Short-Term and Long-Term Prediction of South China Sea SST Based on Multiple Meteorological Factors and Machine Learning

Short-Term and Long-Term Prediction of South China Sea SST Based on Multiple Meteorological Factors and Machine Learning | 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 Article Short-Term and Long-Term Prediction of South China Sea SST Based on Multiple Meteorological Factors and Machine Learning Wenya Ji, Jiyuan Yin, Jianhu Wang, Menglu Wang, Juan Li, Yiqiu Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8257774/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 Sea surface temperature (SST) is a vital component of the climate system, and its spatiotemporal variations significantly influence global climate and ecological equilibrium. In this paper, based on the ERA5 reanalysis data, three machine learning algorithms, namely, Random Forest (RF), XGBoost and LightGBM, are used to construct short-term and long-term SST forecast models for the South China Sea. The input feature variables include seven meteorological and hydrological variables such as SST, 10m u-component of wind (U10), 10m v-component of wind (V10), 2m dewpoint temperature (d2m), 2m temperature (t2m), mean sea level pressure (SLP), and total cloud cover (TCC). Correlation analysis revealed that these meteorological factors are significantly correlated with SST, with the strongest correlations observed for 2-meter dew point temperature and 2-meter air temperature. Model performance is assessed using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results indicate that the RF model exhibits the highest accuracy for both short-term and long-term forecasting models. Furthermore, this study explores high-resolution SST forecasting models for the South China Sea, revealing that total cloud cover (TCC) contributes more to SST predictions than sea surface salinity (SSS), and the model performs well across most areas of the South China Sea (excluding coastal regions), achieving forecasts with a lead time of at least 20 months. These findings demonstrate the feasibility of machine learning algorithms for SST prediction, providing an efficient approach to understanding future SST changes and their potential impacts, while emphasizing the necessity of integrating multiple meteorological factors to enhance forecasting accuracy. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Ocean sciences Correlation analysis Machine learning algorithms Sea surface temperature (SST) South China Sea (SCS) Total cloud cover (TCC) 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-8257774","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":563011865,"identity":"cc467437-4c15-4e45-8356-e06d404a63b1","order_by":0,"name":"Wenya Ji","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenya","middleName":"","lastName":"Ji","suffix":""},{"id":563011866,"identity":"9d44d207-823f-4cb3-a601-6bcdb133d6ba","order_by":1,"name":"Jiyuan Yin","email":"","orcid":"","institution":"South China Sea Institute Of 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Learning","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":"Correlation analysis, Machine learning algorithms, Sea surface temperature (SST), South China Sea (SCS), Total cloud cover (TCC)","lastPublishedDoi":"10.21203/rs.3.rs-8257774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8257774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSea surface temperature (SST) is a vital component of the climate system, and its spatiotemporal variations significantly influence global climate and ecological equilibrium. In this paper, based on the ERA5 reanalysis data, three machine learning algorithms, namely, Random Forest (RF), XGBoost and LightGBM, are used to construct short-term and long-term SST forecast models for the South China Sea. The input feature variables include seven meteorological and hydrological variables such as SST, 10m u-component of wind (U10), 10m v-component of wind (V10), 2m dewpoint temperature (d2m), 2m temperature (t2m), mean sea level pressure (SLP), and total cloud cover (TCC). Correlation analysis revealed that these meteorological factors are significantly correlated with SST, with the strongest correlations observed for 2-meter dew point temperature and 2-meter air temperature. Model performance is assessed using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R\u0026sup2;). The results indicate that the RF model exhibits the highest accuracy for both short-term and long-term forecasting models. Furthermore, this study explores high-resolution SST forecasting models for the South China Sea, revealing that total cloud cover (TCC) contributes more to SST predictions than sea surface salinity (SSS), and the model performs well across most areas of the South China Sea (excluding coastal regions), achieving forecasts with a lead time of at least 20 months. These findings demonstrate the feasibility of machine learning algorithms for SST prediction, providing an efficient approach to understanding future SST changes and their potential impacts, while emphasizing the necessity of integrating multiple meteorological factors to enhance forecasting accuracy.\u003c/p\u003e","manuscriptTitle":"Short-Term and Long-Term Prediction of South China Sea SST Based on Multiple Meteorological Factors and Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 04:48:31","doi":"10.21203/rs.3.rs-8257774/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":"c938914c-7c47-4fb1-bfa4-e01c487a961a","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59958260,"name":"Earth and environmental sciences/Climate sciences"},{"id":59958261,"name":"Earth and environmental sciences/Environmental sciences"},{"id":59958262,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2026-01-27T04:24:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 04:48:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8257774","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8257774","identity":"rs-8257774","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}