Work

Ocean Wave Forecasting With Deep Learning as Alternative to Conventional Models

Year: 2025

Type: article

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 data sets: 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‐hr 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.

Source: Journal of Advances in Modeling Earth Systems

DOI: 10.1029/2025ms005285

Authors: Ziliang Zhang, Huaming Yu, Danqin Ren, Chenyu Zhang, Minghua Sun, Xin Qi

Institutions: Ocean University of China, China Meteorological Administration

Language: English

Topic: Ocean Waves and Remote Sensing

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,650