Mesoscale Westward Eddy Trajectory Prediction With Memory Augmented Neural Network

Accurate prediction of oceanic eddy trajectory is crucial for monitoring ocean climate change, but the complex dynamics mechanism and changeable environmental effects make it difficult. In recent years, many deep-learning methods have been proposed to solve this problem. However, the complexity of high-dimensional models increases the prediction accuracy as well as calculation cost. In this article, a parsimonious and interpretable network with external memory of the Rossby wave is constructed to implement westward mesoscale eddy trajectory prediction. Specifically, 1) fundamental multilayer perceptrons are utilized to extract cross-variable features, and gate recurrent units with fewer gates are employed to capture temporal corrections; 2) an external memory unit to retain the phase speed of long Rossby wave across different scales is designed to maintain simplicity and efficiency within the network; 3) the network structure includes an external memory module responsible for reading the phase speed of long Rossby wave from the external memory unit; and 4) this information is then interacted with Rossby wave related features of the eddy and corrected the output of prediction module to enhance forecasting outcomes. Experiments on dataset benchmarks demonstrate the effectiveness of the proposed method. Our method outperforms the baseline methods in terms of accuracy and computational cost, with mean geodesic distance errors of 7.52 km for three-day prediction while taking lower computational cost and training time.