Forecasting the eddying ocean with a deep neural network.

ABSTRACT: Mesoscale eddies with horizontal scales from tens to hundreds of kilometers are ubiquitous in the upper ocean, dominating the ocean variability from daily to weekly time scales. Their turbulent nature causes great scientific challenges and computational burdens in accurately forecasting the short-term evolution of the ocean states based on conventional physics-driven numerical models. Recently, artificial intelligence (AI)-based methods have achieved competitive forecast performance and greatly increased computational efficiency in weather forecasts, compared to numerical models. Yet, their application to ocean forecasts remains challenging due to the different dynamic characteristics of the atmosphere and the ocean. Here, we develop WenHai, a data-driven eddy-resolving global ocean foreca

SUBMITTER: Cui Y 

PROVIDER: S-EPMC11885467 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature