Peng et al. (2026) Forecasting a Hailstorm in Western China Plateau by Assimilating XPAR Radar Network Data with WRF-FDDA-HLHN

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-03-24
• Authors: Jingyuan Peng, Bo Jiang, Qiuji Ding, Lei Cao, Zhigang Chu, Yueqin Shi, Yi Liu
• DOI: 10.3390/rs18070968

Research Groups

Meteorological research and forecasting groups focused on severe weather in the Yun-Gui Plateau, Western China.

Short Summary

This study evaluates the assimilation of high spatiotemporal resolution X-band phased-array radar (XPAR) data into the WRF model, combined with a humidity adjustment scheme, to improve hailstorm prediction over the Yun-Gui Plateau. It demonstrates that XPAR data assimilation significantly reduces model error and enhances the representation of rapid hail cloud evolution, especially when coupled with humidity adjustments.

Objective

• To evaluate the effectiveness of assimilating high spatiotemporal resolution X-band phased-array radar (XPAR) data, combined with a humidity adjustment scheme, for improving numerical prediction of shallow, rapidly evolving hailstorms in the Yun-Gui Plateau, Western China, using the WRF model.

Study Configuration

• Spatial Scale: Regional scale, focused on the Yun-Gui Plateau, Western China, specifically around Weining.
• Temporal Scale: Nowcasting to short-range forecasting (minutes to a few hours), with data assimilation performed at 1-minute intervals to capture rapid storm evolution.

Methodology and Data

• Models used: Weather Research and Forecast (WRF) model, Hydrometeor and Latent Heat Nudging (HLHN) method, Severe Weather Automatic Nowcast (SWAN) system (for comparative data).
• Data sources: X-band phased-array radar (XPAR) data, operational Severe Weather Automatic Nowcast (SWAN) system radar mosaic data, Vertically Integrated Liquid (VIL) derived from radar data.

Main Results

• Assimilating XPAR data at 1-minute intervals significantly reduces model error and improves the representation of rapid hail cloud evolution compared to using SWAN data.
• A humidity adjustment scheme, based on Vertically Integrated Liquid (VIL) derived from radar data, effectively corrects model analyses of humidity and temperatures, suppressing spurious convection and thereby improving hailstorm forecasts.
• The study recommends joint assimilation of high spatiotemporal resolution XPAR data along with SWAN radar data using the improved WRF-HLHN system for hailstorm prediction in the study region, with potential adaptability to other regions.

Contributions

• Demonstrates the significant value of high spatiotemporal resolution X-band phased-array radar (XPAR) data for improving numerical hailstorm prediction in complex, high-terrain regions.
• Introduces and validates a novel humidity adjustment scheme based on Vertically Integrated Liquid (VIL) to correct humidity and temperature fields, effectively suppressing spurious convection and enhancing forecast accuracy.
• Proposes an improved WRF-HLHN assimilation system tailored for shallow, rapidly evolving hailstorms, offering a robust methodology adaptable for severe weather forecasting in other regions.

Funding

No funding information was provided in the paper text.

Citation

@article{Peng2026Forecasting,
  author = {Peng, Jingyuan and Jiang, Bo and Ding, Qiuji and Cao, Lei and Chu, Zhigang and Shi, Yueqin and Liu, Yi},
  title = {Forecasting a Hailstorm in Western China Plateau by Assimilating XPAR Radar Network Data with WRF-FDDA-HLHN},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18070968},
  url = {https://doi.org/10.3390/rs18070968}
}