  Ocean wave climate has a significant impact on near-shore and off-shore human
activities, and its characterisation 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 spatio-temporal relationship between
North Atlantic wind and significant wave height (Hs) at an off-shore location
in the Bay of Biscay, using a two-stage deep learning model. The first step
uses convolutional neural networks (CNNs) to extract the spatial features that
contribute to Hs. Then, long short-term memory (LSTM) is used to learn the
long-term temporal dependencies between wind and waves.
