Enhancing ENSO predictions with self-attention ConvLSTM and temporal embeddings

Enhancing ENSO predictions with self-attention ConvLSTM and temporal embeddings

El Niño-Southern Oscillation (ENSO), a cyclic climate phenomenon spanning interannual and decadal timescales, exerts substantial impacts on the global weather patterns and ecosystems. Recently, deep learning has brought considerable advances in the accurate prediction of ENSO occurrence. However, th...

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Bibliographic Details
Main Authors: Chuang Rui, Zhengya Sun, Wensheng Zhang, An-An Liu, Zhiqiang Wei
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-02-01
Series:Frontiers in Marine Science
Subjects:
El Niño-Southern Oscillation (ENSO)
deep learning for ENSO prediction
self-attention ConvLSTM
temporal embeddings
spring prediction barrier
Online Access:https://www.frontiersin.org/articles/10.3389/fmars.2024.1334210/full

Internet

https://www.frontiersin.org/articles/10.3389/fmars.2024.1334210/full

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