Tallapragada et al. (2026) Evaluation of GFSv16 for Near‐Real‐Time Data Impact Studies During the Atmospheric River Reconnaissance Program 2022

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

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2026
• Date: 2026-02-11
• Authors: Vijay Tallapragada, Xingren Wu, Minghua Zheng, Luca Delle Monache, F. Martin Ralph, Keqin Wu, Xianglan Li, M. M. Nageswararao, Jia Wang, Anna M. Wilson, Jason M. Cordeira, Shufen Pan, Daniel F. Steinhoff, Patrick Mulrooney
• DOI: 10.1029/2025jd044818

Research Groups

Not specified in the abstract.

Short Summary

This study investigated the impact of assimilating dropsonde data from AR Reconnaissance on improving winter 2022 precipitation forecasts for landfalling Atmospheric Rivers (ARs) on the U.S. West Coast, finding that targeted dropsonde observations significantly enhance forecast accuracy for medium to strong AR events.

Objective

• To investigate the influence of dropsonde data from AR Reconnaissance on improving weather forecasts, specifically AR-related precipitation forecasts for the U.S. West Coast, during winter 2022.

Study Configuration

• Spatial Scale: U.S. West Coast
• Temporal Scale: Winter 2022

Methodology and Data

• Models used: Global Forecast System version 16 (GFSv16)
• Data sources: Dropsonde observations from AR Reconnaissance (AR Recon), operational GFSv16 simulations (with and without assimilated dropsonde observations)

Main Results

• Assimilating dropsonde data improved the forecast accuracy of landfalling AR characteristics and associated precipitation.
• Mixed results were obtained in water vapor transport forecasts.
• Forecast improvement was achieved for medium to strong AR events, especially when dropsondes targeted the AR cores.
• A large error reduction was observed in integrated vapor transport (approximately 20 kg m⁻¹ s⁻¹).
• Precipitation forecasts showed an error reduction of greater than 5 mm d⁻¹ or approximately 20%–40% for strong AR conditions.
• The effectiveness of dropsonde data is influenced by AR characteristics (intensity, track) and the complex terrain of the U.S. West Coast.

Contributions

• Demonstrates the value of targeted field campaigns (AR Recon) in improving AR precipitation forecasts.
• Quantifies the impact of dropsonde assimilation on reducing forecast errors for AR characteristics and precipitation.
• Highlights factors influencing dropsonde effectiveness, such as AR intensity, track, and terrain, for future research and operational improvements.

Funding

Not specified in the abstract.

Citation

@article{Tallapragada2026Evaluation,
  author = {Tallapragada, Vijay and Wu, Xingren and Zheng, Minghua and Monache, Luca Delle and Ralph, F. Martin and Wu, Keqin and Li, Xianglan and Nageswararao, M. M. and Wang, Jia and Wilson, Anna M. and Cordeira, Jason M. and Pan, Shufen and Steinhoff, Daniel F. and Mulrooney, Patrick},
  title = {Evaluation of GFSv16 for Near‐Real‐Time Data Impact Studies During the Atmospheric River Reconnaissance Program 2022},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2026},
  doi = {10.1029/2025jd044818},
  url = {https://doi.org/10.1029/2025jd044818}
}