Optimum Scenario Selection Using Ensemble Clustering and Bayesian Estimation of Realization Probability: Application to Typhoon Hagibis (2019)

Optimum Scenario Selection Using Ensemble Clustering and Bayesian Estimation of Realization Probability: Application to Typhoon Hagibis (2019) | 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 Optimum Scenario Selection Using Ensemble Clustering and Bayesian Estimation of Realization Probability: Application to Typhoon Hagibis (2019) Junpei Yamaguchi, Kosuke Ono This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6794795/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract We describe a novel method for objectively selecting the most probable scenario among four typhoon forecasts. The method uses ensemble clustering to progressively incorporate a sequence of analytical data leading up to the most recent. Each ensemble member and the analytical data were initially projected onto the phase space spanned by the two leading principal components from an empirical orthogonal function analysis of the ensemble clustering. We then employed a particle filter-based Bayesian approach to assess the similarity between the forecasts and the analytical results within phase space. The scenario with the highest probability was then selected as the optimal scenario by application of the selective ensemble method. This new method was applied to Typhoon Hagibis (2019) using the regional ensemble prediction system of the Japan Meteorological Agency (JMA). The selected scenario successfully predicted a mesoscale front and associated coastal heavy rainfall that exceeded 100 mm per 3 hours in Miyagi Prefecture, which JMA's deterministic mesoscale model failed to forecast. Notably, the optimal scenario was identified prior to the onset of heavy rainfall. Statistical analysis of multiple typhoon cases demonstrated that the proposed method allows for optimal scenario selection up to six hours in advance of the target time. These results suggest that the new method can identify more realistic scenarios than operational deterministic forecasts of significant weather phenomena before their occurrence. Ensemble forecast operational forecasting forecasting techniques typhoon Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Jul, 2025 Reviews received at journal 21 Jul, 2025 Reviews received at journal 28 Jun, 2025 Reviewers agreed at journal 28 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 01 Jun, 2025 Submission checks completed at journal 01 Jun, 2025 First submitted to journal 01 Jun, 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. 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-6794795","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466663695,"identity":"158c1810-ec55-496d-aad5-51cc9f5f08b5","order_by":0,"name":"Junpei Yamaguchi","email":"data:image/png;base64,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","orcid":"","institution":"Japan Meteorological Agency","correspondingAuthor":true,"prefix":"","firstName":"Junpei","middleName":"","lastName":"Yamaguchi","suffix":""},{"id":466663696,"identity":"ddb03543-b6f8-4714-a70d-f7b623651fb1","order_by":1,"name":"Kosuke Ono","email":"","orcid":"","institution":"Japan Meteorological Agency","correspondingAuthor":false,"prefix":"","firstName":"Kosuke","middleName":"","lastName":"Ono","suffix":""}],"badges":[],"createdAt":"2025-06-01 09:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6794795/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6794795/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84089048,"identity":"849e5cf0-3731-47ec-933a-073f0256882c","added_by":"auto","created_at":"2025-06-06 15:43:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1942436,"visible":true,"origin":"","legend":"","description":"","filename":"EmpathyandAttitudesTowardPalliativeCareAmongChineseMedicalStudentsACrossSectionalStudy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6794795/v1_covered_0d3c31ee-c163-4b72-9c91-94f979345c06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimum Scenario Selection Using Ensemble Clustering and Bayesian Estimation of Realization Probability: Application to Typhoon Hagibis (2019)","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":"journal-of-the-meteorological-society-of-japan","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44394","submissionUrl":"https://submission.springernature.com/new-submission/44394/3","title":"Journal of the Meteorological Society of Japan","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ensemble forecast, operational forecasting, forecasting techniques, typhoon","lastPublishedDoi":"10.21203/rs.3.rs-6794795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6794795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe describe a novel method for objectively selecting the most probable scenario among four typhoon forecasts. 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