Articles | Volume 18, issue 24
https://doi.org/10.5194/amt-18-7603-2025
© 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/amt-18-7603-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A method for characterizing the spatial organization of deep convective cores in deep convective systems' cloud shield using idealized elementary convective structure decomposition
Université de Toulouse, Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (CNRS/CNES/IRD/UPS), Toulouse, France
Thomas Fiolleau
Université de Toulouse, Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (CNRS/CNES/IRD/UPS), Toulouse, France
Rémy Roca
Université de Toulouse, Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (CNRS/CNES/IRD/UPS), Toulouse, France
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Cited articles
Abramian, S., Muller, C., Risi, C., Fiolleau, T., and Roca, R.: How key features of early development shape deep convective systems, npj Clim. Atmos. Sci., 8, 258, https://doi.org/10.1038/s41612-025-01154-1, 2025.
Anber, U., Wang, S., and Sobel, A.: Response of Atmospheric Convection to Vertical Wind Shear: Cloud-System-Resolving Simulations with Parameterized Large-Scale Circulation. Part II: Effect of Interactive Radiation, Journal of the Atmospheric Sciences, 73, 199–209, https://doi.org/10.1175/JAS-D-15-0151.1, 2016.
Awaka, J., Iguchi, T., and Okamoto, K.: TRMM PR Standard Algorithm 2A23 and its Performance on Bright Band Detection, Journal of the Meteorological Society of Japan. Ser. II, 87A, 31–52, https://doi.org/10.2151/jmsj.87A.31, 2009.
Awaka, J., Le, M., Chandrasekar, V., Yoshida, N., Higashiuwatoko, T., Kubota, T., and Iguchi, T.: Rain Type Classification Algorithm Module for GPM Dual-Frequency Precipitation Radar, https://doi.org/10.1175/JTECH-D-16-0016.1, 2016.
Awaka, J., Le, M., Brodzik, S., Kubota, T., Masaki, T., Chandrasekar, V., and Iguchi, T.: Development of Precipitation Type Classification Algorithms for a Full Scan Mode of GPM Dual-frequency Precipitation Radar, Journal of the Meteorological Society of Japan. Ser. II, 99, 1253–1270, https://doi.org/10.2151/jmsj.2021-061, 2021.
Biagioli, G.: Understanding deep convective organization: simple stochastic approaches and new metrics to bridge the gaps, PhD thesis, Università degli Studi di Trieste, 2023.
Biagioli, G. and Tompkins, A. M.: Measuring Convective Organization, Journal of the Atmospheric Sciences, 80, 2769–2789, https://doi.org/10.1175/JAS-D-23-0103.1, 2023.
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.
Bryan, G. H., Wyngaard, J. C., and Fritsch, J. M.: Resolution Requirements for the Simulation of Deep Moist Convection, Monthly Weather Review, 131, 2394–2416, https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2, 2003.
Chen, Y., Wang, D., Zeng, Z., Huang, L., Li, E., and Xue, Y.: Observational structure and physical features of tropical precipitation systems, Atmospheric Research, 315, 107885, https://doi.org/10.1016/j.atmosres.2024.107885, 2025.
Doswell, C. A., Brooks, H. E., and Maddox, R. A.: Flash Flood Forecasting: An Ingredients-Based Methodology, Weather and Forecasting, 11, 560–581, https://doi.org/10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2, 1996.
Elsaesser, G. S., Roca, R., Fiolleau, T., Del Genio, A. D., and Wu, J.: A Simple Model for Tropical Convective Cloud Shield Area Growth and Decay Rates Informed by Geostationary IR, GPM, and Aqua/AIRS Satellite Data, Journal of Geophysical Research: Atmospheres, 127, e2021JD035599, https://doi.org/10.1029/2021JD035599, 2022.
Feng, Z., Prein, A. F., Kukulies, J., Fiolleau, T., Jones, W. K., Maybee, B., Moon, Z. L., Núñez Ocasio, K. M., Dong, W., Molina, M. J., Albright, M. G., Rajagopal, M., Robledo, V., Song, J., Song, F., Leung, L. R., Varble, A. C., Klein, C., Roca, R., Feng, R., and Mejia, J. F.: Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km-Scale Simulations, Journal of Geophysical Research: Atmospheres, 130, e2024JD042204, https://doi.org/10.1029/2024JD042204, 2025.
Fiolleau, T. and Roca, R.: An Algorithm for the Detection and Tracking of Tropical Mesoscale Convective Systems Using Infrared Images From Geostationary Satellite, IEEE Transactions on Geoscience and Remote Sensing, 51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762, 2013a.
iolleau, T. and Roca, R.: Composite life cycle of tropical mesoscale convective systems from geostationary and low Earth orbit satellite observations: method and sampling considerations, Quarterly Journal of the Royal Meteorological Society, 139, 941–953, https://doi.org/10.1002/qj.2174, 2013b.
Fiolleau, T. and Roca, R.: TOOCAN Database V2.08 – Tracking Of Organized Convection Algorithm using a 3-dimensional segmentation, https://doi.org/10.14768/1BE7FD53-8B81-416E-90D5-002B36B30CF8, 2023.
Fiolleau, T. and Roca, R.: A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020), Earth Syst. Sci. Data, 16, 4021–4050, https://doi.org/10.5194/essd-16-4021-2024, 2024.
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 Transactions on Geoscience and Remote Sensing, 58, 6609–6622, https://doi.org/10.1109/TGRS.2020.2978171, 2020.
Gallus, W. A., Snook, N. A., and Johnson, E. V.: Spring and Summer Severe Weather Reports over the Midwest as a Function of Convective Mode: A Preliminary Study, Weather and Forecasting, 23, 101–113, https://doi.org/10.1175/2007WAF2006120.1, 2008.
Haerter, J. O., Böing, S. J., Henneberg, O., and Nissen, S. B.: Circling in on Convective Organization, Geophysical Research Letters, 46, 7024–7034, https://doi.org/10.1029/2019GL082092, 2019.
Holloway, C. E., Wing, A. A., Bony, S., Muller, C., Masunaga, H., L'Ecuyer, T. S., Turner, D. D., and Zuidema, P.: Observing Convective Aggregation, Surv. Geophys., 38, 1199–1236, https://doi.org/10.1007/s10712-017-9419-1, 2017.
Houze Jr., R. A.: Mesoscale convective systems, Reviews of Geophysics, 42, https://doi.org/10.1029/2004RG000150, 2004.
Houze Jr., R. A. and Betts, A. K.: Convection in GATE, Reviews of Geophysics, 19, 541–576, https://doi.org/10.1029/RG019i004p00541, 1981.
Houze Jr., R. A., Wilton, D. C., and Smull, B. F.: Monsoon convection in the Himalayan region as seen by the TRMM Precipitation Radar, Quarterly Journal of the Royal Meteorological Society, 133, 1389–1411, https://doi.org/10.1002/qj.106, 2007.
Houze Jr., R. A., Rasmussen, K. L., Zuluaga, M. D., and Brodzik, S. R.: The variable nature of convection in the tropics and subtropics: A legacy of 16 years of the Tropical Rainfall Measuring Mission satellite, Reviews of Geophysics, 53, 994–1021, https://doi.org/10.1002/2015RG000488, 2015.
Houze, R. A.: Stratiform Precipitation in Regions of Convection: A Meteorological Paradox?, 1997.
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, Geophysical Research Letters, 48, e2020GL091001, https://doi.org/10.1029/2020GL091001, 2021.
Ji, L., Xu, W., Chen, H., and Liu, N.: Consistency of Vertical Reflectivity Profiles and Echo-Top Heights between Spaceborne Radars Onboard TRMM and GPM, Remote Sensing, 14, 1987, https://doi.org/10.3390/rs14091987, 2022.
Jin, D., Oreopoulos, L., Lee, D., Tan, J., and Kim, K.: A New Organization Metric for Synoptic Scale Tropical Convective Aggregation, Journal of Geophysical Research: Atmospheres, 127, e2022JD036665, https://doi.org/10.1029/2022JD036665, 2022.
Khairoutdinov, M. F. and Randall, D. A.: Cloud Resolving Modeling of the ARM Summer 1997 IOP: Model Formulation, Results, Uncertainties, and Sensitivities, Journal of the Atmospheric Sciences, 60, 607–625, https://doi.org/10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2, 2003.
Kim, D.-S., Maki, M., Shimizu, S., and Lee, D.-I.: X-Band Dual-Polarization Radar Observations of Precipitation Core Development and Structure in a Multi-Cellular Storm over Zoshigaya, Japan, on August 5, 2008, Journal of the Meteorological Society of Japan. Ser. II, 90, 701–719, https://doi.org/10.2151/jmsj.2012-509, 2012.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS), Bulletin of the American Meteorological Society, 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010.
Koren, I., Dror, T., Altaratz, O., and Chekroun, M. D.: Cloud Versus Void Chord Length Distributions (LvL) as a Measure for Cloud Field Organization, Geophysical Research Letters, 51, e2024GL108435, https://doi.org/10.1029/2024GL108435, 2024.
Kukulies, J., Prein, A. F., and Morrison, H.: Simulating Precipitation Efficiency Across the Deep Convective Gray Zone, Journal of Geophysical Research: Atmospheres, 129, e2024JD041924, https://doi.org/10.1029/2024JD041924, 2024.
Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson, J.: The Tropical Rainfall Measuring Mission (TRMM) Sensor Package, Journal of Atmospheric and Oceanic Technology, 15, 809–817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2, 1998.
Lafore, J. P., Chapelon, N., Diop, M., Gueye, B., Largeron, Y., Lepape, S., Ndiaye, O., Parker, D. J., Poan, E., Roca, R., Roehrig, R., Taylor, C., and Moncrieff, M.: Deep Convection, in: Meteorology of Tropical West Africa, John Wiley & Sons, Ltd, 90–129, https://doi.org/10.1002/9781118391297.ch3, 2017.
Lamer, K., Kollias, P., Luke, E. P., Treserras, B. P., Oue, M., and Dolan, B.: Multisensor Agile Adaptive Sampling (MAAS): A Methodology to Collect Radar Observations of Convective Cell Life Cycle, Journal of Atmospheric and Oceanic Technology, 40, 1509–1522, https://doi.org/10.1175/JTECH-D-23-0043.1, 2023.
Le, M. and Chandrasekar, V.: Precipitation Type Classification Method for Dual-Frequency Precipitation Radar (DPR) Onboard the GPM, IEEE Transactions on Geoscience and Remote Sensing, 51, 1784–1790, https://doi.org/10.1109/TGRS.2012.2205698, 2013.
LeMone, M. A., Zipser, E. J., and Trier, S. B.: The Role of Environmental Shear and Thermodynamic Conditions in Determining the Structure and Evolution of Mesoscale Convective Systems during TOGA COARE, Journal of the Atmospheric Sciences, 55, 3493–3518, https://doi.org/10.1175/1520-0469(1998)055<3493:TROESA>2.0.CO;2, 1998.
López, R. E.: Internal Structure and Development Processes of C-Scale Aggregates of Cumulus Clouds, Monthly Weather Review, 106, 1488–1494, https://doi.org/10.1175/1520-0493(1978)106<1488:ISADPO>2.0.CO;2, 1978.
Mandorli, G. and Stubenrauch, C. J.: Assessment of object-based indices to identify convective organization, Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, 2024.
Mapes, B. E.: Gregarious Tropical Convection, Journal of the Atmospheric Sciences, 50, 2026–2037, https://doi.org/10.1175/1520-0469(1993)050<2026:GTC>2.0.CO;2, 1993.
Marinescu, P. J., van den Heever, S. C., Saleeby, S. M., and Kreidenweis, S. M.: The microphysical contributions to and evolution of latent heating profiles in two MC3E MCSs, Journal of Geophysical Research: Atmospheres, 121, 7913–7935, https://doi.org/10.1002/2016JD024762, 2016.
Markowski, P. and Richardson, Y.: Mesoscale Meteorology in Midlatitudes, John Wiley and Sons, https://doi.org/10.1002/9780470682104, 2010.
Moroda, Y., Tsuboki, K., Satoh, S., Nakagawa, K., Ushio, T., and Shimizu, S.: Structure and Evolution of Precipitation Cores in an Isolated Convective Storm Observed by Phased Array Weather Radar, Journal of the Meteorological Society of Japan. Ser. II, 99, 765–784, https://doi.org/10.2151/jmsj.2021-038, 2021.
Muller, C. and Bony, S.: What favors convective aggregation and why?, Geophysical Research Letters, 42, 5626–5634, https://doi.org/10.1002/2015GL064260, 2015.
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, Annual Review of Fluid Mechanics, 54, 133–157, https://doi.org/10.1146/annurev-fluid-022421-011319, 2022.
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, Journal of Geophysical Research: Atmospheres, 129, e2023JD040254, https://doi.org/10.1029/2023JD040254, 2024.
Retsch, M. H., Jakob, C., and Singh, M. S.: Assessing Convective Organization in Tropical Radar Observations, Journal of Geophysical Research: Atmospheres, 125, e2019JD031801, https://doi.org/10.1029/2019JD031801, 2020.
Roca, R., Fiolleau, T., and Bouniol, D.: A Simple Model of the Life Cycle of Mesoscale Convective Systems Cloud Shield in the Tropics, Journal of Climate, 30, 4283–4298, https://doi.org/10.1175/JCLI-D-16-0556.1, 2017.
Roca, R., Fiolleau, T., and Elsaesser, G.: Growth rate of deep convective system cloud shields: satellite observations and km-scale radiative convective equilibrium simulations, Copernicus Meetings, https://doi.org/10.5194/egusphere-egu24-7675, 2024.
Romatschke, U. and Houze, R. A.: Extreme Summer Convection in South America, Journal of Climate, 23, 3761–3791, https://doi.org/10.1175/2010JCLI3465.1, 2010.
Savre, J.: Spatial Dispersion and Statistical Description of Organized Cumulus Cloud Ensembles in Radiative Convective Equilibrium, Journal of Advances in Modeling Earth Systems, 16, e2023MS004096, https://doi.org/10.1029/2023MS004096, 2024.
Schiro, K. A., Sullivan, S. C., Kuo, Y.-H., Su, H., Gentine, P., Elsaesser, G. S., Jiang, J. H., and Neelin, J. D.: Environmental Controls on Tropical Mesoscale Convective System Precipitation Intensity, Journal of the Atmospheric Sciences, 77, 4233–4249, https://doi.org/10.1175/JAS-D-20-0111.1, 2020.
Semie, A. G. and Bony, S.: Relationship Between Precipitation Extremes and Convective Organization Inferred From Satellite Observations, Geophysical Research Letters, 47, e2019GL086927, https://doi.org/10.1029/2019GL086927, 2020.
Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., and Wilheit, T.: The Global Precipitation Measurement (GPM) Mission for Science and Society, Bulletin of the American Meteorological Society, 98, 1679–1695, https://doi.org/10.1175/BAMS-D-15-00306.1, 2017.
Stocker, E. F., Alquaied, F., Bilanow, S., Ji, Y., and Jones, L.: TRMM Version 8 Reprocessing Improvements and Incorporation into the GPM Data Suite, Journal of Atmospheric and Oceanic Technology, 35, 1181–1199, https://doi.org/10.1175/JTECH-D-17-0166.1, 2018.
Takahashi, H., Luo, Z. J., Stephens, G., and Mulholland, J. P.: Revisiting the Land-Ocean Contrasts in Deep Convective Cloud Intensity Using Global Satellite Observations, Geophysical Research Letters, 50, e2022GL102089, https://doi.org/10.1029/2022GL102089, 2023.
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, https://doi.org/10.1175/JCLI-D-11-00258.1, 2012.
Tompkins, A. M. and Semie, A. G.: Organization of tropical convection in low vertical wind shears: Role of updraft entrainment, Journal of Advances in Modeling Earth Systems, 9, 1046–1068, https://doi.org/10.1002/2016MS000802, 2017.
Tseng, C.-Y., Wang, L.-P., and Onof, C.: Modelling convective cell life cycles with a copula-based approach, Hydrol. Earth Syst. Sci., 29, 1–25, https://doi.org/10.5194/hess-29-1-2025, 2025.
Varble, A., Zipser, E. J., Fridlind, A. M., Zhu, P., Ackerman, A. S., Chaboureau, J.-P., Collis, S., Fan, J., Hill, A., and Shipway, B.: Evaluation of cloud-resolving and limited area model intercomparison simulations using TWP-ICE observations: 1. Deep convective updraft properties, Journal of Geophysical Research: Atmospheres, 119, 13891–13918, https://doi.org/10.1002/2013JD021371, 2014.
Weger, R. C., Lee, J., Zhu, T., and Welch, R. M.: Clustering, randomness and regularity in cloud fields: 1. Theoretical considerations, Journal of Geophysical Research: Atmospheres, 97, 20519–20536, https://doi.org/10.1029/92JD02038, 1992.
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, Journal of the Atmospheric Sciences, 75, 425–450, https://doi.org/10.1175/JAS-D-16-0307.1, 2018.
Wing, A. A. and Emanuel, K. A.: Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations, Journal of Advances in Modeling Earth Systems, 6, 59–74, https://doi.org/10.1002/2013MS000269, 2014.
Wing, A. A., Reed, K. A., Satoh, M., Stevens, B., Bony, S., and Ohno, T.: Radiative–convective equilibrium model intercomparison project, Geosci. Model Dev., 11, 793–813, https://doi.org/10.5194/gmd-11-793-2018, 2018.
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, Journal of Advances in Modeling Earth Systems, 12, e2020MS002138, https://doi.org/10.1029/2020MS002138, 2020.
Zipser, E. J. and Lutz, K. R.: The Vertical Profile of Radar Reflectivity of Convective Cells: A Strong Indicator of Storm Intensity and Lightning Probability?, Monthly Weather Review, 122, 1751–1759, https://doi.org/10.1175/1520-0493(1994)122<1751:TVPORR>2.0.CO;2, 1994.
Short summary
Convective systems are the primary drivers of rainfall and climate on Earth, yet the spatial organisation of associated convection remains poorly understood. This study presents a simple approach to describing this organisation. First, the convective field is decomposed into elementary structures. Then, four scores are computed to describe the size, density, spacing scale and departure from randomness of the cores. This method robustly characterises the organisation of convection.
Convective systems are the primary drivers of rainfall and climate on Earth, yet the spatial...