Graph-Based Deep Learning for Sea Surface Temperature Forecasts

TL;DR

This paper explores the use of graph neural networks for global sea surface temperature forecasting, showing improved accuracy over traditional persistence models in most oceans.

Contribution

It introduces graph re-sampling and applies GNNs to SST prediction, demonstrating their potential advantages over grid-based deep learning methods.

Findings

GNNs outperform persistence models in one-month-ahead SST prediction.

Graph re-sampling improves the effectiveness of GNNs for SST forecasting.

GNNs show promise for global environmental spatiotemporal modeling.

Abstract

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.