Regional Ocean Forecasting with Hierarchical Graph Neural Networks

• URL: http://arxiv.org/abs/2410.11807v2
• Date: Wed, 20 Nov 2024 10:33:05 GMT
• Title: Regional Ocean Forecasting with Hierarchical Graph Neural Networks
• Authors: Daniel Holmberg, Emanuela Clementi, Teemu Roos,
• Abstract summary: We introduce SeaCast, a neural network designed for high-resolution, medium-range ocean forecasting. SeaCast employs a graph-based framework to 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.
• Score: 1.4146420810689422
• License:
• Abstract: 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.

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