89th American Meteorological Society Annual Meeting

Monday, 12 January 2009: 4:30 PM
Comparative Lightning Characteristics of a Tornadic and Non—Tornadic Oklahoma Thunderstorm on 24–25 April 2006
Room 131A (Phoenix Convention Center)
Amanda M. Sheffield, Purdue University, West Lafayette, IN; and P. D. Bothwell and J. Schaefer
The scheduled launch of the GOES-R satellite in 2014 will include a Geostationary Lightning Mapper (GLM), which will allow detection of Total lightning. Total lightning consists of Intracloud/Cloud-to-Cloud lightning (IC) and Cloud-to-Ground (CG) lightning. Data from primarily case studies using information from surface based Lightning Maping Array systems indicates that total lightning has potential as a tool for short-term forecasting of severe thunderstorm activity. In particular, previous literature has related the occurrence of lightning jumps, a rapid increase in the lightning flash rate (flashes per minute, or fpm) preceding a peak in the flash rate, to subsequent severe weather occurrence.

To explore this relationship further, lightning characteristics of a tornadic storm and two hailstorms occurring on 24-25 April 2006 within the Oklahoma LMA are examined. The total lightning data from the LMA are augmented by CG flash data from the National Lightning Detection Network. Thus, total lightning, total CG (including positive and negative CG counts) lightning, and total IC lightning are analyzed for these storms. Lightning jumps are found in this data, and the importance of IC lightning to these jumps is seen. Also, the importance of complete IC lightning detection is indicated since the total lightning in the storms consisted of greater than 60-70% IC flashes. Since current land based lightning detection networks either sense only CG data, or have very low detection efficiency for IC strokes, the GOES-R GLM will provide a source of continuous total lightning information over the contiguous United States (CONUS), facilitating the use of lightning rates and characteristics as a short-term forecasting tool.

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