A Deep Learning-Based Bias Correction Method for Predicting Ocean Surface Waves in the Northwest Pacific Ocean

Abstract

Ocean waves, especially extreme waves, are vital for air-sea interaction and shipping. However, current wave models still have significant biases. Based on a numerical wave model and a deep learning model, a BU-Net by adding batch normalization layers to a U-Net, we accurately predict the significant wave height (SWH) of the Northwest Pacific Ocean. For each day in 2017–2021, we conducted a 3-day hindcast experiment using WAVEWATCH3 (WW3) to obtain the SWH forecasts at lead times of 24, 48, and 72 hr, forced by GFS real-time forecast surface winds. After using BU-Net, the mean Root Mean Squared Errors (RMSEs) of the SWH forecast from WW3 at lead times of 24, 48, and 72 hr are reduced by 40%, 38%, and 30%, respectively. During typhoon passages, the drop percentages of RMSEs all exceed 20% for three lead times. Therefore, combining numerical models and deep learning is very promising in wave forecasting.