Enhancing Short-and Long-Term Sea Surface Temperature Forecasting with a Static and Dynamic Learnable Personalized Graph Convolution Network

Sea surface temperature (SST) plays an important role in our Earth's atmosphere, wielding significant influence over both local and global climates and profoundly impacting ecosystems. However, this task presents unique challenges due to the inherent complexity and uncertainty within ocean systems. Recently, deep learning techniques, such as Graph Neural Networks (GNNs), have been employed to address SST forecasting. These methods often grapple with substantial limitations in capturing dynamic spatiotemporal relationships between signals. To tackle this issue, this paper introduces the Static and Dynamic Learnable Personalized Graph Convolution Network (SD-LPGC). This innovative approach comprises two distinct graph learning layers designed to model both stable long-term and short-term evolutionary patterns within multivariate SST signals. Additionally, a learnable personalized convolution layer is integrated to fuse these insights. Our experiments, conducted on real SST datasets, highlight the state-of-the-art performance of the proposed SD-LPGC approach in the realm of SST forecasting, demonstrating its potential to revolutionize this critical task.