Potential Vorticity Analysis of Low Level Thunderstorm Dynamics in an Idealized Supercell Simulation

Abstract

Potential vorticity "thinking" has been used to infer the balanced dynamics of predominately synoptic-scale weather features. This is accomplished through the use of the invertibility principle in which the wind and mass fields can be retrieved from the three-dimensional distribution of potential vorticity (PV). The most common use of PV thinking has been applied to various forms of synoptic-scale cyclogenesis and the sensitivity of certain weather features had on the subsequent development. Utilizing the non-linear balance PV inversion developed by Davis and Emanuel (1991) this study extends the current use of PV diagnostics to atmospheric features and motions on the order of the meso- and storm-scale. This study, in essence becomes a feasibility study to examine whether a primarily synoptic-scale diagnostic tool can be applied to much smaller scales and inherently more complex fields of motion that occur within a supercell thunderstorm. An idealized supercell simulation from the ARW will be used to examine the low level thunderstorm dynamics from a PV perspective. The results show promise that the PV diagnostic can be applied to thunderstorm dynamics given the qualitative results presented here. Refinements are necessary to improve the quantitative accuracy of this technique.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2009
Accession Number
ADA496992

Entities

People

  • Robert T. Davenport

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Atmospheric Motion
  • Boundary Layer
  • Computational Science
  • Convection
  • Coordinate Systems
  • Cyclogenesis
  • Dynamics
  • Environment
  • Equations
  • Grids
  • Inversion
  • Meteorology
  • Physics
  • Simulations
  • Three Dimensional
  • Wind Shear

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Computational Modeling and Simulation
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference