Poster Session P10.4 Taxonomy and analysis of tornado surface marks

Thursday, 14 October 2010
Grand Mesa Ballroom ABC (Hyatt Regency Tech Center)
M. I. Zimmerman, West Virginia Univ., Morgantown, WV; and D. C. Lewellen

Handout (2.8 MB)

As part of a longstanding effort to investigate tornado structure and near-surface intensification, we have employed three-dimensional large-eddy simulations of debris-laden tornadoes to generate an extensive library of surface marks for a wide range of conditions. Included are over 140 damage tracks from simulated tornadoes roughly spanning the expected parameter space governing vortex and debris cloud structure. Parameters varied include corner flow swirl ratio, tornado velocity scale, gravitational forcing, surface-relative translation velocity, debris type, and debris availability at the surface. We have identified several common, prominent classes of marks, and we present here a basic qualitative framework for inferring tornado type, effective debris availability, and potentially near-surface flow structure and (with concurrent knowledge of flow scales aloft) near-surface intensification level. The surface track features that we focus on have been tested for robustness to changes in simulation details such as grid spacing and surface parameterization and for sensitivity to selected physical variations that leave certain critical dimensionless governing parameters unchanged.

Surface tracks contain complex signatures from a handful of competing near-surface physical processes. We can now access fully 3-dimensional simulated tornadoes and their debris clouds to improve upon historical interpretations and analysis of surface marks developed by Fujita and others that were limited by a paucity of available flow data. We have developed algorithms to distill sets of marks down to simpler descriptive forms including characteristic radii, spacings, and shapes. With concurrent Doppler measurements aloft, an approximate vortex radius gleaned from the surface track could provide a measure of near-surface intensification. Shape information can be used to create a composite "mean fingerprint" of the markings within a given track, and we discuss what might be inferred about the near-surface flow structure from quantitative analysis of this fingerprint's properties. These algorithms have been designed so as to be practically applicable to high-contrast aerial photographs of actual surface marks.

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