Stretching along the border of North Dakota and Minnesota, The Red River Valley (RRV) of the North has the highest frequency of reported blizzards within the contiguous United States. Despite the numerous impacts these events have, few systematic studies exist that discuss the meteorological properties of blizzards. As a result, forecasting these events and lesser blowing snow events is an ongoing challenge. This study presents a climatology of atmospheric patterns associated with RRV blizzards for the winter seasons of 1979–1980 and 2017–2018. Patterns were identified using subjective and objective techniques using meteorological fields from the North American Regional Re-analysis (NARR). The RRV experiences, on average, 2.6 events per year. Blizzard frequency is bimodal, with peaks occurring in December and March. The events can largely be typed into four meteorological categories dependent on the forcing that drives the blizzard: Alberta Clippers, Arctic Fronts, Colorado Lows, and Hybrids. The objective classification of these blizzards using a competitive neural network known as the Self-Organizing Map (SOM) demonstrates that gross segregation of the events can be achieved with a small (eight-class) map. This implies that objective analysis techniques can be used to identify these events in weather and climate model output that may aid future forecasting and risk assessment projects.
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