Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO-Reference-Cited by-同舟云学术

Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

Published:2023-07-15 Issue:1 Volume:41 Page:141-154
ISSN:0256-1530
Container-title:Advances in Atmospheric Sciences
language:en
Short-container-title:Adv. Atmos. Sci.

Author:

Wang Tingyu,Huang Ping

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science

Link

https://link.springer.com/content/pdf/10.1007/s00376-023-3001-1.pdf

Reference70 articles.

1. Ashok, K., S. K. Behera, S. A. Rao, H. Y. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, https://doi.org/10.1029/2006JC003798.

2. Baehrens, D., T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. Müller, 2010: How to explain individual classification decisions. The Journal of Machine Learning Research, 11, 1803–1831.

3. Boschetti, F., M. Feng, J. R. Hartog, A. J. Hobday, and X. B. Zhang, 2022: Sea Surface Temperature Predictability at the interface between oceanographic modelling and machine learning. Research Square, 1, https://doi.org/10.21203/rs.3.rs-1721732/v1.

4. Broni-Bedaiko, C., F. A. Katsriku, T. Unemi, M. Atsumi, J.-D. Abdulai, N. Shinomiya, and E. Owusu, 2019: El Niño-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks. Artificial Life and Robotics, 24, 445–451, https://doi.org/10.1007/s10015-019-00540-2.

5. Cachay, S. R., E. Erickson, A. Fender, C. Bucker, E. Pokropek, W. Potosnak, S. Osei, and B. Lütjens, 2021: Graph neural networks for improved El Niño forecasting. Preprint, arXiv, https://doi.org/10.48550/arXiv.2012.01598.

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