Journal of Tropical Oceanography ›› 2024, Vol. 43 ›› Issue (1): 16-27.doi: 10.11978/2023060CSTR: 32234.14.2023060

• Marine Hydrology • Previous Articles     Next Articles

Prediction of mesoscale eddies in the South China Sea based on the PredRNN++ model

ZHAO Jie(), LIN Yanjiang, LIU Ran, DU Rong   

  1. Department of Atmospheric and Oceanic Science, Fudan University, Shanghai 200438, China
  • Received:2023-05-12 Revised:2023-06-08 Online:2024-01-10 Published:2024-01-19

Abstract:

Based on 26 years of data on sea level anomalies, sea surface wind speed anomalies, and sea surface temperature anomalies, using the spatiotemporal series prediction model PredRNN++, this paper predicts the trajectory of mesoscale eddies in the South China Sea and dipole activity in the western South China Sea over a period of 1 to 28 days. The results indicate that the PredRNN++model can comprehensively consider the spatiotemporal evolution characteristics of the entire South China Sea region and the role of environmental wind and temperature fields, and has good performance in short-term (1~2weeks) and medium-term (3~4weeks) forecasting. This model has the ability to predict the generation and disappearance of eddies to a certain extent, and can control the 4-cycle prediction error of eddy trajectories to 42.1 km. For eddies with a lifespan of less than 100 days, the mid-term position and amplitude prediction error are small. In addition, the model can better track the evolution and intensity change of dipole structure at any time point under the monthly average, 4-day average and any forecast time effect in August-November. The prediction error of dipole eddy related attributes is the smallest and there are interannual and type differences. In 2017, the amplitude position, prediction and radius error of eddy 1-4 cycles are the smallest, which are 40~60 km, 3~5 cm and 20~40 km respectively, and the prediction effect of cyclone position is better than that of anticyclone.

Key words: mesoscale eddies, dipole off eastern Vietnam, ocean forecast, deep learning