Reconstructing 3-D Thermohaline Structures for Mesoscale Eddies Using Satellite Observations and Deep Learning
文献类型：期刊论文
| 作者 | Liu, Yingjie2; Wang, Haoyu2; Jiang, Fei2; Zhou, Yuan1; Li, Xiaofeng2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 62页码:16 |
| 关键词 | Deep learning (DL) mesoscale eddies prior knowledge-embedded reconstruction of 3-D thermohaline structure |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2024.3373605 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | Mesoscale eddies are circular water currents found widely in the ocean and significantly impact the ocean's circulation, water distribution, and biology. However, our comprehension of eddies' 3-D structures remains constrained due to the scarcity of in situ data. Therefore, we introduce a novel deep learning (DL) model, 3D-EddyNet, designed for reconstructing the 3-D thermohaline structure of mesoscale eddies. Utilizing multisource satellite data and Argo profiles collected from eddies in the North Pacific Ocean between 2000 and 2015, we optimized the 3D-EddyNet model by adjusting image sizes, introducing a convolutional block attention module (CBAM), and incorporating eddy physical parameters. The results demonstrate remarkable accuracy, with an average root mean square error (RMSE) of 0.32 C-degrees (0.03 psu) for temperature (salinity) within anticyclonic eddies and 0.41 C-degrees (0.04 psu) within cyclonic eddies in the upper 1000 m. We applied 3D-EddyNet to reconstruct 3-D eddy structures in the Kuroshio extension (KE) and the Oyashio current (OC) regions, demonstrating its capability to accurately represent the 3-D thermohaline eddy structures both vertically and horizontally. The consistency in the averaged 3-D eddy structures between our 3D-EddyNet and the ARMOR3D dataset in the KE and OC regions underscores the robust generalizability of our model, indicating the model's ability to infer 3-D eddy structures when Argo profiles are unavailable. The distinctive advantage offered by 3D-EddyNet enhances our ability to understand mesoscale eddy dynamics, overcoming challenges posed by the limited availability of in situ data. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001184968700037 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/185124]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yingjie,Wang, Haoyu,Jiang, Fei,et al. Reconstructing 3-D Thermohaline Structures for Mesoscale Eddies Using Satellite Observations and Deep Learning[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:16. |
| APA | Liu, Yingjie,Wang, Haoyu,Jiang, Fei,Zhou, Yuan,&Li, Xiaofeng.(2024).Reconstructing 3-D Thermohaline Structures for Mesoscale Eddies Using Satellite Observations and Deep Learning.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,16. |
| MLA | Liu, Yingjie,et al."Reconstructing 3-D Thermohaline Structures for Mesoscale Eddies Using Satellite Observations and Deep Learning".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):16. |
入库方式： OAI收割
来源：海洋研究所
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