An Evolving Sea Surface Temperature Predicting Method Based on Multidimensional Spatiotemporal Influences

In global climate researches, marine ecosystem researches, and ocean-related applications, it is of considerable significance to accurately observe and predict sea surface temperature (SST). However, various physical and environmental factors affect the changes in SST, making it highly random and uncertain. Therefore, it is still a challenge to propose a highly accurate SST prediction method. SST prediction methods based on the temporal information usually focus on capturing the temporal influence of the historical SST but ignore the spatial influence in the sea area, so these methods meet the performance bottlenecks. To fuse the multidimensional spatiotemporal influence and further improve the accuracy of the SST prediction, this letter proposed the convolutional gated recurrent unit (GRU) with multilayer perceptron (CGMP) to predict SST in the Bohai Sea and the South China Sea. The convolutional layer of CGMP can capture the neighbor influence effectively in the spatial dimension, making up for the shortcomings of methods that are based on the temporal information and do not consider the spatial information. The GRU layer and the MLP layer of CGMP can process historical information effectively in the temporal dimension. Experiments showed that the prediction performance of CGMP was better than those of other comparison methods in different sea areas, different schemas, and different prediction scales. Besides, the error distribution law in the Bohai Sea daily mean SST prediction was explored.