Journal of Marine Sciences ›› 2024, Vol. 42 ›› Issue (3): 2-27.DOI: 10.3969/j.issn.1001-909X.2024.03.001
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Prospect of artificial intelligence in oceanography
DONG Changming1,2,3(
), WANG Ziyun2, XIE Huarong2, XU Guangjun4, HAN Guoqing5, ZHOU Shuyi6, XIE Wenhong7, SHEN Xiangyu2, HAN Lei8
- 1. Tianchang Research Institute, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
4. College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
5. Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
6. Department of Earth System Science, Tsinghua University, Beijing 100084, China
7. Nanjing Xingyao Technology Co., LTD., Nanjing 210012, China
8. School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
-
Received:2023-10-08Revised:2024-02-23Online:2024-09-15Published:2024-11-25
CLC Number:
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DONG Changming, WANG Ziyun, XIE Huarong, XU Guangjun, HAN Guoqing, ZHOU Shuyi, XIE Wenhong, SHEN Xiangyu, HAN Lei. Prospect of artificial intelligence in oceanography[J]. Journal of Marine Sciences, 2024, 42(3): 2-27.
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Fig.1 Global trends in the growth of ocean data volume from 2009 to 2030[9]
Fig.1 Global trends in the growth of ocean data volume from 2009 to 2030[9]
Fig.2 Remote sensing data acquisition process[14]
Fig.2 Remote sensing data acquisition process[14]
Fig.3 Spatial distribution of global salinity anomalies in January 2016 reconstructed by feedforward neural networks (Figure is repainted from reference [27]. Fig.a shows the salinity anomaly (IAP 0.25°); Fig.b shows the salinity anomaly (IAP 1°); Fig.c shows the salinity anomaly (ARMOR3D); Fig.d shows the salinity anomaly (observations); Fig.e shows the salinity anomaly (IAP 0.25° minus IAP 1° at 100 m); Fig.f shows the ADT anomaly (remote sensing); Fig.g shows the SST anomaly (remote sensing).)
Fig.3 Spatial distribution of global salinity anomalies in January 2016 reconstructed by feedforward neural networks (Figure is repainted from reference [27]. Fig.a shows the salinity anomaly (IAP 0.25°); Fig.b shows the salinity anomaly (IAP 1°); Fig.c shows the salinity anomaly (ARMOR3D); Fig.d shows the salinity anomaly (observations); Fig.e shows the salinity anomaly (IAP 0.25° minus IAP 1° at 100 m); Fig.f shows the ADT anomaly (remote sensing); Fig.g shows the SST anomaly (remote sensing).)
Fig.4 Correlation coefficients for ENSO forecasts using the CNN model (Figure is repainted from reference [72]. Fig.a shows the all-season correlation skill of the three-month moving average Ni?o3.4 index as a function of the forecast lead month in the CNN model (red), SINTEX-F dynamical forecast system (blue), and dynamical forecast systems included in the North American Multi-Model Ensemble project (the other colors); Fig.b and Fig.c show the correlation skill of the Ni?o3.4 index targeted to each calendar month in the CNN model (b) and the SINTEX-F dynamical forecast system (c), hatching highlights the forecasts with correlation skill exceeding 0.5.)
Fig.4 Correlation coefficients for ENSO forecasts using the CNN model (Figure is repainted from reference [72]. Fig.a shows the all-season correlation skill of the three-month moving average Ni?o3.4 index as a function of the forecast lead month in the CNN model (red), SINTEX-F dynamical forecast system (blue), and dynamical forecast systems included in the North American Multi-Model Ensemble project (the other colors); Fig.b and Fig.c show the correlation skill of the Ni?o3.4 index targeted to each calendar month in the CNN model (b) and the SINTEX-F dynamical forecast system (c), hatching highlights the forecasts with correlation skill exceeding 0.5.)
Fig.5 Verification of ANN forecast storm surge level (Figure is repainted from reference [86]. Figures a-c show the storm surge verification results of 2003 Typhoon “Maemi” at Masan Station (a),2002 Typhoon “Rusa” at Tongyoung Station (b), and 2012 Typhoon “Bolaven” at Yeosu Station (c), respectively. MA is the Levenberg-Marquardt backpropagation algorithm in the reference.)
Fig.5 Verification of ANN forecast storm surge level (Figure is repainted from reference [86]. Figures a-c show the storm surge verification results of 2003 Typhoon “Maemi” at Masan Station (a),2002 Typhoon “Rusa” at Tongyoung Station (b), and 2012 Typhoon “Bolaven” at Yeosu Station (c), respectively. MA is the Levenberg-Marquardt backpropagation algorithm in the reference.)
Fig.6 ConvLSTM algorithm intelligently predicts typhoon wave height (Figure is repainted from reference [104]. Figures a-c show the SWH(significant wave height) of Typhoon “Lekima” over the East China Sea at 18:00 on August 8, 2019; figures d-f show the SWH of Typhoon “Tembin” over the South China Sea at 21:00 on December 24, 2017.)
Fig.6 ConvLSTM algorithm intelligently predicts typhoon wave height (Figure is repainted from reference [104]. Figures a-c show the SWH(significant wave height) of Typhoon “Lekima” over the East China Sea at 18:00 on August 8, 2019; figures d-f show the SWH of Typhoon “Tembin” over the South China Sea at 21:00 on December 24, 2017.)
Fig.7 Comparison of mixing coefficients between artificial intelligence parameterization schemes driven by different input parameters and KPP parameterization schemes (Figure is repainted from reference [144]. The driving parameters in figures a-c are temperature, salinity, flow rate (a), temperature and salinity (b),and temperature (c), respectively. In figure, ‘corr’ refers to the correlation coefficient.)
Fig.7 Comparison of mixing coefficients between artificial intelligence parameterization schemes driven by different input parameters and KPP parameterization schemes (Figure is repainted from reference [144]. The driving parameters in figures a-c are temperature, salinity, flow rate (a), temperature and salinity (b),and temperature (c), respectively. In figure, ‘corr’ refers to the correlation coefficient.)
Fig.8 Asymmetrical vortex shedding pattern in the cylinder wake to PINN training data (Figure is repainted from reference [161]. Figure a shows the incompressible flow and dynamic vortex shedding past a circular cylinder at Re=100. The spatio-temporal training data correspond to the depicted rectangular region in the cylinder wake. Figures b-c show the locations of training datapoints for the stream-wise and transverse velocity components, u(t, x, y) and v(t, x, t), respectively.)
Fig.8 Asymmetrical vortex shedding pattern in the cylinder wake to PINN training data (Figure is repainted from reference [161]. Figure a shows the incompressible flow and dynamic vortex shedding past a circular cylinder at Re=100. The spatio-temporal training data correspond to the depicted rectangular region in the cylinder wake. Figures b-c show the locations of training datapoints for the stream-wise and transverse velocity components, u(t, x, y) and v(t, x, t), respectively.)
Fig.9 Zero-shot super-resolution simulation: Navier-Stokes Equation[170] (Top: truth case; bottom: neural operator prediction case. Trained on 64×64×20 dataset, evaluated on 256×256×80.)
Fig.9 Zero-shot super-resolution simulation: Navier-Stokes Equation[170] (Top: truth case; bottom: neural operator prediction case. Trained on 64×64×20 dataset, evaluated on 256×256×80.)
Fig.10 Trends of milestone ML models from 1952 to 2022[193]
Fig.10 Trends of milestone ML models from 1952 to 2022[193]
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