This study presents OceanCastNet (OCN), a machine learning approach for wave forecasting that incorporates wind and wave fields to predict significant wave height, mean wave period, and mean wave direction.We evaluate OCN's performance against the operational ECWAM model using two independent datasets: NDBC buoy and Jason-3 satellite observations. NDBC station validation indicates OCN performs better at 24 stations compared to ECWAM's 10 stations, and Jason-3 satellite validation confirms similar accuracy across 228-hour forecasts. OCN successfully captures wave patterns during extreme weather conditions, demonstrated through Typhoon Goni with prediction errors typically within ±0.5 m. The approach also offers computational efficiency advantages. The results suggest that machine learning approaches can achieve performance comparable to conventional wave forecasting systems for operational wave prediction applications.
@article{arxiv.2406.03848,
title = {Ocean Wave Forecasting with Deep Learning as Alternative to Conventional Models},
author = {Ziliang Zhang and Huaming Yu and Danqin Ren and Chenyu Zhang and Minghua Sun and Xin Qi},
journal= {arXiv preprint arXiv:2406.03848},
year = {2025}
}
Comments
Accepted manuscript. Final version published in Journal of Advances in Modeling Earth Systems: https://doi.org/10.1029/2025MS005285