Zhang et al. (2026) TriPhysGAN-Attn: A Physics-Informed Generative Model for Radar Echo Forecasting via Triple Mechanism Decomposition and Attention Fusion

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Yonghong Zhang, Ziwei Yuan, Professor Atta Badii, Junfei Wang, Peishan Li
• DOI: 10.1109/jstars.2026.3658947

Research Groups

Information not available in the provided text.

Short Summary

This paper introduces TriPhysGAN-Attn, a physics-informed generative model for radar echo forecasting, utilizing triple mechanism decomposition and attention fusion.

Objective

• To develop and evaluate TriPhysGAN-Attn, a physics-informed generative model, for improved radar echo forecasting via triple mechanism decomposition and attention fusion.

Study Configuration

• Spatial Scale: Information not available in the provided text.
• Temporal Scale: Information not available in the provided text.

Methodology and Data

• Models used: TriPhysGAN-Attn (a physics-informed generative adversarial network incorporating triple mechanism decomposition and attention fusion)
• Data sources: Information not available in the provided text.

Main Results

• Information not available in the provided text.

Contributions

• Information not available in the provided text.

Funding

• Information not available in the provided text.

Citation

@article{Zhang2026TriPhysGANAttn,
  author = {Zhang, Yonghong and Yuan, Ziwei and Badii, Professor Atta and Wang, Junfei and Li, Peishan},
  title = {TriPhysGAN-Attn: A Physics-Informed Generative Model for Radar Echo Forecasting via Triple Mechanism Decomposition and Attention Fusion},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3658947},
  url = {https://doi.org/10.1109/jstars.2026.3658947}
}