Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| Retrieval of Subsurface Velocities in the Southern Ocean from Satellite Observations | |
Xiang, Liang1,2,3; Xu, Yongsheng1,2,3,4 ; Sun, Hanwei5; Zhang, Qingjun6; Zhang, Liqiang6; Zhang, Lin7; Zhang, Xiangguang1,2,3; Huang, Chao1,2,3; Zhao, Dandan8
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| 2023-12-01 | |
| 发表期刊 | REMOTE SENSING
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| 卷号 | 15期号:24页码:19 |
| 通讯作者 | Xu, Yongsheng(yongsheng.xu@qdio.ac.cn) |
| 摘要 | Determining the dynamic processes of the subsurface ocean is a critical yet formidable undertaking given the sparse measurement resources available presently. In this study, using the light gradient boosting machine algorithm (LightGBM), we report for the first time a machine learning strategy for retrieving subsurface velocities at 1000 dbar depth in the Southern Ocean from information derived from satellite observations. Argo velocity measurements are used in the training and validation of the LightGBM model. The results show that reconstructed subsurface velocity agrees better with Argo velocity than reanalysis datasets. In particular, the subsurface velocity estimates have a correlation coefficient of 0.78 and an RMSE of 4.09 cm/s, which is much better than the ECCO estimates, GODAS estimates, GLORYS12V1 estimates, and Ora-S5 estimates. The LightGBM model has a higher skill in the reconstruction of subsurface velocity than the random forest and the linear regressor models. The estimated subsurface velocity exhibits a statistically significant increase at 1000 dbar since the 1990s, providing new evidence for the deep acceleration of mean circulation in the Southern Ocean. This study demonstrates the great potential and advantages of statistical methods for subsurface velocity modeling and oceanic dynamical information retrieval. |
| 关键词 | subsurface velocity light gradient boosting machine (LightGBM) The Southern Ocean satellite observations long-term variability |
| DOI | 10.3390/rs15245699 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | NSFC-Shandong Joint Fund Key Project |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001131061600001 |
| 出版者 | MDPI |
| WOS关键词 | EMPIRICAL MODE ; SURFACE ; INTERIOR ; CURRENTS ; HEAT |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.qdio.ac.cn/handle/337002/184243 |
| 专题 | 海洋环流与波动重点实验室 |
| 通讯作者 | Xu, Yongsheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Laoshan Lab, Lab Ocean Dynam & Climate, Qingdao 266237, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Beijing Inst Radio Measurement, Spaceborne Radar Res Ctr, Beijing 100039, Peoples R China 6.China Acad Space Technol, Inst Remote Sensing Satellite, Beijing 100094, Peoples R China 7.Naval Submarine Acad, Qingdao 266199, Peoples R China 8.Qilu Univ Technol, Inst Oceanog Instrumentat, Shandong Acad Sci, Qingdao 266061, Peoples R China |
| 第一作者单位 | 海洋环流与波动重点实验室 |
| 通讯作者单位 | 海洋环流与波动重点实验室 |
| 推荐引用方式 GB/T 7714 | Xiang, Liang,Xu, Yongsheng,Sun, Hanwei,et al. Retrieval of Subsurface Velocities in the Southern Ocean from Satellite Observations[J]. REMOTE SENSING,2023,15(24):19. |
| APA | Xiang, Liang.,Xu, Yongsheng.,Sun, Hanwei.,Zhang, Qingjun.,Zhang, Liqiang.,...&Zhao, Dandan.(2023).Retrieval of Subsurface Velocities in the Southern Ocean from Satellite Observations.REMOTE SENSING,15(24),19. |
| MLA | Xiang, Liang,et al."Retrieval of Subsurface Velocities in the Southern Ocean from Satellite Observations".REMOTE SENSING 15.24(2023):19. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| remotesensing-15-056（4576KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 浏览 | |
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