Reconstruction of Interior Velocity in the Southern Pacific Ocean Using Satellite and Argo Data

Liang Xiang;Yongsheng Xu;Haiwei Sun;Qingjun Zhang;Weiya Kong;Lin Zhang;Xiangguang Zhang;Chao Huang;Dandan Zhao
{"title":"Reconstruction of Interior Velocity in the Southern Pacific Ocean Using Satellite and Argo Data","authors":"Liang Xiang;Yongsheng Xu;Haiwei Sun;Qingjun Zhang;Weiya Kong;Lin Zhang;Xiangguang Zhang;Chao Huang;Dandan Zhao","doi":"10.1109/LGRS.2024.3508023","DOIUrl":null,"url":null,"abstract":"Ocean velocities are essential for understanding how the ocean influences and responds to climate dynamics, making their accurate reconstruction crucial for both climate modeling and predictions. However, reconstructing interior ocean velocities remains a significant challenge due to the sparse distribution of velocity observations and the ocean’s complex dynamics. In this study, we introduce an efficient methodology for reconstructing interior ocean velocities by combining sea surface satellite data—including sea surface height (SSH), temperature, wind, and current—with Argo velocity observations, using the dynamic mode decomposition (DMD) technique. DMD offers the advantage of reducing the dimensionality of interior velocity fields, helping to address the limitations caused by sparse observations. The reconstructed velocity for the Southern Pacific Ocean (SPO) was validated against Argo and acoustic Doppler current profiler (ADCP) velocities, showing a strong correlation than GLORYS12V1 velocities. In particular, the reconstructed velocities have a mean correlation coefficient of 0.78 for the zonal component and 0.74 for the meridional component above 1000 m. Additionally, the reconstructed flow field exhibits a coherent pattern that closely aligns with the eddies observed in SSH. This research significantly contributes to the Global Ocean Monitoring and Observing Program by enhancing both the accuracy and resolution of ocean velocity measurements.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10770283/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Ocean velocities are essential for understanding how the ocean influences and responds to climate dynamics, making their accurate reconstruction crucial for both climate modeling and predictions. However, reconstructing interior ocean velocities remains a significant challenge due to the sparse distribution of velocity observations and the ocean’s complex dynamics. In this study, we introduce an efficient methodology for reconstructing interior ocean velocities by combining sea surface satellite data—including sea surface height (SSH), temperature, wind, and current—with Argo velocity observations, using the dynamic mode decomposition (DMD) technique. DMD offers the advantage of reducing the dimensionality of interior velocity fields, helping to address the limitations caused by sparse observations. The reconstructed velocity for the Southern Pacific Ocean (SPO) was validated against Argo and acoustic Doppler current profiler (ADCP) velocities, showing a strong correlation than GLORYS12V1 velocities. In particular, the reconstructed velocities have a mean correlation coefficient of 0.78 for the zonal component and 0.74 for the meridional component above 1000 m. Additionally, the reconstructed flow field exhibits a coherent pattern that closely aligns with the eddies observed in SSH. This research significantly contributes to the Global Ocean Monitoring and Observing Program by enhancing both the accuracy and resolution of ocean velocity measurements.
利用卫星和阿尔戈数据重建南太平洋内部速度
海洋速度对了解海洋如何影响和响应气候动力学至关重要,因此准确重建海洋速度对气候建模和预测至关重要。然而,由于速度观测数据分布稀少,海洋动力学复杂,重建海洋内部速度仍是一项重大挑战。在这项研究中,我们采用动态模式分解(DMD)技术,将海面卫星数据(包括海面高度(SSH)、温度、风和海流)与 Argo 速度观测数据相结合,介绍了一种重建海洋内部速度的有效方法。动态模态分解技术的优点是降低了内部速度场的维度,有助于解决观测数据稀少所带来的局限性。南太平洋(SPO)的重建速度与 Argo 和声学多普勒海流剖面仪(ADCP)的速度进行了验证,结果显示与 GLORYS12V1 的速度有很强的相关性。此外,重建的流场呈现出一种连贯的模式,与在 SSH 中观测到的漩涡密切吻合。这项研究通过提高海洋速度测量的精度和分辨率,为全球海洋监测和观测计划做出了重大贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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