Ocean Wave Forecasting with Deep Learning as Alternative to Conventional Models

TL;DR

This paper introduces OceanCastNet, a deep learning model for wave forecasting that outperforms some traditional models in accuracy, especially during extreme weather, while also being computationally efficient.

Contribution

The study develops OceanCastNet, a novel machine learning approach that effectively predicts wave parameters and demonstrates superior performance over the operational ECWAM model.

Findings

OCN outperforms ECWAM at 24 NDBC stations

OCN maintains accuracy over 228-hour forecasts with Jason-3 data

OCN accurately predicts wave patterns during Typhoon Goni within ±0.5 m

Abstract

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.