Poster Session P1.40 Winter storm simulations using a local mesoscale model

Monday, 25 June 2007
Summit C (The Yarrow Resort Hotel and Conference Center)
Andrew R. Kimball, NOAA/NWSFO, Wakefield, VA; and J. A. Billet

Handout (1.0 MB)

Major challenges in snowstorm forecasting are often associated with mesoscale features, such as heavy snow bands, shallow cold air layers, and coastal fronts, especially in the Mid Atlantic region. These features are not well resolved by current operational models, and as a result, the timing, location, and movement of these features can be poorly forecasted. Accurate timing and location of mesoscale features during a winter event is especially important to the public, particularly because of the high impacts on transportation and utilities.

The National Weather Service Forecast Office in Wakefield, VA (AKQ) has run a local version of the Weather Research and Forecasting (WRF) Environmental Modeling System (EMS) for three archived snowstorms (24-26 Dec 2004, 4-6 December 2005, and 10-12 February 2006) that impacted portions of the AKQ County Warning Area (CWA). The model runs for all three events were found to show small-scale detail in the depictions of frontogenetic banding, shallow cold air layers east of the Appalachians, and precipitation coverage. Thicknesses at various levels were well depicted providing needed details of the low level cold air. Precipitation accumulations and simulated reflectivity were similar to reality. These depictions of the mesoscale features will aide in the understanding of how these features develop and change with storm evolution, which forecasters can apply in both winter weather watch and warning situations.

This paper will present results of how to use and improve high resolution local mesoscale model depictions of various winter mesoscale scale features. The paper will provide information on the structure of low level cold air and how detailed topography influenced the changes in this cold air. Also by examining simulated reflectivity mesoscale forcing will be demonstrated and how forecasters can adjust this to take the best advantage of the data the local mesoscale model provides them. All this should help improve forecaster understanding and use of local mesoscale models.

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