Forecast of sea surface temperature in the South China Sea based on multi-scale deep learning model

Zhang Yu; Xu Dazhi; Yu Shengbin; Xing Huibin; Guan Yuping

doi:10.12284/hyxb2024034

Volume 46 Issue 5

May 2024

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Zhang Yu，Xu Dazhi，Yu Shengbin, et al. Forecast of sea surface temperature in the South China Sea based on multi-scale deep learning model[J]. Haiyang Xuebao，2024, 46(5)：27–36 doi: 10.12284/hyxb2024034

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Forecast of sea surface temperature in the South China Sea based on multi-scale deep learning model

doi: 10.12284/hyxb2024034

Zhang Yu^{1, 2, 3
,},
Xu Dazhi^{1, 2},
Yu Shengbin^{1, 2},
Xing Huibin^{1, 2},
Guan Yuping^{4, 5
,
,}

1.
South China Sea Marine Forecast and Hazard Mitigation Center, Guangzhou 510310, China
2.
Key Laboratory of Marine Environment Survey Technology and Application, Ministry of Natural Resource, Guangzhou 510310, China
3.
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
4.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
5.
College of Marine Science, University of Chinese Academy of Sciences, Beijing 100049, China

Received Date: 2023-08-28
Rev Recd Date: 2023-11-28

Available Online: 2024-08-19

Publish Date: 2024-05-01

Abstract

Abstract

Sea surface temperature (SST) is one of the most important physical variables of the ocean, which provides the basic information of the climate system. Accurately SST forecasting system has a comprehensive and essential application. In recent years, AI-based SST forecasting methods have become popular and shown great potential. Based on the convolutional long and short-term memory neural network (ConvLSTM), this paper studies the influence of multi-scale input fields on SST prediction in the northern South China Sea. Multi-dimensional ensemble empirical mode decomposition method (MEEMD) is used to decompose the average daily SST into the spatial eigenmodes of differentiated scales. Input different combinations of eigenmodes into ConvLSTM for training and prediction experiments. Results show that when using all four SST eigenmodes, the RMSE of the predicted SST in 1−7 days is 0.4−0.8℃, decrease 0.2−1.2℃ compared with the original SST alone; the MAPE is 1%−6%, decrease 0.5%−10%; the spatial correlation coefficient is 99.5%−96.5%, improve 0.5%−3.5%. Moreover, the randomized experiments also further proved the method has a high universality. The prediction model based on deep learning needs to select the appropriate training data in order to further improve its prediction accuracy. This paper preliminarily explores the integration of artificial intelligence methods and physical concepts in SST prediction, which can provide some reference for future research.
- SST prediction,
- deep learning,
- ConvLSTM,
- MEEMD

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References(38)

References

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