Spatiotemporal wave forecast with transformer-based network: A case study for the northwestern Pacific Ocean

The forecast of ocean waves relies mostly on complex dynamic-based models, which are expensive in computation and demanding in professional skills to run. Diverse deep learning methods have been proposed to tackle this problem, yet the architecture of Transformer (i.e., the self-attention) was seldom tested for such a learning problem. To bridge this gap, we apply a state-of-the-art spatiotemporal attention network, the EarthFormer, and apply it for wave forecasting in the northwestern Pacific. To train and validate the EarthFormer, the fifth-generation atmospheric reanalysis dataset (ERA5) from the European Centre for Medium-Range Weather Forecast product with hourly resolution was adopted, with the sequence of wind as the input and the three key variables (i.e., significance wave height, mean wave period, and mean wave direction) of ocean waves as the output. The prediction can generate accurate wave forecasts up to 12 hours ahead, with considerably low root-mean-squared error (RMSE). Overall, the predicted waves resulted in RMSE for the significance wave height of 0.22 m, for the mean wave period of 0.68 s, and for the mean wave direction of 0.28 rad. These wave height and wave period values are ∼13% and ∼10% of corresponding spatiotemporal mean values. The EarthFormer outperformed a popular spatiotemporal forecast network, the ConvLSTM, in our problem. For site-wise forecasts against buoy observations near Taiwan, the EarthFormer forecast also presents considerably high accuracy comparable to the ERA5 forecasts. This method can therefore provide an accurate and prompt way to forecast the large-scale distribution of waves to better model the marine dynamic and its climate effects, which could have a high potential for disaster prevention and climate modeling.