Global oceanic mesoscale eddies trajectories prediction with knowledge-fused neural network

Zhang, Xinmin; Huang, Baoxiang; Chen, Ge; Ge, Linyao; Radenkovic, Milena; Hou, Guojia

Abstract

Efficient eddy trajectory prediction driven by multiinformation fusion can facilitate the scientific research of oceanography, while the complicated dynamics mechanism makes this issue challenging. Benefiting from ocean observing technology, the eddy trajectory dataset can be qualified for data-intensive research paradigms. In this article, the dynamics mechanism is used to inspire the design idea of the eddy trajectory prediction neural network (termed EddyTPNet) and is also transformed into prior knowledge to guide the learning process. This study is among the first to implement eddy trajectory prediction with physics informed neural network. First, an in-depth analysis of the kinematic characteristics indicates that the longitude and latitude of the trajectory should be decoupled; second, the directional dispersion prior knowledge of global eddy propagation is embedded into the decoder of the EddyTPNet to improve the performance; finally, EddyTPNet predicts global eddy trajectories through pretraining and adapts to complex local regions via model transfer. Extensive experimental results demonstrate that EddyTPNet can reliably forecast the motion of eddies for the next seven days, ensuring a low daily mean geodetic error. This exploratory study provides valuable insights into solving the prediction problem of ocean phenomena by using knowledge-based time-series neural networks.

Citation

Zhang, X., Huang, B., Chen, G., Ge, L., Radenkovic, M., & Hou, G. (2024). Global oceanic mesoscale eddies trajectories prediction with knowledge-fused neural network. IEEE Transactions on Geoscience and Remote Sensing, 62, Article 4205214. https://doi.org/10.1109/tgrs.2024.3388040

• Journal Article Type: Article
• Acceptance Date: Apr 6, 2024
• Online Publication Date: Apr 12, 2024
• Publication Date: 2024
• Deposit Date: Apr 20, 2024
• Publicly Available Date: Apr 25, 2024
• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 62
• Article Number: 4205214
• DOI: https://doi.org/10.1109/tgrs.2024.3388040
• Keywords: Trajectory , Oceans , Surfaces , Predictive models , Dynamics , Deep learning , Pipelines , Deep learning , directional divergence physical information , eddy trajectory prediction , knowledge-fused neural network
• Public URL: https://nottingham-repository.worktribe.com/output/33839331
• Publisher URL: https://ieeexplore.ieee.org/document/10497608