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Understanding Tornadoes And Their Parent Supercells Through Ultra-High Resolution Simulation/Analysis

Wilhelmson, Robert B; Jewett, Brian; Gilmore, Matthew
University of Illinois - Chicago
Start date
End date

This proposal is for a provisional allocation of time on the Blue Waters computer system, due to become operational in 2011, and for travel funds to support technical coordination by various collaborators with the Blue Waters project team and vendor technical team. The project involves a study of the conditions that support tornado development and longevity. The approach is to simulate numerically the interaction between a supercell and an evolving tornado using a sophisticated model of atmospheric dynamics and thermodynamics. These simulations will resolve small-scale, dynamically active features that influence the evolution of the tornado, including the shallow inflow into the tornado and the thin bands of precipitation that form. The numerical model used will be a development of a parallel, storm-scale, atmospheric model named CM1. The work will produce a better understanding of the influence of different environmental conditions within and around the supercell on the genesis and dynamics of tornadoes. The rationale for requiring a resource of the scale of Blue Waters is that, without it, it will not be possible to resolve thin curtains of precipitation, tens of meters thick, which potentially transfer large amounts of angular momentum from cloud-base to ground, the tornado inflow layer, also tens of meters thick, and the influence of low-lying pools of cold air. Being able to diagnose and understand the interplay between microphysics, buoyancy and vorticity dynamics within the supercell, during and after tornado-genesis, requires higher resolution than is possible on existing computing systems. The insights gained from this project should be helpful in improving tornado forecasting capability which has the potential to increase safety through early and more accurate warnings. More general, data generated by this study may be used to improve sub-grid parameterizations for synoptic scale models, improving their ability to forecast severe weather.

Funding Source
United States Nat'l. Science Fndn.
Project number
Predictive Microbiology