1. Paper Review Jennie Bukowski ATS-780 18-APR-2017

2. Introduction
Explore the possibility of using total lightning trends to diagnose severe weather onset
Development of a standardized algorithm to forecast thunderstorm intensity
Previous methods relied only on radar
Based solely on observations of “lightning jumps”
Authors seek to define this quantitatively and then exploit it

3. Lightning Jumps
Rapid increase in total lightning flash rate before tornadic and non-tornadic severe thunderstorms (Williams et al., 1999; Goodman et al., 2005; Steiger et al. 2007)
Three phases of flash activity linked to updraft evolution:
Rapid increase
Updraft intensifies  more ice and graupel collisions and high supercooled water content  more lightning
Maximum
Flash rate peaks when updraft speeds reach a maximum value
Slow decrease
Updraft weakens and flash rate decreases

4. Example: 20-JUL-1986 Microburst
IC flash rate peaks after max. vertical velocity and with max. 30-dBZ height
(adapted from Goodman et al. 1988; Kingsmill and Wakimoto 1991)

5. Methodology 20 spring storms within 160 km of site
ICs: North Alabama Lightning Mapping Array
Reconstruct 4-D lightning channel
Processed with a flash-clustering algorithm - identifies VHF sources as a “flash” or as “noise” (McCaul et al. 2005, 2009)
CGs: National Lightning Detection Network (Cummins et al. (1998, 2006)
20 spring storms within 160 km of site

6. Lightning Jump Algorithm
Tested an average total flash rate over 1 min and 2 min
Time rate of change of total flash rate (finite difference):
Lightning jump is an anomaly compared to moving average total flash rate and detected by some threshold relative to the standard deviation
Average and standard deviation calculated from prior 6-10 min history (1-min averaging) or min history (2-min averaging)

7. Algorithm
Includes tests of 5 tunable parameters with ~ 10,000 combinations
Sampling rate (1 min vs 2 min)
Moving average calculation method (standard vs weighted)
Time interval of moving average calculation
Number of samples for standard deviation calculation
Minimum # of sources to define a flash
Best configuration yields (Wilks 1995):
Highest probability of detection (POD)
Lowest false alarm rate (FAR)
Highest critical success index (CSI

8. Verification
Successful if severe weather occurs within 30 minutes of lightning jump

9. Case Study #1 – Extreme Hailstorm
30-MAR-2002
Rapid updraft growth at UTC
Lightning jump at 0330 UTC
Large hail reports minutes after lightning jump

10. Case Study #2 Tornadic Case
19-MAR-2002
Rapid vertical growth to 1825 UTC
Lightning jump at same time
F1 tornado approx UTC

11. Physical Mechanisms
Strong updraft leads to collisions in mixed phase region
Size sorting in updraft separates charge
Graupel grows large enough to fall out
Increases drag on updraft and weakens it
Downdraft speed increases due to precipitation loading & evaporative cooling
In tornadic cases, strengthening updraft stretches vertical vorticity
Supercell cases may take minutes to descend to ground
Non-supercell cases develops quickly at all levels
Algorithm provides warning only in supercell case

12. Algorithm Test Results
20 thunderstorms produced:
110 severe weather events
16 tornadoes
81 hail events
9 straight-line winds events
1 lacking any severe weather report
15 supercell thunderstorms and 5 multicell and bow echo thunderstorms
8 thunderstorms were tornadic
1 non-supercell tornado

13. Supercell Tornado 1240 UTC 6-May- 2003 Most prolific case in data set
1116 UTC - 17 min prior to F1
1156 UTC, F1 tornado at 1220
7 hits, 3 misses, 0 false alarms

14. Non-Supercell Tornado
Lightning jump 4 minutes prior to 3- body scatter spike in reflectivity
Elevated core of reflectivity

15. Tornadic Non-Supercell
Top = flashes with no singletons removed
Bottom = flashes containing at least 10 VHF sources
Less amplified
Lower jump threshold
False alarm at UTC

16. Algorithm Performance
Non-Severe Storm – All False Alarms
2-min
Optimized identified lightning jumps preceded severe weather by a mean of 22 minutes
67 lightning jumps identified and 41 of these preceded severe weather
POD = 0.74, FAR = 0.40, CSI = 0.49
2 min. ave. highest CSI
1-min

17. Conclusions An algorithm was developed to identify lightning jumps
Use lightning jump identification as an indication of impending severe weather
Exploit the relationship between total lightning activity and updraft evolution
Rapid increases in the total flash rate = updraft intensification
Trend in the flash rate of a storm more useful than individual flash rate
Assist NWS forecasters in warning decision making process