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  2. 海洋研究所
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  4. 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

文献类型：期刊论文

作者Xin, Linchao1,2,3; Hu, Shijian1,2,3; Wang, Fan1,2,3; Xie, Wenhong4; Hu, Dunxin1,2,3; Dong, Changming4
刊名FRONTIERS IN MARINE SCIENCE
出版日期2023-01-26
卷号10页码:10
关键词Indonesian Throughflow sea surface height neural network deep learning CNN
DOI10.3389/fmars.2023.1079286
通讯作者Hu, Shijian(sjhu@qdio.ac.cn)
英文摘要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.
WOS关键词INDIAN-OCEAN ; PACIFIC ; VARIABILITY ; EXCHANGE ; CIRCULATION ; CURRENTS ; IMPACTS ; MODEL
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000928710900001
源URL[http://ir.qdio.ac.cn/handle/337002/183443]  
专题海洋研究所_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
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.

入库方式： OAI收割

来源：海洋研究所

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