Long-Term Prediction of Sea Surface Temperature by Temporal Embedding Transformer With Attention Distilling and Partial Stacked Connection

Long-Term Prediction of Sea Surface Temperature by Temporal Embedding Transformer With Attention Distilling and Partial Stacked Connection

Sea surface temperature (SST) is one of the most important parameters in the global ocean–atmosphere system, and its long-term changes will have a significant impact on global climate and ecosystems. Accurate prediction of SST, therefore, especially the improvement of long-term predictive...

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Bibliographic Details
Main Authors: Hao Dai, Zhigang He, Guomei Wei, Famei Lei, Xining Zhang, Weijie Zhang, Shaoping Shang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Attention distilling
China Sea
long-term prediction
partial stacked connection
sea surface temperature (SST)
temporal transformer
Online Access:https://ieeexplore.ieee.org/document/10414022/

Internet

https://ieeexplore.ieee.org/document/10414022/

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