Learning the spatiotemporal relationship between wind and significant wave height using deep learning

Ocean wave climate has a significant impact on near-shore and off-shore human activities, and its characterization can help in the design of ocean structures such as wave energy converters and sea dikes. Therefore, engineers need long time series of ocean wave parameters. Numerical models are a valuable source of ocean wave data; however, they are computationally expensive. Consequently, statistical and data-driven approaches have gained increasing interest in recent decades. This work investigates the spatiotemporal relationship between North Atlantic wind and significant wave height (H-s) at an off-shore location in the Bay of Biscay, using a two-stage deep learning model. The first step uses convolutional neural networks to extract the spatial features that contribute to H-s. Then, long short-term memory is used to learn the long-term temporal dependencies between wind and waves. Impact Statement In the context of climate change, the climate of ocean waves has major socioeconomic and environmental implications. Since ocean waves are generated by the wind blowing at the ocean surface, understanding the relationship between wind and waves is critical to assessing the impact of climate change on ocean waves. This work contributes to understanding the spatiotemporal relationship between wind conditions and ocean waves using deep learning. We propose a fully data-driven empirical wind-wave model that predicts the significant wave height at a location in the Bay of Biscay using the North Atlantic wind conditions. The proposed method is computationally inexpensive and can provide long time series of future significant wave heights or complete historical data if wind data are available.