Forecasting 3D velocity structures of the Loop Current system in the Gulf of Mexico using a deep learning model

This dataset is the outcome of an ocean current prediction model based on deep learning method. This method was evaluated on the measured energetic currents of the Gulf of Mexico circulation dominated by the Loop Current (LC) at multiple spatial and temporal scales. The approach taken herein consists of dividing the velocity tensor into planes perpendicular to each of the three Cartesian coordinate system directions. A Long Short-Term Memory Recurrent Neural Network, which is best suited to handling long-term dependencies in the data, was thus used to predict the evolution of the velocity field in each plane, along each of the three directions. The predicted tensors, made of the planes perpendicular to each Cartesian direction, revealed that the model’s prediction skills were best for the flow field in the planes perpendicular to the direction of prediction. Furthermore, the fusion of all three predicted tensors significantly increased the overall skills of the flow prediction over the individual model’s predictions. The useful forecast period of this new model was greater than four days, with a root mean square error less than 0.05 cm·s^−1 and a correlation coefficient of 0.6.

This dataset supports this publication: Muhamed Ali, Ali, Hanqi Zhuang, James VanZwieten, Ali K. Ibrahim, and Laurent Chérubin. 2021. A Deep Learning Model for Forecasting Velocity Structures of the Loop Current System in the Gulf of Mexico. Forecasting 3, no. 4: 934-953. https://doi.org/10.3390/forecast3040056