Forecasting One-day Global Ionospheric TEC Maps based on a Modified 3D Convolution U-Net Incorporating Fused Index Features

Forecasting One-day Global Ionospheric TEC Maps based on a Modified 3D Convolution U-Net Incorporating Fused Index Features | 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 Forecasting One-day Global Ionospheric TEC Maps based on a Modified 3D Convolution U-Net Incorporating Fused Index Features Xin Gao, Fang Cheng, Xiaochun Lu, Yibin Yao, Liang Zhang, Yang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6698312/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in GPS Solutions → Version 1 posted 11 You are reading this latest preprint version Abstract Ionospheric Total Electron Content (TEC) serves as a fundamental parameter for characterizing ionospheric morphology. Ionospheric TEC exhibits irregular disturbances driven by solar and geomagnetic activities. TEC forecasting products enhance GNSS positioning precision through error correction and space weather assessment, consequently improving satellite navigation system reliability and space weather warning systems. This paper proposes a modified 3D convolutional U-Net architecture incorporating fused index features for forecasting 1-day global ionospheric TEC maps. The input includes the previous day’s TEC maps and corresponding solar and geomagnetic indices, such as F10.7, SSN, Vsw, IMF Bz, Dst, and Kp indices. Our model comprises an MLP-based fused feature generation module for solar and geomagnetic indices and a 3D convolutional spatiotemporal forecasting module. The primary contribution of this work involves the dimensional expansion of 1D solar and geomagnetic indices to achieve spatiotemporal alignment with 2D TEC grid maps. We benchmark our model against the C1PG model, evaluating prediction performance under different geomagnetic storm intensities. The results demonstrate that our fused index generation module significantly enhances 1-day TEC prediction accuracy during storm periods, particularly in low-latitude regions where our model better captures large-scale TEC anomalies. Compared to the C1PG model, our model reduces forecast errors by 35%-40% at low latitudes. The feature fusion approach provides new insights into the spatiotemporal TEC modeling field. Total Electron Content Solar and Geomagnetic Indices Fused Index Features 3D Convolution Spatiotemporal Forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in GPS Solutions → Version 1 posted Editorial decision: Revision requested 20 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 27 Jul, 2025 Reviews received at journal 20 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 13 Jul, 2025 Editor assigned by journal 23 Jun, 2025 Submission checks completed at journal 20 May, 2025 First submitted to journal 19 May, 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. 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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-6698312","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484702825,"identity":"49ee707e-2f71-423d-9084-204575668abd","order_by":0,"name":"Xin Gao","email":"","orcid":"","institution":"National Time Service Center of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Gao","suffix":""},{"id":484702826,"identity":"e486ee42-9c26-4257-911d-05bdb5d7104c","order_by":1,"name":"Fang 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