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<p>This study presents an Eddy LSIN (LSTM-Informer)&#xa0;framework for the accurate prediction of mesoscale eddy trajectories in the northeastern South China Sea. This comprehensive framework is designed to improve mesoscale eddy trajectory prediction, and it employs a multimodal spatiotemporal eddy representation (STER) dataset, a temporal Informer (T-Informer) network for long-sequence modeling, and a geographically aware combined mean squared error (CMSE) loss function. Mesoscale eddies are critical dynamic processes in oceans, and accurately predicting their trajectories is of great importance in oceanographic research. Leveraging the physical characteristics of mesoscale eddies, the framework is based on the construction of a high-resolution dataset that integrates sea level anomalies, relative vorticity, and kinetic energy features. An autoencoder is employed for feature extraction, resulting in the STER dataset, which reduces prediction errors by approximately 70% (mean squared error)&#xa0;and 82% (mean absolute error)compared to the unprocessed sla-t-u-v dataset. The framework incorporates a long short-term memory model for short-term prediction and a temporally encoded T-Informer model for long-term prediction. To balance numerical accuracy and spatial precision, CMSE is introduced by combining the mean squared error with the Haversine distance-based Geographic error. Experimental results show that the framework achieves average trajectory center errors of approximately 8&#xa0;km for 1-day predictions, 18&#xa0;km for 7-day predictions, and 39&#xa0;km for 20-day predictions. Compared with conventional models, the T-Informer improves the long-sequence prediction accuracy by around 40% and effectively mitigates cumulative errors. Overall, the Eddy LSIN framework demonstrates high accuracy and robustness in short- and long-term mesoscale eddy trajectory prediction and offers a promising approach for advancing predictive oceanography.</p>

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Mesoscale eddy trajectory prediction in the Northeastern South China Sea based on an deep learning framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月18日

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  • Tingyue Han,
  • Dalei Song,
  • Chong Li,
  • Wei Zhao,
  • Xinning Wang

摘要

This study presents an Eddy LSIN (LSTM-Informer) framework for the accurate prediction of mesoscale eddy trajectories in the northeastern South China Sea. This comprehensive framework is designed to improve mesoscale eddy trajectory prediction, and it employs a multimodal spatiotemporal eddy representation (STER) dataset, a temporal Informer (T-Informer) network for long-sequence modeling, and a geographically aware combined mean squared error (CMSE) loss function. Mesoscale eddies are critical dynamic processes in oceans, and accurately predicting their trajectories is of great importance in oceanographic research. Leveraging the physical characteristics of mesoscale eddies, the framework is based on the construction of a high-resolution dataset that integrates sea level anomalies, relative vorticity, and kinetic energy features. An autoencoder is employed for feature extraction, resulting in the STER dataset, which reduces prediction errors by approximately 70% (mean squared error) and 82% (mean absolute error)compared to the unprocessed sla-t-u-v dataset. The framework incorporates a long short-term memory model for short-term prediction and a temporally encoded T-Informer model for long-term prediction. To balance numerical accuracy and spatial precision, CMSE is introduced by combining the mean squared error with the Haversine distance-based Geographic error. Experimental results show that the framework achieves average trajectory center errors of approximately 8 km for 1-day predictions, 18 km for 7-day predictions, and 39 km for 20-day predictions. Compared with conventional models, the T-Informer improves the long-sequence prediction accuracy by around 40% and effectively mitigates cumulative errors. Overall, the Eddy LSIN framework demonstrates high accuracy and robustness in short- and long-term mesoscale eddy trajectory prediction and offers a promising approach for advancing predictive oceanography.