Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-6977-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6977-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Nov 2021
Model description paper |
| 17 Nov 2021
| 17 Nov 2021
ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
Bin Mu
School of Software engineering, Tongji University, Shanghai, 201804,
China
School of Software engineering, Tongji University, Shanghai, 201804,
China
Shijin Yuan
CORRESPONDING AUTHOR
School of Software engineering, Tongji University, Shanghai, 201804,
China
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Cited
21 citations as recorded by crossref.
- Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction B. Mu et al. 10.1016/j.ocemod.2024.102344
- Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0 B. Mu et al. 10.5194/gmd-15-4105-2022
- ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding X. Liang et al. 10.1007/s00382-024-07119-z
- A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses L. Zhou & R. Zhang 10.1007/s00376-021-1368-4
- Deep learning with autoencoders and LSTM for ENSO forecasting C. Ibebuchi & M. Richman 10.1007/s00382-024-07180-8
- Dynamic Neuro-Fuzzy Systems for Forecasting El Niño Southern Oscillation (ENSO) Using Oceanic and Continental Climate Parameters as Inputs M. Lee et al. 10.3390/jmse10081161
- On the relative role of east and west pacific sea surface temperature (SST) gradients in the prediction skill of Central Pacific NINO3.4 SST S. Lekshmi et al. 10.1007/s10236-023-01581-9
- CAU: A Causality Attention Unit for Spatial-Temporal Sequence Forecast B. Qin et al. 10.1109/TMM.2023.3326289
- Deep learning for skillful long-lead ENSO forecasts K. Patil et al. 10.3389/fclim.2022.1058677
- Toward a Learnable Climate Model in the Artificial Intelligence Era G. Huang et al. 10.1007/s00376-024-3305-9
- IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning B. Mu et al. 10.5194/gmd-16-4677-2023
- ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler B. Mu et al. 10.1029/2022MS003132
- A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions L. Zhou & R. Zhang 10.1126/sciadv.adf2827
- A Transformer‐Based Deep Learning Model for Successful Predictions of the 2021 Second‐Year La Niña Condition C. Gao et al. 10.1029/2023GL104034
- CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation M. Sun et al. 10.1029/2023GL105175
- ENSO analysis and prediction using deep learning: A review G. Wang et al. 10.1016/j.neucom.2022.11.078
- Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network Y. Chen et al. 10.1029/2023GL106676
- A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions L. Zhou et al. 10.1016/j.aosl.2023.100330
- Graph Representation Learning and Its Applications: A Survey V. Hoang et al. 10.3390/s23084168
- A New Hybrid Prediction Method of El Niño/La Niña Events by Combining TimesNet and ARIMA Y. Du et al. 10.1109/ACCESS.2023.3319395
- Survey on the Application of Artificial Intelligence in ENSO Forecasting W. Fang et al. 10.3390/math10203793
21 citations as recorded by crossref.
- Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction B. Mu et al. 10.1016/j.ocemod.2024.102344
- Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0 B. Mu et al. 10.5194/gmd-15-4105-2022
- ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding X. Liang et al. 10.1007/s00382-024-07119-z
- A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses L. Zhou & R. Zhang 10.1007/s00376-021-1368-4
- Deep learning with autoencoders and LSTM for ENSO forecasting C. Ibebuchi & M. Richman 10.1007/s00382-024-07180-8
- Dynamic Neuro-Fuzzy Systems for Forecasting El Niño Southern Oscillation (ENSO) Using Oceanic and Continental Climate Parameters as Inputs M. Lee et al. 10.3390/jmse10081161
- On the relative role of east and west pacific sea surface temperature (SST) gradients in the prediction skill of Central Pacific NINO3.4 SST S. Lekshmi et al. 10.1007/s10236-023-01581-9
- CAU: A Causality Attention Unit for Spatial-Temporal Sequence Forecast B. Qin et al. 10.1109/TMM.2023.3326289
- Deep learning for skillful long-lead ENSO forecasts K. Patil et al. 10.3389/fclim.2022.1058677
- Toward a Learnable Climate Model in the Artificial Intelligence Era G. Huang et al. 10.1007/s00376-024-3305-9
- IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning B. Mu et al. 10.5194/gmd-16-4677-2023
- ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler B. Mu et al. 10.1029/2022MS003132
- A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions L. Zhou & R. Zhang 10.1126/sciadv.adf2827
- A Transformer‐Based Deep Learning Model for Successful Predictions of the 2021 Second‐Year La Niña Condition C. Gao et al. 10.1029/2023GL104034
- CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation M. Sun et al. 10.1029/2023GL105175
- ENSO analysis and prediction using deep learning: A review G. Wang et al. 10.1016/j.neucom.2022.11.078
- Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network Y. Chen et al. 10.1029/2023GL106676
- A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions L. Zhou et al. 10.1016/j.aosl.2023.100330
- Graph Representation Learning and Its Applications: A Survey V. Hoang et al. 10.3390/s23084168
- A New Hybrid Prediction Method of El Niño/La Niña Events by Combining TimesNet and ARIMA Y. Du et al. 10.1109/ACCESS.2023.3319395
- Survey on the Application of Artificial Intelligence in ENSO Forecasting W. Fang et al. 10.3390/math10203793
Latest update: 24 Apr 2024
Short summary
Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air–sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we...
