Application of a Deep Learning‐Based Sea Surface Roughness Parameterization in a Global Forecasting System

Abstract Accurately calculating the momentum exchange between the atmosphere and the ocean is essential for global weather forecasting. However, current widely used parameterization schemes face challenges in accurately estimating sea surface roughness . Deep learning (DL) technology offers new opportunities to address this problem. Here, we present a DL‐based parameterization scheme for sea surface roughness, which we have integrated into a global forecasting system. The DL‐based scheme demonstrates advantages over four widely used traditional schemes in offline and online tests. In the offline tests based on observational data, the DL‐based scheme significantly improved root mean square errors (RMSEs) for the calculated , with a decrease of at least compared to the four traditional schemes. In the online tests based on the coupling of the DL‐based scheme with a global forecasting system, the DL‐based scheme also shows advantages over four traditional schemes, especially in forecasting the geopotential height and the wind field for the Southern Hemisphere.