Multi-model prediction of disastrous weather events caused by Typhoon Bebinca and analysis of the brown ocean effect

Multi-model prediction of disastrous weather events caused by Typhoon Bebinca and analysis of the brown ocean effect | 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 Multi-model prediction of disastrous weather events caused by Typhoon Bebinca and analysis of the brown ocean effect Yunlong Du, Zhiren Liu, Xiaoqian Su, Liang Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8071668/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 On September 16th, 2024, Typhoon Bebinca (2413) made landfall in Shanghai at severe typhoon intensity, and maintained intensity as a severe tropical storm for 18 hours, triggering disastrous weather events like heavy rain and strong winds across Shanghai and Jiangsu. Based on land surface observations, the performance of two global models, i.e., the European Center for Medium-Range Weather Forecasts (ECMWF) model, the Global Forecast System (GFS) model, and one regional model, i.e., the Jiangsu Weather Research and Forecasting (JWRF) model, was compared in forecasting the typhoon-caused rain and wind by the metrics of threat score, the prediction accuracy of precipitation location, and extreme values; meanwhile, the maintaining mechanism of Bebinca was investigated based on the JWRF regional model’s forecasts. The main findings are as follows: 1) All three models accurately predicted the spatial distribution and intensity of heavy rain and the spatial distribution of wind speeds, but showed southward biases in their forecasts of the location of heavy rain and overestimation of the wind magnitude. 2) The JWRF model outperformed the other two global models in forecasting the location of heavy rain, especially torrential rain, maximum precipitation, and the wind magnitude. 3) In the 24-h lead time forecasts, the GFS and JWRF models adjusted the location of torrential rain northwards, reaching higher accuracy than their 36-h lead time forecasts. 4) Further diagnostic analysis identified the brown ocean effect and water vapor transport from the East China Sea as the main causes for the typhoon’s maintaining presence. Typhoon Bebinca power grid meteorological disasters regional model forecast verification brown ocean effect 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-8071668","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551429751,"identity":"09007f15-40d2-4357-a24f-331a4e150bf7","order_by":0,"name":"Yunlong Du","email":"","orcid":"","institution":"State Grid Jiangsu Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Du","suffix":""},{"id":551429752,"identity":"acdc7d2e-67a5-4baa-b784-65e1eed1a069","order_by":1,"name":"Zhiren Liu","email":"","orcid":"","institution":"State Grid Jiangsu Electric Power Co., Ltd. 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Based on land surface observations, the performance of two global models, i.e., the European Center for Medium-Range Weather Forecasts (ECMWF) model, the Global Forecast System (GFS) model, and one regional model, i.e., the Jiangsu Weather Research and Forecasting (JWRF) model, was compared in forecasting the typhoon-caused rain and wind by the metrics of threat score, the prediction accuracy of precipitation location, and extreme values; meanwhile, the maintaining mechanism of Bebinca was investigated based on the JWRF regional model\u0026rsquo;s forecasts. 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