Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks

The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST.