Deep learning for predicting wave parameters from wind components in the Adriatic basin

Abstract

Predicting high-resolution ocean wave parameters, such as significant wave height, mean wave period, and direction, in complex coastal regions like the Adriatic Sea is essential but computationally intensive when using traditional physical models. This thesis explores deep learning-based statistical downscaling, using coarse-resolution ERA5 wind fields to generate fine-scale wave predictions. Both deterministic (U-Net, ClimaX) and probabilistic (WGAN-GP, Conditional Flow Matching) models were developed and compared. Results show that deep learning can effectively model the nonlinear relationships between wind and waves. Among the tested approaches, the CFM model achieved the highest accuracy for ensemble mean predictions and offered reliable uncertainty quantification, highlighting its potential for efficient and scalable high-resolution wave forecasting.