Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model

doi:10.3389/fmars.2023.1181182

IOCAS-IR > 海洋环流与波动重点实验室

	Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model
	Qi, Jifeng 1,2,3; Xie, Bowen 4; Li, Delei 1,2; Chi, Jianwei 5; Yin, Baoshu 1,2,3,6; Sun, Guimin 4
	2023-04-24
发表期刊	FRONTIERS IN MARINE SCIENCE
卷号	10 页码:16
通讯作者	Qi, Jifeng([email protected]) ; Yin, Baoshu([email protected])
摘要	Accurately estimating the ocean's subsurface thermohaline structure is essential for advancing our understanding of regional and global ocean dynamics. In this study, we propose a novel neural network model based on Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) to simultaneously estimate the ocean subsurface thermal structure (OSTS) and ocean subsurface salinity structure (OSSS) in the tropical Indian Ocean using satellite observations. The input variables include sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), eastward component of sea surface wind (ESSW), northward component of sea surface wind (NSSW), longitude (LON), and latitude (LAT). We train and validate the model using Argo data, and compare its accuracy with that of the original Convolutional Neural Network (CNN) model using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R-2). Our results show that the CBAM-CNN model outperforms the CNN model, exhibiting superior performance in estimating thermohaline structures in the tropical Indian Ocean. Furthermore, we evaluate the model's accuracy by comparing its estimated OSTS and OSSS at different depths with Argo-derived data, demonstrating that the model effectively captures most observed features using sea surface data. Additionally, the CBAM-CNN model demonstrates good seasonal applicability for OSTS and OSSS estimation. Our study highlights the benefits of using CBAM-CNN for estimating thermohaline structure and offers an efficient and effective method for estimating thermohaline structure in the tropical Indian Ocean.
关键词	ocean thermohaline structure satellite observations machine learning CNN tropical Indian Ocean
DOI	10.3389/fmars.2023.1181182
收录类别	SCI
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology
WOS类目	Environmental Sciences ; Marine & Freshwater Biology
WOS记录号	WOS:000982373800001
出版者	FRONTIERS MEDIA SA
WOS关键词	IN-SITU ; THERMAL STRUCTURE ; TEMPERATURE ; SUBSURFACE ; SALINITY ; CLIMATE ; VARIABILITY ; SYSTEM
引用统计	被引频次：6[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/337002/182958
专题	海洋环流与波动重点实验室海洋生态与环境科学重点实验室
通讯作者	Qi, Jifeng; Yin, Baoshu
作者单位	1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Laoshan Lab, Qingdao, Peoples R China 3.Univ Chinese Acad Sci, Coll Marine Sci, Beijing, Peoples R China 4.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao, Peoples R China 5.Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Peoples R China 6.Chinese Acad Sci, Inst Oceanol, CAS Engn Lab Marine Ranching, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Qi, Jifeng,Xie, Bowen,Li, Delei,et al. Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model[J]. FRONTIERS IN MARINE SCIENCE,2023,10:16.
APA	Qi, Jifeng,Xie, Bowen,Li, Delei,Chi, Jianwei,Yin, Baoshu,&Sun, Guimin.(2023).Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model.FRONTIERS IN MARINE SCIENCE,10,16.
MLA	Qi, Jifeng,et al."Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model".FRONTIERS IN MARINE SCIENCE 10(2023):16.

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