Effects of environmental shear and buoyancy on simulated supercell interactions

Wendt, Nathan Allen

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https://hdl.handle.net/2142/89099 Copy

Description

Title

Effects of environmental shear and buoyancy on simulated supercell interactions

Author(s)

Wendt, Nathan Allen

Issue Date

2015-12-11

Director of Research (if dissertation) or Advisor (if thesis)

Jewett, Brian F

Committee Member(s)

Rauber, Robert M

Department of Study

Atmospheric Sciences

Discipline

Atmospheric Sciences

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• supercell
• supercell interactions
• storm interactions
• severe weather
• numerical modeling

Abstract

This study examines the role that buoyancy and vertical wind shear play in modulating the relationship between storm interactions and storm severity. Using an idealized numerical model, 240 supercell interactions are simulated under systematically varied amounts of buoyancy and vertical wind shear. Small changes in buoyancy or vertical wind shear have signi cant impacts on post-interaction storm morphology. A wide amount of variation in low-level rotation is seen across the simulation suite. Two-cell storm simulations are not always stronger than one-cell control simulations. Migration of low-level vertical vorticity centers is ubiquitous through all runs, but orientation of and interaction between two storms' gust fronts modulates where the vortic- ity center will end up. Gust fronts in better alignment have more vorticity centers reach an updraft where they are stretched and intensi ed. With re- spect to storm mode, higher buoyancy produced less classic supercells while higher shear produced more classic supercells. High precipitation supercells were favored with two-cell simulations where the second cell was directly to the southwest of the control cell. Secondary cells that were close to the con- trol merged quickly and were often stronger than simulations with large cell separation distance. Further questions remain with trajectories and machine learning algorithms are tthe next steps for a more detailed analysis of this large data set.

Graduation Semester

2015-12

Type of Resource

text

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http://hdl.handle.net/2142/89099

Copyright and License Information

Copyright 2015 Nathan Wendt

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