TEC-Enhanced Deep Learning Model for Global 3D Ionospheric Electron Density Prediction

TEC-Enhanced Deep Learning Model for Global 3D Ionospheric Electron Density 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 Research Article TEC-Enhanced Deep Learning Model for Global 3D Ionospheric Electron Density Prediction Changzhi Zhai, Lei Liu, Qi Zhang, Yutian Chen, Fei Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7325550/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 Although deep learning has made significant progress in predicting ionospheric parameters, such models often exhibit high accuracy during the training period but perform poorly when extrapolated to extended periods. In this study, we propose a global three-dimensional (3D) ionospheric electron density prediction model based on a Long Short-Term Memory (LSTM) network, which assimilates TEC observations to improve predictive performance, particularly during extended periods. Long-term electron density (Ne) observations from COSMIC-1 (2009–2019) and corresponding TEC derived from Global Ionospheric Maps (GIM) are used for the model training. During the training period, the model with TEC assimilation exhibited higher correlation on the test set and consistently showed lower root mean square error (RMSE) across different latitudinal regions. COSMIC-2 and Swarm-A observations were used to evaluate the models’ performance during the extrapolation period, and the model with TEC assimilation demonstrated significantly improved performance compared to the model without TEC. During the quiet period from DOY 203 to 207 in 2024, the model without TEC showed a pronounced systematic bias relative to COSMIC-2 observations. By contrast, the residuals of TEC-enhanced model closely followed a standard normal distribution, with the RMSE decreasing by 39.1%. During the geomagnetic storm period from DOY 131 to 135 in 2024, the RMSE of the TEC-enhanced model decreased by 25.6% compared to the model without TEC. The electron density distribution predicted by the TEC-enhanced model showed good agreement with Swarm-A satellite observations, whereas the model without TEC failed to capture the characteristic double-crest structure at low latitudes. LSTM TEC COSMIC 3D electron density prediction 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-7325550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503187687,"identity":"5b401ef2-2606-4608-9a64-7e201b2c70f9","order_by":0,"name":"Changzhi Zhai","email":"","orcid":"","institution":"China University of Geosciences","correspondingAuthor":false,"prefix":"","firstName":"Changzhi","middleName":"","lastName":"Zhai","suffix":""},{"id":503187688,"identity":"150f4b01-6122-450e-aec2-035814cfbfe3","order_by":1,"name":"Lei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYBACPnYGhgMMFQwMbAw8DCCSMGBjBmk5Q6oWBsY2EJN4LcwHD/ycdziaj/3sAYYPZYeJ0cKWcLB32+HcNp68BMYZ54jSwmNwgBekRYLHgJm3jSgt/B8O/p0D1fKXOC08DId5G6BaGInTwmZwWOZYOtAvOQYHe86lE9bCz978+OObGuvc+e1nDB/8KLMmrAUFHCBR/SgYBaNgFIwCXAAARyY03BIJ0MYAAAAASUVORK5CYII=","orcid":"","institution":"University of Michigan–Ann Arbor","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Liu","suffix":""},{"id":503187689,"identity":"094109bf-25b4-427a-8c9b-8a439607935c","order_by":2,"name":"Qi Zhang","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Zhang","suffix":""},{"id":503187690,"identity":"4ea5ea35-3c93-44ba-b1b2-e4197da23484","order_by":3,"name":"Yutian Chen","email":"","orcid":"","institution":"Huaiyin Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yutian","middleName":"","lastName":"Chen","suffix":""},{"id":503187691,"identity":"92cd6075-b29a-4817-8b71-1723441164fc","order_by":4,"name":"Fei Xu","email":"","orcid":"","institution":"Hohai University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-08-08 09:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7325550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7325550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103049820,"identity":"53496d7f-d0ec-4624-a7ee-b46395dab066","added_by":"auto","created_at":"2026-02-20 07:46:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6779677,"visible":true,"origin":"","legend":"","description":"","filename":"TECEnhancedNeModelBasedonLSTM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7325550/v1_covered_2fc85118-abbc-423d-8e79-608ba2955aa6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TEC-Enhanced Deep Learning Model for Global 3D Ionospheric Electron Density Prediction","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":"LSTM, TEC, COSMIC, 3D electron density prediction","lastPublishedDoi":"10.21203/rs.3.rs-7325550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7325550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough deep learning has made significant progress in predicting ionospheric parameters, such models often exhibit high accuracy during the training period but perform poorly when extrapolated to extended periods. In this study, we propose a global three-dimensional (3D) ionospheric electron density prediction model based on a Long Short-Term Memory (LSTM) network, which assimilates TEC observations to improve predictive performance, particularly during extended periods. Long-term electron density (Ne) observations from COSMIC-1 (2009\u0026ndash;2019) and corresponding TEC derived from Global Ionospheric Maps (GIM) are used for the model training. During the training period, the model with TEC assimilation exhibited higher correlation on the test set and consistently showed lower root mean square error (RMSE) across different latitudinal regions. COSMIC-2 and Swarm-A observations were used to evaluate the models\u0026rsquo; performance during the extrapolation period, and the model with TEC assimilation demonstrated significantly improved performance compared to the model without TEC. During the quiet period from DOY 203 to 207 in 2024, the model without TEC showed a pronounced systematic bias relative to COSMIC-2 observations. By contrast, the residuals of TEC-enhanced model closely followed a standard normal distribution, with the RMSE decreasing by 39.1%. During the geomagnetic storm period from DOY 131 to 135 in 2024, the RMSE of the TEC-enhanced model decreased by 25.6% compared to the model without TEC. The electron density distribution predicted by the TEC-enhanced model showed good agreement with Swarm-A satellite observations, whereas the model without TEC failed to capture the characteristic double-crest structure at low latitudes.\u003c/p\u003e","manuscriptTitle":"TEC-Enhanced Deep Learning Model for Global 3D Ionospheric Electron Density Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 12:22:29","doi":"10.21203/rs.3.rs-7325550/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":"796ddd08-164b-4bdf-b0df-d62f34780015","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T22:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 12:22:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7325550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7325550","identity":"rs-7325550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}