Long-Term Forecasting of Long-Lived Oceanic Eddies Using an Improved Informer Framework

Long-term prediction of long-lived mesoscale oceanic eddies plays a crucial role in ocean dynamics research, as its accuracy impacts climate change assessments, ocean energy transport analysis, and marine resource management. However, due to the nonlinear, multi-scale nature of vortex dynamics, it remains challenging. To address this, we propose an Informer-based deep learning framework enhanced with two key innovations: (1) an adaptive feature selection module that dynamically prioritizes input features to improve prediction robustness, and (2) a geospatial loss function based on the Haversine formula to optimize spatial accuracy. In addition, we investigates the unique influence of dipole eddies on trajectory predictability and analyzes how different eddy lifecycle stages affect prediction performance, offering theoretical and empirical insights into these complex patterns. Experiments show that our method outperforms seven baseline models across multiple metrics, achieving a mean geodesic distance of 13.624 km in the representative 30-day input and 15-day prediction setting and reducing error accumulation in long-term forecasts. Specifically, our work focuses on eddies with lifespans exceeding one year, providing dedicated predictive tools tailored for these long-lived ocean phenomena. These advances provide a foundation for future integration of physical constraints and environmental variables, promising enhanced predictive tools for ocean dynamics research.