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Multi-Year ENSO Forecasts Using Parallel Convolutional Neural Networks With Heterogeneous Architecture

Min Ye, Jie Nie, Anan Liu, Zhigang Wang, Lei Huang, Hao Tian, Dehai Song, Zhiqiang Wei
2021 Frontiers in Marine Science  
To solve this problem, we propose a novel parallel deep convolutional neural network (CNN) with a heterogeneous architecture.  ...  The El Niño-Southern Oscillation (ENSO) is one of the main drivers of the interannual climate variability of Earth and can cause a wide range of climate anomalies, so multi year ENSO forecasts are a paramount  ...  Prediction of enso beyond spring predictability barrier using deep convolutional lstm networks. IEEE Geosci. Remote Sens.  ... 
doi:10.3389/fmars.2021.717184 fatcat:v2hvd26jajeorac54kgcetq3sa

ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler

Bin Mu, Bo Qin, Shijin Yuan
2021 Geoscientific Model Development  
On the basis of this coupler, an ENSO deep learning forecast model (named ENSO-ASC) is proposed, whose structure is adapted to the characteristics of the ENSO dynamics, including the encoder and decoder  ...  We firstly tune the model performance to optimal and compare it with the other state-of-the-art ENSO deep learning forecast models.  ...  In ENSO forecasting, such a forecast skill decline is regarded as a persistence barrier and usually occurs in spring (i.e., spring predictability barrier, SPB) (Webster, 1995; Zheng and Zhu, 2010) .  ... 
doi:10.5194/gmd-14-6977-2021 fatcat:devwpqfjard7xkrtrpwd5kwwqu

Physics captured by data-based methods in El Niño prediction [article]

G. Lancia, I. J. Goede, C.Spitoni, H. A. Dijkstra
2022 arXiv   pre-print
Recent machine-learning approaches to El Ni\~no prediction, in particular Convolutional Neural Networks (CNNs), have shown a surprisingly high skill at relatively long lead times.  ...  In an attempt to understand this high skill, we here use data from distorted physics simulations with an intermediate complexity El Ni\~no model to determine what aspects of El Ni\~no physics are represented  ...  The research described above shows that DLMs are a very promising tool in ENSO prediction that can provide useful skill of El Niño forecasts beyond the predictability barriers.  ... 
arXiv:2206.03110v1 fatcat:bltaqg3fczekliwbpxegwloeou

International Research Conference on Smart Computing and Systems Engineering SCSE 2020 Proceedings [Full Conference Proceedings]

2020 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)  
ACKNOWLEDGMENT The authors would like to thank the Department of Census and Department of Irrigation, Sri Lanka for providing the paddy yield and climate data for this study.  ...  Kelaniya for their immense support and encouragement they gave throughout the development phase of the data sets.  ...  "cnn-trad-fpool3" was contained, two convolutional, one linear low-rank, and one Deep Neural Network (DNN) layer.  ... 
doi:10.1109/scse49731.2020.9313027 fatcat:gjk5az2mprgvrpallwh6uhvlfi

Eastern Cardiothoracic Surgical Society 55th Annual Meeting

2018 Innovations (Philadelphia): technology and techniques in cardiothoracic and vascular surgery  
While models based on a form of RNN known as the long short-term memory (LSTM) network have performed successfully on tasks such as image annotation, a shortcoming of LSTM networks is that they are composed  ...  The raw video data is passed through a "deep" or many-layered convolutional neural network for classifying superpixels, an average pooling layer, a layer that performs the POISE algorithm to merge superpixels  ... 
doi:10.1097/imi.0000000000000471 fatcat:m7dkilztybhelp2q6upbwkkyle

Knowledge Extracted from Copernicus Satellite Data

Dumitru Octavian, Schwarz Gottfried, Eltoft Torbjørn, Kræmer Thomas, Wagner Penelope, Hughes Nick, Arthus David, Fleming Andrew, Koubarakis Manolis, Datcu Mihai
2019 Zenodo  
The proposed methodology uses new paradigms from Recurrent Neural Networks and Generative Adversarial Networks, supported by Bayesian and Information Bottleneck concepts. References 1.  ...  During the development of deep learning algorithms, a key activity is to establish a large amount of referenced Earth Observation data.  ...  Popularization platform focus on network popularization of investigation knowledge.  ... 
doi:10.5281/zenodo.3941573 fatcat:zzifwgljifck5bpjnboetsftfu