Track based hazard exposure and loss dataset for Western North Pacific tropical cyclones from 2000 to 2024

Track based hazard exposure and loss dataset for Western North Pacific tropical cyclones from 2000 to 2024 | 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 data-descriptor Track based hazard exposure and loss dataset for Western North Pacific tropical cyclones from 2000 to 2024 Yang Gao, Junchao Su, Jingzhi Zhou, Jingjia Luo, Chunyi Xiang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9326685/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Typhoon disaster research is hampered by data fragmented across incompatible spatial scales and temporal resolutions, hindering the development of data-driven risk modeling. This article presents a track-centric, multi-source dataset of western North Pacific typhoons spanning 2000–2024, comprising 756 tracks and 47,903 track points, organized around IBTrACS trajectories. For each track point, a storm-centered quasi-Lagrangian reference frame integrates ocean variables (Copernicus), socioeconomic dynamics (nighttime lights, population, and GDP-LitPop exposure), and hazard-loss metrics (ERA5 wind/rainfall, EM-DAT) into annual NetCDF products, simultaneously capturing storm evolution and local impact. Internal quality checks confirm numerical reproducibility and physical consistency, while external validation against GDIS event locations and IMERG satellite rainfall further corroborates spatial registration. The dataset supports typhoon risk modeling and impact attribution, as well as disaster chain analysis and spatiotemporal deep learning. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9326685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":627637027,"identity":"4b91ce9f-b1f7-4b5d-8244-ae232d515dc0","order_by":0,"name":"Yang Gao","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Gao","suffix":""},{"id":627637028,"identity":"a543289b-6021-42c5-9c56-018ac1cf57d4","order_by":1,"name":"Junchao Su","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Junchao","middleName":"","lastName":"Su","suffix":""},{"id":627637031,"identity":"b92cd23a-221d-4d18-92db-61957b33f34e","order_by":2,"name":"Jingzhi Zhou","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingzhi","middleName":"","lastName":"Zhou","suffix":""},{"id":627637035,"identity":"2031a4ad-0466-4bda-b27d-a9510099cf36","order_by":3,"name":"Jingjia Luo","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingjia","middleName":"","lastName":"Luo","suffix":""},{"id":627637037,"identity":"ef718d1b-8c70-494e-8a7a-7b00e33a46ed","order_by":4,"name":"Chunyi Xiang","email":"","orcid":"","institution":"China Meteorological Administration","correspondingAuthor":false,"prefix":"","firstName":"Chunyi","middleName":"","lastName":"Xiang","suffix":""},{"id":627637039,"identity":"f70a47be-a1f8-4661-ba1f-d35887095a8f","order_by":5,"name":"Fan Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYLCCDwwMPGAGD7E6GGeAFLORooUZrJJoLbrtvYdf2+44LCM/v4Hxwds2BnlzQlrMzpxLs849c5jH4BgDs+HcNgbDnQ2EtNzIMTPObQNqYWNgk+ZtY0gwOEBIy/03ZsaWQC3ybQzsv4nTcoPH+DEjUAvDMQY2ZuK0nMkxY+xtSwf6JbFZcs45CcMNBLUcP2P84Webtb188+GDH96U2cgTtAUI2CQgNGMDkJAgrB4ImD8QpWwUjIJRMApGLgAAqCM66J5C7boAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Fan","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2026-04-05 13:54:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9326685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9326685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108006764,"identity":"32d83afd-1fb7-4b1e-a989-c7df7240ec06","added_by":"auto","created_at":"2026-04-28 12:56:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9131613,"visible":true,"origin":"","legend":"","description":"","filename":"TrackbasedhazardexposureandlossdatasetforWesternNorthPacifictropicalcyclonesfrom2000to2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9326685/v1_covered_486c10e8-233f-4f8f-8c28-d80496df461e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Track based hazard exposure and loss dataset for Western North Pacific tropical cyclones from 2000 to 2024","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9326685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9326685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTyphoon disaster research is hampered by data fragmented across incompatible spatial scales and temporal resolutions, hindering the development of data-driven risk modeling. 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