutenberg University Mainz, Johann-Joachim-Becher-Weg 21, 55128 Mainz, Rhineland-Palatinate, Germany
Holger Tost
Institute for Physics of the Atmosphere, Johannes Gutenberg University Mainz, Johann-Joachim-Becher-Weg 21, 55128 Mainz, Rhineland-Palatinate, Germany
Related authors
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
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.
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
Short summary
Short summary
We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Chun Hang Chau, Peter Hoor, and Holger Tost
Atmos. Chem. Phys., 25, 13123–13140, https://doi.org/10.5194/acp-25-13123-2025, https://doi.org/10.5194/acp-25-13123-2025, 2025
Short summary
Short summary
This study examines how the turbulence in the upper troposphere/lower stratosphere (UTLS) could modify the tracer distribution under different situations. Using a multi-scale chemistry model with a novel diagnostic, we found that both the pre-existing tracer gradient and the dynamical and thermodynamical forcing play a role in modifying the tracer distribution. These results allow further research on the UTLS turbulent mixing and its implications for the climate system.
Lasse Moormann, Friederike Fachinger, Frank Drewnick, and Holger Tost
EGUsphere, https://doi.org/10.5194/egusphere-2025-3862, https://doi.org/10.5194/egusphere-2025-3862, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We examine boundary layer (BL) processes during summer rain in Germany, focusing on air mass exchange and precipitation effects. Using drone and ground observations, and ICON (ICOsahedral Nonhydrostatic) model data, we link delayed BL breakup and weak vertical mixing to aerosol formation and chemical processes. ICON predicts mixing layer height under stable conditions but underestimates it during cold pool events, enhancing understanding of frontal weather scenarios and atmospheric changes.
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
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.
Sina Jost, Ralf Weigel, Konrad Kandler, Luis Valero, Jessica Girdwood, Chris Stopford, Warren Stanley, Luca K. Eichhorn, Christian von Glahn, and Holger Tost
Atmos. Meas. Tech., 18, 4397–4412, https://doi.org/10.5194/amt-18-4397-2025, https://doi.org/10.5194/amt-18-4397-2025, 2025
Short summary
Short summary
For the balloon-borne detection of particles (diameter 0.4 < Dp < 40 µm), the Universal Cloud and Aerosol Sounding System (UCASS) was used, whose sample flow is determined by GPS-measured ascent rates. In flights, actual UCASS sample flows rarely match the ascent rates. Errors are minimised by real-time detection of the UCASS flows, e.g. by implementing a thermal flow sensor (TFS) within the UCASS. The TFSs were tested in flight and calibrated at up to 10 m s−1 and at variable angles of attack.
Ryan Vella, Sergey Gromov, Clara M. Nussbaumer, Laura Stecher, Matthias Kohl, Samuel Ruhl, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Atmos. Chem. Phys., 25, 9885–9904, https://doi.org/10.5194/acp-25-9885-2025, https://doi.org/10.5194/acp-25-9885-2025, 2025
Short summary
Short summary
This work examines the impact of replacing forests with farmland and grazing areas on atmospheric composition. Using a global climate–chemistry model, we found that deforestation reduces biogenic volatile organic compounds (BVOCs), increases farming emissions, and shifts ozone chemistry. These changes result in a slight cooling effect on the climate. Restoring natural vegetation could reverse some of these effects.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
Short summary
Short summary
SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
Adrienne Jeske and Holger Tost
EGUsphere, https://doi.org/10.5194/egusphere-2025-293, https://doi.org/10.5194/egusphere-2025-293, 2025
Short summary
Short summary
Thunderstorms lead to a vertical redistribution of tracers throughout the troposphere. We applied a new tool, the convective exchange matrix, in historical simulations with a chemistry-climate model to investigate the trends in convective transport. This reveals that convection reaches higher but deep convection occurs less often in the time period from 2011 to 2020 than in the 1980ies. Thus, convective transport towards the upper troposphere has declined as an adaptation to climate change.
Ryan Vella, Matthew Forrest, Andrea Pozzer, Alexandra P. Tsimpidi, Thomas Hickler, Jos Lelieveld, and Holger Tost
Atmos. Chem. Phys., 25, 243–262, https://doi.org/10.5194/acp-25-243-2025, https://doi.org/10.5194/acp-25-243-2025, 2025
Short summary
Short summary
This study examines how land cover changes influence biogenic volatile organic compound (BVOC) emissions and atmospheric states. Using a coupled chemistry–climate–vegetation model, we compare present-day land cover (deforested for crops and grazing) with natural vegetation and an extreme reforestation scenario. We find that vegetation changes significantly impact global BVOC emissions and organic aerosols but have a relatively small effect on total aerosols, clouds, and radiative effects.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
Short summary
Short summary
The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Edward Groot, Patrick Kuntze, Annette Miltenberger, and Holger Tost
Weather Clim. Dynam., 5, 779–803, https://doi.org/10.5194/wcd-5-779-2024, https://doi.org/10.5194/wcd-5-779-2024, 2024
Short summary
Short summary
Deep convective clouds (thunderstorms), which may cause severe weather, tend to coherently organise into structured cloud systems. Accurate representation of these systems in models is difficult due to their complex dynamics and, in numerical simulations, the dependence of their dynamics on resolution. Here, the effect of convective organisation and geometry on their outflow winds (altitudes of 7–14 km) is investigated. Representation of their dynamics and outflows improves at higher resolution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
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
Short summary
Short summary
We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Ryan Vella, Andrea Pozzer, Matthew Forrest, Jos Lelieveld, Thomas Hickler, and Holger Tost
Biogeosciences, 20, 4391–4412, https://doi.org/10.5194/bg-20-4391-2023, https://doi.org/10.5194/bg-20-4391-2023, 2023
Short summary
Short summary
We investigated the effect of the El Niño–Southern Oscillation (ENSO) on biogenic volatile organic compound (BVOC) emissions from plants. ENSO events can cause a significant increase in these emissions, which have a long-term impact on the Earth's atmosphere. Persistent ENSO conditions can cause long-term changes in vegetation, resulting in even higher BVOC emissions. We link ENSO-induced emission anomalies with driving atmospheric and vegetational variables.
Edward Groot and Holger Tost
Atmos. Chem. Phys., 23, 6065–6081, https://doi.org/10.5194/acp-23-6065-2023, https://doi.org/10.5194/acp-23-6065-2023, 2023
Short summary
Short summary
It is shown that the outflow from cumulonimbus clouds or thunderstorms in the upper troposphere and lower stratosphere in idealized high-resolution simulations (LESs) depends linearly on the net amount of latent heat released by the cloud for fixed geometry of the clouds. However, it is shown that, in more realistic situations, convective organization and aggregation (collecting mechanisms of cumulonimbus clouds) affect the amount of outflow non-linearly through non-idealized geometry.
Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost
Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
Short summary
Short summary
Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
Edward Groot and Holger Tost
Atmos. Chem. Phys., 23, 565–585, https://doi.org/10.5194/acp-23-565-2023, https://doi.org/10.5194/acp-23-565-2023, 2023
Short summary
Short summary
Thunderstorm systems play an important role in the dynamics of the Earth’s atmosphere, and some of them form a well-organised line: squall lines. Simulations of such squall lines with very small initial perturbations are compared to a reference simulation. The evolution of perturbations and processes amplifying them are analysed. It is shown that the formation of new secondary thunderstorm cells (after the initial primary cells) directly ahead of the line affects the spread strongly.
Mohamed Abdelkader, Georgiy Stenchikov, Andrea Pozzer, Holger Tost, and Jos Lelieveld
Atmos. Chem. Phys., 23, 471–500, https://doi.org/10.5194/acp-23-471-2023, https://doi.org/10.5194/acp-23-471-2023, 2023
Short summary
Short summary
We study the effect of injected volcanic ash, water vapor, and SO2 on the development of the volcanic cloud and the stratospheric aerosol optical depth (AOD). Both are sensitive to the initial injection height and to the aging of the volcanic ash shaped by heterogeneous chemistry coupled with the ozone cycle. The paper explains the large differences in AOD for different injection scenarios, which could improve the estimate of the radiative forcing of volcanic eruptions.
Andrea Pozzer, Simon F. Reifenberg, Vinod Kumar, Bruno Franco, Matthias Kohl, Domenico Taraborrelli, Sergey Gromov, Sebastian Ehrhart, Patrick Jöckel, Rolf Sander, Veronica Fall, Simon Rosanka, Vlassis Karydis, Dimitris Akritidis, Tamara Emmerichs, Monica Crippa, Diego Guizzardi, Johannes W. Kaiser, Lieven Clarisse, Astrid Kiendler-Scharr, Holger Tost, and Alexandra Tsimpidi
Geosci. Model Dev., 15, 2673–2710, https://doi.org/10.5194/gmd-15-2673-2022, https://doi.org/10.5194/gmd-15-2673-2022, 2022
Short summary
Short summary
A newly developed setup of the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) is evaluated here. A comprehensive organic degradation mechanism is used and coupled with a volatility base model.
The results show that the model reproduces most of the tracers and aerosols satisfactorily but shows discrepancies for oxygenated organic gases. It is also shown that this model configuration can be used for further research in atmospheric chemistry.
Ralf Weigel, Christoph Mahnke, Manuel Baumgartner, Martina Krämer, Peter Spichtinger, Nicole Spelten, Armin Afchine, Christian Rolf, Silvia Viciani, Francesco D'Amato, Holger Tost, and Stephan Borrmann
Atmos. Chem. Phys., 21, 13455–13481, https://doi.org/10.5194/acp-21-13455-2021, https://doi.org/10.5194/acp-21-13455-2021, 2021
Short summary
Short summary
In July and August 2017, the StratoClim mission took place in Nepal with eight flights of the M-55 Geophysica at up to 20 km in the Asian monsoon anticyclone. New particle formation (NPF) next to cloud ice was detected in situ by abundant nucleation-mode aerosols (> 6 nm) along with ice particles (> 3 µm). NPF was observed mainly below the tropopause, down to 15 % being non-volatile residues. Observed intra-cloud NPF indicates its importance for the composition in the tropical tropopause layer.
Vinod Kumar, Julia Remmers, Steffen Beirle, Joachim Fallmann, Astrid Kerkweg, Jos Lelieveld, Mariano Mertens, Andrea Pozzer, Benedikt Steil, Marc Barra, Holger Tost, and Thomas Wagner
Atmos. Meas. Tech., 14, 5241–5269, https://doi.org/10.5194/amt-14-5241-2021, https://doi.org/10.5194/amt-14-5241-2021, 2021
Short summary
Short summary
We present high-resolution regional atmospheric chemistry model simulations focused around Germany. We highlight the importance of spatial resolution of the model itself as well as the input emissions inventory and short-scale temporal variability of emissions for simulations. We propose a consistent approach for evaluating the simulated vertical distribution of NO2 using MAX-DOAS measurements while also considering its spatial sensitivity volume and change in sensitivity within this volume.
Sara Bacer, Sylvia C. Sullivan, Odran Sourdeval, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Atmos. Chem. Phys., 21, 1485–1505, https://doi.org/10.5194/acp-21-1485-2021, https://doi.org/10.5194/acp-21-1485-2021, 2021
Short summary
Short summary
We investigate the relative importance of the rates of both microphysical processes and unphysical correction terms that act as sources or sinks of ice crystals in cold clouds. By means of numerical simulations performed with a global chemistry–climate model, we assess the relevance of these rates at global and regional scales. This estimation is of fundamental importance to assign priority to the development of microphysics parameterizations and compare model output with observations.
Edward Groot and Holger Tost
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1142, https://doi.org/10.5194/acp-2020-1142, 2020
Publication in ACP not foreseen
Short summary
Short summary
Sensitivities and variability of upper tropospheric flow (~10 km height) resulting immediately and as a direct consequence of (thunder)storm activity have been modeled in detail down to resolutions of 100–200 m and explored for different (organisation/) storm types. It is shown that the amount of water condensation explains much of emerging variability in upper atmospheric flow. Part of the effects on the nearby upper atmospheric flow is suggested to be explained by (organisation/) storm type.
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
Brüning, S.: Convective cloud trajectories from 3D radar reflectivities, Zenodo [data set], https://doi.org/10.5281/zenodo.14724401, 2025b. a
Brüning, S. and Tost, H.: A machine-learning-based perspective on deep convective clouds and their organisation in 3D – Part 2: Spatial–temporal patterns of convective organisation, Atmos. Chem. Phys., 25, 10797–10822, https://doi.org/10.5194/acp-25-10797-2025, 2025. a
Brüning, S., Niebler, S., and Tost, H.: Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data, Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, 2024. a, b, c, d
Chen, P.-J., Chen, W.-T., Wu, C.-M., and Yo, T.-S.: Convective Cloud Regimes From a Classification of Object-Based CloudSat Observations Over Asian-Australian Monsoon Areas, Geophys. Res. Lett., 48, e2021GL092733, https://doi.org/10.1029/2021GL092733, 2021. a, b
Chen, S. S. and Houze, R. A.: Diurnal variation and life‐cycle of deep convective systems over the tropical pacific warm pool, Q. J. Roy. Meteor. Soc., 123, 357–388, https://doi.org/10.1002/qj.49712353806, 1997. a
CloudSat Data Processing Center: Data Products, CloudSat DPC, https://www.cloudsat.cira.colostate.edu/data-products, last access: 12 December 2024, 2024. a
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, Routledge, 2 edn., https://doi.org/10.4324/9780203771587, 2013. a
Crook, J., Klein, C., Folwell, S., Taylor, C. M., Parker, D. J., Stratton, R., and Stein, T.: Assessment of the Representation of West African Storm Lifecycles in Convection-Permitting Simulations, Earth Space Sci., 6, 818–835, https://doi.org/10.1029/2018EA000491, 2019. a
Cui, W., Dong, X., Xi, B., and Feng, Z.: Climatology of Linear Mesoscale Convective System Morphology in the United States Based on the Random-Forests Method, J. Climate, 34, 7257–7276, https://doi.org/10.1175/JCLI-D-20-0862.1, 2021. a, b, c, d
Deng, M., Mace, G. G., and Wang, Z.: Anvil Productivities of Tropical Deep Convective Clusters and Their Regional Differences, J. Atmos. Sci., 73, 3467–3487, https://doi.org/10.1175/JAS-D-15-0239.1, 2016. a, b, c
Dixon, M. and Wiener, G.: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting–A Radar-based Methodology, J. Atmos. Ocean. Tech., 10, 785–797, https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2, 1993. a
Eisinger, M., Marnas, F., Wallace, K., Kubota, T., Tomiyama, N., Ohno, Y., Tanaka, T., Tomita, E., Wehr, T., and Bernaerts, D.: The EarthCARE mission: science data processing chain overview, Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, 2024. a
Esmaili, R. B., Tian, Y., Vila, D. A., and Kim, K.-M.: A Lagrangian analysis of cold cloud clusters and their life cycles with satellite observations, J. Geophys. Res.-Atmos., 121, 11723–11738, https://doi.org/10.1002/2016JD025653, 2016. a
EUMETSAT Data Services: High Rate SEVIRI Level 1.5 Image Data – MSG – 0 degree, https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:HRSEVIRI, last access: 12 December 2024, 2024. a
Feng, Z., Hardin, J., Barnes, H. C., Li, J., Leung, L. R., Varble, A., and Zhang, Z.: PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis, Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, 2023. a, b
Finkensieper, S., Meirink, J. F., van Zadelhoff, G.-J., Hanschmann, T., Benas, N., Stengel, M., Fuchs, P., Hollmann, R., Kaiser, J., and Werscheck, M.: CLAAS-2.1: CM SAF CLoud property dAtAset using SEVIRI – Edition 2.1, Satellite Application Facility on Climate Monitoring (CM SAF), https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01, 2020. a
Fiolleau, T. and Roca, R.: An Algorithm for the Detection and Tracking of Tropical Mesoscale Convective Systems Using Infrared Images From Geostationary Satellite, IEEE T. Geosci. Remote., 51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762, 2013. a, b
Futyan, J. M. and Genio, A. D. D.: Deep Convective System Evolution over Africa and the Tropical Atlantic, J. Climate, 20, 5041–5060, https://doi.org/10.1175/JCLI4297.1, 2007. a, b, c
Ganetis, S. A., Colle, B. A., Yuter, S. E., and Hoban, N. P.: Environmental Conditions Associated with Observed Snowband Structures within Northeast U.S. Winter Storms, Mon. Weather Rev., 146, 3675–3690, https://doi.org/10.1175/MWR-D-18-0054.1, 2018. a, b
Gupta, S., Wang, D., Giangrande, S. E., Biscaro, T. S., and Jensen, M. P.: Lifecycle of updrafts and mass flux in isolated deep convection over the Amazon rainforest: insights from cell tracking, Atmos. Chem. Phys., 24, 4487–4510, https://doi.org/10.5194/acp-24-4487-2024, 2024. a, b
Haberlie, A. M. and Ashley, W. S.: A Method for Identifying Midlatitude Mesoscale Convective Systems in Radar Mosaics. Part II: Tracking, Journal of Applied Meteorology and Climatology, 57, 1599–1621, https://doi.org/10.1175/JAMC-D-17-0294.1, 2018. a
Hartmann, D. L., Hendon, H. H., and Houze, R. A.: Some Implications of the Mesoscale Circulations in Tropical Cloud Clusters for Large-Scale Dynamics and Climate, J. Atmos. Sci., 41, 113–121, https://doi.org/10.1175/1520-0469(1984)041<0113:SIOTMC>2.0.CO;2, 1984. a
Hartmann, D. L., Gasparini, B., Berry, S. E., and Blossey, P. N.: The Life Cycle and Net Radiative Effect of Tropical Anvil Clouds, J. Adv. Model. Earth Sy., 10, 3012–3029, https://doi.org/10.1029/2018MS001484, 2018. a
Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C., Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with spaceborne W-band radar, J. Geophys. Res.-Atmos., 114, D00A22, https://doi.org/10.1029/2008JD009973, 2009. a, b
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019. a, b, c, d
Holmlund, K., Grandell, J., Schmetz, J., Stuhlmann, R., Bojkov, B., Munro, R., Lekouara, M., Coppens, D., Viticchie, B., August, T., Theodore, B., Watts, P., Dobber, M., Fowler, G., Bojinski, S., Schmid, A., Salonen, K., Tjemkes, S., Aminou, D., and Blythe, P.: Meteosat Third Generation (MTG): Continuation and Innovation of Observations from Geostationary Orbit, Bulletin of the American Meteorological Society, 102, E990–E1015, https://doi.org/10.1175/BAMS-D-19-0304.1, 2021. a
Houze, R. A. and Hobbs, P. V.: Organization and Structure of Precipitating Cloud Systems, Adv. Geophys., 24, 225–315, https://doi.org/10.1016/S0065-2687(08)60521-X, 1982. a, b
Houze Jr., R. A.: Observed structure of mesoscale convective systems and implications for large-scale heating, Q. J. Roy. Meteor. Soc., 115, 425–461, https://doi.org/10.1002/qj.49711548702, 1989. a
Houze Jr., R. A.: Mesoscale convective systems, Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004RG000150, 2004. a, b
Hu, X., Ge, J., Li, W., Du, J., Li, Q., and Mu, Q.: Vertical Structure of Tropical Deep Convective Systems at Different Life Stages From CloudSat Observations, J. Geophys. Res.-Atmos., 126, e2021JD035115, https://doi.org/10.1029/2021JD035115, 2021. a, b, c
Igel, M. R., Drager, A. J., and van den Heever, S. C.: A CloudSat Cloud-Object Partitioning Technique and Assessment and Integration of Deep Convective Anvil Sensitivities to Sea Surface Temperature, J. Geophys. Res.-Atmos., 119, 10515–10535, https://doi.org/10.1002/2014JD021717, 2014. a, b, c
Jones, W. K., Stengel, M., and Stier, P.: A Lagrangian perspective on the lifecycle and cloud radiative effect of deep convective clouds over Africa, Atmos. Chem. Phys., 24, 5165–5180, https://doi.org/10.5194/acp-24-5165-2024, 2024. a, b
Kikuchi, M. and Suzuki, K.: Characterizing Vertical Particle Structure of Precipitating Cloud System From Multiplatform Measurements of A-Train Constellation, Geophys. Res. Lett., 46, 1040–1048, https://doi.org/10.1029/2018GL081244, 2019. a
Kniffka, A., Knippertz, P., and Fink, A. H.: The role of low-level clouds in the West African monsoon system, Atmos. Chem. Phys., 19, 1623–1647, https://doi.org/10.5194/acp-19-1623-2019, 2019. a
Kukulies, J., Chen, D., and Curio, J.: The Role of Mesoscale Convective Systems in Precipitation in the Tibetan Plateau Region, J. Geophys. Res.-Atmos., 126, e2021JD035279, https://doi.org/10.1029/2021JD035279, 2021. a
Lakshmanan, V. and Kain, J. S.: A Gaussian Mixture Model Approach to Forecast Verification, Weather and Forecasting, 25, 908–920, https://doi.org/10.1175/2010WAF2222355.1, 2010. a
Leary, C. A. and Houze, R. A.: The Contribution of Mesoscale Motions to the Mass and Heat Fluxes of an Intense Tropical Convective System, J. Atmos. Sci., 37, 784–796, https://doi.org/10.1175/1520-0469(1980)037<0784:TCOMMT>2.0.CO;2, 1980. a
Li, W., Zhang, F., Yu, Y., Iwabuchi, H., Shen, Z., Wang, G., and Zhang, Y.: The semi-diurnal cycle of deep convective systems over Eastern China and its surrounding seas in summer based on an automatic tracking algorithm, Clim. Dynam., 56, 357–379, https://doi.org/10.1007/s00382-020-05474-1, 2021. a
Liu, C. and Zipser, E. J.: Diurnal cycles of precipitation, clouds, and lightning in the tropics from 9 years of TRMM observations, Geophys. Res. Lett., 35, L04819, https://doi.org/10.1029/2007GL032437, 2008. a
Machado, L. A. T., Rossow, W. B., Guedes, R. L., and Walker, A. W.: Life Cycle Variations of Mesoscale Convective Systems over the Americas, Mon. Weather Rev., 126, 1630–1654, https://doi.org/10.1175/1520-0493(1998)126<1630:LCVOMC>2.0.CO;2, 1998. a
Maddox, R. A.: Mesoscale Convective Complexes, Bulletin of the American Meteorological Society, 61, 1374–1387, https://doi.org/10.1175/1520-0477(1980)061<1374:MCC>2.0.CO;2, 1980. a
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor Detection Using Cloudsat–An Earth-Orbiting 94-GHz Cloud Radar, J. Atmos. Ocean. Tech., 25, 519–533, https://doi.org/10.1175/2007JTECHA1006.1, 2008. a, b
Masunaga, H. and Luo, Z. J.: Convective and large-scale mass flux profiles over tropical oceans determined from synergistic analysis of a suite of satellite observations, J. Geophys. Res.-Atmos., 121, 7958–7974, https://doi.org/10.1002/2016JD024753, 2016. a, b, c
Mecikalski, J. R., MacKenzie, W. M., Koenig, M., and Muller, S.: Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part I: Infrared Fields, Journal of Applied Meteorology and Climatology, 49, 521–534, https://doi.org/10.1175/2009JAMC2344.1, 2010. a
Meyer, F.: Topographic distance and watershed lines, Signal Process., 38, 113–125, https://doi.org/10.1016/0165-1684(94)90060-4, 1994. a
Nesbitt, S. W. and Zipser, E. J.: The Diurnal Cycle of Rainfall and Convective Intensity according to Three Years of TRMM Measurements, J. Climate, 16, 1456–1475, https://doi.org/10.1175/1520-0442(2003)016<1456:TDCORA>2.0.CO;2, 2003. a
Nicholson, S. E.: The ITCZ and the Seasonal Cycle over Equatorial Africa, Bulletin of the American Meteorological Society, 99, 337–348, https://doi.org/10.1175/BAMS-D-16-0287.1, 2018. a
Oreopoulos, L., Cho, N., and Lee, D.: New insights about cloud vertical structure from CloudSat and CALIPSO observations, J. Geophys. Res.-Atmos., 122, 9280–9300, https://doi.org/10.1002/2017JD026629, 2017. a, b
Pilewskie, J. A. and L'Ecuyer, T. S.: The Global Nature of Early-Afternoon and Late-Night Convection Through the Eyes of the A-Train, J. Geophys. Res.-Atmos., 127, e2022JD036438, https://doi.org/10.1029/2022JD036438, 2022. a, b
Prein, A. F., Feng, Z., Fiolleau, T., Moon, Z. L., Núñez Ocasio, K. M., Kukulies, J., Roca, R., Varble, A. C., Rehbein, A., Liu, C., Ikeda, K., Mu, Y., and Rasmussen, R. M.: Km-Scale Simulations of Mesoscale Convective Systems Over South America–A Feature Tracker Intercomparison, J. Geophys. Res.-Atmos., 129, e2023JD040254, https://doi.org/10.1029/2023JD040254, 2024. a, b, c, d
Raut, B. A., Jackson, R., Picel, M., Collis, S. M., Bergemann, M., and Jakob, C.: An Adaptive Tracking Algorithm for Convection in Simulated and Remote Sensing Data, Journal of Applied Meteorology and Climatology, 60, 513–526, https://doi.org/10.1175/JAMC-D-20-0119.1, 2021. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by: Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., 9351, 234–241, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a
Sassen, K. and Wang, Z.: Classifying clouds around the globe with the CloudSat radar: 1-year of results, Geophys. Res. Lett., 35, L04805, https://doi.org/10.1029/2007GL032591, 2008. a
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An introduction to Metetsat second generation (MSG), Bulletin of the American Meteorological Society, 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002. a, b
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a
Sokolowsky, G. A., Freeman, S. W., Jones, W. K., Kukulies, J., Senf, F., Marinescu, P. J., Heikenfeld, M., Brunner, K. N., Bruning, E. C., Collis, S. M., Jackson, R. C., Leung, G. R., Pfeifer, N., Raut, B. A., Saleeby, S. M., Stier, P., and van den Heever, S. C.: tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena, Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, 2024. a, b, c
Steiner, M., Houze, R. A., and Yuter, S. E.: Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data, Journal of Applied Meteorology and Climatology, 34, 1978–2007, https://doi.org/10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2, 1995. a
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M., Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J., Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang, Z., and Marchand, R.: CloudSat mission: Performance and early science after the first year of operation, J. Geophys. Res.-Atmos., 113, D00A18, https://doi.org/10.1029/2008JD009982, 2008. a
Takahashi, H. and Luo, Z.: Where is the level of neutral buoyancy for deep convection?, Geophys. Res. Lett., 39, L15809, https://doi.org/10.1029/2012GL052638, 2012. a
Taylor, S., Stier, P., White, B., Finkensieper, S., and Stengel, M.: Evaluating the diurnal cycle in cloud top temperature from SEVIRI, Atmos. Chem. Phys., 17, 7035–7053, https://doi.org/10.5194/acp-17-7035-2017, 2017. a, b
Tomkins, L. M., Yuter, S. E., and Miller, M. A.: Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms, Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024, 2024. a
Vondou, D. A.: Spatio-Temporal Variability of Western Central African Convection from Infrared Observations, Atmosphere, 3, 377–399, https://doi.org/10.3390/atmos3030377, 2012. a, b
Wall, C. J., Norris, J. R., Gasparini, B., Smith, W. L., Thieman, M. M., and Sourdeval, O.: Observational Evidence that Radiative Heating Modifies the Life Cycle of Tropical Anvil Clouds, J. Climate, 33, 8621–8640, https://doi.org/10.1175/JCLI-D-20-0204.1, 2020. a
Wang, X., Cui, C., Cui, W., and Shi, Y.: Modes of mesoscale convective system organization during Meiyu season over the Yangtze River basin, Acta Meteorol. Sin., 28, 111–126, https://doi.org/10.1007/s13351-014-0108-4, 2014. a
Wielicki, B. A., Cess, R. D., King, M. D., Randall, D. A., and Harrison, E. F.: Mission to Planet Earth: Role of Clouds and Radiation in Climate, Bulletin of the American Meteorological Society, 76, 2125–2154, https://doi.org/10.1175/1520-0477(1995)076<2125:MTPERO>2.0.CO;2, 1995. a
Wilcox, E. M., Yuan, T., and Song, H.: Deep convective cloud system size and structure across the global tropics and subtropics, Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, 2023. a, b
Zipser, E. J. and LeMone, M. A.: Cumulonimbus Vertical Velocity Events in GATE. Part II: Synthesis and Model Core Structure, J. Atmos. Sci., 37, 2458–2469, https://doi.org/10.1175/1520-0469(1980)037<2458:CVVEIG>2.0.CO;2, 1980. a, b
Zipser, E. J., Cecil, D. J., Liu, C., Nesbitt, S. W., and Yorty, D. P.: Where are the most intense thunderstorms on earth?, Bulletin of the American Meteorological Society, 87, 1057–1072, https://doi.org/10.1175/BAMS-87-8-1057, 2006. 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.
This study analyses the temporal variability and life cycle of 3D convective clouds...
Altmetrics
Final-revised paper
Preprint