INTELLIGENT PREDICTION OF SEA SURFACE TEMPERATURE IN THE SOUTH CHINA SEA BASED ON DEEP LEARNING
Sea surface temperature(SST)is one of the crucial factors affecting ocean and climate change,and accurately predicting SST variations is vital for marine ecological environments,meteorology,and navigation.Traditional SST prediction methods typically rely on numerical models,which have high computational costs.A rapid and intelligent forecasting model for SST in the South China Sea(SCS)is developed based on a deep learning model(3D U-Net),using SST,sea surface height anomalies(SSHA),and sea surface wind(SSW)as input variables.Results indicate that compared to the convolutional long short-term memory(ConvLSTM)model,the 3D U-Net model shows higher accuracy across all prediction times,with a root mean square error(RMSE)of 0.53 ℃ and a Pearson correlation coefficient(R)of 0.96.In various seasons and regions of the SCS,the 3D U-Net model exhibits consistently smaller prediction errors and maintains robust performance during monsoon seasons.Moreover,in predicting marine heatwave(MHW)events in the SCS in 2021,the model achieves over 80％accuracy in most sea areas.The overall precision and recall rates are 0.89 and 0.45 respectively.Sensitivity experiments reveal that SSHA and SSW significantly influence the model's predictive performance,playing different roles at various forecasting stages.Therefore,the 3D U-Net model,combined with multi-source sea surface data,can predict SST in the SCS quickly and precisely,offering a new method for predicting MHW events.
sea surface temperature3D U-Net modeldeep learningSouth China Seamarine heatwave
万方数据
维普
