[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124023-en":3,"doc-seo-124023-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},124023,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Meet the challenge of predictability desert - a machine learning model that outperforms conventional global subseasonal forecast models - Supplementary Notes","Supplementary Notes detail how the FuXi-S2S machine learning subseasonal forecasting system improves predictability by introducing flow-dependent perturbations into hidden features. Experiments compare Perlin-noise-only initialization versus fixed and flow-dependent perturbation strategies, producing 42-day forecasts and assessing skill against a baseline FuXi-S2S configuration. Results include TCC and RMSE evaluations across regions and variables, plus energy-spectrum analyses showing how the models’ ensemble mean and members align with ERA5 benchmarks over increasing lead times.","arXiv:2312.09926v2 [[physics. ao-ph](physics. ao-ph) ] 5 Jul 2024  \nSupplementary Information: Meet the challenge of predictability desert: a machine learning model that outperforms conventional global subseasonal forecast models  \nLei Chen 1,2†, Xiaohui Zhong 1†, Hao Li 1*†, Jie Wu3†, Bo  \nLu3,4*, Deliang Chen5 , Shang-Ping Xie6 , Libo Wu7,8,9 , Qingchen Chao3 , Chensen Lin 1 , Zixin Hu 1 and Yuan  \nQi2,1*  \n1 Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, 200433, China.  \n2 Shanghai Academy of Artificial Intelligence for Science, Shanghai, 200232, China.  \n3 China Meteorological Administration Key Laboratory for Climate Prediction Studies, National Climate Center, Beijing,  \n100081, China.  \n4 Xiong’an Institute of Meteorological Artificial Intelligence, Xiong’an, China.  \n5 University of Gothenburg, Sweden.  \n6 Scripps Institution of Oceanography, University of California San Diego, USA.  \n7 School of Data Science, Fudan University, Shanghai, 200433, China.  \n8 Institute for Big Data, Fudan University, Shanghai, 200433, China.  \n9 MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, 200433, China.  \n*Corresponding author(s). E-mail(s): lihao [lh@fudan.edu.cn](lh@fudan.edu.cn) ; [bolu@cma.gov.cn](bolu@cma.gov.cn) ; [qiyuan@fudan.edu.cn](qiyuan@fudan.edu.cn) ;  \nContributing authors: [cltpys@163.com](cltpys@163.com) ; [x7zhong@gmail.com](x7zhong@gmail.com) ;  \n[wujie@cma.gov.com](wujie@cma.gov.com) ; [deliang@gvc.gu.se](deliang@gvc.gu.se) ; [sxie@ucsd.edu](sxie@ucsd.edu) ;  \n2 FuXi-S2S  \n[wulibo@fudan.edu.cn](wulibo@fudan.edu.cn) ; [chaoqc@cma.gov.cn](chaoqc@cma.gov.cn) ; [linchensen@fudan.edu.cn](linchensen@fudan.edu.cn) ; [huzixin@fudan.edu.cn](huzixin@fudan.edu.cn) ;  \n†These authors contributed equally to this work.  \nContents of this file  \nSupplementary Notes 1 to 8 Supplementary Figures 1 to 16 Supplementary Table 1  \nSupplementary Notes  \n1 Effectiveness of flow-dependent perturbations  \nThis section discusses the effect of incorporating the flow-dependent perturbations into the model’s hidden features to enhance performance in subseasonal forecasts. We conducted experiments using FuXi-S2S models which exclusively employ Perlin noise in the initial conditions or combine Perlin noise in the initial conditions with fixed perturbations added into the hidden features, to generate 42-day forecasts. Subsequently we evaluate their performance in comparison with the original FuXi-S2S model.  \nSupplementary Figure 1 presents a comparison of the globally-averaged and latitude-weighted TCC for TP. This analysis encompasses all testing data from the period spanning from 2017 to 2021 . The FuXi-S2S model, which incorporates flow-dependent perturbations into its hidden features, consistently exhibits considerably improved forecast performance in comparison to the FuXi-S2S model that incorporates fixed Gaussian noise into the hidden features, across all forecast lead times. Furthermore, the introduction of flowdependent perturbations has extended the FuXi-S2S model’s skillful MJO prediction from 22 days to 36 days.  \n2 Deterministic forecast metrics comparison  \nSupplementary Figure 3 presents a comparison of latitude-weighted TCC between FuXi-S2S and ECMWF S2S. It examines TP, T2M, Z500, and OLR across four geographical regions: in the extra-tropics (90°S - 30°S and 30°N- 90°N), in the tropics (30°S-30°N), over land, and over the ocean. Within the extra-tropical regions, FuXi-S2S consistently exhibits superior performance compared to ECMWF S2S for all four variables. In tropical regions, FuXi-S2S outperforms ECMWF S2S for TP and OLR, while achieving comparable accuracy in T2M and Z500 . Over land areas, FuXi-S2S demonstrates consistently higher TCC values for TP, Z500, and OLR.  \nFuXi-S2S 3  \nSupplementary Figure 4 presents a comparison of the globally-averaged and latitude-weighted root mean square error (RMSE) of the ensemble mean between ECMWF S","cbCaipR5ln1QXw39","https://ap.wps.com/l/cbCaipR5ln1QXw39","pdf",14360687,1,58,"English","en",105,"# Supplementary Notes\n## Effectiveness of flow-dependent perturbations\n## Deterministic forecast metrics comparison\n## Energy spectra analysis across forecast lead times","[{\"question\":\"What is the main purpose of the supplementary notes?\",\"answer\":\"They investigate how flow-dependent perturbations added to the FuXi-S2S model’s hidden features improve subseasonal forecast performance and skill duration.\"},{\"question\":\"How do the notes evaluate forecast effectiveness?\",\"answer\":\"They compare metrics such as TCC and RMSE between FuXi-S2S variants and ECMWF S2S across multiple variables, regions, and forecast lead times.\"},{\"question\":\"What do the energy-spectrum results indicate about model behavior over longer lead times?\",\"answer\":\"At longer wavelengths, selected ensemble members from FuXi-S2S and ECMWF S2S better align with ERA5, while at shorter wavelengths FuXi-S2S shows a gradual reduction in energy, implying smoother forecasts at smaller scales.\"}]","Meet the challenge of predictability desert - a machine learning model that outperforms conventional global subseasonal forecast models - Supplementary Notes | PDF",1785819910,146,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"meet-the-challenge-of-predictability-desert-a-machine-learning-model-that-outperforms-conventional-global-subseasonal-forecast-models-supplementary-notes","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/meet-the-challenge-of-predictability-desert-a-machine-learning-model-that-outperforms-conventional-global-subseasonal-forecast-models-supplementary-notes/124023/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main purpose of the supplementary notes?","Question",{"text":75,"@type":76},"They investigate how flow-dependent perturbations added to the FuXi-S2S model’s hidden features improve subseasonal forecast performance and skill duration.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the notes evaluate forecast effectiveness?",{"text":80,"@type":76},"They compare metrics such as TCC and RMSE between FuXi-S2S variants and ECMWF S2S across multiple variables, regions, and forecast lead times.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the energy-spectrum results indicate about model behavior over longer lead times?",{"text":84,"@type":76},"At longer wavelengths, selected ensemble members from FuXi-S2S and ECMWF S2S better align with ERA5, while at shorter wavelengths FuXi-S2S shows a gradual reduction in energy, implying smoother forecasts at smaller scales.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]