Verification of Storm Prediction Center Winter Weather Mesoscale Discussions

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Thursday, 8 January 2015: 4:45 PM
232A-C (Phoenix Convention Center - West and North Buildings)
Christopher D. McCray, Lyndon State College, Lyndonville, VT; and C. J. Melick, W. F. Bunting, I. L. Jirak, A. E. Cohen, A. R. Dean, P. Marsh, and J. L. Guyer

The Storm Prediction Center (SPC) has issued winter weather mesoscale discussions (MDs) since 1997 to alert meteorologists at National Weather Service (NWS) Weather Forecast Offices (WFOs) and other partners of the near-term potential for heavy snowfall or freezing rain rates, winter mixed precipitation events, and for the initiation of mesoscale blizzard conditions. To date, SPC has had no means of objectively verifying these discussions. An evaluation of mesoscale discussions from January 2007 to April 2014 is performed to determine the spatial and temporal characteristics of the various types of MDs issued. To aid in enhancing SPC winter weather operations, a verification method is developed using gridded surface station precipitation type observations, local storm reports (LSRs), and quantitative precipitation estimation (QPE) data. From the surface observations and LSRs, a gridded dominant precipitation type product is generated and used alongside gauge-corrected National Mosaic & Multi-Sensor QPE (NMQ) and a snow-to-liquid ratio climatology to calculate estimated hourly snowfall accumulations. Similar methods utilizing these datasets are developed for verifying blizzard, freezing rain, and mixed precipitation MDs.

A case study is performed for a heavy snow event over the upper Midwest, from which it is determined that a 40 km neighborhood verification method, similar to that used in the verification of SPC convective forecasts, best represents MD forecast accuracy. All datasets used are available in real-time, and products developed such as dominant precipitation type can be incorporated into operational systems for use in analysis and forecasting. The verification system developed is also flexible, allowing for the implementation of new methods and datasets as they become available.