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.
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References
Braakmann-Folgmann A, Roscher R, Wenzel S, et al. 2017. Sea level anomaly prediction using recurrent neural networks. arXiv preprint arXiv: 1710.07099
Cho K, Van Merriënboer B, Gulcehre C, et al. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv: 1406.1078v1, 1724–1734
De Bézenac E, Pajot A, Gallinari P. 2019. Deep learning for physical processes: Incorporating prior scientific knowledge. Journal of Statistical Mechanics: Theory and Experiment, 2019(12): 124009, doi: https://doi.org/10.1088/1742-5468/ab3195
Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9(8): 1735–1780, doi: https://doi.org/10.1162/neco.1997.9.8.1735
Huang X J, Shan J J, Vaidya V. 2017. Lung nodule detection in CT using 3d convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging. Melbourne, VIC, Australia: IEEE, 379–383
Iudicone D, Santoleri R, Marullo S, et al. 1998. Sea level variability and surface eddy statistics in the Mediterranean Sea from TOPEX/POSEIDON data. Journal of Geophysical Research: Oceans, 103(C2): 2995–3011, doi: https://doi.org/10.1029/97JC01577
Jacobs G A, Hogan P J, Whitmer K R. 1999. Effects of eddy variability on the circulation of the Japan/East Sea. Journal of Oceanography, 55(2): 247–256, doi: https://doi.org/10.1023/A:1007898131004
Ji Shuiwang, Xu Wei, Yang Ming, et al. 2013. 3D Convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1): 221–231, doi: https://doi.org/10.1109/TPAMI.2012.59
Kumar N K, Savitha R, Al Mamun A. 2017. Regional ocean wave height prediction using sequential learning neural networks. Ocean Engineering, 129: 605–612, doi: https://doi.org/10.1016/j.oceaneng.2016.10.033
Ma Xiaolei, Tao Zhimin, Wang Yinhai, et al. 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54: 187–197, doi: https://doi.org/10.1016/j.trc.2015.03.014
Mason E, Pascual A, McWilliams J C. 2014. A new sea surface height-based code for oceanic mesoscale eddy tracking. Journal of Atmospheric and Oceanic Technology, 31(5): 1181–1188, doi: https://doi.org/10.1175/JTECH-D-14-00019.1
McWilliams J C. 1985. Submesoscale, coherent vortices in the ocean. Reviews of Geophysics, 23(2): 165–182, doi: https://doi.org/10.1029/RG023i002p00165
Morrow R, Coleman R, Church J, et al. 1994. Surface eddy momentum flux and velocity variances in the Southern Ocean from Geosat altimetry. Journal of Physical Oceanography, 24(10): 2050–2071, doi: https://doi.org/10.1175/1520-0485(1994)024<2050:SEMFAV>2.0.CO;2
Reckinger S, Fox-Kemper B, Bachman S, et al. 2014. Anisotropic mesoscale eddy transport in ocean general circulation models. In: 67th Annual Meeting of the Aps Division of Fluid Dynamics. San Francisco, California: Bulletin of the American Physical Society, 59 (20): 23–25
Seki M P, Bidigare R R, Lumpkin R, et al. 2001. Mesoscale cyclonic eddies and pelagic fisheries in Hawaiian waters. In: MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings. Honolulu, HI, USA: IEEE
Shi Xinglian, Chen Zhourong, Wang Hao, et al. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 802–810
Shin H C, Roth H R, Gao Mingchen, et al. 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5): 1285–1298, doi: https://doi.org/10.1109/TMI.2016.2528162
Song Tao, Wang Zihe, Xie Pengfei, et al. 2020. A novel dual path gated recurrent unit model for sea surface salinity prediction. Journal of Atmospheric and Oceanic Technology, 37(2): 317–325, doi: https://doi.org/10.1175/JTECH-D-19-0168.1
Soong Y S, Hu J H, Ho C R, et al. 1995. Cold-core eddy detected in South China Sea. Eos, Transactions American Geophysical Union, 76(35): 345–347
Szegedy C, Vanhoucke V, Ioffe S, et al. 2016. Rethinking the Inception Architecture for Computer Vision. Las Vegas, NV, USA: IEEE, 2818–2826
Szegedy C, Liu Wei, Jia Yangqing, et al. 2015. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 1–9
Wang Liping, Koblinsky C J, Howden S. 2000. Mesoscale variability in the South China Sea from the TOPEX/Poseidon altimetry data. Deep Sea Research Part I: Oceanographic Research Papers, 47(4): 681–708, doi: https://doi.org/10.1016/S0967-0637(99)00068-0
Weiss J B, Grooms I. 2017. Assimilation of ocean sea-surface height observations of mesoscale eddies. Chaos, 27(12): 126803, doi:https://doi.org/10.1063/1.4986088
Yang Fengyu, Feng Tao, Xu Ganyang, et al. 2020. Applied method for water-body segmentation based on mask R-CNN. Journal of Applied Remote Sensing, 14(1): 014502
Zeng Xiangming, Li Yizhen, He Ruoying. 2015. Predictability of the loop current variation and eddy shedding process in the Gulf of Mexico using an artificial neural network approach. Journal of Atmospheric and Oceanic Technology, 32(5): 1098–1111, doi: https://doi.org/10.1175/JTECH-D-14-00176.1
Zeng Xuezhi, Peng Shiqiu, Li Zhijin, et al. 2014. A reanalysis dataset of the South China Sea. Scientific Data, 1: 140052, doi: https://doi.org/10.1038/sdata.2014.52
Zhang Qin, Wang Hui, Dong Junyu, et al. 2017. Prediction of sea surface temperature using long short-term memory. IEEE Geoscience and Remote Sensing Letters, 14(10): 1745–1749, doi: https://doi.org/10.1109/LGRS.2017.2733548
Zhang Zhengguang, Wang Wei, Qiu Bo. 2014a. Oceanic mass transport by mesoscale eddies. Science, 34(6194): 322–324
Zhang Chunhua, Xi Xiaoliang, Liu Songtao, et al. 2014b. A mesoscale eddy detection method of specific intensity and scale from SSH image in the South China Sea and the Northwest Pacific. Science China Earth Sciences, 57(8): 1897–1906, doi: https://doi.org/10.1007/s11430-014-4839-y
Zhang Yuanyuan, Zhao Dong, Sun Jiande, et al. 2016. Adaptive convolutional neural network and its application in face recognition. Neural Processing Letters, 43(2): 389–399, doi: https://doi.org/10.1007/s11063-015-9420-y
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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.
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Song, T., Han, N., Zhu, Y. et al. Application of deep learning technique to the sea surface height prediction in the South China Sea. Acta Oceanol. Sin. 40, 68–76 (2021). https://doi.org/10.1007/s13131-021-1735-0
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DOI: https://doi.org/10.1007/s13131-021-1735-0