Bitzer, Phillip M.

Carey, Lawrence D.

Cecil, Daniel J.

Chronis, Themis

Koshak, William J.

Schultz, Christopher J.

NASA Technical Reports Server

Integrating Space-Borne Lightning Characteristics and Ground-Based 
Metrics  for  Assessing  Thunderstorm  Intensity
1- NASA Marshall Space Flight Center, Huntsville, AL
2- Earth System Science Center, University of Alabama-Huntsville, Huntsville, AL
2- Department of Atmospheric Science, University of Alabama-Huntsville, Huntsville, AL
This work is supported by NASA A.25 Severe Storms Research Award NNM11AA01A
https://ntrs.nasa.gov/search.jsp?R=20170005528 2019-08-31T08:16:16+00:00Z
Motivation • How do Geostationary Lightning Mapper 
observations align with 
ground based relationships 
between lightning and storm 
intensity?
• What are some of the 
characteristics of sub-flash 
properties in a variety of 
storms?
Image of  the Geostationary Lightning Mapper,  courtesy of 
www.goes-r.gov
GOAL: take a first glance of well 
characterized storms to determine 
how GLM properties can enhance 
thunderstorm intensity 
measurements.  
Why ground based 
networks won’t suffice 
2 NLDN 
locations 
NLDN 
location for a 
very large 
lightning flash
Lightning Mapping Arrays (LMA)  
- limited in range (~150 km from 
domain center). A
- Detect different parts of the 
lightning flash.
LF/VLF (e.g., National Lightning 
Detection Network and Earth 
Networks) 
- have larger domains
- detect fewer flashes/strokes than 
LMA or TRMM-LIS
- Bitzer et al. 2016, JTECH.
- No spatial component. 
LMA (dots), GLM (colored boxes) and NLDN (minus signs) overlaid for a single flash 
Schultz et al. (2017), J. Operational Meteorology, in review
The Temporary Solution
Tropical Rainfall Measuring Mission-
Lightning Instrument Sensor (TRMM-LIS; 
Kummerow et al. 1998, Christian et al. 2000)
Data for Analysis
• Lightning data
• Weather Service Radar, 88D (NEXRAD) radar information 
Methods
• 68 TRMM overpasses that were coincident 
with a 1500 thunderstorm database in Schultz 
(2015).
• These storms contained:
• Normalized flash rate based on satellite 
overpass duration.
• Used Conditional Probability Metrics to 
quantify utility in identifying severe 
thunderstorms.
Radar Reflectivity
Updraft Volume vs Flash Rate
Blue box 
represents time of 
TRMM overpass
LMA Flash Rate vs TRMM Flash Rate
• Strong correlation between 
LMA flash rate and TRMM-LIS 
flash rate.
• Range from LMA and small 
parallax offsets have not been 
accounted for yet.
N=48
Reflectivity Profile of Thunderstorms vs 
TRMM-LIS Flash Rate
• As expected, different 
reflectivity profiles 
produce different flash 
rates.
• For the most part, 
LMA flash rates are 
higher than TRMM-
LIS flash rates.
• Useful though to see 
how the order of 
“intensity” changes 
with different flash 
properties. 
Less Intense                                                                                                                 More Intense
MESH and TRMM Flash Rate
• Chronis et al. (2015) demonstrated 
that storms with lightning jumps and 
higher flash rates had larger MESH 
values.
• Schultz et al. (2016) showed that 
combining storms with MESH and 
lightning jumps objectively 
identified severe storms better than 
MESH alone. 
Conditional Probability of a storm being identified as severe:
MESH Alone (MESH≥ 25.4 mm): 48.6%  (18/37)                         Flash Rate ≥10 flash min-1: 69.6% (16/23)
Flash Rate ≤10 flash min-1: 40.0% (18/45)
What are the range of LIS event rates?
• More flashes should 
result in higher event 
rates.
• R-correlation with 
number of flashes is 
strong
• R=0.93
Reflectivity Profile of Thunderstorms vs 
TRMM-LIS Event Rate
• Slight 
differences 
in 
reflectivity 
profiles if the 
event rate is 
chosen as the 
intensity 
metric 
instead of 
flash rate. 
Less Intense                                                                                                                 More Intense
MESH and TRMM event rates
• Highest event rates don’t 
necessarily correspond to the 
strongest mesh values.
• Seem to separate severe from 
non-severe in this limited 
sample. 
• Conditional probabilities 
slightly higher than using 
flash rates.
Conditional Probability of a storm being identified as severe:
MESH Alone (MESH≥ 25.4 mm): 48.6% (18/37)              Events Alone (≥100 events min-1): 76.9% (30/39)
Events Alone (≥200 events min-1): 73.5% (25/24)
Events Alone (≤100 events min-1): 13.7% (4/29)
Events Alone (≤200 events min-1): 17.5% (6/34)
TRMM Flash Rate vs Events per Flash
• Flash rates > 20 flashes min-1 tend 
to have fewer than 50 events per 
flash.
• Probability of severity increased in 
this sample as flash rate increased 
and events per flash decreased. 
• 25 of 34 severe storms reached 10 flash 
min-1 threshold for 2σ lightning jump 
algorithm.
• 6 of 34 non-severe reached this flash 
rate threshold.
Evidence of Flash Rate vs Flash Size 
Relationships?
• Increasing flash rate results in a 
decrease in flash size.
• Provides an idea of kinematic 
texture (i.e., updraft location, 
turbulence).
Schultz et al. (2017), WAF, EOR
Discussion/Conclusions
• TRMM flash rates are in good correlation with the LMA flash rates.
• Flash rate and intensity metrics to extend to the satellite realm.
• Event rates and events per flash show additional promise of helping discern 
storm intensity.
• This work did not incorporate any lightning jump information.


Integrating Space-Borne Lightning Characteristics and Ground Based Metrics for Assessing Thunderstorm Intensity

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