SpatioTECformer: Global ionospheric VTEC Forecasting via spatio-temporal modeling and adaptive driver fusion

SpatioTECformer: Global ionospheric VTEC Forecasting via spatio-temporal modeling and adaptive driver fusion | 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 SpatioTECformer: Global ionospheric VTEC Forecasting via spatio-temporal modeling and adaptive driver fusion Jiawen Chen, Han Zhao, Jiageng Chi, Da Xu, Qiong Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6700653/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 The ionosphere plays a vital role in energy exchange between Earth and outer space, and its dynamic variations significantly impact satellite navigation and radio communication. Vertical Total Electron Content (VTEC), a key parameter that quantifies ionospheric electron density, is critical for space weather monitoring and Global Navigation Satellite System (GNSS) error correction. However, current forecasting models struggle to accurately represent spatial heterogeneity, capture long-range temporal dependencies, and remain robust under external disturbances such as geomagnetic storms. We propose SpatioTECformer, a novel model that integrates local and global spatiotemporal features to accurately forecast VTEC dynamics. It employs a Transformer encoder for long-sequence temporal modeling, an enhanced convolutional neural network (CNN) module for spatial feature extraction, and an adaptive feature fusion module to integrate solar wind and geomagnetic indices. Validation on Global Ionospheric Map (GIM) data from the Center for Orbit Determination in Europe (CODE) shows that SpatioTECformer achieves state-of-the-art performance, with RMSE and MAE of 1.80 and 1.23 TECU in 2014 (high solar activity), and improved values of 0.78 and 0.58 TECU in 2017 (low solar activity). The model exhibits superior robustness and predictive accuracy across global regions, particularly within the Equatorial Ionization Anomaly (EIA) zone and under geomagnetic disturbance conditions. The source code is publicly available at: https://github.com/jiawenchen1011/SpatioTECformer . Ionospheric Forecasting Vertical Total Electron Content (VTEC) Spatiotemporal Modeling Transformer 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-6700653","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471675454,"identity":"d2a6fb3b-a8c7-459c-9fab-02c452c5e411","order_by":0,"name":"Jiawen Chen","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jiawen","middleName":"","lastName":"Chen","suffix":""},{"id":471675455,"identity":"218b1288-fc5c-4025-a19b-b1c707e4ba92","order_by":1,"name":"Han Zhao","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhao","suffix":""},{"id":471675456,"identity":"92064a6d-0f6a-4beb-87bd-724e84780f38","order_by":2,"name":"Jiageng Chi","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jiageng","middleName":"","lastName":"Chi","suffix":""},{"id":471675457,"identity":"fec76139-1932-4ad9-ad43-bf84df8138f1","order_by":3,"name":"Da Xu","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Da","middleName":"","lastName":"Xu","suffix":""},{"id":471675458,"identity":"908fcfec-090c-4379-bdfd-46d2cbd9bb0e","order_by":4,"name":"Qiong Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYJCCA0Asx9gOYrKRoMWYsZkULSCQ2MBMrBaDG8kbD3zcUZve3MxjwPCh7DAD/+wGQlrSCg7OPHM8txGohXHGucMMEncO4NdidiPH4DBv2zGwFmbetsMMBhIJxGlJZwRp+UuClpoEsBZGYrTYn3kG9EvbAcPGZraCgz3n0nkkbhDQItmevPnDx7Y6ecP25o0PfpRZy/HPIKAFCAyA+DCDYQMkTnkIqodqqWOQJ0bpKBgFo2AUjEwAAGH0RibhBlnnAAAAAElFTkSuQmCC","orcid":"","institution":"Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-05-19 16:09:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6700653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6700653/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103646557,"identity":"4028c0d5-9314-43b6-8972-b19ae1759b89","added_by":"auto","created_at":"2026-02-28 10:56:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1938047,"visible":true,"origin":"","legend":"","description":"","filename":"SpatioTECformerGlobalionosphericVTECForecastingviaspatiotemporalmodelingandadaptivedriverfusion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6700653/v1_covered_93222a19-106c-41db-aee7-3f6b6e79d6d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SpatioTECformer: Global ionospheric VTEC Forecasting via spatio-temporal modeling and adaptive driver fusion","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":"Ionospheric Forecasting, Vertical Total Electron Content (VTEC), Spatiotemporal Modeling, Transformer","lastPublishedDoi":"10.21203/rs.3.rs-6700653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6700653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ionosphere plays a vital role in energy exchange between Earth and outer space, and its dynamic variations significantly impact satellite navigation and radio communication. Vertical Total Electron Content (VTEC), a key parameter that quantifies ionospheric electron density, is critical for space weather monitoring and Global Navigation Satellite System (GNSS) error correction. However, current forecasting models struggle to accurately represent spatial heterogeneity, capture long-range temporal dependencies, and remain robust under external disturbances such as geomagnetic storms. We propose SpatioTECformer, a novel model that integrates local and global spatiotemporal features to accurately forecast VTEC dynamics. It employs a Transformer encoder for long-sequence temporal modeling, an enhanced convolutional neural network (CNN) module for spatial feature extraction, and an adaptive feature fusion module to integrate solar wind and geomagnetic indices. Validation on Global Ionospheric Map (GIM) data from the Center for Orbit Determination in Europe (CODE) shows that SpatioTECformer achieves state-of-the-art performance, with RMSE and MAE of 1.80 and 1.23 TECU in 2014 (high solar activity), and improved values of 0.78 and 0.58 TECU in 2017 (low solar activity). The model exhibits superior robustness and predictive accuracy across global regions, particularly within the Equatorial Ionization Anomaly (EIA) zone and under geomagnetic disturbance conditions. The source code is publicly available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jiawenchen1011/SpatioTECformer\u003c/span\u003e\u003cspan address=\"https://github.com/jiawenchen1011/SpatioTECformer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e","manuscriptTitle":"SpatioTECformer: Global ionospheric VTEC Forecasting via spatio-temporal modeling and adaptive driver fusion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 18:36:36","doi":"10.21203/rs.3.rs-6700653/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":"38005cb2-e896-4e7e-8a6e-42927290660a","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-28T10:55:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 18:36:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6700653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6700653","identity":"rs-6700653","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}