A Climatology of Lee Cyclones across the Central United States, 1980–2021
Journal Article
Overview
abstract
Abstract; The central United States is a favorable region for cyclogenesis east of the Rocky Mountains. This region’s distinctive location in the center of the continental United States and juxtaposition with complex topography contribute to unique forecast challenges, including uncertainties in the prediction of extratropical cyclone (ETC) intensity and location, as well as concomitant areas of heavy precipitation and strong winds. Consequently, this study utilizes machine learning techniques to investigate the variability of cold-season (October–May) ETC characteristics across the central United States. ETCs are identified within the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) during 1980–2021 using a method for ETC detection adopted from previously published work. This ETC dataset subsequently facilitates an analysis of the monthly and seasonal frequency of central U.S. ETCs, for which an increase in cold-season ETC frequency is found for both winter and springtime ETCs. A self-organizing map (SOM) is trained on mean sea level pressure anomalies from ERA5 during ETC events and used to examine the variability in large-scale weather regimes conducive to central U.S. ETCs. These regimes are categorized into five clusters: 1) the Mississippi River anticyclone, 2) the Canadian anticyclone, 3) the Northern Plains cyclone, 4) the Central Plains cyclone, and 5) the Southern Plains cyclone clusters. One main finding is a significant increase of ETCs that are categorized into the Canadian anticyclone cluster, indicating that a subset of weaker central U.S. ETCs are becoming more common. The extent to which the characteristics of near-ETC environments (e.g., moisture sources, upper-level jet stream structure, quasigeostrophic ascent, and precipitation) vary as a function of each SOM cluster is also investigated.; ; Significance Statement; Extratropical cyclones frequently produce strong precipitation and winds across the central United States, resulting in profound societal impacts. This study investigates the various atmospheric environments (e.g., moisture sources, upper-level jet stream structure, and precipitation) that accompany cyclone development in this region. Machine learning methods are used to identify distinct categories of cyclone events, and the different upper- and lower-level environments associated with these cyclone categories are explored. We find that while central U.S. ETCs have been increasing during 1980–2021, weaker ETCs associated with higher pressures and less moisture are particularly becoming more common.;
