Hengqian Yan, Ren Zhang, Huizan Wang, Senliang Bao, Yongchui Zhang, Mei Hong
{"title":"从卫星数据估算每日次表层温盐结构:内嵌经验正交函数的深度网络","authors":"Hengqian Yan, Ren Zhang, Huizan Wang, Senliang Bao, Yongchui Zhang, Mei Hong","doi":"10.1016/j.dsr.2024.104257","DOIUrl":null,"url":null,"abstract":"<div><p>Estimating subsurface thermohaline structure from concurrent satellite data is a meaningful way to enrich internal oceanic observations. As a powerful tool for data mining, many studies have used machine learning in subsurface reconstruction, but most conventional applications have been purely black-box in nature without further consideration of oceanic characteristics. Instead, proposed here for the first time is a semi-explicit deep network for reconstructing the oceanic interior from surface data. Named EEFFNN, the method embeds empirical orthogonal functions extracted from reanalysis data (the EE part of the name) into the inner framework of a feed-forward neural network (the FFNN part of the name). Comparison with Argo profiles and reanalysis data shows that EEFFNN can significantly outperform conventional machine-learning algorithms in estimating subsurface thermohaline structures and especially subsurface-intensified eddies. Also, EEFFNN can perform thermohaline reconstruction in one pass, making it more lightweight than “shallow” machine-learning algorithms such as random forest. Overall, EEFFNN shows promise for being applied to operational thermohaline reconstruction in the near future.</p></div>","PeriodicalId":51009,"journal":{"name":"Deep-Sea Research Part I-Oceanographic Research Papers","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating daily subsurface thermohaline structure from satellite data: A deep network with embedded empirical orthogonal functions\",\"authors\":\"Hengqian Yan, Ren Zhang, Huizan Wang, Senliang Bao, Yongchui Zhang, Mei Hong\",\"doi\":\"10.1016/j.dsr.2024.104257\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Estimating subsurface thermohaline structure from concurrent satellite data is a meaningful way to enrich internal oceanic observations. As a powerful tool for data mining, many studies have used machine learning in subsurface reconstruction, but most conventional applications have been purely black-box in nature without further consideration of oceanic characteristics. Instead, proposed here for the first time is a semi-explicit deep network for reconstructing the oceanic interior from surface data. Named EEFFNN, the method embeds empirical orthogonal functions extracted from reanalysis data (the EE part of the name) into the inner framework of a feed-forward neural network (the FFNN part of the name). Comparison with Argo profiles and reanalysis data shows that EEFFNN can significantly outperform conventional machine-learning algorithms in estimating subsurface thermohaline structures and especially subsurface-intensified eddies. Also, EEFFNN can perform thermohaline reconstruction in one pass, making it more lightweight than “shallow” machine-learning algorithms such as random forest. Overall, EEFFNN shows promise for being applied to operational thermohaline reconstruction in the near future.</p></div>\",\"PeriodicalId\":51009,\"journal\":{\"name\":\"Deep-Sea Research Part I-Oceanographic Research Papers\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Deep-Sea Research Part I-Oceanographic Research Papers\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S096706372400027X\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OCEANOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Deep-Sea Research Part I-Oceanographic Research Papers","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S096706372400027X","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OCEANOGRAPHY","Score":null,"Total":0}
Estimating daily subsurface thermohaline structure from satellite data: A deep network with embedded empirical orthogonal functions
Estimating subsurface thermohaline structure from concurrent satellite data is a meaningful way to enrich internal oceanic observations. As a powerful tool for data mining, many studies have used machine learning in subsurface reconstruction, but most conventional applications have been purely black-box in nature without further consideration of oceanic characteristics. Instead, proposed here for the first time is a semi-explicit deep network for reconstructing the oceanic interior from surface data. Named EEFFNN, the method embeds empirical orthogonal functions extracted from reanalysis data (the EE part of the name) into the inner framework of a feed-forward neural network (the FFNN part of the name). Comparison with Argo profiles and reanalysis data shows that EEFFNN can significantly outperform conventional machine-learning algorithms in estimating subsurface thermohaline structures and especially subsurface-intensified eddies. Also, EEFFNN can perform thermohaline reconstruction in one pass, making it more lightweight than “shallow” machine-learning algorithms such as random forest. Overall, EEFFNN shows promise for being applied to operational thermohaline reconstruction in the near future.
期刊介绍:
Deep-Sea Research Part I: Oceanographic Research Papers is devoted to the publication of the results of original scientific research, including theoretical work of evident oceanographic applicability; and the solution of instrumental or methodological problems with evidence of successful use. The journal is distinguished by its interdisciplinary nature and its breadth, covering the geological, physical, chemical and biological aspects of the ocean and its boundaries with the sea floor and the atmosphere. In addition to regular "Research Papers" and "Instruments and Methods" papers, briefer communications may be published as "Notes". Supplemental matter, such as extensive data tables or graphs and multimedia content, may be published as electronic appendices.