Sea surface temperature (SST) forecasts help with managing the marine
ecosystem and the aquaculture impacted by anthropogenic climate change.
Numerical dynamical models are resource intensive for SST forecasts; machine
learning (ML) models could reduce high computational requirements and have been
in the focus of the research community recently. ML models normally require a
large amount of data for training. Environmental data are collected on
regularly-spaced grids, so early work mainly used grid-based deep learning (DL)
for prediction. However, both grid data and the corresponding DL approaches
have inherent problems. As geometric DL has emerged, graphs as a more
generalized data structure and graph neural networks (GNNs) have been
introduced to the spatiotemporal domains. In this work, we preliminarily
explored graph re-sampling and GNNs for global SST forecasts, and GNNs show
better one month ahead SST prediction than the persistence model in most oceans
in terms of root mean square errors.

该研究尝试使用图重新采样和图神经网络应用于全球海表温度预测，并发现其在大多数海洋中以均方根误差为衡量标准比持续模型具有更好的一个月的预测效果。