Hao et al. (2025) ENSOFarseer: Probabilistic Deep Learning for Cross-Scale Spatiotemporal Teleconnections Insight in Skilful ENSO Prediction

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2025
• Date: 2025-12-23
• Authors: Rixu Hao, Yu Zhao, Xiong Deng
• DOI: 10.1109/tgrs.2025.3647494

Research Groups

Not available in the provided text.

Short Summary

Not available in the provided text. The paper focuses on utilizing probabilistic deep learning to achieve skillful El Niño-Southern Oscillation (ENSO) prediction and to gain insight into cross-scale spatiotemporal teleconnections.

Objective

• To achieve skilful El Niño-Southern Oscillation (ENSO) prediction.
• To gain insight into cross-scale spatiotemporal teleconnections.

Study Configuration

• Spatial Scale: Cross-scale (as indicated by "Cross-Scale Spatiotemporal Teleconnections"). Specific scales are not detailed in the provided text.
• Temporal Scale: Not detailed in the provided text, but ENSO prediction typically involves interannual to decadal scales. "Cross-Scale Spatiotemporal Teleconnections" implies a range of temporal scales.

Methodology and Data

• Models used: Probabilistic Deep Learning.
• Data sources: Not available in the provided text.

Main Results

Not available in the provided text.

Contributions

Not available in the provided text.

Funding

Not available in the provided text.

Citation

@article{Hao2025ENSOFarseer,
  author = {Hao, Rixu and Zhao, Yu and Deng, Xiong},
  title = {ENSOFarseer: Probabilistic Deep Learning for Cross-Scale Spatiotemporal Teleconnections Insight in Skilful ENSO Prediction},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2025},
  doi = {10.1109/tgrs.2025.3647494},
  url = {https://doi.org/10.1109/tgrs.2025.3647494}
}