Predicting Surface Stokes Drift with Deep Learning

Stokes drift refers to the net horizontal displacement of water particles under the influence of wave action, playing a crucial role in the transport of heat, salt, nutrients, and pollutants in the ocean. Accurate estimation of Stokes drift is essential for understanding ocean dynamics and material transport. This study utilizes two deep learning models (Earthformer and ConvLSTM) to predict surface Stokes drift, using wind and water depth as input variables. We designed three control experiments to evaluate the impact of different training objectives on the experimental results. In Exp. 1, the model used the two Stokes drift components (us, vs) as the training objectives. In Exp. 2, the objectives were the two components (us, vs) plus the direction θ. In Exp. 3, the model employed the magnitude |us| and the direction θ of the Stokes drift as the training objectives. The results indicate that using the magnitude and direction (Exp. 3) significantly reduces the RMSE for magnitude, direction, and each component (us, vs) by up to 33.3%, compared to the other two strategies. Moreover, the approach of choosing magnitude and direction as the training objectives can also be applied to the prediction of other vector variables, such as ocean currents and winds.