  Sea surface temperature (SST) is uniquely important to the Earth's atmosphere
since its dynamics are a major force in shaping local and global climate and
profoundly affect our ecosystems. Accurate forecasting of SST brings
significant economic and social implications, for example, better preparation
for extreme weather such as severe droughts or tropical cyclones months ahead.
However, such a task faces unique challenges due to the intrinsic complexity
and uncertainty of ocean systems. Recently, deep learning techniques, such as
graphical neural networks (GNN), have been applied to address this task. Even
though these methods have some success, they frequently have serious drawbacks
when it comes to investigating dynamic spatiotemporal dependencies between
signals. To solve this problem, this paper proposes a novel static and dynamic
learnable personalized graph convolution network (SD-LPGC). Specifically, two
graph learning layers are first constructed to respectively model the stable
long-term and short-term evolutionary patterns hidden in the multivariate SST
signals. Then, a learnable personalized convolution layer is designed to fuse
this information. Our experiments on real SST datasets demonstrate the
state-of-the-art performances of the proposed approach on the forecasting task.
