Ocean Wave Forecasting with Deep Learning as Alternative to Conventional Models
This provides an efficient alternative to conventional models for operational wave prediction, though it is incremental as it builds on existing machine learning approaches.
The study tackled ocean wave forecasting by developing OceanCastNet (OCN), a deep learning model that predicts wave parameters using wind and wave fields, and found it outperformed the conventional ECWAM model at more NDBC stations (24 vs. 10) and maintained similar accuracy in satellite data over 228-hour forecasts, with errors within ±0.5 m during extreme weather.
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 $\pm$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.