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

文献类型：期刊论文

作者Qi, Jifeng3,4,5; Xie, Bowen6; Li, Delei3,4; Chi, Jianwei2; Yin, Baoshu1,3,4,5; Sun, Guimin6
刊名FRONTIERS IN MARINE SCIENCE
出版日期2023-04-24
卷号10页码:16
关键词ocean thermohaline structure satellite observations machine learning CNN tropical Indian Ocean
DOI10.3389/fmars.2023.1181182
通讯作者Qi, Jifeng(jfqi@qdio.ac.cn) ; Yin, Baoshu(bsyin@qdio.ac.cn)
英文摘要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.
WOS关键词IN-SITU ; THERMAL STRUCTURE ; TEMPERATURE ; SUBSURFACE ; SALINITY ; CLIMATE ; VARIABILITY ; SYSTEM
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology
语种英语
WOS记录号WOS:000982373800001
出版者FRONTIERS MEDIA SA
源URL[http://ir.qdio.ac.cn/handle/337002/182957]  
专题海洋研究所_海洋环流与波动重点实验室
海洋研究所_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 Engn Lab Marine Ranching, Qingdao, Peoples R China
2.Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
4.Laoshan Lab, Qingdao, Peoples R China
5.Univ Chinese Acad Sci, Coll Marine Sci, Beijing, Peoples R China
6.Qingdao Univ Sci & Technol, Sch Math & Phys, 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.

入库方式： OAI收割

来源：海洋研究所

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