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    The Utility of Total Lightning in Diagnosing Single-cell Thunderstorm Severity in the Central Appalachian Mountains Region

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    Date
    2014-05-04
    Author
    Miller, Paul Wesley
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    Abstract
    Recent severe weather research has examined the potential role of total lightning patterns in the severe thunderstorm warning-decision process although none to-date have examined these patterns in explicitly weak-shear environments. Total lightning flashes detected by the Earth Networks Total Lightning Network (ENTLN) during the 2012-13 convective seasons (1 May – 31 August) over a region of the Central Appalachian Mountains were clustered into likely discrete thunderstorms and subsequently classified as either single-cell or multicell/supercell storm modes. The classification of storms was determined using a storm index (SI) which was informed by current National Weather Service (NWS) identification techniques. The 36 days meeting the minimum threshold of lightning activity were divided into 24 lightning-defined (LD) single-cell thunderstorm days and 12 LD multicell/supercell days. LD single-cell days possessed statistically significant lower 0000 UTC 0-6 km wind shear (13.8 knots) than LD multicell/supercell days (26.5 knots) consistent with traditional expectations of single-cell and multicell/supercell environments respectively. The popular 2σ total lightning jump algorithm was applied to all flashes associated with 470 individual LD thunderstorms. The frequencies of the storms’ total lightning jumps were then compared against any associated severe weather reports as an accuracy assessment. The overall performance of the algorithm among both categories was much poorer than in previous studies. While probability of detections (POD) of the 2σ algorithm were comparable to previous research, false alarm rates (FAR) were much greater than previously documented. Given these results, the 2σ algorithm does not appear fit for operational use in a weak shear environment.
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    http://hdl.handle.net/10919/56976
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    • Masters Theses [21552]

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