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DOI: 10.1002/2016GL071269
论文题名:
Oceanic eddy detection and lifetime forecast using machine learning methods
作者: Ashkezari M.D.; Hill C.N.; Follett C.N.; Forget G.; Follows M.J.
刊名: Geophysical Research Letters
ISSN: 0094-8350
EISSN: 1944-8081
出版年: 2016
卷: 43, 期:23
起始页码: 12234
结束页码: 12241
语种: 英语
英文关键词: eddy ; eddy lifetime ; machine learning ; ocean ; remote sensing
Scopus关键词: Artificial intelligence ; Machine components ; Remote sensing ; Eddy ; Eddy lifetime ; Eddy structures ; Geostrophic velocity anomalies ; Machine learning approaches ; Machine learning methods ; Machine learning models ; Ocean ; Learning systems
英文摘要: We report a novel altimetry-based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency. ©2016. American Geophysical Union. All Rights Reserved.
URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006847852&doi=10.1002%2f2016GL071269&partnerID=40&md5=490493937cf1c291042e5d84c55459d0
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/9313
Appears in Collections:科学计划与规划
气候变化与战略

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作者单位: Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States

Recommended Citation:
Ashkezari M.D.,Hill C.N.,Follett C.N.,et al. Oceanic eddy detection and lifetime forecast using machine learning methods[J]. Geophysical Research Letters,2016-01-01,43(23).
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