Forecasting of Sea Surface Temperature in Eastern Tropical Pacific by a Hybrid Multiscale Spatial–Temporal Model Combining Error Correction Map

Sea surface temperature (SST) is one of the most important parameters in the global ocean-atmosphere system. Predicting SST can help to analyze and identify extreme weather and protect the marine environment in advance. Traditional numerical and machine learning methods tend to ignore spatial features. The single model in existing deep learning methods suffers from weakening spatial features and reducing the ability of discriminating time-series information. At the same time, the influence of physical ocean phenomena is rarely considered, which may lead to inaccurate prediction results. Based on the spatial–temporal characteristics and physical laws of the SST field, this article proposes a hybrid multiscale spatial–temporal model combining error correction map (ECM-HMSTM) to predict the SST. First, the ECM-HMSTM can comprehensively extract the spatial–temporal features of the SST field at different scales, and thus, the SST prediction map can be obtained. Second, by a new error correction approach based on the activity of tropical instability waves (TIWs), the ECM-HMSTM can effectively predict the variation characteristics of TIWs signals, which results in producing the ECMs. Third, by fusing the two above maps, the SST field in the tropical eastern Pacific Ocean after five days is predicted. The experimental results show that the accuracy of the ECM-HMSTM was improved by 10.3% compared with the current state-of-the-art deep convolution model. Moreover, the SST predicted by the ECM-HMSTM performs well in characterizing the intensity of TIWs. Therefore, this article provides a strategy for effective short-term prediction of SST fields, which may aid the prediction and analysis of ocean phenomena and climate.