Hierarchical stacked spatiotemporal self-attention network for sea surface temperature forecasting

Sea surface temperature (SST) is a highly complex spatiotemporal variable, which stems from its susceptibility to non-linear dynamical processes and substantial spatiotemporal variability. In particular, accurately forecasting small-scale SST is a formidable challenge due to the compounded effects of diverse physical processes spanning across various scales. In this study, we employ deep learning methods to mine the ocean’s evolutionary patterns, as the ocean’s dynamic mechanisms are inherently embedded in spatiotemporal data. We propose a hierarchical stacked spatiotemporal self-attention mechanism (HSSSA) network architecture. The hierarchical stacked encoder–decoder architecture provides the capability for feature fusion and extraction at different scales. The spatial self-attention and temporal self-attention modules simultaneously focus on information from different spatial locations and time steps, allowing the exploration of spatiotemporal patterns in the complex dynamics of the ocean. The experiments are conducted on a high-resolution East China Sea dataset ($1/10°\times 1/10°$) to demonstrate the forecast performance of the proposed model for refined ocean variables. The 15-day forecasts indicate that the HSSSA method outperforms the EOF-ARIMA and CNN-Transformer methods.