Xu et al. (2026) A Dual-Stage Reconstruction and Optimization Deep Learning Framework for Generating High-Precision Seamless Precipitable Water Vapor Across the Mainland United States

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Chaoqian Xu, Xiuguang Yao, Yibin Yao, Qi Zhang, Xiongwei Ma, Yuqing Liu, Yunzheng Huang, Yiyang Zhao, Zheyan Liu, Liang Zhang
• DOI: 10.1109/tgrs.2026.3658271

Research Groups

[Information not available in the provided text.]

Short Summary

This paper introduces a dual-stage deep learning framework designed to generate high-precision, seamless precipitable water vapor data across the Mainland United States.

Objective

• To develop and apply a dual-stage reconstruction and optimization deep learning framework for generating high-precision, seamless precipitable water vapor (PWV) across the Mainland United States.

Study Configuration

• Spatial Scale: Mainland United States.
• Temporal Scale: [Information not available in the provided text.]

Methodology and Data

• Models used: A dual-stage reconstruction and optimization deep learning framework.
• Data sources: Precipitable Water Vapor (PWV) data. [Specific sources 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{Xu2026DualStage,
  author = {Xu, Chaoqian and Yao, Xiuguang and Yao, Yibin and Zhang, Qi and Ma, Xiongwei and Liu, Yuqing and Huang, Yunzheng and Zhao, Yiyang and Liu, Zheyan and Zhang, Liang},
  title = {A Dual-Stage Reconstruction and Optimization Deep Learning Framework for Generating High-Precision Seamless Precipitable Water Vapor Across the Mainland United States},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3658271},
  url = {https://doi.org/10.1109/tgrs.2026.3658271}
}