Ocean Reanalysis Data‐Driven Deep Learning Forecast for Sea Surface Multivariate in the South China Sea

Most prediction models based on artificial neural networks (ANNs) are site‐specific and do not provide simultaneous spatial information similar to numerical schemes. Such ANNs do not account for the correlations across grid points or the dynamic balance among different variables, which is an obvious physical defect. Moreover, such methods generally perform well on a single scale, while the actual marine environmental variability is multiscale. To cope with these issues, a data‐driven hybrid model based on ocean reanalysis is developed. This model combines empirical orthogonal function (EOF) analysis and complete ensemble empirical mode decomposition (CEEMD) with ANN (referred to as EOF‐CEEMD‐ANN). The results demonstrate that the EOF‐CEEMD‐ANN model is efficient for mid‐term predictions of sea surface multivariate including sea surface height (SSH), temperature (SST), salinity (SSS), and velocity (SSV) in the entire South China Sea (SCS) region. During the 30 days forecast window, the root‐mean‐square errors (RMSEs) of this model forecasts for SSH, SST, SSS, U, and V at the end of the forecast window are about 0.042 m, 0.52°C, 0.08 psu, 0.073 m/s, and 0.064 m/s, respectively, which are much smaller than those with persistence and optimal climatic normal (OCN) prediction. The anomaly correlation coefficients (ACCs) are approximately 0.75, 0.66, 0.73, 0.69, and 0.71, respectively. Case studies show that eddies and their evolutions can be simulated well by this model. Plain Language Summary Ocean prediction technique based on artificial neural networks generally performs well at individual point, and predicts on a certain daily or monthly scale. In this study, a hybrid model of empirical orthogonal function (EOF)‐complete ensemble empirical mode decomposition (CEEMD)‐artificial neural network (ANN) can effectively solve these problems. EOF analysis transforms the spatial‐temporal prediction problem into a time series prediction problem. It can reduce computational effort and dimensionality, capture spatial relationships, and consider correlations between different variables. Then, CEEMD can improve the predictability of time series. To serve as the basis, ANNs are used to predict CEEMD‐derived time series from the PCs corresponding to EOFs. This work is expected to provide a reference for spatial domain prediction of marine. Key Points A data‐driven prediction model based on empirical orthogonal function (EOF) analysis, complete ensemble empirical mode decomp