Fully AI-driven regional system for skillful tropical cyclone forecasting

Fully AI-driven regional system for skillful tropical cyclone forecasting | 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 Research Article Fully AI-driven regional system for skillful tropical cyclone forecasting Hongxiong Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7772125/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 Forecasting tropical cyclones (TCs) remains one of the most enduring challenges in numerical weather prediction, particularly in terms of storm intensity and track at medium to extended lead times. Recent advances in artificial intelligence (AI)-based global models have demonstrated strong skill in capturing large-scale atmospheric dynamics; however, their coarse spatial resolution and limited variable suites constrain their ability to produce storm-resolving forecasts critical for high-impact events. Here, we introduce the first fully AI-driven regional TC forecasting system, in which the European Centre for Medium-Range Weather Forecasts’ AI Forecast System (AIFS) is run locally to provide all initial and lateral boundary conditions for a convection-permitting regional model. This framework operates using a single atmospheric analysis and incorporates dynamic vortex initialization and spectral nudging to enhance storm realism and environmental consistency. Applied to Northwest Pacific TCs, the system demonstrated substantial improvement in intensity forecasts relative to those of the AIFS alone, whilst also delivering more accurate track guidance compared with that of conventional regional systems driven by physics-based global models. These gains highlight the system’s ability to simultaneously deliver accurate forecasts of both track and intensity—a capability traditionally considered difficult to achieve within a single framework. The proposed approach establishes a scalable, efficient, and operationally viable paradigm for tropical cyclone prediction based entirely on AI-driven regional modeling. Atmospheric Sciences Typhoon intensity AI weather forecasting Vortex dynamic initialization Weather Research and Forecasting model Full Text Additional Declarations The authors declare no competing interests. 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-7772125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524183648,"identity":"5dba1aae-1021-4d00-ac15-413e85e27e38","order_by":0,"name":"Hongxiong Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYFACHgaGDyRrYZxBshZmHpI0GNzIPSZtU7NNnoF/8TEJhpo7hLVIzshLNs45dtuwQeJZmgTDsWeEtfBL5Bg+zm24zdggccbYgLHhMGEtbBI5BoctG27bE68FbAtjw+3EBv4ewwdEaZHseWNs2HPsdnKbBFvig4RjRGgxOJ5jJvGj5rZtP//hAwc+1BChBQ7YJBIYGBJI0AAE/AdIUz8KRsEoGAUjBwAAqU83Gh+rcmwAAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Meteorological Sciences","correspondingAuthor":true,"prefix":"","firstName":"Hongxiong","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-10-03 09:36:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7772125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7772125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92822600,"identity":"ce1cd3d0-996b-4a66-bc83-dc0ba23a282f","added_by":"auto","created_at":"2025-10-06 02:58:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8884209,"visible":true,"origin":"","legend":"","description":"","filename":"aifsDriventyphoon20251003preprint.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7772125/v1_covered_7c1b6be1-63e7-48ad-86d4-a6b62f3b67c4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFully AI-driven regional system for skillful tropical cyclone forecasting\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China ","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":"Typhoon intensity, AI weather forecasting, Vortex dynamic initialization, Weather Research and Forecasting model","lastPublishedDoi":"10.21203/rs.3.rs-7772125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7772125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForecasting tropical cyclones (TCs) remains one of the most enduring challenges in numerical weather prediction, particularly in terms of storm intensity and track at medium to extended lead times. Recent advances in artificial intelligence (AI)-based global models have demonstrated strong skill in capturing large-scale atmospheric dynamics; however, their coarse spatial resolution and limited variable suites constrain their ability to produce storm-resolving forecasts critical for high-impact events. Here, we introduce the first fully AI-driven regional TC forecasting system, in which the European Centre for Medium-Range Weather Forecasts’ AI Forecast System (AIFS) is run locally to provide all initial and lateral boundary conditions for a convection-permitting regional model. This framework operates using a single atmospheric analysis and incorporates dynamic vortex initialization and spectral nudging to enhance storm realism and environmental consistency. Applied to Northwest Pacific TCs, the system demonstrated substantial improvement in intensity forecasts relative to those of the AIFS alone, whilst also delivering more accurate track guidance compared with that of conventional regional systems driven by physics-based global models. These gains highlight the system’s ability to simultaneously deliver accurate forecasts of both track and intensity—a capability traditionally considered difficult to achieve within a single framework. The proposed approach establishes a scalable, efficient, and operationally viable paradigm for tropical cyclone prediction based entirely on AI-driven regional modeling.\u003c/p\u003e","manuscriptTitle":"Fully AI-driven regional system for skillful tropical cyclone forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 02:50:41","doi":"10.21203/rs.3.rs-7772125/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"02182844-4ddd-4c1a-ad5e-e9fbd877290b","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55715842,"name":"Atmospheric Sciences"}],"tags":[],"updatedAt":"2025-10-06T02:50:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 02:50:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7772125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7772125","identity":"rs-7772125","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}