1. 1 Comparative Lightning Characteristics of a Tornadic and Non-Tornadic Oklahoma Thunderstorm on April 24 - 25, 2006 Amanda Sheffield Purdue University West Lafayette, IN Dr. Phillip Bothwell Dr. Phillip Bothwell Dr. Joe Schaefer Storm Prediction Center Norman, OK

2. 2 Outline Objective Objective Background Background Methodology Methodology Results Results Conclusion/Future Work Conclusion/Future Work

3. 3 Objective Evaluate importance of real time total lightning data as will be provided from the GOES-R 2014 mission Evaluate importance of real time total lightning data as will be provided from the GOES-R 2014 mission Relate total lightning to severe (and non- severe) weather events Relate total lightning to severe (and non- severe) weather events www.goes-r.gov/overview/index.html

4. 4 Background Lightning is divided into two main categories: Lightning is divided into two main categories: Cloud-to-Ground (CG) Lightning Cloud-to-Ground (CG) Lightning Positive Flash (lowers positive charge) Positive Flash (lowers positive charge) Negative Flash (lowers negative charge) Negative Flash (lowers negative charge) In-Cloud (IC) Lightning In-Cloud (IC) Lightning Total Lightning = CG + IC (MacGorman, 1993)

5. 5 Background cont. OTD & LIS provide polar orbiting, twice daily total lightning observations OTD & LIS provide polar orbiting, twice daily total lightning observations GOES-R Geostationary Lightning Mapper (GLM) (2014) will deliver 24/7 total lightning observations GOES-R Geostationary Lightning Mapper (GLM) (2014) will deliver 24/7 total lightning observations Applications and Benefits: Applications and Benefits: Predict the onset of tornadoes, hail, microbursts, flash floods; Predict the onset of tornadoes, hail, microbursts, flash floods; Track thunderstorms and warn of approaching lightning threats; Track thunderstorms and warn of approaching lightning threats; Improve airline routing around thunderstorms; improving safety, saving fuel, and reducing delays; TAFs Improve airline routing around thunderstorms; improving safety, saving fuel, and reducing delays; TAFs Provide real-time hazardous weather information, improving the efficiency of emergency management; Provide real-time hazardous weather information, improving the efficiency of emergency management; (Steve Goodman presentation to SPC, May 2008)

6. 6 Background cont. Applications and Benefits cont. Applications and Benefits cont. NWP/Data Assimilation; NWP/Data Assimilation; Locate lightning strikes known to cause forest fires and reduce response times; Locate lightning strikes known to cause forest fires and reduce response times; Multi-sensor precipitation algorithms; Multi-sensor precipitation algorithms; Assess the role of thunderstorms and deep convection in global climate; Assess the role of thunderstorms and deep convection in global climate; Provide a new data source to improve air quality / chemistry forecasts. Provide a new data source to improve air quality / chemistry forecasts.

7. 7 Oklahoma Lightning Mapping Array (LMA) Current ground based instrumentation that measures total lightning over small geographic areas Current ground based instrumentation that measures total lightning over small geographic areas Developed by New Mexico Institute of Mining and Technology, reliably operational from 2004 Developed by New Mexico Institute of Mining and Technology, reliably operational from 2004 Source points mapped for an individual lightning flash, to reveal its location and the development of its structure Source points mapped for an individual lightning flash, to reveal its location and the development of its structure Other LMA sites are: Other LMA sites are: Northern Alabama (Huntsville) Northern Alabama (Huntsville) Washington DC Washington DC Kennedy Space Center Kennedy Space Center Dallas/Ft Worth Dallas/Ft Worth Houston Houston Tucson Tucson White Sands White Sands

8. 8 http://www.nssl.noaa.gov/observations/ltmap.php Region within which the system provides most accurate three- dimensional locations of lightning channel segments Oklahoma Lightning Mapping Array (LMA)

9. 9 National Lightning Detection Network (NLDN) NWS US Network based to detect Cloud- to-Ground (CG) Lightning Flashes NWS US Network based to detect Cloud- to-Ground (CG) Lightning Flashes Recent addition to detect In-Cloud (IC) flashes, though with only 10 - 25% detection efficiency across the US. Recent addition to detect In-Cloud (IC) flashes, though with only 10 - 25% detection efficiency across the US. In comparison, GOES-R Lightning Mapper is expected to detect 80 – 90+% of cloud flashes In comparison, GOES-R Lightning Mapper is expected to detect 80 – 90+% of cloud flashes (MacGorman 2006)

10. 10 Lightning Jump* - precursor to severe weather * Rapid increase in the lightning flash rate, number of flashes per minute (fpm), preceding a peak Determined by looking at the flash rate per minute (fpm min -1 ) Determined by looking at the flash rate per minute (fpm min -1 ) (Figure from Williams, 1999) fpm

11. 11 First termed in Williams et al, 1999, in The behavior of total lightning activity in severe Florida thunderstorms First termed in Williams et al, 1999, in The behavior of total lightning activity in severe Florida thunderstorms 2007 MS Thesis of Patrick Gatlin at University of Alabama in Huntsville qualified the lightning jump as greater than one standard deviation of the flash rate per minute (fpm min -1 ) in order to identify peaks 2007 MS Thesis of Patrick Gatlin at University of Alabama in Huntsville qualified the lightning jump as greater than one standard deviation of the flash rate per minute (fpm min -1 ) in order to identify peaks Lightning Jump* - precursor to severe weather fpm

12. 12 Methodology Objective: Find severe weather events occurring on the same day/environmental conditions to determine relation to lightning characteristics Objective: Find severe weather events occurring on the same day/environmental conditions to determine relation to lightning characteristics Restrictions: Restrictions: Oklahoma LMA: 100 km radius from the sensors for optimum 3 dimensional detection Oklahoma LMA: 100 km radius from the sensors for optimum 3 dimensional detection Storm Reports from 2004 – 2007 for severe storms since reliable data from OK LMA and readily available archived data Storm Reports from 2004 – 2007 for severe storms since reliable data from OK LMA and readily available archived data

13. 13 New Mexico Tech XLMA Software XLMA – lightning display software XLMA – lightning display software Use XLMA software on selected storms in order to: Use XLMA software on selected storms in order to: Calculate Total number of flashes (IC & CG) with software Flash Algorithm Calculate Total number of flashes (IC & CG) with software Flash Algorithm 10 minute and 5 minute time bins from Start to End of storm 10 minute and 5 minute time bins from Start to End of storm

14. 14 XLMA & NLDN XLMA software provides Total flash count XLMA software provides Total flash count Developed method to separate the Total Flash count into In-Cloud and Cloud-to-Ground Flashes Developed method to separate the Total Flash count into In-Cloud and Cloud-to-Ground Flashes Plot NLDN data visually using counts in 10 km grid box in a 5 minute bin in order to calculate total number of CG Flashes Plot NLDN data visually using counts in 10 km grid box in a 5 minute bin in order to calculate total number of CG Flashes Further determined Positive & Negative CG flashes Further determined Positive & Negative CG flashes XLMA Total Flashes – NLDN CG Flashes = IC Flashes estimate XLMA Total Flashes – NLDN CG Flashes = IC Flashes estimate

15. 15 Final Data for Storm Analysis Storm Reports Storm Reports Radar Data Radar Data Lightning: Lightning: Total (CG + IC) Flash counts Total (CG + IC) Flash counts Positive and Negative CG Flash counts Positive and Negative CG Flash counts IC (=Total – CG) Flash counts IC (=Total – CG) Flash counts

16. 16 Lightning Flash Rates With Total number of flashes, CG flashes, and IC flashes, calculated from each: With Total number of flashes, CG flashes, and IC flashes, calculated from each: Flash Per Minute (fpm) = (Number of Flashes)/(5 minutes time bins of storm) Flash Per Minute (fpm) = (Number of Flashes)/(5 minutes time bins of storm) Identified Lightning Jump Identified Lightning Jump fpm min -1 fpm min -1 Compare with severe weather occurrences in the storms Compare with severe weather occurrences in the storms

17. 17 Results Storms Storms 1 Tornadic 1 Tornadic 2 F1 tornados (0023–0038 UTC, 0030–0042 UTC) 2 F1 tornados (0023–0038 UTC, 0030–0042 UTC) Hail 1 – 2.75 inch (0015 UTC) Hail 1 – 2.75 inch (0015 UTC) 2 Hailstorms 2 Hailstorms Northern Cell (2220–215 UTC): 0.75 – 3.00 inch Hail Northern Cell (2220–215 UTC): 0.75 – 3.00 inch Hail Southern Cell (2240-400 UTC): 0.88 – 1.75 inch Hail Southern Cell (2240-400 UTC): 0.88 – 1.75 inch Hail Case Study: April 24-25, 2006

18. 18 El Reno tornadoes 100 km radius of Oklahoma LMA April 24 – 25, 2006 Severe Weather 04/24/2006 0000 UTC – 04/25/2006 1200 UTC Radar Image (4 km) on April 25, 2006 0029 UTC Green Dots indicate Hail Reports, with size in inches * 100

19. 19 1.00 – 2.75 inch Hail, 2 F1 Tornadoes 0.75 – 3.00 inch Hail 0.88 – 1.75 inch Hail, Wind Report IC & CG Percentages of Total Lightning

20. 20 2.75 inch Hail: 12 2 F1 tornados: 14,15 1.75 inch Hail: 19 1.25 inch Hail: 22 1.75 inch Hail: 24 1.00 inch Hail: 27 Lightning Jump

21. 21 0.75 inch Hail: 7, 8, 14 1.00 inch Hail: 15 3.00 inch Hail: 17 0.75 inch Hail: 18 0.88 inch Hail: 18 0.88 inch Hail: 23 0.75 inch Hail: 30 1.00 inch Hail: 35 Lightning Jump

22. 22 1.75 inch Hail: 14 1.00 inch Hail: 40 Wind Report: 51 0.88 inch Hail: 53 Lightning Jump

23. 23 2.75 inch Hail: 12 2 F1 tornados: 14,15 1.75 inch Hail: 19 1.25 inch Hail: 22 1.75 inch Hail: 24 1.00 inch Hail: 27

24. 24 Conclusion GOES-R Geostationary Lightning Mapper (GLM) will provide 24/7 total lightning information and climatology that is currently only available in smaller areas and time (LMAs, LIS/OTD) GOES-R Geostationary Lightning Mapper (GLM) will provide 24/7 total lightning information and climatology that is currently only available in smaller areas and time (LMAs, LIS/OTD) Case Study here emphasized the importance of total lightning information, including In-Cloud Lightning, to: Case Study here emphasized the importance of total lightning information, including In-Cloud Lightning, to: Flash Rates & Lightning Jumps Flash Rates & Lightning Jumps Storm related events Storm related events Comparison of Percentages of Total Lightning Comparison of Percentages of Total Lightning

25. 25 Future Work More cases studies leading up to the launch of the GOES-R satellite in 2014 More cases studies leading up to the launch of the GOES-R satellite in 2014 More in-depth storm study to compare to lightning characteristics, including dynamics and microphysics of storms, in order to determine storm evolution More in-depth storm study to compare to lightning characteristics, including dynamics and microphysics of storms, in order to determine storm evolution Use of radar and new technology, like the Dual- Pol/Phased Array Radar, in combination with current lightning data and areas that are simulating the GOES-R GLM capabilities Use of radar and new technology, like the Dual- Pol/Phased Array Radar, in combination with current lightning data and areas that are simulating the GOES-R GLM capabilities Exploration of lightning characteristics in other geographic regions and environments leading up to the GOES-R launch Exploration of lightning characteristics in other geographic regions and environments leading up to the GOES-R launch With launch of GOES-R, continuous coverage of total lightning information and climatology With launch of GOES-R, continuous coverage of total lightning information and climatology

26. 26 References Gatlin, P., (2007). Severe Weather Precursors in the Lightning Activity of Tennessee Valley Thunderstorms. Unpublished master’s thesis. The University of Alabama, Huntsville, AL. Gatlin, P., (2007). Severe Weather Precursors in the Lightning Activity of Tennessee Valley Thunderstorms. Unpublished master’s thesis. The University of Alabama, Huntsville, AL. Goodman, S., presentation at National Weather Center, Norman, OK, May 14, 2008. Goodman, S., presentation at National Weather Center, Norman, OK, May 14, 2008. MacGorman, D. R., 1993. Lightning in tornadic storms: a review. In: Church, C., Burgess, D., Doswell, C., Davies-Jones, R., (Eds.), The Tornado: Its Structure, Dynamics, Prediction and Hazards, Geophysical Monograph, Vol 79, American Geophysical Union. MacGorman, D. R., 1993. Lightning in tornadic storms: a review. In: Church, C., Burgess, D., Doswell, C., Davies-Jones, R., (Eds.), The Tornado: Its Structure, Dynamics, Prediction and Hazards, Geophysical Monograph, Vol 79, American Geophysical Union. MacGorman, D., I. Apostolakopoulos, A. Nierow, J. Cramer, N. Demetriades, and P. Krehbiel (2006). Improved Timeliness of thunderstorm Detection from Mapping a Larger Fraction of Lightning Flashes. Paper presented at the 1 st International Lightning Meteorology Conference, 26-27 April, 2006. Retrieved from the 2006 ILMC Website: http://www.vaisala.com/newsandmedia/events/ildcilmc/pastconferences/2006%20ilmc%20pap ers MacGorman, D., I. Apostolakopoulos, A. Nierow, J. Cramer, N. Demetriades, and P. Krehbiel (2006). Improved Timeliness of thunderstorm Detection from Mapping a Larger Fraction of Lightning Flashes. Paper presented at the 1 st International Lightning Meteorology Conference, 26-27 April, 2006. Retrieved from the 2006 ILMC Website: http://www.vaisala.com/newsandmedia/events/ildcilmc/pastconferences/2006%20ilmc%20pap ers National Climactic Data Center. (2008). Storm Events. Retrieved May 2008, from: http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwEvent~Storms National Climactic Data Center. (2008). Storm Events. Retrieved May 2008, from: http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwEvent~Storms NOAA National Severe Storms Laboratory. (2008). Field Observing Systems: Oklahoma Lightning Mapping Array. Retrieved June 2008, from: http://www.nssl.noaa.gov/observations/ltmap.php NOAA National Severe Storms Laboratory. (2008). Field Observing Systems: Oklahoma Lightning Mapping Array. Retrieved June 2008, from: http://www.nssl.noaa.gov/observations/ltmap.php National Weather Service Forecast Office, Norman, OK. (2008). Preliminary Storm Data Reports. Retrieved June 2008, from: http://www.srh.noaa.gov/oun/stormdata/oun200604.pdf National Weather Service Forecast Office, Norman, OK. (2008). Preliminary Storm Data Reports. Retrieved June 2008, from: http://www.srh.noaa.gov/oun/stormdata/oun200604.pdf Storm Prediction Center. (2008). Storm Reports. Retrieved May 2008, from: http://www.spc.noaa.gov/climo/ Storm Prediction Center. (2008). Storm Reports. Retrieved May 2008, from: http://www.spc.noaa.gov/climo/ Williams, E. R., B. Boldi, A. Matlin, M. Weber, S. Hodanish, D. Sharp, S. Goodman, R. Raghavan, and D. Buechler, 1999: The behavior of total lightning activity in severe Florida thunderstorms. Atmos. Res., 51, 245-265. Williams, E. R., B. Boldi, A. Matlin, M. Weber, S. Hodanish, D. Sharp, S. Goodman, R. Raghavan, and D. Buechler, 1999: The behavior of total lightning activity in severe Florida thunderstorms. Atmos. Res., 51, 245-265.