Forecast of sea surface temperature in the South China Sea based on multi-scale deep learning model
Sea surface temperature(SST)is one of the most important physical variables of the ocean,which provides the basic information of the climate system.Accurately SST forecasting system has a comprehensive and essential application.In recent years,AI-based SST forecasting methods have become popular and shown great po-tential.Based on the convolutional long and short-term memory neural network(ConvLSTM),this paper studies the influence of multi-scale input fields on SST prediction in the northern South China Sea.Multi-dimensional en-semble empirical mode decomposition method(MEEMD)is used to decompose the average daily SST into the spa-tial eigenmodes of differentiated scales.Input different combinations of eigenmodes into ConvLSTM for training and prediction experiments.Results show that when using all four SST eigenmodes,the RMSE of the predicted SST in 1-7 days is 0.4-0.8℃,decrease 0.2-1.2℃ compared with the original SST alone;the MAPE is 1％-6％,de-crease 0.5％-10％;the spatial correlation coefficient is 99.5％-96.5％,improve 0.5％-3.5％.Moreover,the random-ized experiments also further proved the method has a high universality.The prediction model based on deep learn-ing needs to select the appropriate training data in order to further improve its prediction accuracy.This paper pre-liminarily explores the integration of artificial intelligence methods and physical concepts in SST prediction,which can provide some reference for future research.
万方数据
维普
