Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height

doi:10.3389/fmars.2023.1079286

IOCAS-IR > 海洋环流与波动重点实验室

	Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height
	Xin, Linchao 1,2,3; Hu, Shijian 1,2,3; Wang, Fan1,2,3 ; Xie, Wenhong 4; Hu, Dunxin 1,2,3; Dong, Changming 4
	2023-01-26
发表期刊	FRONTIERS IN MARINE SCIENCE
卷号	10 页码:10
通讯作者	Hu, Shijian(sjhu@qdio.ac.cn)
摘要	The Indonesian Throughflow (ITF) connects the tropical Pacific and Indian Oceans and is critical to the regional and global climate systems. Previous research indicates that the Indo-Pacific pressure gradient is a major driver of the ITF, implying the possibility of forecasting ITF transport by the sea surface height (SSH) of the Indo-Pacific Ocean. Here we used a deep-learning approach with the convolutional neural network (CNN) model to reproduce ITF transport. The CNN model was trained with a random selection of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and verified with residual components of the CMIP6 simulations. A test of the training results showed that the CNN model with SSH is able to reproduce approximately 90% of the total variance of ITF transport. The CNN model with CMIP6 was then transformed to the Simple Ocean Data Assimilation (SODA) dataset and this transformed model reproduced approximately 80% of the total variance of ITF transport in the SODA. A time series of ITF transport, verified by Monitoring the ITF (MITF) and International Nusantara Stratification and Transport (INSTANT) measurements of ITF, was then produced by the model using satellite observations from 1993 to 2021. We discovered that the CNN model can make a valid prediction with a lead time of 7 months, implying that the ITF transport can be predicted using the deep-learning approach with SSH data.
关键词	Indonesian Throughflow sea surface height neural network deep learning CNN
DOI	10.3389/fmars.2023.1079286
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:000928710900001
出版者	FRONTIERS MEDIA SA
WOS关键词	INDIAN-OCEAN ; PACIFIC ; VARIABILITY ; EXCHANGE ; CIRCULATION ; CURRENTS ; IMPACTS ; MODEL
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/337002/183444
专题	海洋环流与波动重点实验室
通讯作者	Hu, Shijian
作者单位	1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Pilot Natl Lab Marine Sci & Technol Qingdao, Qingdao, Peoples R China 3.Univ Chinese Acad Sci, Coll Marine Sci, Qingdao, Peoples R China 4.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing, Peoples R China
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Xin, Linchao,Hu, Shijian,Wang, Fan,et al. Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height[J]. FRONTIERS IN MARINE SCIENCE,2023,10:10.
APA	Xin, Linchao,Hu, Shijian,Wang, Fan,Xie, Wenhong,Hu, Dunxin,&Dong, Changming.(2023).Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height.FRONTIERS IN MARINE SCIENCE,10,10.
MLA	Xin, Linchao,et al."Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height".FRONTIERS IN MARINE SCIENCE 10(2023):10.