Machine-Learned Mapping Functions for the Asymmetric Troposphere

Machine-Learned Mapping Functions for the Asymmetric Troposphere | 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 Machine-Learned Mapping Functions for the Asymmetric Troposphere Zhenyi Zhang, Benedikt Soja This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7692086/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 Accurate empirical tropospheric mapping functions (MFs) are essential for high-precision geodetic applications such as GNSS and VLBI. However, traditional MFs assume azimuthal symmetry or explicitly model gradients on regular grids, limiting their ability to represent real atmospheric condition. To address the limitation, this study designs a novel physics-inspired machine learning-based framework implemented with Multilayer Perceptron and develops global empirical asymmetric MFs on five years of ERA5-based ray-tracing data. Compared to the widely used GPT3 model, our models reduce global RMSE by up to 12.0% for hydrostatic and 2.8% for wet components and remain competitive or superior when GPT3 is used with gradient corrections. The improvements are particularly evident at low elevation angles, high-altitude sites, and over continental regions. To our knowledge, this work marks the first global machine learning-based empirical MF models that directly capture atmospheric asymmetry, offering a flexible and physically consistent alternative to traditional models in space geodesy. Space geodesy Tropospheric mapping function Machine learning Asymmetric troposphere Ray-tracing ERA5 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-7692086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519262809,"identity":"a25e87d0-88f0-4656-9f1d-0e951a1e3b77","order_by":0,"name":"Zhenyi 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Troposphere\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"ETH Zurich","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":"Space geodesy, Tropospheric mapping function, Machine learning, Asymmetric troposphere, Ray-tracing, ERA5","lastPublishedDoi":"10.21203/rs.3.rs-7692086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7692086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate empirical tropospheric mapping functions (MFs) are essential for high-precision geodetic applications such as GNSS and VLBI. However, traditional MFs assume azimuthal symmetry or explicitly model gradients on regular grids, limiting their ability to represent real atmospheric condition. To address the limitation, this study designs a novel physics-inspired machine learning-based framework implemented with Multilayer Perceptron and develops global empirical asymmetric MFs on five years of ERA5-based ray-tracing data. Compared to the widely used GPT3 model, our models reduce global RMSE by up to 12.0% for hydrostatic and 2.8% for wet components and remain competitive or superior when GPT3 is used with gradient corrections. The improvements are particularly evident at low elevation angles, high-altitude sites, and over continental regions. 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