Zhao et al. (2025) Impact of Joint Assimilating AWS and Radar Observations on the Analysis and Forecast of a Squall Line with Complex Terrain

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

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-11-28
• Authors: Ruonan Zhao, Dongmei Xu, Cong Li, Zhixin He
• DOI: 10.3390/rs17233860

Research Groups

This study investigates the impact of assimilating radar and automatic weather station (AWS) observations on a squall line case, finding that joint assimilation, particularly with AWS assimilated before radar, significantly enhances the representation of convective structures and improves forecast accuracy.

Objective

• To investigate the impacts of assimilating radar and automatic weather station (AWS) observations, both independently and jointly, for a squall line case over complex terrain.
• To determine the optimal assimilation strategy for improving the simulation and prediction of severe convective systems.

Study Configuration

• Spatial Scale: Mesoscale, regional (complex terrain in China).
• Temporal Scale: Short-term forecast (1-2 hours), specific squall line event (30 May 2024).

Methodology and Data

• Models used: WRF-3DVar system
• Data sources: Radar observations, Automatic Weather Station (AWS) observations

Main Results

• Radar data assimilation with spatial truncation enhanced convective structures and reduced false echoes by approximately 40%.
• Enlarging variance and correlation length scales in radar assimilation increased reflectivity by 5–10 dBZ but introduced false signals and positional errors.
• A balanced scheme for radar assimilation yielded the highest skill scores.
• Assimilation of AWS alone provided limited improvements.
• Radar assimilation alone introduced localized structures that rapidly decayed within 1–2 hours due to absent boundary-layer constraints.
• Joint assimilation demonstrated clear benefits in spatial continuity and vertical consistency.
• Assimilation order was a decisive factor; assimilating AWS prior to radar optimized low-level thermodynamic and dynamic conditions.
• AWS-before-radar assimilation strengthened cold pool structures by approximately 2 K, enhanced updrafts by over 20%, and improved wind distribution.
• This order also enhanced the coupling between cold pools and updrafts, improving simulation accuracy in both horizontal and vertical structures.

Contributions

• Highlights the critical role of AWS-radar joint assimilation in capturing the dynamical characteristics of squall lines.
• Provides valuable insights for advancing the prediction of severe convective systems.
• Demonstrates the importance of assimilation order, specifically assimilating AWS observations before radar, for optimizing boundary-layer conditions and improving cold pool-updraft coupling.

Funding

Citation

@article{Zhao2025Impact,
  author = {Zhao, Ruonan and Xu, Dongmei and Li, Cong and He, Zhixin},
  title = {Impact of Joint Assimilating AWS and Radar Observations on the Analysis and Forecast of a Squall Line with Complex Terrain},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17233860},
  url = {https://doi.org/10.3390/rs17233860}
}