Learning Subseasonal-to-Seasonal Global Ocean Forecasting on a Hierarchical Triangle Mesh
Year: 2025
Type: conference-paper
Abstract: Poster presentation given at the Tackling Climate Change with Machine Learning - ICLR 2025 Workshop. Abstract The accuracy of medium-range weather predictions has steadily improved over the years. However, risk mitigation strategies for weather extremes require longer-term forecasts. Subseasonal-to-seasonal forecasts address this need, traditionally relying on ensembles of physics-based atmosphere-ocean coupled simulations. More recently, data-driven models emerged as a competitive alternative. Still, these models lack a description of the ocean, whose coupling becomes critical at these timescales. To address this issue, we propose a data-driven model for subseasonal-to-seasonal global ocean forecasting based on graph neural networks. We describe and motivate architectural choices, and present improvements based on analogies with finite element methods required to deal with irregular domains.
Author: Campanella, Stefano
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7107938860
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