Su et al. (2026) Rainfall Amount Forecast Using GNSS-PWV Based on Machine Learning Fusion Strategy and the Constraint of Rainfall Event

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Mingkun Su, C. L. Philip Chen, Zhao Li, Weiping Jiang, Yang Gao, Junna Shang, Xingyu Zhou
• DOI: 10.1109/tgrs.2026.3659172

Research Groups

[Information not provided in the given text.]

Short Summary

This paper focuses on forecasting rainfall amount by integrating Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) data with a machine learning fusion strategy, further constrained by the characteristics of rainfall events.

Objective

• To develop and evaluate a method for forecasting rainfall amount using GNSS-PWV observations, a machine learning fusion strategy, and the constraint of rainfall event characteristics.

Study Configuration

• Spatial Scale: [Information not provided in the given text.]
• Temporal Scale: Forecast of rainfall amount, likely short-term or event-based.

Methodology and Data

• Models used: Machine Learning Fusion Strategy (specific models not named).
• Data sources: GNSS-PWV (Global Navigation Satellite System - Precipitable Water Vapor) observations.

Main Results

[Information not provided in the given text.]

Contributions

[Information not provided in the given text.]

Funding

[Information not provided in the given text.]

Citation

@article{Su2026Rainfall,
  author = {Su, Mingkun and Chen, C. L. Philip and Li, Zhao and Jiang, Weiping and Gao, Yang and Shang, Junna and Zhou, Xingyu},
  title = {Rainfall Amount Forecast Using GNSS-PWV Based on Machine Learning Fusion Strategy and the Constraint of Rainfall Event},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3659172},
  url = {https://doi.org/10.1109/tgrs.2026.3659172}
}