Boomsma et al. (2025) Leveraging Meteorological Reanalysis Models to Characterize Wintertime Cold Air Pool Events Across the Western United States from 2000 to 2022

Identification

• Journal: Atmosphere
• Year: 2025
• Date: 2025-11-24
• Authors: Jacob Boomsma, Heather A. Holmes
• DOI: 10.3390/atmos16121325

Research Groups

• Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USA
• Department of Chemical Engineering, University of Utah, Salt Lake City, UT, USA

Short Summary

This study develops and evaluates an automated method to classify wintertime Cold Air Pool (CAP) events using the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA) model outputs across the Western United States from 2000 to 2022. The results demonstrate that the ERA model, particularly when adjusted with surface observations, provides a reasonable and consistent estimate of CAP conditions, performing similarly to radiosonde observations for classification.

Objective

• To develop and evaluate an automated method for classifying wintertime Cold Air Pool (CAP) events using meteorological reanalysis model outputs, specifically ERA, for use with large datasets and in regions without radiosonde observations.
• To compare CAP strength, temperature, and wind speed differences between observations and model outputs to assess the effectiveness of using gridded model data for CAP classification.

Study Configuration

• Spatial Scale: Western United States, covering 13 locations (Salt Lake City, Reno, Boise, Denver, Las Vegas, Medford, Ogden, Provo, Bakersfield, Fresno, Modesto, Sacramento, Visalia). ERA model horizontal resolution is 0.25 degrees (approximately 31 km). Vertical profiles are analyzed from the surface up to 1.5 times the mean ridge height.
• Temporal Scale: 22-year winter period from 2000/2001 to 2021/2022, with winter defined as 15 November to 15 February of each year. Analysis focuses on afternoon radiosonde observations (00Z).

Methodology and Data

• Models used: European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA). An adjusted version, ERA_Adj, where ERA's surface temperature is replaced with observed surface temperature.
• Data sources:
  • Radiosonde observations (00Z) from the University of Wyoming for six cities (Boise, Denver, Las Vegas, Medford, Reno, Salt Lake City).
  • Automated Surface Observing System (ASOS) surface weather station data from MesoWest for all 13 study locations.
  • A qualitative CAP classification dataset, generated by visual inspection of radiosonde profiles for three winters (2010/2011, 2015/2016, 2021/2022), used for method evaluation.
  • Automated CAP classification method based on Valley Heat Deficit (VHD) and normalized VHD (VHD_norm), incorporating a variable integration height and a potential temperature gradient threshold (G = 6 K km^-1).
  • A surface wind speed threshold of ≤ 4 m s^-1 for CAP classification.

Main Results

• The automated CAP classification method showed high agreement (80–95%) with manually determined CAPs from radiosonde observations across all locations and three test winters.
• ERA model outputs demonstrated similar performance to radiosonde observations in identifying CAP events, with ERA_Adj generally improving classification accuracy, particularly in areas with complex terrain like Medford.
• ERA typically underestimated the number of CAP events compared to radiosonde observations, primarily due to its coarser vertical resolution and challenges in accurately modeling the atmospheric surface layer.
• VHDnorm thresholds for CAP classification varied by location, with radiosonde-derived thresholds generally lower than those from ERA or ERAAdj.
• Locations in the Central Valley of California exhibited the highest average number of CAP days, often influenced by the stable marine layer.
• Persistent Cold Air Pool (PCAP) events showed significant inter-annual variability, with increases observed during winters with heavy snowfall (e.g., Denver 2006–2007) and drought conditions (e.g., Reno and Medford 2013–2014).

Contributions

• Developed a novel automated CAP classification method that incorporates a variable integration height, allowing for the detection of elevated stable layers above the mean ridge height, which were previously missed by static methods.
• Evaluated the effectiveness of using a relatively coarse meteorological reanalysis model (ERA) for CAP classification in regions without radiosonde observations, demonstrating its utility despite resolution limitations.
• Provided a consistent and automated approach for characterizing CAP and PCAP events over large spatial and temporal scales, which is crucial for regional air quality and human health effects studies.
• Highlighted the importance of adjusting model surface temperatures with observational data (ERA_Adj) to improve CAP classification accuracy in complex terrain.

Funding

• NIH National Institute of Environmental Health Sciences (NIEHS) (R01ES032810)
• Center for High Performance Computing at the University of Utah

Citation

@article{Boomsma2025Leveraging,
  author = {Boomsma, Jacob and Holmes, Heather A.},
  title = {Leveraging Meteorological Reanalysis Models to Characterize Wintertime Cold Air Pool Events Across the Western United States from 2000 to 2022},
  journal = {Atmosphere},
  year = {2025},
  doi = {10.3390/atmos16121325},
  url = {https://doi.org/10.3390/atmos16121325}
}