Mid-Term Simultaneous Spatiotemporal Prediction of Sea Surface Height Anomaly and Sea Surface Temperature Using Satellite Data in the South China Sea

Abstract

Marine forecasting techniques based on data-driven method generally treat each variable as independent and analyze the time series of a single and specific variable, while the real marine environment is the result of the interaction of multiple variables. In this letter, a data-driven method combining the empirical orthogonal function of multivariate (MEOF), complete ensemble empirical mode decomposition (CEEMD), and multilayer perceptron (MEOF-CEEMD-MLP in brief) is proposed to perform mid-term prediction of daily sea surface height anomaly (SSHA) and sea surface temperature (SST) simultaneously, considering that there is a correlation between them in the real marine environment. In this model, application of MEOF not only considers the correlation between SSHA and SST but also establishes the temporal and spatial relationship between discrete points, making predictions more accurate. A case study in the South China Sea (SCS) that predicts the daily SSHA and SST 30 days ahead shows that MEOF-CEEMD-MLP model is highly promising for mid-term daily prediction of SSHA and SST simultaneously. Also, the correlation between these two kinds of ocean variables can be simulated very well by this prediction model.