Assessing the Influence of Topography on Convective Storm Environments Using High-resolution Operational Model Output

Open Access
Katona, Branden Thomas
Graduate Program:
Master of Science
Document Type:
Master Thesis
Date of Defense:
April 08, 2016
Committee Members:
  • Paul Markowski, Thesis Advisor
  • Yvette Pamela Richardson, Thesis Advisor
  • Matthew Robert Kumjian, Thesis Advisor
  • convection
  • topography
  • severe storms
  • storms
  • convective environments
Convective storms regularly occur in complex topography, yet relatively little is known about how topography affects convective environments and storms. The first step toward understanding topographic effects on storms is to investigate how topography affects storm environments. Unfortunately, topographic effects on storm environments are not easily observed directly given the spatial-resolution of the current rawinsonde network. Instead, we resort to output from the High-Resolution Rapid Refresh (HRRR). The HRRR’s 3-km grid spacing can resolve larger-scale topographic effects. Widely used convective storm forecasting parameters obtained from the High-Resolution Rapid Refresh model are averaged during convective days from 2013–2015. Most of the day-to-day variability due to synoptic- and mesoscale influences is removed by the averaging. The remaining variability is attributable to hemispheric-scale meridional temperature and pressure gradients along with topographical influences where meso-β-scale anomalies exist. The anomalies, especially those related to low-level wind shear, are sensitive to low-level wind direction, which dictates where local winds blow upslope or downslope. Tornado tracks from 1950–2015 are compared with anomalies within the mean fields of the convective forecasting parameters. Several anomalies are examined with mean soundings or hodographs at points within and just outside of the anomaly of interest to investigate their dynamic or thermodynamic origins. Statistical significance of local maxima and minima is demonstrated by comparing the amplitudes of the anomalies to bootstrapped estimates of the standard errors.