An Empirical Model for Atmospheric Temperature Estimation Using a Geographic Grid Neural Network

An Empirical Model for Atmospheric Temperature Estimation Using a Geographic Grid Neural Network | 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 An Empirical Model for Atmospheric Temperature Estimation Using a Geographic Grid Neural Network Maohua Ding, Chiyu Chen, Xiaodong Luo, Zhiyao Feng, Zhuoyue Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7541639/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 This study presents an empirical model for atmospheric temperature (T) estimation based on a Geographic Grid Neural Network (GGNN) framework. As a critical parameter in GNSS tropospheric tomography, T facilitates the conversion of wet refractivity (Nw) to water vapor density (ρv). The proposed GGNNTemp model integrates grid-specific neural networks across a 1°×1° geographic grid, where each node employs a multilayer perceptron with month and altitude as inputs to predict temperature. Comparative evaluations against traditional neural networks and the GPT2w model demonstrate the superior performance of the GGNNTemp model, achieving a BIAS value of 0.01 K and an RMSE of 1.60 K—significantly outperforming GPT2w’s 12.37 K RMSE. The model exhibits consistent accuracy across varying latitudes, altitudes (up to 10 km), and seasons, with ρv conversion errors maintained below 5‰. This work highlights the efficacy of GGNN technology in modeling geophysical parameters and advances its potential for broader applications in GNSS meteorology. Geographic Grid Neural Network atmospheric temperature estimation GNSS tropospheric tomography GNSS wet refractivity water vapor density 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-7541639","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561214362,"identity":"5b75c914-2af9-49de-b1da-ce3c01f2bf4a","order_by":0,"name":"Maohua Ding","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Maohua","middleName":"","lastName":"Ding","suffix":""},{"id":561214363,"identity":"649c80a6-1db0-453d-b4b6-865eabb5aeb2","order_by":1,"name":"Chiyu Chen","email":"","orcid":"","institution":"Yangzhou 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refractivity, water vapor density","lastPublishedDoi":"10.21203/rs.3.rs-7541639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7541639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study presents an empirical model for atmospheric temperature (T) estimation based on a Geographic Grid Neural Network (GGNN) framework. As a critical parameter in GNSS tropospheric tomography, T facilitates the conversion of wet refractivity (Nw) to water vapor density (ρv). The proposed GGNNTemp model integrates grid-specific neural networks across a 1°×1° geographic grid, where each node employs a multilayer perceptron with month and altitude as inputs to predict temperature. Comparative evaluations against traditional neural networks and the GPT2w model demonstrate the superior performance of the GGNNTemp model, achieving a BIAS value of 0.01 K and an RMSE of 1.60 K—significantly outperforming GPT2w’s 12.37 K RMSE. The model exhibits consistent accuracy across varying latitudes, altitudes (up to 10 km), and seasons, with ρv conversion errors maintained below 5‰. This work highlights the efficacy of GGNN technology in modeling geophysical parameters and advances its potential for broader applications in GNSS meteorology.","manuscriptTitle":"An Empirical Model for Atmospheric Temperature Estimation Using a Geographic Grid Neural Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 04:23:15","doi":"10.21203/rs.3.rs-7541639/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":"3a07f19a-0855-47e9-baaa-728f090d515e","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T21:38:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 04:23:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7541639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7541639","identity":"rs-7541639","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}