Detailed understanding of the structure and variability of winds in the core of tornadoes remains incomplete. While recent advances in observational technology (particularly Doppler radars) have revealed the inner-core structure of the primary tangential circulation, large uncertainties remain regarding the secondary (radial and vertical) circulations, and there remains a paucity of information concerning flow near the surface (below 50 meters) where most of the inflow to tornadoes resides. Previous simulations of tornado-producing supercells portray believable, detailed structures of the tornado near the surface, but their inherent complexity has inhibited understanding of the most basic dynamics of tornadoes. Simpler tornado-chamber-type simulations facilitate understanding, but almost all previous simulations have used prescribed inflow and outflow boundary conditions that preclude interactions between the tornado and the larger environment that creates it. The research in this project will bridge the gaps between theory (primarily from highly idealized, low-Reynolds-number, axisymmetric simulations), aforementioned storm-scale simulations that are far more complex, and observations (which always have limited spatial and temporal coverage). A widely-used numerical model (CM1) will be used to perform simulations of tornado-like vortices of increasing complexity, ranging from axisymmetric simulations with no-slip lower boundary conditions and constant eddy viscosity to three-dimensional large eddy simulation (LES) with realistic surface roughness. In addition, select high resolution direct numerical simulations (DNS) will be used to assess the accuracy of subgrid turbulence parameterizations, especially near the surface. Output from these simulations will also be used to assess the extent to which current (and perhaps future) instruments and analysis procedures are capable of reconstructing three-dimensional wind fields from limited observational data.
Broader impacts of this effort will include improved assessment of the near-surface wind fields of tornadoes as a function of vortex size, intensity, and over different surface conditions, which will in-turn help to guide the collection analysis of data from past and future field campaigns. The extent to which data obtained from recent field campaigns (such as VORTEX2) can accurately diagnose tornado wind fields will be evaluated, leading to better analyses of data from past campaigns and better designs for future campaigns. The project will support education mentoring and career development of a post-doctoral fellow.
