Sun et al. (2026) A Physics-Constrained Network for Daily Gap-Free Gridded Wind Speed Product Considering High Wind Speeds From CYGNSS

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Weichen Sun, Dongkai Yang, Feng Wang, Xiangchao Ma, Chuanrui Tan
• DOI: 10.1109/tgrs.2026.3651797

Research Groups

Not explicitly stated in the provided text.

Short Summary

This paper introduces a physics-constrained neural network designed to generate a daily, gap-free gridded wind speed product, specifically focusing on the accurate representation of high wind speeds, by leveraging data from the CYGNSS satellite constellation.

Objective

• To develop and implement a physics-constrained network for the creation of a daily, gap-free gridded wind speed product, with a particular emphasis on improving the accuracy and representation of high wind speeds, using observations from the CYGNSS mission.

Study Configuration

• Spatial Scale: Gridded product (specific resolution or geographical extent not explicitly stated).
• Temporal Scale: Daily.

Methodology and Data

• Models used: Physics-Constrained Network (a type of neural network incorporating physical principles).
• Data sources: Cyclone Global Navigation Satellite System (CYGNSS) satellite observations.

Main Results

Specific quantitative results are not explicitly stated in the provided text. The study aims to successfully produce a daily, gap-free gridded wind speed product that effectively considers and represents high wind speeds.

Contributions

• Introduction of a novel physics-constrained network architecture for wind speed product generation.
• Development of a methodology to create daily, gap-free gridded wind speed products.
• Enhanced capability to accurately capture and represent high wind speeds within the generated product.
• Effective integration and utilization of CYGNSS satellite data for global wind speed mapping and reconstruction.

Funding

Not explicitly stated in the provided text.

Citation

@article{Sun2026PhysicsConstrained,
  author = {Sun, Weichen and Yang, Dongkai and Wang, Feng and Ma, Xiangchao and Tan, Chuanrui},
  title = {A Physics-Constrained Network for Daily Gap-Free Gridded Wind Speed Product Considering High Wind Speeds From CYGNSS},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3651797},
  url = {https://doi.org/10.1109/tgrs.2026.3651797}
}