Gong et al. (2025) Exploring the impacts of assimilating AMSR2 and GNSS PWV data on rainfall prediction in South China

Identification

• Journal: Atmospheric Research
• Year: 2025
• Date: 2025-11-21
• Authors: Yangzhao Gong, Zhizhao Liu, Hong Liang, Yunchang Cao, Hui Su, Yuting Sun
• DOI: 10.1016/j.atmosres.2025.108642

Research Groups

• State Key Laboratory of Climate Resilience for Coastal Cities, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
• Otto Poon Research Institute for Climate-Resilient Infrastructure (RICRI), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
• Meteorological Observation Center, China Meteorological Administration (CMA), Beijing, China
• State Key Laboratory of Climate Resilience for Coastal Cities, Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
• Heavy Rainfall Research Center of China, China Meteorological Administration Basin Heavy Rainfall Key Laboratory, Institute of Heavy Rain, China Meteorological Administration, Wuhan, China

Short Summary

This study investigates the impact of assimilating AMSR2 and GNSS Precipitable Water Vapor (PWV) data into the WRF model using 3DVAR on rainfall prediction for extreme events in South China, demonstrating significant improvements in both forecast scores and spatial patterns.

Objective

• To comprehensively examine the impacts of assimilating AMSR2 and GNSS PWV data on rainfall forecasting performance, specifically for extreme rainfall events in South China, using the WRF model with 3DVAR.

Study Configuration

• Spatial Scale: South China
• Temporal Scale: April 01 to 30, 2024 (for extreme rainfall events)

Methodology and Data

• Models used: Weather Research and Forecasting (WRF) model, Three-Dimensional Variational Data Assimilation (3DVAR) method
• Data sources:
  • Assimilated: Advanced Microwave Scanning Radiometer 2 (AMSR2) PWV from GCOM-W1 satellite, Global Navigation Satellite System (GNSS) PWV.
  • Assessed by: Rainfall observations from densely distributed automatic weather stations.

Main Results

• Assimilating AMSR2 and GNSS PWV data improves rainfall forecasting performance in terms of both rainfall forecast scores and spatial patterns.
• For intense-precipitation days (accumulated rainfall ≥ 150 mm within 24 hours):
  • Assimilating AMSR2 PWV improved the equitable threat score (ETS) by up to 0.020 (26.3% improvement).
  • Assimilating GNSS PWV improved the ETS by up to 0.021 (27.6% improvement).
  • Assimilating both AMSR2 and GNSS PWV simultaneously improved the ETS by up to 0.030 (39.5% improvement).

Contributions

• Demonstrates the effectiveness of assimilating both satellite-based (AMSR2) and ground-based (GNSS) PWV data for enhancing rainfall prediction, particularly for extreme events.
• Provides quantitative evidence of significant improvements in rainfall forecast scores (ETS) and spatial patterns by combining these two powerful water vapor observation sources in a 3DVAR framework within the WRF model.
• Highlights the potential for improved operational rainfall forecasting in regions prone to extreme rainfall, like South China.

Funding

• Not specified in the provided text.

Citation

@article{Gong2025Exploring,
  author = {Gong, Yangzhao and Liu, Zhizhao and Liang, Hong and Cao, Yunchang and Su, Hui and Sun, Yuting},
  title = {Exploring the impacts of assimilating AMSR2 and GNSS PWV data on rainfall prediction in South China},
  journal = {Atmospheric Research},
  year = {2025},
  doi = {10.1016/j.atmosres.2025.108642},
  url = {https://doi.org/10.1016/j.atmosres.2025.108642}
}