Leveraging Meteorological Reanalysis Models to Characterize Wintertime Cold Air Pool Events Across the Western United States from 2000 to 2022

Wintertime cold air pools (CAPs) are common across the Western United States and result in cold, dense air trapped in valley basins. The CAPs are characterized by a stable atmospheric boundary layer, leading to cold air and low wind speeds. While CAP formation occurs nightly, the CAP conditions can persist into daytime and often last for multiple days (i.e., persistent cold air pool or PCAP), resulting in poor air quality in populated areas. The presence and strength of CAPs can be calculated using data from radiosondes, surface weather stations at varying elevations, and indirectly through air pollution monitors. Because vertical profile data are often limited to twice daily radiosondes, and are spatially sparse, numerical models can be a useful substitute. This work uses the European Centre for Medium-Range Weather Forecasts (ECMWFs) Reanalysis v5 (ERA) atmospheric reanalysis to provide data to classify wintertime CAP events without radiosonde observations. An automated CAP classification method using ERA outputs is evaluated using afternoon radiosonde observations in six cities (Salt Lake City, Utah; Reno, Nevada; Boise, Idaho; Denver, Colorado; Las Vegas, Nevada; Medford, and Oregon). Using this CAP determination method, days with CAP events are analyzed in 13 locations, 6 with radiosonde observations and 7 without, including the Central valley of California. The CAP classification method is evaluated at these 13 locations across the Western US over the study period of 2000–2022. The results show that the ERA model performs similarly to the radiosonde observations when used to identify CAP events. Therefore, ERA can be used to provide a reasonable estimate of CAP conditions when radiosonde data are unavailable. Providing consistent CAP classifications across space and time are necessary for regional scale CAP studies, such as human health effects modeling over large spatial and temporal scales.