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  • Author or Editor: Rodger A. Brown x
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Vincent T. Wood
and
Rodger A. Brown

Abstract

A tornadic vortex signature (TVS) is a degraded Doppler velocity signature that occurs when the tangential velocity core region of a tornado is smaller than the effective beamwidth of a sampling Doppler radar. Early Doppler radar simulations, which used a uniform reflectivity distribution across an idealized Rankine vortex, showed that the extreme Doppler velocity peaks of a TVS profile are separated by approximately one beamwidth. The simulations also indicated that neither the size nor the strength of the tornado is recoverable from a TVS. The current study was undertaken to investigate how the TVS might change if vortices having more realistic tangential velocity profiles were considered. The one-celled (axial updraft only) Burgers–Rott vortex model and the two-celled (annular updraft with axial downdraft) Sullivan vortex model were selected. Results of the simulations show that the TVS peaks still are separated by approximately one beamwidth—signifying that the TVS not only is unaffected by the size or strength of a tornado but also is unaffected by whether the tornado structure consists of one or two cells.

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Tetsuya Fujita
,
Kenneth A. Styber
, and
Rodger A. Brown

Abstract

During the month of July 1960, a mesometeorological field network was established in an area of 30 by 40 mi centered around San Francisco Mountain, Arizona. Network instrumentation included 33 micro-barographs, 10 hygrothermographs, 10 Beckman-Whitley wind recorders, about 165 nonrecording rain gauges, and 165 hail gauges. Daily precipitation amounts were carefully studied in order to relate them with the characteristics of moisture inflow into the network area. An analysis of the 22 July case over the network area revealed that a very small low-pressure area formed over the heated side of the mountain slope, providing a field of convergence leading to the morning cumulus convection. AS time went on, this low dissipated and cumulonimbus convection occurred. The mesometeorological network was found to be most suitable for the investigation of cumulus to cumulonimbus convection over the San Francisco Mountain area.

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David John Gagne II
,
Amy McGovern
,
Jeffrey B. Basara
, and
Rodger A. Brown

Abstract

Oklahoma Mesonet surface data and North American Regional Reanalysis data were integrated with the tracks of over 900 tornadic and nontornadic supercell thunderstorms in Oklahoma from 1994 to 2003 to observe the evolution of near-storm environments with data currently available to operational forecasters. These data are used to train a complex data-mining algorithm that can analyze the variability of meteorological data in both space and time and produce a probabilistic prediction of tornadogenesis given variables describing the near-storm environment. The algorithm was assessed for utility in four ways. First, its probability forecasts were scored. The algorithm did produce some useful skill in discriminating between tornadic and nontornadic supercells as well as in producing reliable probabilities. Second, its selection of relevant attributes was assessed for physical significance. Surface thermodynamic parameters, instability, and bulk wind shear were among the most significant attributes. Third, the algorithm’s skill was compared with the skill of single variables commonly used for tornado prediction. The algorithm did noticeably outperform all of the single variables, including composite parameters. Fourth, the situational variations of the predictions from the algorithm were shown in case studies. They revealed instances both in which the algorithm excelled and in which the algorithm was limited.

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