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On the climatology, multiscale dynamics, and predictability of convective snow bursts in the northern United States

PI Shawn Milrad

The goal is to broadly address non-lake effect snow squalls (hereafter referred to as “snow bursts”), defined as short-duration (< 6 h) mesoscale phenomena that can have substantial impacts on aviation and human interests during the cool season.

The project contains four primary components that will serve to increase dynamical understanding and forecast skill of snow burst events, using a combination of observational and modeling techniques:

  • A climatology of snow bursts for three different regions across the northern U.S., providing insight into event frequency and regional differences.
  • A multiscale dynamical analysis using observational and modeling tools (e.g., reanalysis, observed soundings), aimed at identifying synoptic-scale and mesoscale precursors and characteristics of snow bursts. It is not assumed a priori that snow burst dynamics or thermodynamics are uniform either within a region (e.g., the foothills of the Rockies) or across regions of varying characteristics (e.g., the foothills of the Rockies vs. the Northeast.)
  • High-resolution numerical simulations: Prior research suggests that high-resolution convection-explicit numerical models are able to simulate some snow bursts, and provide dynamical insight that coarser reanalysis products cannot. High-resolution simulations among different dynamical regimes of snow burst events will also serve to identify ranges in key physical attributes, such as ascent-forcing mechanisms and instabilities. Simulating at least 10 cases in each dynamical regime will provide insight into which snow burst regime high-resolution numerical models can more readily reproduce, and which may pose more of a forecast challenge.
  • Development of end user products that will enhance operational forecasts and aviation decision-making. Proposed tools include a dynamically based snow burst index and forecast decision tree. These tools will lead to improved forecasts and better warnings, and can be implemented into automated and human forecasting systems, which will result in reduced hazards to aviation, life, and property.

Research Dates

08/01/2013

Researchers

  • Shawn Milrad
    Department
    Applied Aviation Sciences Department
    Degrees
    Ph.D., M.S., McGill University
    B.S., Cornell University

Tags: applied aviation sciences applied meteorology college of aviation daytona beach campus

Categories: Faculty-Staff