From monochromatic waves to realistic tides: deep learning for short-term forecasting of coastal ocean.

In this study, a hybrid architecture combining convolutional neural networks for spatial reconstruction and long short-term memory networks for temporal forecasting is used to predict sea-level variations in the German Bight. This new framework is applied to a series of sea level data ranging from academic to realistic data. Experiments with monochromatic waves demonstrate the model's ability to deliver accurate short-term forecasts with minimal errors. Forecasts of real tidal constituents, including M2 and the sum of M2 and M4 tides, confirm robust model performance over lead times up to 48 h. A key result is that deep learning can reconstruct basin-wide sea level from a limited number of coastal gauge stations. Therefore, in the forecast experiments, adding data from coastal observations (mimicking data assimilation) significantly improves prediction accuracy. The study highlights the potential of deep learning to supplement traditional numerical models, particularly in regions with dense observational coverage. Key factors influencing model performance are identified, among them spatial signal complexity and steepness of gradients. An overall result is that deep learning can complement numerical models in operational ocean forecasting and provide a valuable tool for evidence-based coastal management in data-rich regions.