Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting

Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting

Sea surface height anomaly (SSHA) plays a pivotal role in ocean dynamics and climate systems. This article develops a graph-based memory recall recurrent neural network (GMR-Net) to achieve accurate and reliable mid-term spatiotemporal prediction of the SSHA field. The proposed method designs a newl...

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Bibliographic Details
Main Authors: Yuan Zhou, Tian Ren, Keran Chen, Le Gao, Xiaofeng Li
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Sea-surface height anomaly (SSHA)
deep learning (DL)
spatiotemporal prediction
Rossby waves
Online Access:https://ieeexplore.ieee.org/document/10458077/

Internet

https://ieeexplore.ieee.org/document/10458077/

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