[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128313-en":3,"doc-seo-128313-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128313,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A machine-learning-based perspective on deep convective clouds and their organisation in 3D - Part 2 - Spatial–temporal patterns of convective organisation","This series of papers investigates spatio-temporal patterns of convective cloud occurrence and organisation using a machine-learning-based method to extrapolate contiguous 3D cloud fields from 2D satellite data. Part 2 studies convective organisation in tropical West Africa from March to August 2019 and relates organisation to 3D cloud properties and core structures. Organisation is quantified with SCAI, COP and ROME indices capturing different spatial clustering aspects. Results connect stronger organisation with larger cloud areas, lower tops and core heights, and shorter lifespans, while weak organisation links to smaller clouds and fewer cores. The frequency of organisation increases in the Northern Hemisphere during boreal summer, consistent with northward ITCZ migration, with coastal/Atlantic emergence followed by inland Sahel shifts, and oceanic regions showing slightly stronger organisation overall.","Atmos. Chem. Phys., 25, 10797–10822, 2025 [https://doi.org/10.5194/acp-25-10797-2025](https://doi.org/10.5194/acp-25-10797-2025)[ ](https://doi.org/10.5194/acp-25-10797-2025)© Author(s) 2025 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nA machine-learning-based perspective on deep convective clouds and their organisation in 3D – Part 2:  \nSpatial–temporal patterns of convective organisation  \nSarah Brüning and Holger Tost  \nInstitute for Physics of the Atmosphere, Johannes Gutenberg University Mainz, Johann-Joachim-Becher-Weg 21, 55128 Mainz, Rhineland-Palatinate, Germany Correspondence: Sarah Brüning ([sbruenin@uni-mainz.de](sbruenin@uni-mainz.de))  \nReceived: 27 January 2025 – Discussion started: 5 February 2025  \nRevised: 2 July 2025 – Accepted: 3 July 2025 – Published: 19 September 2025  \nAbstract. This series of papers explores spatio-temporal patterns of convective cloud occurrence and organisation. We use a machine-learning-based method to extrapolate a contiguous 3D cloud field of 2D satellite data. In Part 2, we focus on convective organisation in tropical West Africa between March and August 2019, examining how it relates to the 3D properties of convective clouds and their core structures. We quantify organisation using three indices (SCAI, COP, ROME) to capture different aspects of spatial cloud clustering. Our findings highlight how cloud properties may interact with organisation. Hence, strong organisation may occur with larger cloud areas, lower cloud tops and core heights, and shorter lifespans compared to the average convective system. In contrast, weak organisation may be associated with smaller clouds and fewer cores but similarly shorter lifespans. We find an increasing frequency of convective organisation in the Northern Hemisphere during boreal summer months, likely linked to the northward migration of the Intertropical Convergence Zone (ITCZ) . From March to May, patches of strong convective organisation emerge along the African coastlines and over the southern Atlantic Ocean. Between June and August, hotspots shift inland, particularly across the Sahel and wider West African Plains. Notably, oceanic regions show slightly stronger organisation overall. However, overlapping regions of strong and weak organisation may complicate the interpretation of regional statistics. While the machine-learning-based 3D perspective helps bridge observational gaps in the representation of cloud structures, the inherent complexity and variability of convective organisation highlight the need for continued investigation.  \n1 Introduction  \nAtmospheric convection plays an essential role in the climate system through its contribution to weather and climate variability (Brune et al., 2020) . In the tropics, we observe convective clouds forming as spatially connected structures of extensive size (Houze, 1977) . These mesoscale convective systems (MCSs) are one of the main drivers for the transport of heat and moisture through the atmosphere. Furthermore, they affect the hydrological and radiative variability on Earth (Hartmann et al., 1984) . The spatial clustering of convective systems – also known as convective organisation – may promote the occurrence of severe weather events such as hail and floods (Becker et al., 2021) . However, a robust assessment of  \nthe connection between convective organisation and extreme weather, in particular in a future climate under global warming, expresses the need for further research.  \nAlthough the term “convective organisation” has become increasingly popular in climate research, it is often used vaguely. Mapes and Neale (2011) broadly summarise organisation as “non-randomness in meteorological fields in convecting regions”. This definition induces a clustering of deep convective cells which is ubiquitous in the atmosphere, particularly in the tropics. However, the underlying mechanisms remain insufficiently understood (Muller and Bony, 2015) . While convective","cbCaikhFtcqGwxgk","https://ap.wps.com/l/cbCaikhFtcqGwxgk","pdf",10566382,4,1,26,"English","en",105,"# Introduction\n## Convective organisation and its definitions\n## Self-aggregation of convection and mechanisms\n## Motivation for metrics in observational data","[{\"question\":\"What is the goal of Part 2 in this series of papers?\",\"answer\":\"To study how convective organisation varies in space and time in tropical West Africa between March and August 2019, and how it relates to 3D cloud properties and core structures.\"},{\"question\":\"How is the 3D cloud information obtained from satellite data?\",\"answer\":\"A machine-learning-based method extrapolates a contiguous 3D cloud field from 2D satellite observations.\"},{\"question\":\"Which indices are used to quantify convective organisation?\",\"answer\":\"Three indices—SCAI, COP, and ROME—are used to capture different aspects of spatial cloud clustering.\"}]","A machine-learning-based perspective on deep convective clouds and their organisation in 3D - Part 2 - Spatial–temporal patterns of convective organisation | PDF",1785946786,66,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"a-machine-learning-based-perspective-on-deep-convective-clouds-and-their-organisation-in-3d-part-2-spatialtemporal-patterns-of-convective-organisation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/a-machine-learning-based-perspective-on-deep-convective-clouds-and-their-organisation-in-3d-part-2-spatialtemporal-patterns-of-convective-organisation/128313/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the goal of Part 2 in this series of papers?","Question",{"text":76,"@type":77},"To study how convective organisation varies in space and time in tropical West Africa between March and August 2019, and how it relates to 3D cloud properties and core structures.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the 3D cloud information obtained from satellite data?",{"text":81,"@type":77},"A machine-learning-based method extrapolates a contiguous 3D cloud field from 2D satellite observations.",{"name":83,"@type":74,"acceptedAnswer":84},"Which indices are used to quantify convective organisation?",{"text":85,"@type":77},"Three indices—SCAI, COP, and ROME—are used to capture different aspects of spatial cloud clustering.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]