15 citations as recorded by crossref.

1. AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al. https://doi.org/10.1093/nsr/nwag063
2. The predictability study of oceanic deep learning models: Taking Kuroshio intrusion into South China Sea as an example Q. Wang et al. https://doi.org/10.1016/j.ocemod.2025.102622
3. The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model B. Qin et al. https://doi.org/10.1002/qj.4882
4. ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler B. Mu et al. https://doi.org/10.1029/2022MS003132
5. CAU: A Causality Attention Unit for Spatial-Temporal Sequence Forecast B. Qin et al. https://doi.org/10.1109/TMM.2023.3326289
6. Deep Learning Model for ENSO Forecasting Using Multiple-Scale Spatiotemporal Information Y. Wang et al. https://doi.org/10.1109/TGRS.2025.3529322
7. A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions L. Zhou & R. Zhang https://doi.org/10.1126/sciadv.adf2827
8. DeepBlue: Advanced convolutional neural network applications for ocean remote sensing H. Wang & X. Li https://doi.org/10.1109/MGRS.2023.3343623
9. Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction B. Mu et al. https://doi.org/10.1016/j.ocemod.2024.102344
10. ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding X. Liang et al. https://doi.org/10.1007/s00382-024-07119-z
11. IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning B. Mu et al. https://doi.org/10.5194/gmd-16-4677-2023
12. A deep learning-based bias correction model for Arctic sea ice concentration towards MITgcm S. Yuan et al. https://doi.org/10.1016/j.ocemod.2024.102326
13. Characteristics of summer typhoon activities over China during 2020–2022 and their relationships with the Triple La Niña events Q. Zeng et al. https://doi.org/10.1016/j.atmosres.2026.108798
14. Incorporating heat budget dynamics in a Transformer-based deep learning model for skillful ENSO prediction B. Mu et al. https://doi.org/10.1038/s41612-024-00741-y
15. Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method B. Mu et al. https://doi.org/10.3390/jmse11050910