[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123922-en":3,"doc-seo-123922-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123922,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","A machine learning model that outperforms conventional global subseasonal forecast models - read online free","Skillful subseasonal forecasts are essential for decision-making in agriculture, disaster preparedness, extreme-event mitigation, and water resource management, yet remain difficult because both atmospheric initial conditions and surface boundary conditions limit predictability. A new machine learning approach, FuXi Subseasonal-to-Season (FuXi-S2S), produces global daily-mean forecasts up to 42 days and improves precipitation and outgoing longwave radiation skill over the ECMWF state-of-the-art model. The gains stem from better uncertainty capture and more accurate Madden-Julian Oscillation prediction, extending skill from 30 to 36 days, while supporting discovery of precursor signals for Earth system research.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nA machine learning model that outperforms conventional global subseasonal forecast models.  \nPermalink  \n[https://escholarship.org/uc/item/6c6841zm](https://escholarship.org/uc/item/6c6841zm)  \nJournal  \nNature Communications, 15(1)  \nAuthors  \nChen, Lei  \nZhong, Xiaohui Li, Hao  \net al.  \nPublication Date  \n2024-07-30  \nDOI  \n10.1038/s41467-024-50714-1  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nArticle [https://doi.org/10.1038/s41467-024-50714-1](https://doi.org/10.1038/s41467-024-50714-1)  \nA machine learning model that outperforms conventional global subseasonal  \nforecast models  \nReceived: 7 February 2024  \n\n| Accepted: 19 July 2024 |\n| --- |\n|  |\n| Check for updates |\n\nLei Chen1,2,10, Xiaohui Zhong 1,10, Hao Li 1,10 , Jie Wu3,10, Bo Lu 3,4 , Deliang Chen 5, Shang-Ping Xie 6, Libo Wu 7,8,9, Qingchen Chao3, Chensen Lin 1, Zixin Hu1 & Yuan Qi1,2   \nSkillful subseasonal forecasts are crucial for various sectors of society but posea grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF’s state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.  \nSubseasonal forecasting, which predicts weather patterns from 2 to 6 weeks in advance, bridges a critical gap between short-term weather forecasts, typically up to 15 days, and longer-term climate forecasts that extend to seasonal and longer timescales1. Forecasting at this intermediate subseasonal timescale is indispensable for a variety of applications, including agricultural planning, disaster preparedness, mitigating impacts of extreme events such as heatwaves, droughts,  \nfloods, and cold spells, and water resource management2–5. Despite its significant socioeconomic benefits, subseasonal forecasting has historically not received sufficient attention compared to mediumrange weather and climate predictions. This gap existed because accurate subseasonal forecasts were once considered nearly impossible. Subseasonal forecasts are particularly challenging as they rely on both atmospheric initial conditions, essential in short-term  \n1Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China. 2Shanghai Academy of Artificial Intelligence for Science, Shanghai, China. 3China Meteorological Administration Key Laboratory for Climate Prediction Studies, National Climate Center, Beijing, China. 4Xiong’an Institute of Meteorological Artificial Intelligence, Xiong’an, China. 5University of Gothenburg, Gothenburg, Sweden. 6Scripps Institution of Oceanography, University of California San Diego, San Diego, CA, USA. 7School of Data Science, Fudan University, Shanghai, China. 8Institute for Big Data, Fudan","cbCaivkOV3QXhimt","https://ap.wps.com/l/cbCaivkOV3QXhimt","pdf",2464967,1,15,"English","en",105,"# Overview\n## Subseasonal forecasting challenges and societal value\n## FuXi-S2S model approach and training data\n# Performance and key improvements\n## Outperforming ECMWF subseasonal-to-seasonal baselines\n## Capturing uncertainty and precipitation skill\n## Extending MJO prediction and teleconnections\n# Implications for Earth system science\n## Using precursor signals for research","[{\"question\":\"What time range does the FuXi-S2S model forecast?\",\"answer\":\"FuXi-S2S provides global daily-mean forecasts up to 42 days, covering subseasonal timescales.\"},{\"question\":\"How does FuXi-S2S improve upon conventional ECMWF subseasonal-to-seasonal modeling?\",\"answer\":\"It outperforms the ECMWF state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts, particularly for total precipitation and outgoing longwave radiation.\"},{\"question\":\"Why is the Madden-Julian Oscillation (MJO) prediction better with FuXi-S2S?\",\"answer\":\"The improved performance is attributed to FuXi-S2S’s stronger capability to capture forecast uncertainty and accurately predict the MJO, extending skill from 30 to 36 days.\"}]","A machine learning model that outperforms conventional global subseasonal forecast models - 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