Using Surface Observations in Conjunction with Remotely Sensed Observations to Understand the PBL Processes in Long Lake-Axis Parallel (LLAP) Snows

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Sunday, 2 February 2014
Hall C3 (The Georgia World Congress Center )
Alycia Gilliland, Metropolitan State University of Denver, Denver, CO

The eastern Great-Lakes region experiences some of the highest snowfall rates in the world, resulting from Lake-Effect snows. These storms often cripple entire cities and can hamper transportation for days. Long-Lake Axis-Parallel (LLAP) snow bands are single, intense bands which form in a well-mixed, unstable layer and exhibit convective characteristics. These areas of convection can, and often do, result in tremendous snowfall accumulations in a small, localized area. These same areas have proven difficult to forecast, and the overall level of understanding of the inner dynamics and structure these LLAP snow bands is poor (at best) because they have not been extensively studied in the past. The NSF-EAGER funded project focused their study in and around the Tug Hill plateau area of western New York, which historically experiences high snowfall amounts, resulting from LLAP snowstorms. In-Situ and remotely sensed data was gathered on seven LLAP events during the winter of 2010-2011. Many features, similar to those seen in warm-season convective storms were identified within these LLAP snow bands. This radar data, combined with surface measurements and observations were compared to find any correlation that may or may not exist, in an effort to understand the processes and to be able predict these snowstorms with greater confidence and accuracy in the near future.