Forecasting the eddying ocean with a deep neural network - Yingzhe Cui; Ruohan Wu; Xiang Zhang; Ziqi Zhu; Bo Liu; Jun Shi; Junshi Chen; Hailong Liu; Shenghui Zhou; Liang Su; Zhao Jing; Hong An; et al. (Nature Communications)
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 forecast system (GOFS), by training a deep neural network (DNN). The bulk formulae on momentum, heat, and freshwater fluxes are incorporated into the DNN to improve the representation of air-sea interactions. Ocean dynamics is exploited in the DNN architecture design to preserve ocean mesoscale eddy variability. WenHai outperforms a state-of-the-art eddy-resolving numerical GOFS and AI-based GOFS for the temperature profile, salinity profile, sea surface temperature, sea level anomaly, and near-surface current forecasts led by 1 day to at least 10 days. Our results highlight expertise-guided deep learning as a promising pathway for enhancing the global ocean forecast capacity.