IOCAS-IR  > 海洋环流与波动重点实验室
Characteristics of Global Ocean Abnormal Mesoscale Eddies Derived From the Fusion of Sea Surface Height and Temperature Data by Deep Learning
Liu, Yingjie1,2; Zheng, Quanan3; Li, Xiaofeng1,2
2021-09-16
Source PublicationGEOPHYSICAL RESEARCH LETTERS
ISSN0094-8276
Volume48Issue:17Pages:11
Corresponding AuthorLi, Xiaofeng(xiaofeng.li@ieee.org)
AbstractRecent satellite sea surface height (SSH) and sea surface temperature (SST) observations have shown that abnormal eddies, that is, warm cyclonic eddies and cold anticyclonic eddies occur sporadically in some regions, which triggers an essential question on the spatiotemporal distribution of abnormal eddies in the global ocean. In this study, a deep learning framework was developed to systematically mine information from the synergy of satellite-sensed global SSH and SST data over the 1996-2015, 20-year period. Abnormal eddies account for a surprising one-third of total eddies and are active along the Equatorial Current and high unstable currents. Normal (abnormal) eddies are stronger in winter (summer) in the North Hemisphere and vice versa in the Southern Hemisphere. The annual mean amplitudes of normal eddies are larger than that of abnormal eddies. Crucially, the daily number of normal (abnormal) eddies increased (decreased) 9.68 (11.80) every year.
Keywordmeososcale eddies abnormal eddies multi-source remote sensing data deep learning data fusion statistical analysis of spatiotemporal characteristics
DOI10.1029/2021GL094772
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42000000] ; Major Scientific and Technological Innovation Projects of Shandong Province[2019JZZY010102] ; Key Project of the Center for Ocean Mega-Science, Chinese Academy of Sciences[COMS2019R02] ; CAS Program[Y9KY04101L] ; National Natural Science Foundation of China[U2006211]
WOS Research AreaGeology
WOS SubjectGeosciences, Multidisciplinary
WOS IDWOS:000694653200068
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/176362
Collection海洋环流与波动重点实验室
Corresponding AuthorLi, Xiaofeng
Affiliation1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Peoples R China
3.Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA
First Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences;  Center for Ocean Mega-Science, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences;  Center for Ocean Mega-Science, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liu, Yingjie,Zheng, Quanan,Li, Xiaofeng. Characteristics of Global Ocean Abnormal Mesoscale Eddies Derived From the Fusion of Sea Surface Height and Temperature Data by Deep Learning[J]. GEOPHYSICAL RESEARCH LETTERS,2021,48(17):11.
APA Liu, Yingjie,Zheng, Quanan,&Li, Xiaofeng.(2021).Characteristics of Global Ocean Abnormal Mesoscale Eddies Derived From the Fusion of Sea Surface Height and Temperature Data by Deep Learning.GEOPHYSICAL RESEARCH LETTERS,48(17),11.
MLA Liu, Yingjie,et al."Characteristics of Global Ocean Abnormal Mesoscale Eddies Derived From the Fusion of Sea Surface Height and Temperature Data by Deep Learning".GEOPHYSICAL RESEARCH LETTERS 48.17(2021):11.
Files in This Item:
File Name/Size DocType Version Access License
2021GL094772.pdf(1562KB)期刊论文出版稿延迟开放CC BY-NC-SAView 2023-7-1后可获取
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu, Yingjie]'s Articles
[Zheng, Quanan]'s Articles
[Li, Xiaofeng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Yingjie]'s Articles
[Zheng, Quanan]'s Articles
[Li, Xiaofeng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Yingjie]'s Articles
[Zheng, Quanan]'s Articles
[Li, Xiaofeng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 2021GL094772.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.