RTCP-Net: Tropical cyclone generation prediction model based on multi-source information fusion

RTCP-Net: Tropical cyclone generation prediction model based on multi-source information fusion | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article RTCP-Net: Tropical cyclone generation prediction model based on multi-source information fusion Wei Tian, Xiaotian Li, Jianqiao Fan, Haikun Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6603584/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Tropical cyclones are among the most destructive extreme weather phenomena in nature, and accurately predicting whether a tropical cloud cluster will develop into a tropical cyclone is crucial for disaster prevention and mitigation. Considering the insufficient extraction of numerous key features in tropical cloud cluster data by previous deep learning-based tropical cyclogenesis prediction studies, in response, this paper proposes the Real Time Tropical Cyclogenesis Prediction-Net (RTCP-Net) based on multi-source information fusion. The model computes convective core maps and polar coordinate representations from infrared images of tropical cloud clusters and employs ResNet along with self-attention mechanism to extract their spatiotemporal features. Experimental results demonstrate that the proposed model achieves high accuracy and stability, it attains a detection rate of 99.4% and a false alarm rate of 0.36% when predicting the formation of tropical cyclones 24 hours in advance. Notably, the model not only ensures potential of real-time prediction capabilities from satellite data but also surpasses the accuracy of models that utilize reanalysis data. Earth and environmental sciences/Natural hazards Physical sciences/Mathematics and computing Tropical cyclogenesis prediction Deep learning Feature fusion Polar coordinate Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Nov, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviews received at journal 12 Jul, 2025 Reviewers agreed at journal 30 Jun, 2025 Reviewers invited by journal 15 Jun, 2025 Editor assigned by journal 15 Jun, 2025 Editor invited by journal 23 May, 2025 Submission checks completed at journal 12 May, 2025 First submitted to journal 12 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6603584","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472047910,"identity":"4b7caf29-c67f-48aa-894a-90a119f1051c","order_by":0,"name":"Wei Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYFAC5gMMDAZglgFxGngY2BJAiiVI0cIDVkmCFnuJnG+PeQrq6hjYm7dJMNTcIcIWidztxjwGhyUYeI6VSTAce0aEFuncbdI8BgckGCRyzCQYGw4ToyXnGVBLnQSD/BvitbABtTADbeEhVsv9Z2aScwwOS7bxpBVbJBwjQgt7z+FnEm/+1PHzsx/eeONDDRFa4IANRCSQoGEUjIJRMApGAR4AAP2JLGcWTFMLAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Tian","suffix":""},{"id":472047911,"identity":"7d45f6b3-2fdb-4953-858d-71cddb1a6765","order_by":1,"name":"Xiaotian Li","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaotian","middleName":"","lastName":"Li","suffix":""},{"id":472047912,"identity":"d5d8b29a-9bc5-40c9-84a6-377ffa5c39f2","order_by":2,"name":"Jianqiao Fan","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jianqiao","middleName":"","lastName":"Fan","suffix":""},{"id":472047913,"identity":"66384773-9c18-49f3-808f-408e873e4f65","order_by":3,"name":"Haikun Zhao","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Haikun","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-05-06 13:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6603584/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6603584/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-33207-z","type":"published","date":"2026-01-08T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100069040,"identity":"db73008b-c9fd-47e7-b04d-332d54c71483","added_by":"auto","created_at":"2026-01-12 16:05:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1091772,"visible":true,"origin":"","legend":"","description":"","filename":"gpcsycmjhdxjwhrmwntmpgkjzhbwfsrk.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6603584/v1_covered_3cf88d82-78bd-4569-aa09-34f6bf6752ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"RTCP-Net: Tropical cyclone generation prediction model based on multi-source information fusion","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tropical cyclogenesis prediction, Deep learning, Feature fusion, Polar coordinate","lastPublishedDoi":"10.21203/rs.3.rs-6603584/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6603584/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Tropical cyclones are among the most destructive extreme weather phenomena in nature, and accurately predicting whether a tropical cloud cluster will develop into a tropical cyclone is crucial for disaster prevention and mitigation. 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