A Deep Learning‐Based Bias Correction Method for Predicting Ocean Surface Waves in the Northwest Pacific Ocean
Danyi Sun, Wenyu Huang, Yong Luo, Jing‐Jia Luo, Jonathon S. Wright, Haohuan Fu, Bin Wang
Abstract
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.