Journal of Tropical Oceanography ›› 2022, Vol. 41 ›› Issue (2): 1-15.doi: 10.11978/2021070CSTR: 32234.14.2021070

• Marine Hydrology •     Next Articles

Research and application of global three-dimensional thermohaline reconstruction technology based on neural network

NIE Wangchen1(), WANG Xidong1,2(), CHEN Zhiqiang1, HE Zikang1, FAN Kaigui1   

  1. 1. Key Laboratory of Research on Marine Hazards Forecasting, Ministry of Natural Resources, College of Oceanography, Hohai University, Nanjing 210098, China
    2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
  • Received:2021-06-06 Revised:2021-07-28 Online:2022-03-10 Published:2021-08-16
  • Contact: WANG Xidong E-mail:191311040024@hhu.edu.cn;191311040024@hhu.edu.cn;xidong_wang@hhu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41776004)

Abstract:

We apply the FOAGRNN (fruit fly optimization algorithm, FOA; generalized regression neural network, GRNN) method and SODA (simple ocean data assimilation) reanalysis data to construct a global ocean projection relationship model between sea-surface variables (sea surface height, SSH; sea surface temperature, SST; sea surface salinity, SSS) and subsurface thermohaline field. The remote sensing observations are utilized to evaluate the applicability of this global surface-subsurface reconstruction model. First, an ideal reconstruction test is executed using the independent SODA data in 2016. The idealized reconstruction results show that the global mean root mean square error (MRMSE) values of the reconstructed temperature and salinity are 0.36 ℃ and 0.08‰, which are reduced by about 50% and 60% compared to those of the WOA13 (World Ocean Atlas), respectively. Then, the satellite observations (Input field) and Argo profiles (verification field) are inputted to evaluate the practical application performance of the model. The results again indicate that our reconstruction model can reasonably reconstruct the thermohaline structures, and the MRMSE values of the reconstructed temperature and salinity are 0.79 ℃ and 0.16‰, which are 27% and 11% lower than those in the WOA13, respectively. Specifically, the RMSE of temperature is small at the sea surface and in the deep ocean, and the largest value exists in the thermocline layer with a maximum value of 1.35 ℃ at 100 m, and then quickly decreases to 0.81 ℃ at 250 m. The RMSE of salinity mostly decreases as depth increases, and has the largest peak of about 0.25‰ around 25 m. Finally, the analysis of Argo floats’ tracks and the statistics of regional water mass confirm that the reconstructed model can better describe the interior characteristics of the three-dimensional thermohaline field.

Key words: FOAGRNN, three-dimensional ocean temperature field, reconstruction, satellite observation data, SODA reanalysis dataset

CLC Number: 

  • P731.1