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.
| Original language | English |
|---|---|
| Article number | e2022GL100916 |
| Journal | Geophysical Research Letters |
| Volume | 49 |
| Issue number | 23 |
| DOIs | |
| State | Published - Dec 16 2022 |
| Externally published | Yes |
Funding
We thank the editor and two anonymous reviewers for constructive comments that led to the improvement of the manuscript. This work was jointly supported by National Key R&D Program of China (2020YFA0608000), National Natural Science Foundation of China (42275154), and Tsinghua University Initiative Scientific Research Program (2019Z07L02011).