Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021GL094772 |
Characteristics of Global Ocean Abnormal Mesoscale Eddies Derived from the Fusion of Sea Surface Height and Temperature Data by Deep Learning | |
Yingjie Liu; Quanan Zheng; Xiaofeng Li | |
2021-08-25 | |
发表期刊 | Geophysical Research Letters
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出版年 | 2021 |
英文摘要 | Recent satellite sea surface height (SSH) and sea surface temperature (SST) observations have shown that abnormal eddies, i.e., 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 paper, 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. |
领域 | 气候变化 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/336680 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Yingjie Liu,Quanan Zheng,Xiaofeng Li. 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. |
APA | Yingjie Liu,Quanan Zheng,&Xiaofeng Li.(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. |
MLA | Yingjie Liu,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 (2021). |
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