Lin et al. (2026) FANO: Fourier Advection Neural Operator for Weather Prediction

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Kenghong Lin, Huiwei Lin, Baoquan Zhang, Yangjinxi Ge, Dong Liu, Xutao Li, Yunming Ye, Chuyao Luo
• DOI: 10.1109/tgrs.2026.3655726

Research Groups

[Not provided in the given text.]

Short Summary

This paper introduces FANO, a Fourier Advection Neural Operator, as a novel deep learning architecture specifically designed for weather prediction.

Objective

• To develop and evaluate a Fourier Advection Neural Operator (FANO) for the purpose of weather prediction.

Study Configuration

• Spatial Scale: [Not provided in the given text.]
• Temporal Scale: [Not provided in the given text.]

Methodology and Data

• Models used: FANO (Fourier Advection Neural Operator)
• Data sources: [Not provided in the given text.]

Main Results

[Not provided in the given text.]

Contributions

• Introduction of FANO as a novel neural operator architecture for weather prediction.
• Potential for advancing the state-of-the-art in data-driven weather forecasting through the application of Fourier Advection Neural Operators.

Funding

[Not provided in the given text.]

Citation

@article{Lin2026FANO,
  author = {Lin, Kenghong and Lin, Huiwei and Zhang, Baoquan and Ge, Yangjinxi and Liu, Dong and Li, Xutao and Ye, Yunming and Luo, Chuyao},
  title = {FANO: Fourier Advection Neural Operator for Weather Prediction},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3655726},
  url = {https://doi.org/10.1109/tgrs.2026.3655726}
}