Radar-Based Surface Snowfall Partitioning Near Marquette, Michigan During the 2012-2013 Winter Season

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Sunday, 4 January 2015
Mitch Ziesemer, University of Wisconsin-Madison, Madison, WI; and M. S. Kulie and T. L'Ecuyer

Lake effect snow greatly impacts the Great Lakes region since it can result in large quantities of snow that influence the local economy and society. Lake effect snow is a product of cold weather outbreaks that are guided by surface wind orientation, air-lake temperature differences, and other local surface weather characteristics. A 2012-2013 winter data set of radar reflectivity from the Marquette, Michigan National Weather Service WSR-88D radar provides the foundation to classify the frequency of occurrence and contribution to annual snowfall accumulation from shallow lake effect snow events. This study will highlight snowfall partitioning (lake effect versus deeper synoptically-produced snow) results from two perspectives. First, a lake effect snow dataset was created through visual inspection by looking for key radar reflectivity features to differentiate lake effect from synoptic snow events. Total and partitioned radar-derived snow accumulation derived from this data set for the 2012-2013 winter will be presented. Differences between radar-derived and ground-based accumulations will also be highlighted. For instance, ground observations indicate a high magnitude of lake effect snow on the shoreline west of Marquette, but this peak is not distinguishable via the radar-based snow accumulations. Topography is a likely cause for the discontinuity and confirms the need for further research into the shortcomings that could be fixed in the radar-based detection of lake effect snow events. Second, preliminary algorithm development work will be described to discriminate between lake effect and synoptic snow events. This algorithm utilizes multiple parameters (e.g., height-dependent reflectivity thresholds, reflectivity by pixel standard deviation, surface wind direction, etc.) to classify two-dimensional radar scenes as lake effect or synoptic snowfall. This algorithm will allow a multi-year snowfall partitioning data set to be created in an automated fashion that can be used for climatological and model evaluation studies.