Composite Analysis with Cyclone Alignment Network Reveals Features of Boreal Spring Rapidly Intensifying Cyclones over the Mongolian Plateau-Northeast China Plain

Composite Analysis with Cyclone Alignment Network Reveals Features of Boreal Spring Rapidly Intensifying Cyclones over the Mongolian Plateau-Northeast China Plain | 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 Composite Analysis with Cyclone Alignment Network Reveals Features of Boreal Spring Rapidly Intensifying Cyclones over the Mongolian Plateau-Northeast China Plain Ruipeng Sun, Yina Diao, Jianping Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6224469/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Traditional composite analysis in cyclone studies, which compares variables at grid points using the cyclone center as a reference, is limited by spatial misalignments caused by cyclone rotation. This misalignment blurs composite results and hinders accurate structural analysis. To solve this issue, the Cyclone Alignment Network (CAN) method is proposed. CAN aligns variables into a unified coordinate system by learning an affine transformation matrix, improving classification and composite outcomes. Specifically designed for cyclones, CAN uses a Transformer structure with Rotary Position Embedding (RoPE) to effectively capture relative positional information, rather than typical Convolutional Neural Network (CNN). Its classification network, referencing cyclone development equations, concentrates coordinate transformation within the affine matrix. Evaluations on a cyclone dataset show CAN-based composites outperform traditional methods, demonstrating more significant results and reasonable variable coupling. CAN reveals key common features: cyclone rapid intensification in spring is dominated by cold air activity, topography significantly impacts intensification, and downstream ridge structures potentially influence intensification by causing anomaly subsidence, leading to low-level dynamic uplift and limited baroclinic energy release. CAN effectively analyzes cyclone circulation and structure. Earth and environmental sciences/Climate sciences/Atmospheric science Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Physical sciences/Mathematics and computing/Computational science extra-tropical cyclones cyclone rapid intensification composite analysis machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Jun, 2025 Reviews received at journal 22 May, 2025 Reviewers agreed at journal 11 May, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Editor assigned by journal 24 Mar, 2025 Editor invited by journal 17 Mar, 2025 Submission checks completed at journal 14 Mar, 2025 First submitted to journal 14 Mar, 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-6224469","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434173419,"identity":"8e899185-fa47-4db3-bbc9-fb8f0807b9c3","order_by":0,"name":"Ruipeng Sun","email":"","orcid":"","institution":"Ocean University of China","correspondingAuthor":false,"prefix":"","firstName":"Ruipeng","middleName":"","lastName":"Sun","suffix":""},{"id":434173420,"identity":"fdc6e981-b824-469d-bf21-cb9b8ffdf74d","order_by":1,"name":"Yina Diao","email":"","orcid":"","institution":"Ocean University of China","correspondingAuthor":false,"prefix":"","firstName":"Yina","middleName":"","lastName":"Diao","suffix":""},{"id":434173421,"identity":"5d9e8511-ed1d-4586-ad80-e036d4a9ab6c","order_by":2,"name":"Jianping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoklEQVRIiWNgGAWjYBACPoYDjA8gzAQitbAxHGA2IFULA5sEiVoYzx6r+Nhmx8DPnmPA8HMHUbacS7s5sy2ZQbLnjQFj7xmitJwxu83bdoDB4EaOATNjG5FaikFa7EnSwgy2RYJ4LeeSJWecS+aROPOs4GAvMVr4Jc4e/PChzE6Ovz1544OfxGhhkIAEEg+IOECMBqA1PcSpGwWjYBSMghEMAH6nMPA/o1HAAAAAAElFTkSuQmCC","orcid":"","institution":"Ocean University of China","correspondingAuthor":true,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-14 08:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6224469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6224469/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-24367-z","type":"published","date":"2025-11-19T15:58:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96650212,"identity":"e2f35358-e2d5-43f5-b8ac-b6e8420d8bc1","added_by":"auto","created_at":"2025-11-24 16:09:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1453924,"visible":true,"origin":"","legend":"","description":"","filename":"CompositeAnalysiswithCycloneAlignmentNetworkRevealsFeaturesofBorealSpringRapidlyIntensifyingCyclonesovertheMongolianPlateauNortheastChinaPlain.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6224469/v1_covered_da8ee0f2-8081-4879-94d1-9b9118506660.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Composite Analysis with Cyclone Alignment Network Reveals Features of Boreal Spring Rapidly Intensifying Cyclones over the Mongolian Plateau-Northeast China Plain","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":"extra-tropical cyclones, cyclone rapid intensification, composite analysis, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6224469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6224469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional composite analysis in cyclone studies, which compares variables at grid points using the cyclone center as a reference, is limited by spatial misalignments caused by cyclone rotation. This misalignment blurs composite results and hinders accurate structural analysis.\u003c/p\u003e \u003cp\u003eTo solve this issue, the Cyclone Alignment Network (CAN) method is proposed. CAN aligns variables into a unified coordinate system by learning an affine transformation matrix, improving classification and composite outcomes. Specifically designed for cyclones, CAN uses a Transformer structure with Rotary Position Embedding (RoPE) to effectively capture relative positional information, rather than typical Convolutional Neural Network (CNN). Its classification network, referencing cyclone development equations, concentrates coordinate transformation within the affine matrix.\u003c/p\u003e \u003cp\u003eEvaluations on a cyclone dataset show CAN-based composites outperform traditional methods, demonstrating more significant results and reasonable variable coupling. 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