A Climatology and Characteristics of Midwest U.S. Heavy Snowfall Events

Quantitative snowfall forecasting in winter storms has historically been a difficult task due in part to the mesoscale nature of snowband formation. Quantifying characteristics of Midwest heavy snow fall events in terms of characteristics of the radar reflectivity fields and associated environments over a large sample of events will enable forecasters to better predict impactful winter storm hazards. The goal of this study is to expand upon Midwest heavy snowfall climatology using composite radar imagery from 13 National Weather Service radars to detect and characterize snowband features in Midwest United States winter storms during 1991–2020 and to derive common characteristics from synoptic environment composites.

We identified heavy snowfall event dates (i.e., ≥4” of snow in 24 h) by analyzing National Weather Service Cooperative Observer snowfall data for the Midwest states of Indiana, Illinois, Missouri, Iowa, Wisconsin, and Minnesota. We utilize a snowband detection algorithm to identify locally enhanced reflectivity features in regional radar maps. Information on snowband features such as average snowband length, width, aspect ratio, and duration are determined. Examination of environmental variables in reanalysis data (e.g., ERA5) provides additional information on the broad-scale conditions associated with heavy snowfall events in the Midwest.