Beyond Isotropic Uncertainty in Tropical Cyclone Tracks with Adaptive Conformal Prediction

Beyond Isotropic Uncertainty in Tropical Cyclone Tracks with Adaptive Conformal Prediction | 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 Beyond Isotropic Uncertainty in Tropical Cyclone Tracks with Adaptive Conformal Prediction Xuepeng Chen, Yaqiang Wang, Jing-Jia Luo, Fan Meng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9195108/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Tropical cyclones are among the most destructive natural hazards, necessitating reliable uncertainty quantification in track forecasting. However, traditional probability prediction has problems such as isotropic regions and difficulty in reflecting geometric distortion. This paper proposes an adaptive uncertainty quantification framework based on distribution-free conformal prediction. This framework utilizes deep learning models to construct anisotropic probability ellipses and integrates an online calibration mechanism to achieve joint dynamic adjustment of the ellipse scale and principal axis direction. Results demonstrate our proposed framework moves beyond conventional methods, offering a more expressive and operationally relevant characterization of forecast uncertainty. Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards Tropical cyclones Uncertainty quantification Deep learning Conformal Prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 22 Mar, 2026 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. We do this by developing innovative software and high quality services for the global research community. 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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-9195108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614921484,"identity":"f98d29c9-a8fe-4a28-81e6-c717e017c1db","order_by":0,"name":"Xuepeng Chen","email":"","orcid":"","institution":"State Key Laboratory of Climate System Prediction and Risk Management (CPRM)","correspondingAuthor":false,"prefix":"","firstName":"Xuepeng","middleName":"","lastName":"Chen","suffix":""},{"id":614921485,"identity":"c92d0942-b86f-4fc1-be52-e39127a4d576","order_by":1,"name":"Yaqiang Wang","email":"","orcid":"","institution":"State Key Laboratory of Severe Weather Meteorological Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yaqiang","middleName":"","lastName":"Wang","suffix":""},{"id":614921486,"identity":"4244417f-fcf4-4a24-aca0-1c942816b331","order_by":2,"name":"Jing-Jia Luo","email":"","orcid":"","institution":"State Key Laboratory of Climate System Prediction and Risk Management (CPRM)","correspondingAuthor":false,"prefix":"","firstName":"Jing-Jia","middleName":"","lastName":"Luo","suffix":""},{"id":614921487,"identity":"13b0ea6c-45e5-47a8-a169-a84f017bae22","order_by":3,"name":"Fan Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYLCCDwwMPGAGD7E6GGeAFLORooUZrJJoLbrtvYdf2+44LCM/v4Hxwds2BnlzQlrMzpxLs849c5jH4BgDs+HcNgbDnQ2EtNzIMTPObQNqYWNgk+ZtY0gwOEBIy/03ZsaWQC3ybQzsv4nTcoPH+DEjUAvDMQY2ZuK0nMkxY+xtSwf6JbFZcs45CcMNBLUcP2P84Webtb188+GDH96U2cgTtAUI2CQgNGMDkJAgrB4ImD8QpWwUjIJRMApGLgAAqCM66J5C7boAAAAASUVORK5CYII=","orcid":"","institution":"State Key Laboratory of Climate System Prediction and Risk Management (CPRM)","correspondingAuthor":true,"prefix":"","firstName":"Fan","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2026-03-23 03:39:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9195108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9195108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904620,"identity":"a4f2dc0e-3c1b-442b-9f37-376b7fe87b99","added_by":"auto","created_at":"2026-04-01 10:09:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13403248,"visible":true,"origin":"","legend":"","description":"","filename":"npjtrack2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9195108/v1_covered_95b01a47-9157-493c-b757-2c7a0b173301.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Isotropic Uncertainty in Tropical Cyclone Tracks with Adaptive Conformal Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tropical cyclones, Uncertainty quantification , Deep learning, Conformal Prediction","lastPublishedDoi":"10.21203/rs.3.rs-9195108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9195108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Tropical cyclones are among the most destructive natural hazards, necessitating reliable uncertainty quantification in track forecasting. 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