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
33 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
- Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al. 10.3389/fmars.2024.1396322
- Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich 10.3390/cli12110189
- Deep learning with autoencoders and LSTM for ENSO forecasting C. Ibebuchi & M. Richman 10.1007/s00382-024-07180-8
- A transformer-based coupled ocean-atmosphere model for ENSO studies R. Zhang et al. 10.1016/j.scib.2024.04.048
- The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model B. Qin et al. 10.1002/qj.4882
- Incorporating heat budget dynamics in a Transformer-based deep learning model for skillful ENSO prediction B. Mu et al. 10.1038/s41612-024-00741-y
- 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
- 基于纯数据驱动的Transformer模型对2023~2024年热带太平洋气候状态的实时预测 荣. 张 et al. 10.1360/N072024-0038
- Extreme Meteorological Drought Events over China (1951–2022): Migration Patterns, Diversity of Temperature Extremes, and Decadal Variations Z. Liu et al. 10.1007/s00376-024-4004-2
- Advances in Air–Sea Interactions, Climate Variability, and Predictability W. Zhang et al. 10.3390/atmos15121422
- ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler B. Mu et al. 10.1029/2022MS003132
- 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
- Explainable El Niño predictability from climate mode interactions S. Zhao et al. 10.1038/s41586-024-07534-6
- 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
- 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
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. 10.1016/j.acags.2024.100201
- 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
- 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
- Explainable AI in lengthening ENSO prediction from western north pacific precursor L. Deng et al. 10.1016/j.ocemod.2024.102431
- Toward a Learnable Climate Model in the Artificial Intelligence Era G. Huang et al. 10.1007/s00376-024-3305-9
- Real-time predictions of the 2023–2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model R. Zhang et al. 10.1007/s11430-024-1396-x
- 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
- Graph Representation Learning and Its Applications: A Survey V. Hoang et al. 10.3390/s23084168
- Survey on the Application of Artificial Intelligence in ENSO Forecasting W. Fang et al. 10.3390/math10203793
33 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
- Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al. 10.3389/fmars.2024.1396322
- Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich 10.3390/cli12110189
- Deep learning with autoencoders and LSTM for ENSO forecasting C. Ibebuchi & M. Richman 10.1007/s00382-024-07180-8
- A transformer-based coupled ocean-atmosphere model for ENSO studies R. Zhang et al. 10.1016/j.scib.2024.04.048
- The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model B. Qin et al. 10.1002/qj.4882
- Incorporating heat budget dynamics in a Transformer-based deep learning model for skillful ENSO prediction B. Mu et al. 10.1038/s41612-024-00741-y
- 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
- 基于纯数据驱动的Transformer模型对2023~2024年热带太平洋气候状态的实时预测 荣. 张 et al. 10.1360/N072024-0038
- Extreme Meteorological Drought Events over China (1951–2022): Migration Patterns, Diversity of Temperature Extremes, and Decadal Variations Z. Liu et al. 10.1007/s00376-024-4004-2
- Advances in Air–Sea Interactions, Climate Variability, and Predictability W. Zhang et al. 10.3390/atmos15121422
- ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler B. Mu et al. 10.1029/2022MS003132
- 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
- Explainable El Niño predictability from climate mode interactions S. Zhao et al. 10.1038/s41586-024-07534-6
- 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
- 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
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. 10.1016/j.acags.2024.100201
- 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
- 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
- Explainable AI in lengthening ENSO prediction from western north pacific precursor L. Deng et al. 10.1016/j.ocemod.2024.102431
- Toward a Learnable Climate Model in the Artificial Intelligence Era G. Huang et al. 10.1007/s00376-024-3305-9
- Real-time predictions of the 2023–2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model R. Zhang et al. 10.1007/s11430-024-1396-x
- 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
- Graph Representation Learning and Its Applications: A Survey V. Hoang et al. 10.3390/s23084168
- Survey on the Application of Artificial Intelligence in ENSO Forecasting W. Fang et al. 10.3390/math10203793
Latest update: 13 Dec 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...
