[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123577-en":3,"doc-seo-123577-105":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},123577,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Composite Analysis-Based Machine Learning for Prediction of Tropical Cyclone-Induced Sea Surface Height Anomaly","Sea surface height anomaly (SSHA) induced by tropical cyclones (TCs) is tightly linked to upper-ocean oscillations and is a key proxy for thermocline structure and ocean heat content. However, accurate TC-driven SSHA prediction remains insufficiently studied. This work introduces a composite analysis-based random forest framework that predicts daily TC-induced SSHA using TC characteristics and pre-storm upper-ocean parameters as inputs, forecasting up to 30 days after passage. Results indicate strong skill in capturing both SSHA amplitude and temporal evolution across different TC intensities. Using a 5°×5° TC-centered box, the method achieves root mean square error of 0.024 m over the Western North Pacific, outperforming alternative machine learning approaches and a numerical model, with consistent performance in the South China Sea and Western North Pacific subtropical regions.","Kent Academic Repository  \nCui, Hongxing, Tang, Danling, Liu, Huizeng, Sui, Yi and Gu, Xiaowei (2023) Composite Analysis-Based Machine Learning for Prediction of Tropical Cyclone-Induced Sea Surface Height Anomaly. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing . ISSN 1939-1404. (In press)  \nDownloaded from  \n[https://kar.kent.ac.uk/100189/](https://kar.kent.ac.uk/100189/ The University of Kent)[ The University of Kent](https://kar.kent.ac.uk/100189/ The University of Kent)'s Academic Repository KAR  \nThe version of record is available from  \nThis document version  \nAuthor's Accepted Manuscript  \nDOI for this version  \nLicence for this version  \nUNSPECIFIED  \nAdditional information  \nVersions of research works  \nVersions of Record  \nIf this version is the version of record, it is the same as the published version available on the publisher's web site. Cite as the published version.  \nAuthor Accepted Manuscripts  \nIf this document is identified as the Author Accepted Manuscript it is the version after peer review but before typesetting, copy editing or publisher branding. Cite as Surname, Initial. (Year) 'Title of article'. To be published in Title of Journal , Volume and issue numbers [peer-reviewed accepted version] . Available at: DOI or URL (Accessed: date) .  \nEnquiries  \nIf you have questions about this [document contact ](document contact ResearchSupport@kent.ac.uk. Please)[ResearchSupport@kent.ac.uk](document contact ResearchSupport@kent.ac.uk. Please)[. Please](document contact ResearchSupport@kent.ac.uk. Please) include the URL of the record in KAR. If you believe that your, or a third party's rights have been compromised through this document please see our Take Down policy (available from [https://www.kent.ac.uk/guides/kar-the-kent-academic-repository\\#policies](https://www.kent.ac.uk/guides/kar-the-kent-academic-repository#policies)) .  \nComposite Analysis-Based Machine Learning for Prediction of Tropical Cyclone-Induced Sea Surface Height Anomaly  \nHongxing Cui1,2 , Danling Tang1*, Huizeng Liu3 , Yi Sui4 , 1 , and Xiaowei Gu5  \n1 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou); Guangdong Remote Sensing Center for Marine Ecology and Environment, Guangzhou 511458, China;  \n2 Department of Ocean Science, HongKong University of Science and Technology, HongKong, China;  \n3 Institute for Advanced Study, Shenzhen University, 518060 Shenzhen, China;  \n4 Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada;  \n5 School of Computing, University of Kent, Canterbury, CT2 7NZ, UK;  \n* Correspondence: [lingzistdl@126.com](lingzistdl@126.com)  \nAbstract—Sea surface height anomaly (SSHA) induced by tropical cyclones (TCs) is closely associated with oscillations and is a crucial proxy for thermocline structure and ocean heat content in the upper ocean. The prediction of TC-induced SSHA, however, has been rarely investigated. This study presents a new composite analysis-based random forest (RF) approach to predict daily TCinduced SSHA. The proposed method utilizes TC’s characteristics and pre-storm upper oceanic parameters as input features to predict TC-induced SSHA up to 30 days after TC passage. Simulation results suggest that the proposed method is skillful at inferring both the amplitude and temporal evolution of SSHA induced by TCs of different intensity groups. Using a TC-centered 5°×5° box, the proposed method achieves highly accurate prediction of TC-induced SSHA over the Western North Pacific with root mean square error of 0.024m, outperforming alternative machine learning methods and the numerical model. Moreover, the proposed method also demonstrated good prediction performance in different geographical regions, i.e., the South China Sea and the Western North Pacific subtropical ocean. The study provides insight into the application of machine learning in improving the prediction of SSHA influenced by extreme weather conditions.","cbCaiczWsrRX3Wb8","https://ap.wps.com/l/cbCaiczWsrRX3Wb8","pdf",1807629,1,11,"English","en",105,"# Abstract\n# Index Terms\n# I. Introduction","[{\"question\":\"What does the study aim to predict?\",\"answer\":\"The study predicts daily sea surface height anomaly (SSHA) induced by tropical cyclones (TCs), forecasting up to 30 days after TC passage.\"},{\"question\":\"What machine learning method is proposed?\",\"answer\":\"A composite analysis-based random forest (RF) approach is proposed, using TC characteristics and pre-storm upper-ocean parameters as input features.\"},{\"question\":\"How accurate is the proposed prediction method?\",\"answer\":\"Over the Western North Pacific, the method achieves a root mean square error of 0.024 m using a 5°×5° TC-centered box, and it outperforms alternative machine learning methods and a numerical model.\"}]","Composite Analysis-Based Machine Learning for Prediction of Tropical Cyclone-Induced Sea Surface Height Anomaly | PDF",1785817438,28,{"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},"composite-analysis-based-machine-learning-for-prediction-of-tropical-cyclone-induced-sea-surface-height-anomaly","",{"@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/composite-analysis-based-machine-learning-for-prediction-of-tropical-cyclone-induced-sea-surface-height-anomaly/123577/",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 does the study aim to predict?","Question",{"text":75,"@type":76},"The study predicts daily sea surface height anomaly (SSHA) induced by tropical cyclones (TCs), forecasting up to 30 days after TC passage.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning method is proposed?",{"text":80,"@type":76},"A composite analysis-based random forest (RF) approach is proposed, using TC characteristics and pre-storm upper-ocean parameters as input features.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the proposed prediction method?",{"text":84,"@type":76},"Over the Western North Pacific, the method achieves a root mean square error of 0.024 m using a 5°×5° TC-centered box, and it outperforms alternative machine learning methods and a numerical model.","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"]