Reconstructing 3-D Thermohaline Structures for Mesoscale Eddies Using Satellite Observations and Deep Learning

doi:10.1109/TGRS.2024.3373605

IOCAS-IR > 海洋环流与波动重点实验室

	Reconstructing 3-D Thermohaline Structures for Mesoscale Eddies Using Satellite Observations and Deep Learning
	Liu, Yingjie 1; Wang, Haoyu 1; Jiang, Fei 1; Zhou, Yuan 2; Li, Xiaofeng 1
	2024
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	62 页码:16
通讯作者	Li, Xiaofeng([email protected])
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.
关键词	Deep learning (DL) mesoscale eddies prior knowledge-embedded reconstruction of 3-D thermohaline structure
DOI	10.1109/TGRS.2024.3373605
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001184968700037
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/185125
专题	海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, 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	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.