Evaluating ecPoint and EMOS for Ensemble Post-processing of Precipitation Forecast

Evaluating ecPoint and EMOS for Ensemble Post-processing of Precipitation Forecast | 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 Short Report Evaluating ecPoint and EMOS for Ensemble Post-processing of Precipitation Forecast Sonum Stejik, Pu Liu, Wang Jialing, Yong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8355829/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 In this study, we evaluate two advanced post-processing techniques—Ensemble Model Output Statistics (EMOS) and the point-based European Centre for ECMWF statistical ensemble method (ecPoint)—for calibrating ensemble precipitation forecasts. A comprehensive assessment of performance of these ensemble post-processing methods is conducted using the CMA Global Ensemble Forecasting System (CMA-GEPS) forecast over eastern China. The results demonstrate that both methods significantly reduce systematic biases and improve the reliability and dispersion of ensemble forecasts. Notably, improvement on forecast accuracy is observed even under convective weather conditions, and early warnings of extreme precipitation events. Overall, while both methods show comparable performance, they exhibit distinct behaviors across different regions. The ecPoint method slightly outperforms EMOS in terms of Continuous Ranked Probability Score (CRPS) and provides improved resolution and early warning capabilities at various precipitation thresholds. NWP Post-processing Ensemble forecast Probability prediction ecPoint EMOS 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-8355829","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":561521353,"identity":"d90c6489-1b42-489c-a3c1-e666e72af30e","order_by":0,"name":"Sonum Stejik","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sonum","middleName":"","lastName":"Stejik","suffix":""},{"id":561521356,"identity":"741743f8-1a6a-4c05-a3d7-1ec56a6191c2","order_by":1,"name":"Pu Liu","email":"","orcid":"","institution":"Nanjing Meteorological 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