基于深度学习的越南沿海夏季上升流和叶绿素的月度变化和年际变化

IF 3.3 2区 地球科学 Q1 OCEANOGRAPHY
Qiang Lian, Haiyuan Lin, Yawen Huang, Shuwen Zhang, Zhaoyun Chen
{"title":"基于深度学习的越南沿海夏季上升流和叶绿素的月度变化和年际变化","authors":"Qiang Lian,&nbsp;Haiyuan Lin,&nbsp;Yawen Huang,&nbsp;Shuwen Zhang,&nbsp;Zhaoyun Chen","doi":"10.1029/2024JC021362","DOIUrl":null,"url":null,"abstract":"<p>The deep learning technique is used to automatically identify upwelling regions off the Vietnam coast using Sea Surface Temperature (SST) data from satellite imagery. After delineating these upwelling areas, this study is pioneering in deriving the upwelling probability off the Vietnam coast, pinpointing two significant upwelling hotspots. To deepen the understanding of the individual impacts of wind and sea surface height anomalies on monthly and spatial variations in upwelling probability and chlorophyll distribution, the Empirical Orthogonal Function method was employed to analyze this data set. The upwelling probability demonstrates a close relationship with wind and sea surface height anomalies, while chlorophyll concentration correlates with the strength of the southwesterly wind. The southwesterly wind, Ekman pumping, eddy dipole, and northeastward jet have been identified as key drivers of the formation and spatial variability of upwelling in summer, albeit with varying contributions to different parts of the coastal region. The distribution of upwelling probability correlates with chlorophyll variation off the northern Vietnam coast. High chlorophyll concentration off the southern Vietnam coast is primarily influenced by the southwesterly wind that carries the Mekong River plume eastward. Furthermore, two abnormal scenarios in upwelling and chlorophyll concentration during 2010 and 2018 were analyzed, attributed to the abnormal southwesterly monsoon and coastal circulation influenced by an El Niño event and a weak La Niña event with a positive Indian Ocean Dipole, respectively. This study elucidates the dynamic processes underlying the intricate chlorophyll distribution over the continental shelf, which is influenced by both the river plume and upwelling.</p>","PeriodicalId":54340,"journal":{"name":"Journal of Geophysical Research-Oceans","volume":"129 11","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Monthly and Interannual Variations in Upwelling and Chlorophyll off the Vietnam Coast in Summer Based on Deep Learning\",\"authors\":\"Qiang Lian,&nbsp;Haiyuan Lin,&nbsp;Yawen Huang,&nbsp;Shuwen Zhang,&nbsp;Zhaoyun Chen\",\"doi\":\"10.1029/2024JC021362\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The deep learning technique is used to automatically identify upwelling regions off the Vietnam coast using Sea Surface Temperature (SST) data from satellite imagery. After delineating these upwelling areas, this study is pioneering in deriving the upwelling probability off the Vietnam coast, pinpointing two significant upwelling hotspots. To deepen the understanding of the individual impacts of wind and sea surface height anomalies on monthly and spatial variations in upwelling probability and chlorophyll distribution, the Empirical Orthogonal Function method was employed to analyze this data set. The upwelling probability demonstrates a close relationship with wind and sea surface height anomalies, while chlorophyll concentration correlates with the strength of the southwesterly wind. The southwesterly wind, Ekman pumping, eddy dipole, and northeastward jet have been identified as key drivers of the formation and spatial variability of upwelling in summer, albeit with varying contributions to different parts of the coastal region. The distribution of upwelling probability correlates with chlorophyll variation off the northern Vietnam coast. High chlorophyll concentration off the southern Vietnam coast is primarily influenced by the southwesterly wind that carries the Mekong River plume eastward. Furthermore, two abnormal scenarios in upwelling and chlorophyll concentration during 2010 and 2018 were analyzed, attributed to the abnormal southwesterly monsoon and coastal circulation influenced by an El Niño event and a weak La Niña event with a positive Indian Ocean Dipole, respectively. This study elucidates the dynamic processes underlying the intricate chlorophyll distribution over the continental shelf, which is influenced by both the river plume and upwelling.</p>\",\"PeriodicalId\":54340,\"journal\":{\"name\":\"Journal of Geophysical Research-Oceans\",\"volume\":\"129 11\",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Geophysical Research-Oceans\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1029/2024JC021362\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OCEANOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research-Oceans","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024JC021362","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OCEANOGRAPHY","Score":null,"Total":0}
引用次数: 0

摘要

利用卫星图像中的海面温度(SST)数据,采用深度学习技术自动识别越南沿海的上升流区域。在划定这些上升流区域后,本研究开创性地推导出了越南沿海上升流概率,精确定位了两个重要的上升流热点。为了加深理解风和海面高度异常对上升流概率和叶绿素分布的月度和空间变化的单独影响,采用了经验正交函数法对该数据集进行分析。上升流概率与风力和海面高度异常有密切关系,而叶绿素浓度则与西南风的强度相关。西南风、埃克曼抽气、涡偶极子和东北向喷流被认为是夏季上升流形成和空间变化的主要驱动力,尽管它们对沿岸地区不同部分的贡献各不相同。上升流的分布与越南北部沿岸的叶绿素变化有关。越南南部沿海叶绿素浓度较高,主要是受西南风的影响,西南风携带湄公河羽流向东移动。此外,还分析了 2010 年和 2018 年上升流和叶绿素浓度的两种异常情况,分别归因于受厄尔尼诺事件和印度洋偶极子正作用的弱拉尼娜事件影响的异常西南季风和沿岸环流。该研究阐明了大陆架上受河流羽流和上升流影响的复杂叶绿素分布的动态过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monthly and Interannual Variations in Upwelling and Chlorophyll off the Vietnam Coast in Summer Based on Deep Learning

The deep learning technique is used to automatically identify upwelling regions off the Vietnam coast using Sea Surface Temperature (SST) data from satellite imagery. After delineating these upwelling areas, this study is pioneering in deriving the upwelling probability off the Vietnam coast, pinpointing two significant upwelling hotspots. To deepen the understanding of the individual impacts of wind and sea surface height anomalies on monthly and spatial variations in upwelling probability and chlorophyll distribution, the Empirical Orthogonal Function method was employed to analyze this data set. The upwelling probability demonstrates a close relationship with wind and sea surface height anomalies, while chlorophyll concentration correlates with the strength of the southwesterly wind. The southwesterly wind, Ekman pumping, eddy dipole, and northeastward jet have been identified as key drivers of the formation and spatial variability of upwelling in summer, albeit with varying contributions to different parts of the coastal region. The distribution of upwelling probability correlates with chlorophyll variation off the northern Vietnam coast. High chlorophyll concentration off the southern Vietnam coast is primarily influenced by the southwesterly wind that carries the Mekong River plume eastward. Furthermore, two abnormal scenarios in upwelling and chlorophyll concentration during 2010 and 2018 were analyzed, attributed to the abnormal southwesterly monsoon and coastal circulation influenced by an El Niño event and a weak La Niña event with a positive Indian Ocean Dipole, respectively. This study elucidates the dynamic processes underlying the intricate chlorophyll distribution over the continental shelf, which is influenced by both the river plume and upwelling.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Geophysical Research-Oceans
Journal of Geophysical Research-Oceans Earth and Planetary Sciences-Oceanography
CiteScore
7.00
自引率
13.90%
发文量
429
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信