Application of deep learning technique to the sea surface height prediction in the South China Sea

Tao Song; Ningsheng Han; Yuhang Zhu; Zhongwei Li; Yineng Li; Shaotian Li; Shiqiu Peng

doi:10.1007/s13131-021-1735-0

Volume 40 Issue 7

Jul. 2021

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Article Navigation > Acta Oceanologica Sinica > 2021 > 40(7): 68-76

Tao Song, Ningsheng Han, Yuhang Zhu, Zhongwei Li, Yineng Li, Shaotian Li, Shiqiu Peng. Application of deep learning technique to the sea surface height prediction in the South China Sea[J]. Acta Oceanologica Sinica, 2021, 40(7): 68-76. doi: 10.1007/s13131-021-1735-0

Citation:

Tao Song, Ningsheng Han, Yuhang Zhu, Zhongwei Li, Yineng Li, Shaotian Li, Shiqiu Peng. Application of deep learning technique to the sea surface height prediction in the South China Sea[J]. Acta Oceanologica Sinica, 2021, 40(7): 68-76. doi: 10.1007/s13131-021-1735-0

Citation:

Tao Song, Ningsheng Han, Yuhang Zhu, Zhongwei Li, Yineng Li, Shaotian Li, Shiqiu Peng. Application of deep learning technique to the sea surface height prediction in the South China Sea[J]. Acta Oceanologica Sinica, 2021, 40(7): 68-76. doi: 10.1007/s13131-021-1735-0

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Application of deep learning technique to the sea surface height prediction in the South China Sea

doi: 10.1007/s13131-021-1735-0

Tao Song^{1, 6},
Ningsheng Han¹,
Yuhang Zhu^{2, 3, 5},
Zhongwei Li¹,
Yineng Li^{2, 3, 4},
Shaotian Li^{2, 3},
Shiqiu Peng^{2, 3, 4, 5
,
,}

1.
College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao 266580, China
2.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
3.
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
4.
Key Laboratory of Science and Technology on Operational Oceanography, Chinese Academy of Sciences, Guangzhou 511458, China
5.
Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Bubei Gulf University, Qinzhou 535011, China
6.
Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Boadilla del Monte 28660, Madrid, Spain

Funds: The National Key Research and Development Program under contract Nos 2018YFC1406204 and 2018YFC1406201; the Guangdong Special Support Program under contract No. 2019BT2H594; the Taishan Scholar Foundation under contract No. tsqn201812029; the National Natural Science Foundation of China under contract Nos U1811464, 61572522, 61572523, 61672033, 61672248, 61873280, 41676016 and 41776028; the Natural Science Foundation of Shandong Province under contract Nos ZR2019MF012 and 2019GGX101067; the Fundamental Research Funds of Central Universities under contract Nos 18CX02152A and 19CX05003A-6; the fund of the Shandong Province Innovation Researching Group under contract No. 2019KJN014; the Key Special Project for Introduced Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) under contract No. GML2019ZD0303.

More Information

Corresponding author: Email: speng@scsio.ac.cn
Received Date: 2020-08-26
Accepted Date: 2020-09-16

Available Online: 2021-05-12

Publish Date: 2021-07-25

Abstract

Abstract

A deep-learning-based method, called ConvLSTMP3, is developed to predict the sea surface heights (SSHs). ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs, in which the spatial features are “learned” by convolutional operations while the temporal features are tracked by long short term memory (LSTM). Trained by a reanalysis dataset of the South China Sea (SCS), ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in summer. Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4% averaged over a 15-d prediction period. In particular, ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean model. Given the much less computation in the prediction required by ConvLSTMP3, our study suggests that the deep learning technique is very useful and effective in the SSH prediction, and could be an alternative way in the operational prediction for ocean environments in the future.
- deep learning,
- sea surface height prediction,
- convolutional operation,
- long short term memory

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References(30)

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