Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7795-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7795-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessment of object-based indices to identify convective organization
Giulio Mandorli
CORRESPONDING AUTHOR
Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace, (LMD/IPSL), Sorbonne Université, Ecole Polytechnique, CNRS, Paris, France
Claudia J. Stubenrauch
Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace, (LMD/IPSL), Sorbonne Université, Ecole Polytechnique, CNRS, Paris, France
Related authors
Xiaoting Chen, Claudia J. Stubenrauch, and Giulio Mandorli
Atmos. Chem. Phys., 25, 6857–6880, https://doi.org/10.5194/acp-25-6857-2025, https://doi.org/10.5194/acp-25-6857-2025, 2025
Short summary
Short summary
Strongly precipitating mesoscale convective systems produce a large amount of diabatic heating of the atmosphere, influencing atmospheric circulation. Their complete 3D description, attained by machine learning techniques in combination with satellite observations, has enabled a detailed study of the relationship between latent and radiative heating in these cloud systems. Convective organization increases both the average and the vertical gradient of radiative effects of the mesoscale convective systems.
Claudia J. Stubenrauch, Giulio Mandorli, and Elisabeth Lemaitre
Atmos. Chem. Phys., 23, 5867–5884, https://doi.org/10.5194/acp-23-5867-2023, https://doi.org/10.5194/acp-23-5867-2023, 2023
Short summary
Short summary
Organized convection leads to large convective cloud systems and intense rain and may change with a warming climate. Their complete 3D description, attained by machine learning techniques in combination with various satellite observations, together with a cloud system concept, link convection to anvil properties, while convective organization can be identified by the horizontal structure of intense rain.
Claudia J. Stubenrauch, Xiaoting Chen, Laurent Li, and Anthony J. Baran
EGUsphere, https://doi.org/10.5194/egusphere-2026-5049, https://doi.org/10.5194/egusphere-2026-5049, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Atmospheric heating gradients caused by tropical upper tropospheric ice clouds (cirrus) influence the atmospheric circulation which then affects patterns of precipitation. While their overall radiative effect leads to a strengthening of the Hadley circulation, semi-transparent cirrus weaken the circulation. This means that in a warmer climate changes in their optical depth may then counteract or enhance the weakened Hadley circulation.
Xiaoting Chen, Claudia J. Stubenrauch, and Giulio Mandorli
Atmos. Chem. Phys., 25, 6857–6880, https://doi.org/10.5194/acp-25-6857-2025, https://doi.org/10.5194/acp-25-6857-2025, 2025
Short summary
Short summary
Strongly precipitating mesoscale convective systems produce a large amount of diabatic heating of the atmosphere, influencing atmospheric circulation. Their complete 3D description, attained by machine learning techniques in combination with satellite observations, has enabled a detailed study of the relationship between latent and radiative heating in these cloud systems. Convective organization increases both the average and the vertical gradient of radiative effects of the mesoscale convective systems.
Claudia J. Stubenrauch, Giulio Mandorli, and Elisabeth Lemaitre
Atmos. Chem. Phys., 23, 5867–5884, https://doi.org/10.5194/acp-23-5867-2023, https://doi.org/10.5194/acp-23-5867-2023, 2023
Short summary
Short summary
Organized convection leads to large convective cloud systems and intense rain and may change with a warming climate. Their complete 3D description, attained by machine learning techniques in combination with various satellite observations, together with a cloud system concept, link convection to anvil properties, while convective organization can be identified by the horizontal structure of intense rain.
Cited articles
Bao, J., Sherwood, S. C., Colin, M., and Dixit, V.: The Robust Relationship Between Extreme Precipitation and Convective Organization in Idealized Numerical Modeling Simulations, J. Adv. Model. Earth Sy., 9, 2291–2303, https://doi.org/10.1002/2017MS001125, 2017. a
Besag, J.: Discussion on Dr Ripley's Paper, J. Roy. Stat. Soc. B, 39, 192–212, https://doi.org/10.1111/j.2517-6161.1977.tb01616.x, 1977. a
Biagioli, G. and Tompkins, A. M.: Measuring Convective Organization, J. Atmos. Sci., 80, 2769–2789, https://doi.org/10.1175/JAS-D-23-0103.1, 2023. a, b, c
Bony, S., Semie, A., Kramer, R. J., Soden, B., Tompkins, A. M., and Emanuel, K. A.: Observed Modulation of the Tropical Radiation Budget by Deep Convective Organization and Lower-Tropospheric Stability, AGU Advances, 1, e2019AV000155, https://doi.org/10.1029/2019AV000155, 2020. a, b, c, d
Bretherton, C. S., Blossey, P. N., and Khairoutdinov, M.: An Energy-Balance Analysis of Deep Convective Self-Aggregation above Uniform SST, J. Atmos. Sci., 62, 4273–4292, https://doi.org/10.1175/JAS3614.1, 2005. a, b
Cacciari, M., Salam, G. P., and Soyez, G.: The anti-kt jet clustering algorithm, JHEP, 04, 063, https://doi.org/10.1088/1126-6708/2008/04/063, 2008. a
Cronin, T. W. and Wing, A. A.: Clouds, Circulation, and Climate Sensitivity in a Radiative-Convective Equilibrium Channel Model, J. Adv. Model. Earth Sy., 9, 2883–2905, https://doi.org/10.1002/2017MS001111, 2017. a, b
Fiolleau, T. and Roca, R.: TOOCAN Database V2.08 – Tracking Of Organized Convection Algorithm using a 3-dimensional segmentation, IPSL Data Catalog [data set], https://doi.org/10.14768/1be7fd53-8b81-416e-90d5-002b36b30cf8, 2023. a
Fiolleau, T., Roca, R., Cloché, S., Bouniol, D., and Raberanto, P.: Homogenization of Geostationary Infrared Imager Channels for Cold Cloud Studies Using Megha-Tropiques/ScaRaB, IEEE T. Geosci. Remote, 58, 6609–6622, https://doi.org/10.1109/TGRS.2020.2978171, 2020. a
Held, I. M., Hemler, R. S., and Ramaswamy, V.: Radiative-Convective Equilibrium with Explicit Two-Dimensional Moist Convection, J. Atmos. Sci., 50, 3909–3927, https://doi.org/10.1175/1520-0469(1993)050<3909:RCEWET>2.0.CO;2, 1993. a
Holloway, C. E. and Woolnough, S. J.: The sensitivity of convective aggregation to diabatic processes in idealized radiative-convective equilibrium simulations, J. Adv. Model. Earth Sy., 8, 166–195, https://doi.org/10.1002/2015MS000511, 2016. a
Janssens, M., Vilà-Guerau de Arellano, J., Scheffer, M., Antonissen, C., Siebesma, A. P., and Glassmeier, F.: Cloud Patterns in the Trades Have Four Interpretable Dimensions, Geophys. Res. Lett., 48, e2020GL091001, https://doi.org/10.1029/2020GL091001, 2021. a
Kadoya, T. and Masunaga, H.: New Observational Metrics of Convective Self-Aggregation: Methodology and a Case Study, J. Meteorol. Soc. Jpn. Ser. II, 96, 535–548, https://doi.org/10.2151/jmsj.2018-054, 2018. a, b
Mandorli, G.: gmandorl/Assessment_of_the_object-based_indices_to_identify_convective_organization: before_submission (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.8287752, 2023. a
Muller, C., Yang, D., Craig, G., Cronin, T., Fildier, B., Haerter, J. O., Hohenegger, C., Mapes, B., Randall, D., Shamekh, S., and Sherwood, S. C.: Spontaneous Aggregation of Convective Storms, Annu. Rev. Fluid Mech., 54, 133–157, https://doi.org/10.1146/annurev-fluid-022421-011319, 2022. a, b
Muller, C. J. and Held, I. M.: Detailed Investigation of the Self-Aggregation of Convection in Cloud-Resolving Simulations, J. Atmos. Sci., 69, 2551–2565, https://doi.org/10.1175/JAS-D-11-0257.1, 2012. a
Muller, C. J. and Romps, D. M.: Acceleration of tropical cyclogenesis by self-aggregation feedbacks, P. Natl. Acad. Sci. USA, 115, 2930–2935, https://doi.org/10.1073/pnas.1719967115, 2018. a, b
Prein, A., Feng, Z., Fiolleau, T., Moon, Z., Núñez Ocasio, K., Kukulies, J., Roca, R., Varble, A., Rehbein, A., Liu, C., Ikeda, K., Mu, Y., and Rasmussen, R.: Km-Scale Simulations of Mesoscale Convective Systems (MCSs) Over South America – A Feature Tracker Intercomparison, ESSOAR [data set], https://doi.org/10.22541/essoar.169841723.36785590/v1, 2023. a
Pscheidt, I., Senf, F., Heinze, R., Deneke, H., Trömel, S., and Hohenegger, C.: How organized is deep convection over Germany?, Q. J. Roy. Meteor. Soc., 145, 2366–2384, https://doi.org/10.1002/qj.3552, 2019. a
Retsch, M. H., Jakob, C., and Singh, M. S.: Assessing Convective Organization in Tropical Radar Observations, J. Geophys. Res.-Atmos., 125, e2019JD031801, https://doi.org/10.1029/2019JD031801, 2020. a, b, c, d
Ripley, B. D.: The second-order analysis of stationary point processes, J. Appl. Probab., 13, 255–266, https://doi.org/10.2307/3212829, 1976. a
Ripley, B. D.: Modelling Spatial Patterns, J. Roy. Stat. Soc. B, 39, 172–192, https://doi.org/10.1111/j.2517-6161.1977.tb01615.x, 1977. a
Ripley, B. D.: Wiley Series in Probability and Statistics, pp. 253–260, John Wiley & Sons, Ltd, ISBN 9780471725213, https://doi.org/10.1002/0471725218.scard, 1981. a
Semie, A. G. and Bony, S.: Relationship Between Precipitation Extremes and Convective Organization Inferred From Satellite Observations, Geophys. Res. Lett., 47, e2019GL086927, https://doi.org/10.1029/2019GL086927, 2020. a, b
Stein, T. H. M., Holloway, C. E., Tobin, I., and Bony, S.: Observed Relationships between Cloud Vertical Structure and Convective Aggregation over Tropical Ocean, J. Climate, 30, 2187–2207, https://doi.org/10.1175/JCLI-D-16-0125.1, 2017. a
Tan, J., Jakob, C., Rossow, W. B., and Tselioudis, G.: Increases in tropical rainfall driven by changes in frequency of organized deep convection, Nature, 519, 451–454, https://doi.org/10.1038/nature14339, 2015. a
Tobin, I., Bony, S., and Roca, R.: Observational Evidence for Relationships between the Degree of Aggregation of Deep Convection, Water Vapor, Surface Fluxes, and Radiation, J. Climate, 25, 6885–6904, https://doi.org/10.1175/JCLI-D-11-00258.1, 2012. a, b, c, d
Tobin, I., Bony, S., Holloway, C. E., Grandpeix, J.-Y., Sèze, G., Coppin, D., Woolnough, S. J., and Roca, R.: Does convective aggregation need to be represented in cumulus parameterizations?, J. Adv. Model. Earth Sy., 5, 692–703, https://doi.org/10.1002/jame.20047, 2013. a, b
Tompkins, A. M.: Organization of Tropical Convection in Low Vertical Wind Shears: The Role of Water Vapor, J. Atmos. Sci., 58, 529–545, https://doi.org/10.1175/1520-0469(2001)058<0529:OOTCIL>2.0.CO;2, 2001. a, b
Tompkins, A. M. and Semie, A. G.: Organization of tropical convection in low vertical wind shears: Role of updraft entrainment, J. Adv. Model. Earth Sy., 9, 1046–1068, https://doi.org/10.1002/2016MS000802, 2017. a, b, c, d
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., Yu, T., and scikit-image contributors: scikit-image: image processing in Python, 2, e453, https://doi.org/10.7717/peerj.453, 2014. a
Weger, R. C., Lee, J., Zhu, T., and Welch, R. M.: Clustering, randomness and regularity in cloud fields: 1. Theoretical considerations, J. Geophys. Res.-Atmos., 97, 20519–20536, https://doi.org/10.1029/92JD02038, 1992. a, b
White, B. A., Buchanan, A. M., Birch, C. E., Stier, P., and Pearson, K. J.: Quantifying the Effects of Horizontal Grid Length and Parameterized Convection on the Degree of Convective Organization Using a Metric of the Potential for Convective Interaction, J. Atmos. Sci., 75, 425–450, https://doi.org/10.1175/JAS-D-16-0307.1, 2018. a, b, c
Wing, A. A. and Emanuel, K. A.: Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations, J. Adv. Model. Earth Sy., 6, 59–74, https://doi.org/10.1002/2013MS000269, 2014. a, b, c
Wing, A. A., Emanuel, K., Holloway, C. E., and Muller, C.: Convective Self-Aggregation in Numerical Simulations: A Review, Surv. Geophys., 38, 1173–1197, https://doi.org/10.1007/s10712-017-9408-4, 2017. a, b
Wing, A. A., Stauffer, C. L., Becker, T., Reed, K. A., Ahn, M.-S., Arnold, N. P., Bony, S., Branson, M., Bryan, G. H., Chaboureau, J.-P., De Roode, S. R., Gayatri, K., Hohenegger, C., Hu, I.-K., Jansson, F., Jones, T. R., Khairoutdinov, M., Kim, D., Martin, Z. K., Matsugishi, S., Medeiros, B., Miura, H., Moon, Y., Müller, S. K., Ohno, T., Popp, M., Prabhakaran, T., Randall, D., Rios-Berrios, R., Rochetin, N., Roehrig, R., Romps, D. M., Ruppert Jr., J. H., Satoh, M., Silvers, L. G., Singh, M. S., Stevens, B., Tomassini, L., van Heerwaarden, C. C., Wang, S., and Zhao, M.: Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations, J. Adv. Model. Earth Sy., 12, e2020MS002138, https://doi.org/10.1029/2020MS002138, 2020. a
Xu, K.-M., Hu, Y., and Wong, T.: Convective Aggregation and Indices Examined from CERES Cloud Object Data, J. Geophys. Res.-Atmos., 124, 13604–13624, https://doi.org/10.1029/2019JD030816, 2019. a, b, c
Short summary
In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
In recent years, several studies focused their attention on the disposition of convection. Lots...