Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4021-2024
© 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/essd-16-4021-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020)
Université de Toulouse, LEGOS (CNRS/UT3), Toulouse, France
Rémy Roca
Université de Toulouse, LEGOS (CNRS/UT3), Toulouse, France
Related authors
Louis Netz, Thomas Fiolleau, and Rémy Roca
EGUsphere, https://doi.org/10.5194/egusphere-2025-2247, https://doi.org/10.5194/egusphere-2025-2247, 2025
Short summary
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.
Hannes Konrad, Rémy Roca, Anja Niedorf, Stephan Finkensieper, Marc Schröder, Sophie Cloché, Giulia Panegrossi, Paolo Sanò, Christopher Kidd, Rômulo Augusto Jucá Oliveira, Karsten Fennig, Thomas Sikorski, Madeleine Lemoine, and Rainer Hollmann
Earth Syst. Sci. Data, 17, 4097–4124, https://doi.org/10.5194/essd-17-4097-2025, https://doi.org/10.5194/essd-17-4097-2025, 2025
Short summary
Short summary
GIRAFE v1 is a global satellite-based precipitation dataset covering 2002 to 2022. It combines high-accuracy microwave and high-resolution infrared observations for retrieving daily precipitation, a respective sampling uncertainty for a more robust analysis, and monthly means. It is intended and suitable for climate monitoring and research and allows studies on water management, agriculture, and disaster risk reduction. A continuous extension from 2023 onwards will be implemented in 2025.
Louis Netz, Thomas Fiolleau, and Rémy Roca
EGUsphere, https://doi.org/10.5194/egusphere-2025-2247, https://doi.org/10.5194/egusphere-2025-2247, 2025
Short summary
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.
Cited articles
Arkin, P. A.: The Relationship between Fractional Coverage of High Cloud and Rainfall Accumulations during GATE over the B-Scale Array, https://doi.org/10.1175/1520-0493(1979)107<1382:TRBFCO>2.0.CO;2, 1979.
Arnaud, Y., Desbois, M., and Maizi, J.: Automatic Tracking and Characterization of African Convective Systems on Meteosat Pictures, J. Appl. Meteor. Climatol, 31, 443–453, https://doi.org/10.1175/1520-0450(1992)031<0443:ATACOA>2.0.CO;2, 1992.
Autones, F. and Moisselin, J.-M.: Algorithm Theoretical Basis Document for “Rapid Development Thunderstorms” (RDT-PGE11 v3.0), Scientific documentation of SAF/NWC, code SAF/NWC/CDOP2/MFT/SCI/ATBD/11, 2013.
Bellerby, T., Hsu, K., and Sorooshian, S.: LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration, J. Hydrometeorol., 10, 1081–1095, https://doi.org/10.1175/2009jhm1091.1, 2009.
Berthet, S., Roca, R., Duvel, J. P., and Fiolleau, T.: Subseasonal variability of mesoscale convective systems over the tropical northeastern Pacific, Q. J. Roy. Meteor. Soc., 143, 1086–1094, https://doi.org/10.1002/qj.2992, 2017.
Boer, E. R. and Ramanathan, V.: Lagrangian approach for deriving cloud characteristics from satellite observations and its implications to cloud parameterization, J. Geophys. Res.-Atmos., 102, 21383–21399, https://doi.org/10.1029/97JD00930, 1997.
Bouniol, D., Roca, R., Fiolleau, T., and Poan, D. E.: Macrophysical, Microphysical, and Radiative Properties of Tropical Mesoscale Convective Systems over Their Life Cycle, J. Climate, 29, 3353–3371, https://doi.org/10.1175/jcli-d-15-0551.1, 2016.
Bouniol, D., Roca, R., Fiolleau, T., and Raberanto, P.: Life cycle-resolved observation of radiative properties of mesoscale convective systems, J. Appl. Meteorol. Climatol., 60, 1091–1104, https://doi.org/10.1175/JAMC-D-20-0244.1, 2021.
Carvalho, L. M. V. and Jones, C.: A Satellite Method to Identify Structural Properties of Mesoscale Convective Systems Based on the Maximum Spatial Correlation Tracking Technique (MASCOTTE), J. Appl. Meteorol., 40, 1683–1701, https://doi.org/10.1175/1520-0450(2001)040<1683:ASMTIS>2.0.CO;2, 2001.
Dias, J., Tulich, S. N., and Kiladis, G. N.: An Object-Based Approach to Assessing the Organization of Tropical Convection, J. Atmos. Sci., 69, 2488–2504, https://doi.org/10.1175/JAS-D-11-0293.1, 2012.
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, J. Geophys. Res.-Atmos., 127, e2021JD035599, https://doi.org/10.1029/2021JD035599, 2022.
Endlich, R. M. and Wolf, D. E.: Automatic Cloud Tracking Applied to GOES and METEOSAT Observations, J. Appl. Meteorol. Climatol., 20, 309–319, https://doi.org/10.1175/1520-0450(1981)020<0309:ACTATG>2.0.CO;2, 1981.
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., 121, 11723–11738, https://doi.org/10.1002/2016JD025653, 2016.
Evans, J. L. and Shemo, R. E.: A Procedure for Automated Satellite-Based Identification and Climatology Development of Various Classes of Organized Convection, J. Appl. Meteorol. Climatol., 35, 638–652, https://doi.org/10.1175/1520-0450(1996)035<0638:APFASB>2.0.CO;2, 1996.
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze, R. A., Li, J., Hardin, J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective System Database Using Satellite-Derived Cloud Tops, Surface Precipitation, and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021.
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.
Fiolleau, T.: Animation of convective situation segmented by TOOCAN from HIMAWARI IR data over the Western Pacific region in October 2015, TIB AV Portal [video], https://doi.org/10.5446/68200, 2024.
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, 2013a.
Fiolleau, T. and Roca, R.: Composite life cycle of tropical mesoscale convective systems from geostationary and low Earth orbit satellite observations: method and sampling considerations, Q. J. Roy. Meteor. Soc., 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, IPSL Data Catalog [data set], https://doi.org/10.14768/1be7fd53-8b81-416e-90d5-002b36b30cf8, 2023a.
Fiolleau, T. and Roca, R.: CACATOES database V1.04, IPSL Data Catalog [data set], https://doi.org/10.14768/98569eea-d056-412d-9f52-73ea07b9cdca, 2023b.
Fiolleau, T. and Roca, R.: GEOgrid_coldcloud: a 2012–2020 global homogenized infrared dataset from a fleet of geostationary satellites, IPSL Data Catalog [data set], https://doi.org/10.14768/93f138f5-a553-4691-96ed-952fd32d2fc3, 2023c.
Fiolleau, T., Roca, R., Cloche, 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.
Guilloteau, C. and Foufoula-Georgiou, E.: Life Cycle of Precipitating Cloud Systems from Synergistic Satellite Observations: Evolution of Macrophysical Properties and Precipitation Statistics from Geostationary Cloud tracking and GPM Active and Passive Microwave Measurements, J. Hydrometeorol., https://doi.org/10.1175/jhm-d-23-0185.1, 2024.
Harper, B., Kepert, J., and Ginger, J.: Wind speed time averaging conversions for tropical cyclone conditions, in: Proc. 28th Conference Hurricanes and Tropical Meteorology (AMS, 2008), 27 April–2 May 2008, Palms Foyer, USA, 2008.
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.
Hennon, C. C., Helms, C. N., Knapp, K. R., and Bowen, A. R.: An objective algorithm for detecting and tracking tropical cloud clusters: Implications for tropical cyclogenesis prediction, J. Atmos. Ocean. Tech., 28, 1007–1018, https://doi.org/10.1175/2010JTECHA1522.1, 2011.
Hennon, C. C., Papin, P. P., Zarzar, C. M., Michael, J. R., Adam Caudill, J., Douglas, C. R., Groetsema, W. C., Lacy, J. H., Maye, Z. D., Reid, J. L., Scales, M. A., Talley, M. D., and Helms, C. N.: Tropical cloud cluster climatology, variability, and genesis productivity, J. Climate, 26, 3046–3066, https://doi.org/10.1175/JCLI-D-12-00387.1, 2013.
Hewison, T. J., Wu, X., Yu, F., Tahara, Y., Hu, X., Kim, D., and Koenig, M.: GSICS inter-calibration of infrared channels of geostationary imagers using metop/IASI, IEEE T. Geosci. Remote, 51, 1160–1170, https://doi.org/10.1109/TGRS.2013.2238544, 2013.
Hodges, K. I.: A General-Method For Tracking Analysis And Its Application To Meteorological Data, Mon. Weather Rev., 122, 2573–2586, https://doi.org/10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2, 1994.
Houze Jr., R. A.: Mesoscale Convective Systems, Rev. Geophys., 42, 1–43, https://doi.org/10.1029/2004RG000150, 2004.
Houze, R. A.: 100 Years of Research on Mesoscale Convective Systems, Meteorological Monographs, AMSMONOGRAPHS-D-18-0001.1, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0001.1, 2018.
Huang, X., Hu, C., Huang, X., Chu, Y., Tseng, Y., Zhang, G. J., and Lin, Y.: A long-term tropical mesoscale convective systems dataset based on a novel objective automatic tracking algorithm, Clim. Dynam., 51, 3145–3159, https://doi.org/10.1007/s00382-018-4071-0, 2018.
Jirak, I. L., Cotton, W. R., and Mcanelly, R. L.: Satellite and Radar Survey of Mesoscale Convective System Development, Mon. Weather Rev., 131, 2428–2449, https://doi.org/10.1175/1520-0493(2003)131<2428:SARSOM>2.0.CO;2, 2003.
Jones, W. K., Christensen, M. W., and Stier, P.: A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations, Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, 2023.
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), B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010.
Knapp, K. R., Ansari, S., Bain, C. L., Bourassa, M. A., Dickinson, M. J., Funk, C., Helms, C. N., Hennon, C. C., Holmes, C. D., Huffman, G. J., Kossin, J. P., Lee, H. T., Loew, A., and Magnusdottir, G.: Globally Gridded Satellite observations for climate studies, B. Am. Meteorol. Soc., 92, 893–907, https://doi.org/10.1175/2011BAMS3039.1, 2011.
Kruk, M. C., Knapp, K. R., and Levinson, D. H.: A technique for combining global tropical cyclone best track data, J. Atmos. Ocean. Tech., 27, 680–692, https://doi.org/10.1175/2009JTECHA1627.1, 2010.
Laing, A. G. and Fritsch, J. M.: The global population of mesoscale convective complexes, Q. J. Roy. Meteor. Soc., 123, 389–405, https://doi.org/10.1002/qj.49712353807, 1997.
Laing, A. G. and Fritsch, J. M.: The Large-Scale Environments of the Global Populations of Mesoscale Convective Complexes, Mon. Weather Rev., 128, 2756–2776, 2000.
Liu, C., Zipser, E. J., and Nesbitt, S. W.: Global Distribution of Tropical Deep Convection: Different Perspectives from TRMM Infrared and Radar Data, J. Climate, 20, 489–503, https://doi.org/10.1175/jcli4023.1, 2007.
Liu, C., Zipser, E. J., Cecil, D. J., Nesbitt, S. W., and Sherwood, S.: A cloud and precipitation feature database from nine years of TRMM observations, J. Appl. Meteorol. Climatol., 47, 2712–2728, https://doi.org/10.1175/2008JAMC1890.1, 2008.
Machado, L. A. T. and Rossow, W. B.: Structural Characteristics and Radiative Properties of Tropical Cloud Clusters, Mon. Weather Rev., 121, 3234–3260, https://doi.org/10.1175/1520-0493(1993)121<3234:SCARPO>2.0.CO;2, 1993.
Machado, L. A. T., Desbois, M., Duvel, J.-P., Machado, L. A. T., Desbois, M., and Duvel, J.-P.: Structural Characteristics of Deep Convective Systems over Tropical Africa and the Atlantic Ocean, Mont. Weather Rev., 120, 392–406, https://doi.org/10.1175/1520-0493(1992)120<0392:SCODCS>2.0.CO;2, 1992.
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, 1998.
Maddox, R. A.: Mesoscale Convective Complexes, B. Am. Meteorol. Soc., 61, 1374–1387, 1980.
Mapes, B. and Houze, R. A.: Cloud Clusters and Superclusters over the Oceanic Warm Pool, Mon. Weather Rev., 121, 1398–1415, https://doi.org/10.1175/1520-0493(1993)121<1398:CCASOT>2.0.CO;2, 1993.
Mapes, B., Milliff, R., and Morzel, J.: Composite life cycle of maritime tropical mesoscale convective systems in scatterometer and microwave satellite observations, J. Atmos. Sci., 66, 199–208, https://doi.org/10.1175/2008JAS2746.1, 2009.
Mathon, V. and Laurent, H.: Life cycle of Sahelian mesoscale convective cloud systems, Q. J. Roy., Meteor. Soc., 127, 377–406, https://doi.org/10.1002/qj.49712757208, 2001.
Mohr, K. I. and Zipser, E. J.: Mesoscale Convective Systems Defined by Their 85-GHz Ice Scattering Signature: Size and Intensity Comparison over Tropical Oceans and Continents, Mon. Weather Rev., 124, 2417–2437, https://doi.org/10.1175/1520-0493(1996)124<2417:mcsdbt>2.0.co;2, 1996.
Ocasio, K. M. N., Evans, J. L., and Young, G. S.: Tracking mesoscale convective systems that are potential candidates for tropical cyclogenesis, Mon. Weather Rev., 148, 655–669, https://doi.org/10.1175/MWR-D-19-0070.1, 2020.
Ostlund, S.: Computer Software for Rainfall Analyses and Echo Tracking of Digitized Radar Data, NOAA technical memorandum ERL WMPO, 15, 1974.
Poujol, B., Prein, A. F., and Newman, A. J.: Kilometer-scale modeling projects a tripling of Alaskan convective storms in future climate, Clim. Dynam., 55, 3543–3564, https://doi.org/10.1007/s00382-020-05466-1, 2020.
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.
Rajagopal, M., Russell, J., Skok, G., and Zipser, E.: Tracking Mesoscale Convective Systems in IMERG and Regional Variability of Their Properties in the Tropics, J. Geophys. Res.-Atmos., 128, e2023JD038563, https://doi.org/10.1029/2023JD038563, 2023.
Roca, R. and Fiolleau, T.: Extreme precipitation in the tropics is closely associated with long-lived convective systems, Commun. Earth Environ., 1, 18, https://doi.org/10.1038/s43247-020-00015-4, 2020.
Roca, R. and Ramanathan, V.: Scale dependence of monsoonal convective systems over the Indian Ocean, J. Climate, 13, 1286–1298, https://doi.org/10.1175/1520-0442(2000)013<1286:SDOMCS>2.0.CO;2, 2000.
Roca, R., Berges, J. C., Brogniez, H., Capderou, M., Chambon, P., Chomette, O., Cloche, S., Fiolleau, T., Jobard, I., and Lemond, J.: On the water and energy cycles in the Tropics, Comptes Rendus Geosciences, 342, 390–402, https://doi.org/10.1016/j.crte.2010.01.003, 2010.
Roca, R., Aublanc, J., Chambon, P., Fiolleau, T., and Viltard, N.: Robust observational quantification of the contribution of mesoscale convective systems to rainfall in the tropics, J. Climate, 27, 4952–4958, https://doi.org/10.1175/JCLI-D-13-00628.1, 2014.
Roca, R., Fiolleau, T., and Bouniol, D.: A simple model of the life cycle of mesoscale convective systems cloud shield in the tropics, J. Climate, 30, 4283–4298, https://doi.org/10.1175/JCLI-D-16-0556.1, 2017.
Roca, R., Bouniol, D., and Fiolleau, T.: On the Duration and Life Cycle of Precipitation Systems in the Tropics, in: Satellite Precipitation Measurement: Volume 2, edited by: Levizzani, V., Kidd, C., Kirschbaum, D., Kummerow, C., Nakamura, K., and Turk, F., Springer International Publishing, Cham, 729–744, https://doi.org/10.1007/978-3-030-35798-6_14, 2020.
Roca, R., Fiolleau, T., Viju, J., and Schulz, J.: METEOSAT Long-Term Observations Reveal Changes in Convective organization over Tropical Africa and Atlantic Ocean, Surv. Geophys., in review, 2024.
Schröder, M., König, M., and Schmetz, J.: Deep convection observed by the Spinning Enhanced Visible and Infrared Imager on board Meteosat 8: Spatial distribution and temporal evolution over Africa in summer and winter 2006, J. Geophys. Res.-Atmos., 114, 1–14, https://doi.org/10.1029/2008JD010653, 2009.
Szantai, A., Six, B., Cloché, S., and Sèze, G.: megha-tropiques MTTM Megha-Tropiques Technical Memorandum Quality of geostationary satellite images Megha-Tropiques Quality of geostationary satellite images, https://meghatropiques.ipsl.fr/download/megha-tropiques-technical-memorandum-n3/ (last access: 9 September 2024), 2011.
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.
Tsakraklides, G. and Evans, J. L.: Global and regional diurnal variations of organized convection, J. Climate, 16, 1562–1572, https://doi.org/10.1175/1520-0442-16.10.1562, 2003.
Vant-Hull, B., Rossow, W., and Pearl, C.: American Meteorological Society Global Comparisons of Regional Life Cycle Properties and Motion of Multiday Convective Systems: Tropical and Midlatitude Land and Ocean, J. Climate, 29, 5837–5858, https://doi.org/10.1175/JCLI-D-15-0698.1, 2016.
Wilcox, E. M.: Spatial and temporal scales of precipitating tropical cloud systems in satellite imagery and the NCAR CCM3, J. Climate, 16, 3545–3559, https://doi.org/10.1175/1520-0442(2003)016<3545:SATSOP>2.0.CO;2, 2003.
Williams, M. and Houze, R. A.: Satellite-Observed Characteristics of Winter Monsoon Cloud Clusters, Mon. Weather Rev., 115, 505–519, https://doi.org/10.1175/1520-0493(1987)115<0505:SOCOWM>2.0.CO;2, 1987.
WMO: Measurement of surface wind. Guide to Meteorological Instruments and Methods of Observations, 5th edn., World Meteorological Organization Tech. Rep. WMO-8, 1983.
Woodley, W. L., Griffith, C. G., Griffin, J. S., and Stromatt, S. C.: The Inference of GATE Convective Rainfall from SMS-1 Imagery, J. Appl. Meteorol., 19, 388–408, 1980.
Short summary
This paper presents a database of tropical deep convective systems over the 2012–2020 period, built from a cloud-tracking algorithm called TOOCAN, which has been applied to homogenized infrared observations from a fleet of geostationary satellites. This database aims to analyze the tropical deep convective systems, the evolution of their associated characteristics over their life cycle, their organization, and their importance in the hydrological and energy cycle.
This paper presents a database of tropical deep convective systems over the 2012–2020 period,...
Altmetrics
Final-revised paper
Preprint