Simulating the February 2014 North Carolina Snow Event

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Sunday, 4 January 2015
Warren E. Pettee, University of North Carolina at Charlotte, Charlotte, NC; and B. I. Magi and M. D. Eastin

The 11-14 February 2014 Snow Event over Western North Carolina provides an excellent case study for evaluating the forecasting skill of globally-initialized Weather Research and Forecasting (Global-WRF) simulations. Direct observations showed that the Snow Event produced complex patterns of rain, ice, and snow over the Charlotte area, and complex vertical temperature profiles. Global-WRF simulations of the Snow Event are evaluated against these observations. This study evaluates the forecasting effectiveness from 7 days prior to the event to 12 hours prior to the event.

Each Global-WRF simulation is initialized at 00 UTC and 12 UTC each day from 4-10 February, with each simulation running until 14 February 2014 at 00 UTC. The spatial resolution of the coarse domain is 111 km by 111 km. Three nested domains are also configured to capture the mesoscale features of the event. The first nested domain is located over the continental United States with a spatial resolution of 33 km by 33 km. The second domain is nested over the Southeast US (VA/NC/SC/GA) with a spatial resolution of 12 km by 12 km. The third domain is nested over North Carolina with a spatial resolution of 4 km by 4 km.

Evaluation of the model's forecasting skill will primarily compare precipitation observations to simulations over the course of the Snow Event. Preliminary results show liquid precipitation values within 200% of the observed values (1.2 inches in the 11-Feb 00 UTC simulation, 2.9 inches observed), and a surface pressure field indicative of the event over 180 hours prior. The vertical temperature profiles also consistently indicate a cold air damming event within 48 hours of the actual occurrence. At this range of accuracy, the Global-WRF could become an effective forecast tool for similar complex winter events. Additional research will include testing WRF forecasting skill for the Snow Event against the choice of physics schemes, and evaluating the robustness of the forecast as a function of lead time.