Regional Ocean Forecasting with Hierarchical Graph Neural Networks
Holmberg, Daniel, Clementi, Emanuela, Roos, Teemu
–arXiv.org Artificial Intelligence
Accurate ocean forecasting systems are vital for understanding marine dynamics, which play a crucial role in environmental management and climate adaptation strategies. Traditional numerical solvers, while effective, are computationally expensive and time-consuming. Recent advancements in machine learning have revolutionized weather forecasting, offering fast and energy-efficient alternatives. Building on these advancements, we introduce SeaCast, a neural network designed for high-resolution, medium-range ocean forecasting. SeaCast employs a graph-based framework to effectively handle the complex geometry of ocean grids and integrates external forcing data tailored to the regional ocean context. Our approach is validated through experiments at a high spatial resolution using the operational numerical model of the Mediterranean Sea provided by the Copernicus Marine Service, along with both numerical and data-driven atmospheric forcings.
arXiv.org Artificial Intelligence
Nov-20-2024
- Country:
- Atlantic Ocean > Mediterranean Sea (0.35)
- Genre:
- Research Report (1.00)
- Industry:
- Transportation > Marine (0.34)
- Technology: