Eddy-Resolving Global Ocean Forecasting with Multi-Scale Graph Neural Networks
This addresses the challenge of accurate multi-scale ocean forecasting for climate and marine applications, but it is incremental as it builds on existing data-driven ocean models.
The study tackled global eddy-resolving ocean forecasting by proposing a multi-scale graph neural network model, which improved short-term prediction skill and enhanced representation of multi-scale ocean variability, as shown by reduced root mean square error and accurate surface kinetic energy spectra.
Research on data-driven ocean models has progressed rapidly in recent years; however, the application of these models to global eddy-resolving ocean forecasting remains limited. The accurate representation of ocean dynamics across a wide range of spatial scales remains a major challenge in such applications. This study proposes a multi-scale graph neural network-based ocean model for 10-day global forecasting that improves short-term prediction skill and enhances the representation of multi-scale ocean variability. The model employs an encoder-processor-decoder architecture and uses two spherical meshes with different resolutions to better capture the multi-scale nature of ocean dynamics. In addition, the model incorporates surface atmospheric variables along with ocean state variables as node inputs to improve short-term prediction accuracy by representing atmospheric forcing. Evaluation using surface kinetic energy spectra and case studies shows that the model accurately represents a broad range of spatial scales, while root mean square error comparisons demonstrate improved skill in short-term predictions. These results indicate that the proposed model delivers more accurate short-term forecasts and improved representation of multi-scale ocean dynamics, thereby highlighting its potential to advance data-driven, eddy-resolving global ocean forecasting.