An Attention-Based Context Fusion Network for Spatiotemporal Prediction of Sea Surface Temperature

Sea surface temperature (SST) is a fundamental parameter in the field of oceanography as it significantly influences various physical, chemical, and biological processes within the marine environment. In this study, we propose an attention-based context fusion network (ACFN) model for short-term prediction of SST based on the operational SST and sea ice analysis (OSTIA) data. The ACFN model integrates an attention-based context fusion block into the convolutional long short-term memory (ConvLSTM) model. Specifically, the attention mechanism generates attention maps sequentially across both the channel and spatial dimensions, allowing for the detailed exploration of intricate spatiotemporal correlations between the previous context state and the current input state in ConvLSTM. To assess the performance of the ACFN model, we apply it to predict SST in the Bohai Sea with lead times ranging from one to ten days. The results demonstrate that our proposed model outperforms several state-of-the-art models, i.e., ConvLSTM, predictive RNN (PredRNN), SimVP, and motion details RNN (MoDeRNN), in terms of mean absolute error (MAE) and coefficient of determination (
$R^{2}$
). In particular, our analysis reveals that the prediction errors are relatively higher in the coastal areas compared to those in the central Bohai Sea.