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
53 citations as recorded by crossref.
- Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al.
- Deep learning with autoencoders and LSTM for ENSO forecasting C. Ibebuchi & M. Richman
- A flexible Regional Ocean Modeling System-based hybrid coupled model for El Niño–Southern Oscillation studies – model formulation and performance evaluation Y. Yu et al.
- Incorporating heat budget dynamics in a Transformer-based deep learning model for skillful ENSO prediction B. Mu et al.
- 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.
- Extreme Meteorological Drought Events over China (1951–2022): Migration Patterns, Diversity of Temperature Extremes, and Decadal Variations Z. Liu et al.
- Advances in Air–Sea Interactions, Climate Variability, and Predictability W. Zhang et al.
- AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al.
- ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler B. Mu et al.
- ENSO analysis and prediction using deep learning: A review G. Wang et al.
- Explainable El Niño predictability from climate mode interactions S. Zhao et al.
- A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions L. Zhou et al.
- A New Hybrid Prediction Method of El Niño/La Niña Events by Combining TimesNet and ARIMA Y. Du et al.
- Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II S. Zhang et al.
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al.
- Deep Learning for Ocean Forecasting: A Comprehensive Review of Methods, Applications, and Datasets R. Hao et al.
- ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding X. Liang et al.
- A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses L. Zhou & R. Zhang
- Dynamic Neuro-Fuzzy Systems for Forecasting El Niño Southern Oscillation (ENSO) Using Oceanic and Continental Climate Parameters as Inputs M. Lee et al.
- Physics-informed spatio-temporal graph neural networks for evapotranspiration prediction: Case of the Korean Peninsula K. Gyamfi et al.
- CAU: A Causality Attention Unit for Spatial-Temporal Sequence Forecast B. Qin et al.
- Deep learning for skillful long-lead ENSO forecasts K. Patil et al.
- Explainable AI in lengthening ENSO prediction from western north pacific precursor L. Deng et al.
- Toward a Learnable Climate Model in the Artificial Intelligence Era G. Huang et al.
- EAAC-S2S: East Asian Atmospheric Circulation S2S Forecasting with a Deep Learning Model Considering Multi-Sphere Coupling B. Mu et al.
- A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions L. Zhou & R. Zhang
- CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation M. Sun et al.
- Metrics matters: A deep assessment of deep learning CNN method for ENSO forecast M. Naisipour et al.
- Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction B. Mu et al.
- 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
- A dynamic graph-based multiscale spatio-temporal feature enhancement network applied to ENSO prediction W. Shao & G. Tong
- A deep residual intelligent model for ENSO prediction by incorporating coupled model forecast data C. Song et al.
- A transformer-based coupled ocean-atmosphere model for ENSO studies R. Zhang et al.
- An RCUNet-based sea surface wind stress model with multi-day time sequence information incorporated and its applications to ENSO modeling S. Du & R. Zhang
- The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model B. Qin et al.
- AI-based Correction of Wave Forecasts Using the Transformer-enhanced UNet Model Y. Cao et al.
- IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning B. Mu et al.
- ENSOFarseer: Probabilistic Deep Learning for Cross-Scale Spatiotemporal Teleconnections Insight in Skilful ENSO Prediction R. Hao et al.
- Toward long-range ENSO prediction with an explainable deep learning model Q. Chen et al.
- Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network Y. Chen et al.
- On the Use of Graphs for Satellite Image Time Series: A comprehensive review C. Dufourg et al.
- Integrating an CNOP analysis into a deep learning model to identify optimal initial errors for 2020–2022 La Niña prediction L. Tao et al.
- Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0 B. Mu et al.
- Explainable physics-guided attention network for long-lead ENSO forecasts S. Wu et al.
- 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.
- Monthly Temperature Prediction in the Han River Basin, South Korea, Using Long Short-Term Memory (LSTM) and Multiple Linear Regression (MLR) Models C. Kim et al.
- Real-time predictions of the 2023–2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model R. Zhang et al.
- Adaptive composite loss and dual-branch architecture enable high-fidelity bidirectional design of acoustic metamaterials Z. Wang et al.
- A Transformer‐Based Deep Learning Model for Successful Predictions of the 2021 Second‐Year La Niña Condition C. Gao et al.
- Graph Representation Learning and Its Applications: A Survey V. Hoang et al.
- DP-BICNN: A Bidirectional Information Compensation Neural Network Coupled With Data-Driven and Physical Information for Sea Surface Temperature Prediction X. Liu et al.
- The 3D-Geoformer for ENSO studies: a Transformer-based model with integrated gradient methods for enhanced explainability L. Zhou & R. Zhang
- Survey on the Application of Artificial Intelligence in ENSO Forecasting W. Fang et al.
53 citations as recorded by crossref.
- Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al.
- Deep learning with autoencoders and LSTM for ENSO forecasting C. Ibebuchi & M. Richman
- A flexible Regional Ocean Modeling System-based hybrid coupled model for El Niño–Southern Oscillation studies – model formulation and performance evaluation Y. Yu et al.
- Incorporating heat budget dynamics in a Transformer-based deep learning model for skillful ENSO prediction B. Mu et al.
- 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.
- Extreme Meteorological Drought Events over China (1951–2022): Migration Patterns, Diversity of Temperature Extremes, and Decadal Variations Z. Liu et al.
- Advances in Air–Sea Interactions, Climate Variability, and Predictability W. Zhang et al.
- AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al.
- ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler B. Mu et al.
- ENSO analysis and prediction using deep learning: A review G. Wang et al.
- Explainable El Niño predictability from climate mode interactions S. Zhao et al.
- A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions L. Zhou et al.
- A New Hybrid Prediction Method of El Niño/La Niña Events by Combining TimesNet and ARIMA Y. Du et al.
- Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II S. Zhang et al.
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al.
- Deep Learning for Ocean Forecasting: A Comprehensive Review of Methods, Applications, and Datasets R. Hao et al.
- ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding X. Liang et al.
- A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses L. Zhou & R. Zhang
- Dynamic Neuro-Fuzzy Systems for Forecasting El Niño Southern Oscillation (ENSO) Using Oceanic and Continental Climate Parameters as Inputs M. Lee et al.
- Physics-informed spatio-temporal graph neural networks for evapotranspiration prediction: Case of the Korean Peninsula K. Gyamfi et al.
- CAU: A Causality Attention Unit for Spatial-Temporal Sequence Forecast B. Qin et al.
- Deep learning for skillful long-lead ENSO forecasts K. Patil et al.
- Explainable AI in lengthening ENSO prediction from western north pacific precursor L. Deng et al.
- Toward a Learnable Climate Model in the Artificial Intelligence Era G. Huang et al.
- EAAC-S2S: East Asian Atmospheric Circulation S2S Forecasting with a Deep Learning Model Considering Multi-Sphere Coupling B. Mu et al.
- A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions L. Zhou & R. Zhang
- CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation M. Sun et al.
- Metrics matters: A deep assessment of deep learning CNN method for ENSO forecast M. Naisipour et al.
- Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction B. Mu et al.
- 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
- A dynamic graph-based multiscale spatio-temporal feature enhancement network applied to ENSO prediction W. Shao & G. Tong
- A deep residual intelligent model for ENSO prediction by incorporating coupled model forecast data C. Song et al.
- A transformer-based coupled ocean-atmosphere model for ENSO studies R. Zhang et al.
- An RCUNet-based sea surface wind stress model with multi-day time sequence information incorporated and its applications to ENSO modeling S. Du & R. Zhang
- The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model B. Qin et al.
- AI-based Correction of Wave Forecasts Using the Transformer-enhanced UNet Model Y. Cao et al.
- IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning B. Mu et al.
- ENSOFarseer: Probabilistic Deep Learning for Cross-Scale Spatiotemporal Teleconnections Insight in Skilful ENSO Prediction R. Hao et al.
- Toward long-range ENSO prediction with an explainable deep learning model Q. Chen et al.
- Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network Y. Chen et al.
- On the Use of Graphs for Satellite Image Time Series: A comprehensive review C. Dufourg et al.
- Integrating an CNOP analysis into a deep learning model to identify optimal initial errors for 2020–2022 La Niña prediction L. Tao et al.
- Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0 B. Mu et al.
- Explainable physics-guided attention network for long-lead ENSO forecasts S. Wu et al.
- 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.
- Monthly Temperature Prediction in the Han River Basin, South Korea, Using Long Short-Term Memory (LSTM) and Multiple Linear Regression (MLR) Models C. Kim et al.
- Real-time predictions of the 2023–2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model R. Zhang et al.
- Adaptive composite loss and dual-branch architecture enable high-fidelity bidirectional design of acoustic metamaterials Z. Wang et al.
- A Transformer‐Based Deep Learning Model for Successful Predictions of the 2021 Second‐Year La Niña Condition C. Gao et al.
- Graph Representation Learning and Its Applications: A Survey V. Hoang et al.
- DP-BICNN: A Bidirectional Information Compensation Neural Network Coupled With Data-Driven and Physical Information for Sea Surface Temperature Prediction X. Liu et al.
- The 3D-Geoformer for ENSO studies: a Transformer-based model with integrated gradient methods for enhanced explainability L. Zhou & R. Zhang
- Survey on the Application of Artificial Intelligence in ENSO Forecasting W. Fang et al.
Saved (final revised paper)
Latest update: 20 May 2026
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...
