MesoGRU: Deep Learning Framework for Mesoscale Eddy Trajectory Prediction

Abstract

This work presents a novel framework, named MesoGRU, to accurately predict trajectories of mesoscale eddies (MEs), which is beneficial to study the natural ocean phenomena and perform statistical analysis of oceanic data. MesoGRU first extracts ME trajectory data and establishes two datasets according to the information of location and date in the South China Sea (SCS). Then it effectively processes SCS trajectory data and integrates SLA data and AVISO data into our combined ME dataset (CDME). Furthermore, a designated neural network with a new loss function [(weighted mean square estimation (WMSE)] is designed to learn the characteristics of ME trajectories. After thoroughly analyzing correlations of every two features, the MesoGRU network iteratively extracts key ME features and renormalizes the future time sequence of eddy trajectories. Our verification results demonstrate that MesoGRU has conducted a significant prediction improvement. The mean daily center error of ME trajectory prediction with our scheme is about 8 km and its center error for seven-day forecasting is approximately 18.2 km. Moreover, MesoGRU achieves a higher matching ratio of ME trajectory forecasting compared to other deep learning methods with a single dataset, indicating MesoGRU can predict trajectories as a state-of-the-art method.