Resumen
Mesoscale eddies are a common occurrence in the Kuroshio Extension (KE) that have a major impact on the levels of salinity and heat transport in the Northwest Pacific, the strength of the Kuroshio jet, and the fluctuations of the Kuroshio?s trajectory. In this study, a purely data-driven machine learning model, Temporal Modes-Enhanced Neural Network (TMENN), is proposed to forecast the spatiotemporal variation of mesoscale eddies based on daily sea surface height (SSH) data over a 20-year period (2000?2019) in the Kuroshio Extension. To reduce computational costs and facilitate faster forecasting, raw SSH data are decomposed into spatial modes and temporal modes (principal components, PCs) by empirical orthogonal functions (EOF) analysis, and the first 117 PCs (a total of 8384 PCs), wherein the cumulative variance contribution rate reaches 95%, are selected solely as the predictors of TMENN to train and forecast. Forecasting reconstruction results show that the model can reliably forecast the evolution of the eddy in the KE for about 30 days. Additionally, three classical mesoscale eddy processes are selected to verify the accuracy of the model, namely cold eddy attachment, warm eddy shedding, and attachment, and the results indicate that the model can well capture the evolution process of mesoscale eddies.