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Abstract
Predicting
the
timing
and
location
of
lightning
initiation
is
a
challenging
but
important
task
for
operational
meteorology,
as
many
people
are
killed
or
injured
each
year
by
lightning.
The
advent
of
the
GOES-‐16
satellite
offers
the
potential
for
new
answers
to
this
challenge.
This
study
takes
advantage
of
newly
available
high-‐
resolution
infrared
imagery
from
GOES-‐16,
along
with
lightning
data
from
ground-‐
based
detection
systems,
in
order
to
determine
any
features
or
trends
that
might
help
predict
the
onset
of
lightning
in
the
cloud.
Using
an
operational
NWS
AWIPS
workstation,
cloud
temperature
data
for
over
60
thunderstorm
cells
in
four
regions
of
the
CONUS
were
collected
at
5-‐minute
intervals
and
entered
into
spreadsheets,
along
with
5-‐minute
total
lightning
counts.
The
data
collection
period
for
each
storm
was
30
minutes
before
lightning
initiation
to
30
minutes
after.
Preliminary
results
indicate
that
lightning
initiation
in
storms
over
the
US
Northeast
and
Midwest
occurs
with
cloud
temperatures
in
the
range
-‐20
°C
to
-‐40
°C,
averaging
about
-‐30
°C.
However
for
storms
in
the
Southeast,
Great
Plains
and
Rockies
the
corresponding
temperatures
were
colder,
averaging
about
-‐40
°C
to
-‐45
°C.
The
period
of
most
rapid
cooling
typically
begins
a
few
minutes
before
lightning
initiation
but
also
continues
for
some
time
after.
Further
efforts
to
relate
lightning
initiation
temperature
to
environmental
factors
have
thus
far
been
generally
inconclusive.
Aaron
Haegele
Meteorology,
Geology
Minor
Dr.
M.
Majcen,
Dr.
C.
Kauffman,
Dr.
G.
Gould,
Bill
Meloy
Keywords:
Lightning,
Remote
Sensing,
GOES-‐16,
Cloud-‐top
Temperature
Acknowledgements
I
would
like
to
thank
my
Thesis
Advisor
Dr.
Mario
Majcen
and
my
Thesis
Committee
members
consisting
of
Dr.
Chad
Kauffman,
Dr.
Greg
Gould,
and
Bill
Meloy.
I
would
also
like
to
express
my
gratitude
towards
the
University
Honors
Program
(UHP)
at
California
University
of
Pennsylvania
for
the
unparalleled
opportunities
given
to
me.
I
would
also
like
to
extend
my
gratitude
to
the
staff
of
the
National
Weather
Service
Office
in
Philadelphia/Mt.
Holly
NJ
for
aiding
in
my
research
during
my
internship
in
the
summer
of
2017.
Finally
I
would
like
to
recognize
the
NOAA
Ernest
F.
Hollings
Undergraduate
Scholarship
Program
for
providing
me
with
tuition
assistance
to
support
me
during
my
studies
at
California
University
of
Pennsylvania
as
well
as
a
paid
research
internship
in
the
summer
of
2017.
Haegele
1
1.
Introduction
Lightning
is
one
of
the
most
spectacular
weather
phenomena,
yet
our
scientific
knowledge
of
exactly
how
and
when
it
will
occur
remains
an
elusive
research
problem.
Each
year
lightning
strikes
the
United
States
about
25
million
times
and
is
responsible
for
striking
400+
people
causing
about
47
fatalities
and
hundreds
of
injuries
in
the
U.S.
alone
(NOAA
2015).
Annually,
lightning
causes
numerous
economic
impacts
including
damages
of
$1
billion
in
insured
losses
(NOAA
2010),
$2
billion
in
aviation
operating
costs
(Golding
2005),
thirty
percent
of
all
power
outages
in
the
U.S.
(Crum
and
Forster
2014),
and
24,600
fires,
both
house
and
wildfire,
averaging
about
$407
million
in
damages
and
an
additional
16
fatalities
(Ahrens
2013).
The
need
to
better
understand
inter-‐related
lightning
processes
and
be
able
to
predict
the
onset
of
lightning
is
critical
to
ensuring
the
safety
of
both
life
and
property.
a. Lightning
as
a
Risk/Danger
Lightning
and
related
threats
pose
serious
risks/dangers
to
people,
various
business
enterprises,
and
the
environment
in
the
United
States
and
around
the
globe.
In
the
United
States,
lightning
strikes
are
annually
responsible
for
about
47
fatalities
and
hundreds
of
injuries,
with
the
greatest
concentration
in
Florida
(NOAA
2015).
Despite
the
fact
that
the
U.S.
population
has
increased
by
about
a
factor
of
four
since
the
early
twentieth
century,
lightning
fatalities
have
decreased
from
more
than
400
to
less
than
50.
During
these
years,
large
improvements
in
the
quality
of
buildings,
workplace
environment,
and
overall
knowledge
and
education
of
weather
Haegele
2
amongst
the
public
and
scientists
have
lead
to
a
decrease
in
fatalities.
Lightning
fatality
incidents
have
shifted
from
agriculture
and
other
outdoor
work
to
largely
outdoor
recreation
incidents
(Holle
2016).
Globally,
lightning
fatality
records
are
much
less
complete.
One
study
by
Cardoso
et
al.
(2011)
estimates
6000
global
fatalities
and
another
study
by
Holle
and
Lopez
(2003)
estimates
as
much
as
24,000
annual
fatalities.
The
amount
of
annual
lightning
related
fatalities
differ
greatly
between
developed
and
developing
countries.
Lesser-‐developed
countries
have
less
accessible
lightning
safe
structures,
lack
of
awareness
or
education
about
lightning
safety,
and
high
rate
of
labor-‐
intensive
manual
agriculture
(Holle
2016).
Many
developing
countries
lie
near
the
equator
and
the
Inter-‐Tropical
Convergence
Zone
(ITCZ),
making
the
population
prone
to
frequent
lightning
threats.
The
combination
of
frequent
lightning
threats
alongside
poor
infrastructure
and
lifestyles
prone
to
the
dangers
of
lightning
(i.e.
agriculture
based
economy
focused
on
manual
labor,
poor
education,
and
lack
of
scientific
knowledge)
cause
many
developing
countries
to
have
the
greatest
amounts
of
annual
lightning
fatalities
(Holle
2016).
In
the
United
States
about
0.3
people
in
1
million
die
from
lightning
strikes.
Similarly
in
European
countries
the
figure
is
about
0.2
per
million,
however
in
Zimbabwe
it
is
about
20
per
million
and
in
Malawi
it
is
84
per
million
(Mulder
et
al.
2012).
Lightning
is
responsible
for
24,600
fires
each
year
(including
house
and
wildfires)
causing
about
$407
million
in
damage,
upwards
of
$1
billion
in
insured
losses
and
an
additional
16
fatalities.
Lightning-‐caused
house
fires
represent
about
5%
of
all
residential
claims,
averaging
about
1
claim
for
every
57
lightning
strikes.
Haegele
3
In
1996,
the
National
Park
Service
alone
reported
517
lightning
caused
fires
with
a
total
cost
of
damages
of
over
$19.5
million.
(Crum
and
Forster
2014).
Wildfires
started
by
lightning
burn
an
average
of
5.5
million
acres
annually
(NOAA
2012).
Lightning-‐caused
wildfires
tend
to
be
larger,
burning
on
average
402
acres,
as
opposed
to
human-‐caused
wildfires
fires,
which
burn
45
acres
(Ahrens
2013).
Lightning
related
threats
affect
U.S.
industry,
especially
the
aviation
and
energy
enterprises.
Each
year,
lightning
and
related
thunderstorm
risks
are
responsible
for
$2
billion
in
aviation
operating
costs
and
passenger
delays.
Thunderstorm
weather
hazards
are
responsible
for
an
average
of
10
million
minutes
of
delay
annually
for
U.S.
airports
(Golding
2005).
Lightning
poses
a
serious
safety
risk
to
airport
personnel
that
work
outdoors
servicing
gate-‐side
aircraft
and
maintaining
airport
grounds.
Safety
procedures
trigger
ramp
closures
and
suspend
outdoor
work
including
baggage
handling,
food
and
fuel
supplying,
mechanical
servicing
and
other
related
work.
There
are
many
uncertainties
in
the
decision
making
process
for
lightning
related
weather
delays,
including
lightning
detection
and
forecasting.
NCAR
(National
Center
for
Atmospheric
Research)
is
working
toward
a
better
characterization
of
the
true
lightning
hazard
that
is
needed
as
a
basis
for
improving
the
safety
of
outdoor
personnel
and
minimizing
avoidable
operational
inefficiencies.
Ongoing
research
is
combining
multiple
sources
of
relevant
information
(e.g.,
radar
and
various
lightning
data)
for
a
robust
diagnosis
of
lightning
threats
in
a
project
called
BoltAlert.
Most
importantly
a
nowcasting
component
is
developed
that
enables
recognition
of
lightning
threats
prior
to
impact
allowing
for
proactive
actions
(NCAR
2017).
Haegele
4
Lightning
is
a
frequent
cause
of
power
outages
to
the
United
States
electrical
grid.
Lightning
strikes
can
hit
electrical
equipment
directly
or
indirectly
by
striking
nearby
objects
such
as
trees
that
may
fall
onto
utility
infrastructure.
Lightning
is
responsible
for
about
30%
of
U.S.
power
outages
totaling
about
$1
billion
in
costs.
Other
energy
enterprises
such
as
nuclear
power
plants
and
offshore
oil
rigs
are
often
at
frequent
risks
due
to
lightning
(Crum
and
Forster
2014).
b. Lightning
Processes
Despite
its
global
abundance,
lightning
and
the
electrification
processes,
which
govern
initiation,
are
not
fully
understood.
This
is
largely
because
lightning
formation
processes
are
complex
and
difficult
to
observe
at
all
scales
in
nature.
Lightning
discharges
span
15
orders
of
magnitude
in
scale,
from
atomic-‐scale
electron
transfer
to
thunderstorm
dynamics
tens
or
hundreds
of
kilometers
in
size
(Harris
et
al.
2010).
Lightning
initiation
is
caused
by
the
electrification
process
within
a
thunderstorm
cloud,
therefore
the
beginning
of
the
electrification
process
begins
with
cloud
dynamics
and
properties
of
a
thunderstorm.
Lightning
is
caused
by
differential
charge
distributions
within
the
thunderstorm
cloud
and
the
ground
surface.
In
simplest
terms,
moisture,
atmospheric
instability,
and
a
lifting
mechanism
are
the
ingredients
for
thunderstorm
development.
Thunderstorms
go
through
life
stages
including
growth,
development,
electrification,
and
dissipation.
Higher
amounts
of
moisture
(generally
dewpoints
greater
than
55
°F),
allow
for
sufficient
latent
heat
release
leading
to
greater
instability.
Atmospheric
instability
Haegele
5
allows
air
from
low
levels
of
the
atmosphere
to
rise
into
upper
levels,
supporting
deep
convection
and
thunderstorm
cloud
development.
Various
features
including
frontal
boundaries,
dry
lines,
outflow
boundaries,
upslope
flow,
low-‐pressure
systems,
differential
heating,
and
low
level
warm
air
or
moisture
advection
may
trigger
lifting
mechanisms
(NWS
2004).
During
the
growth
stage,
rising
moist
unstable
air
leads
to
convective
growth
of
cumulus
clouds
due
to
strong
updrafts.
Continued
growth
occurs
as
the
cumulus
cloud
structure
develops
into
a
towering
cumulus
with
an
anvil
top.
At
the
development
stage,
the
towering
cloud
structures
(containing
mainly
liquid
particles)
rise
above
the
freezing
level
and
critical
temperature
levels
such
as
-‐15°C
where
glaciation
occurs.
Cloud
droplets
grow
in
size
until
the
updrafts
are
no
longer
able
to
suspend
the
droplets,
at
which
time
precipitation
occurs.
Meanwhile,
cool
dry
air
flows
downward
in
the
cloud
structure
creating
a
downdraft.
At
this
point
the
cloud
structure
is
effectively
called
a
cumulonimbus
cloud
as
it
contains
an
updraft,
downdraft,
and
precipitation.
Once
precipitation
processes
within
the
cloud
have
formed,
different
phased
(water,
mixed,
super-‐cooled
and
ice)
droplets
create
electric
charges
within
the
cloud,
known
as
the
electrification
stage.
Eventually
the
downdraft
in
the
cloud
structure
becomes
stronger
than
the
updraft
causing
a
net
loss
of
energy
to
the
structure
and
therefore
is
known
as
the
dissipation
stage
(Jensenius
2012).
For
purposes
of
this
study,
the
dynamics
and
processes
leading
up
to
and
during
the
electrification
stage
will
be
presented
in
most
detail.
Many
microphysical
processes
occur
prior
to
yielding
lightning
within
strongly
developed
Haegele
6
cumulonimbus
clouds.
Lightning
occurs
in
cold
clouds
containing
water,
supercooled
water,
and
ice.
Initially,
a
convective
cumulus
cloud
contains
only
water
droplets.
However
strong
updrafts
carry
unstable
moist
air
and
water
droplets
above
the
freezing
level.
Precipitation
processes
usually
begin
to
occur
once
cloud
tops
reach
the
freezing
level.
Cloud
droplets
remain
liquid
(supercooled)
until
the
cloud
tops
ascend
to
temperatures
around
the
-‐15°C
where
freezing
known
as
glaciation
occurs.
Droplets,
under
strong
updraft
environments,
may
continue
to
ascend
into
levels
from
-‐15°C
to
-‐25°C.
In
this
region
supercooled
droplets
freeze
to
form
larger
ice
crystals,
graupel,
or
hail.
These
larger
frozen
particles
are
heavy
and
therefore
either
descend
through
the
cloud
or
are
suspended
by
the
updraft.
Lighter
water
droplets
and
ice
crystals
from
lower
levels
in
the
cloud
are
ascended
by
the
updraft
and
collide
with
the
heavier
frozen
particles
in
central
part
of
the
cloud
structure
(Harris
et
al.
2010).
The
collision
of
ascending
liquid
droplets
and
suspended
or
descending
ice
particles
is
largely
believed
to
be
the
reason
for
differential
charge
generation
and
distribution
within
a
thunderstorm
cloud.
Collision
events
are
most
numerous
in
the
mixed
phase
region
of
the
cloud
and
are
proportional
to
the
intensity
of
the
updraft.
The
smaller
ascending
liquid
droplets
and
ice
crystals
become
positively
charged
while
the
larger
suspended
or
descending
frozen
particles
become
negatively
charged.
The
updraft
carries
the
positive
charges
toward
the
top
of
the
cloud
to
temperature
levels
exceeding
-‐40°C,
while
the
heavier
ice
particles
are
either
suspended
in
the
middle
or
fall
toward
the
lower
part
of
the
cloud
due
to
gravity
and
the
downdraft
(Gremillion
and
Orville
1999).
A
thin
layer
of
positive
charges
Haegele
7
develops
at
the
base
of
the
cloud
structure
due
to
warmer
air
temperatures
and
liquid
precipitation
properties.
Figure
1.0
shows
a
typical
charge
distribution
within
a
thunderstorm
cloud.
The
Earth’s
surface
is
naturally
negatively
charged.
However
the
Earth’s
surface
is
influenced
by
charges
within
overlying
clouds.
The
abundant
amount
of
accumulated
negative
charges
within
the
mixed
phase
region
of
clouds
induces
a
proportional
positive
charge
upon
the
ground
surface
(and
anything
touching
the
ground)
beneath
the
thunderstorm
cloud.
The
electric
field
and
gradient
increases
until
the
insulating
properties
of
the
air
break
down,
resulting
in
electric
discharge
in
the
form
of
a
lightning
flash
or
strike.
The
resulting
lightning
discharges
negative
charges
to
one
or
more
locations
on
the
ground
(negative
flash).
Lightning
can
also
be
a
positive
flash
where
there
is
a
discharge
between
positive
charges
within
a
cloud
and
negative
charges
on
the
ground.
Positive
cloud-‐to-‐
ground
(CG)
lightning
strikes
are
much
less
common
than
negative
strikes;
90-‐95%
of
all
CG
lightning
is
negatively
charged
(Harris
et
al.
2010).
Haegele
8
Figure
1.0
Charge
Distributions
within
Thunderstorm
Cloud
and
Ground
Surface
(ALDIS
2013)
Electric
discharges
do
not
have
to
connect
differential
charges
from
the
cloud
to
the
ground
surface.
In
fact,
cloud-‐to-‐ground
(CG)
lightning
is
less
frequent
than
inter-‐cloud
and
intra-‐cloud
(IC)
lightning.
Inter-‐cloud
lightning
is
caused
by
a
discharge
from
negative
charges
in
one
cloud
to
positive
charges
in
another
cloud
whereas
intra-‐cloud
lightning
is
caused
by
a
discharge
from
negative
charges
to
positive
charges
within
the
same
cloud.
Intra-‐cloud
lightning
flashes
tend
to
briefly
neutralize
charge
between
the
upper
positive
and
mid-‐lower
level
negative
charge
regions
within
the
cloud.
IC
lightning
is
more
abundant
and
usually
precedes
CG
lightning
in
a
thunderstorm
(Medici
et
al.
2017).
A
study
by
Melick
et
al.
(2015)
determined
that
the
total
lightning
across
the
contiguous
U.S.
is
composed
of
85-‐
91%
IC
lightning
with
the
small
remainder
composed
of
CG.
Figure
1.1
explains
the
Haegele
9
different
types
of
lightning
(e.g.
cloud-‐to-‐ground,
inter-‐cloud
and
intra-‐cloud)
and
their
respective
charge
distributions.
Figure
1.1
Different
types
of
lightning
and
respective
charge
distributions
(Beroual
and
Fofana
2016)
c. Forecasting
&
Detection
Operational
meteorologists
use
a
combination
of
radar,
satellite
imagery,
and
even
recently
developed
numerical
weather
models
to
forecast
the
onset
of
lightning.
Over
the
years
much
research
regarding
relationships
between
different
types
of
lightning
and
radar
and
satellite
signatures
have
been
explored.
Many
of
these
studies
have
made
significant
contributions
to
operational
meteorology
and
the
understanding
of
lightning
processes.
Radar
studies
(using
WSR
88-‐D)
in
the
Haegele
10
late
1980s
and
1990s
relating
radar
reflectivity
to
temperature
heights
have
proven
useful
in
determining
CG
lightning
activity.
From
these
studies
it
has
been
widely
accepted
that
lightning
frequently
occurs
when
the
40
dBz
echo
reaches
approximately
the
-‐10
°C
height
and
also
that
cloud
tops
usually
have
to
exceed
9
km
for
CG
lightning
to
occur.
Michimoto
(1991)
discovered
in
their
study
that
the
first
CG
lightning
discharge
occurred
approximately
five
minutes
after
the
30
dBz
echo
reached
the
-‐20
°C
height,
therefore
providing
a
possible
proxy
to
the
onset
of
CG
lightning
(Gremillion
and
Orville
1999).
More
recently
a
research
group
from
University
of
Alabama-‐Huntsville
(UAH)
examined
summertime
lightning
activity
and
found
the
best
index
for
CG
flash
initiation
is
a
25
dBz
echo
at
-‐20°C,
and
a
25
dBz
echo
at
the
-‐15
°C
for
IC
flash
initiation
(Mecikalski
et
al.
2013).
Geostationary
satellite
studies
since
the
1990s
have
shown
significant
usefulness
for
nowcasting
the
development
of
severe
storms
and
estimating
storm
intensity
with
lightning
flash
rates.
Infrared
(IR)
interest
fields
from
GOES
satellites
have
proven
to
be
able
to
identify
locations
of
cloud-‐top
glaciation
within
convective
systems
and
also
identify
locations
of
strong
updrafts,
two
proxies
for
lightning
initiation.
Various
satellite-‐measured
parameters
including
cloud-‐top
height,
temperature,
and
phase
have
been
used
to
gain
understanding
of
possible
relationships
to
lightning
initiation
(Mecikalski
et
al.
2013).
Numerical
Weather
Models
such
as
the
Weather
Research
and
Forecasting
Model
(WRF)
contains
derived
variables
such
as
the
potential
electrical
energy
(Ep)
and
lightning
potential
index
(LPI).
The
two
variables
use
similar
microphysical
Haegele
11
processes
to
predict
the
possible
production
of
CG
and
IC
lightning
in
convection,
allowing
forecasts
(Lynn
et
al.
2012).
Lightning
detection,
until
the
advent
of
GOES-‐R,
has
been
observed
by
numerous
ground-‐based
networks
sensitive
to
electromagnetic
pulses
emitted
by
lightning
strikes.
Cloud-‐to-‐ground
lightning
detecting
networks
were
the
first
invented
and
became
operationally
used
starting
in
1989
with
the
implementation
of
the
National
Lightning
Data
Network
(NLDN)
operated
by
Vaisala
Inc.
There
are
numerous
ground-‐based
networks
but
the
two
used
in
U.S.
operational
meteorology
are
the
NLDN
and
a
newer
system
developed
by
Earth
Networks
called
Earth
Networks
Total
Lightning
Network
(ENTLN).
Most
ground-‐based
networks
were
originally
only
able
to
detect
CG
strikes
and
not
IC.
The
ENTLN
lead
the
way
in
detecting
total
lightning
and
differentiating
between
CG
and
IC
flashes
(Thompson
et
al.
2014).
Lightning
Mapping
Arrays
(LMA)
provide
3-‐D
mapping
of
lightning
channels
over
a
specific
area.
Up
to
thousands
of
points
can
be
mapped
for
an
individual
lightning
flash
to
reveal
its
location
and
structure.
LMAs
are
used
extensively
in
research
to
investigate
lightning
characteristics
and
how
it’s
related
to
updrafts,
precipitation
and
severe
storm
processes.
National
research
LMAs
currently
exist
in
Oklahoma,
Texas,
Northern
Alabama,
Washington
D.C.
and
the
Kennedy
Space
Center
(NSSL
2012).
The
Geostationary
Lightning
Mapper
(GLM)
on
board
GOES-‐R
satellites
is
an
incredibly
powerful
lightning-‐monitoring
instrument
that
is
capable
of
detecting
and
differentiating
between
lightning
types
at
90%
accuracy.
The
GLM
uses
optical
Haegele
12
technology
to
detect
visible
radiances
from
clouds
and
collects
information
such
as
the
frequency,
location,
and
extent
of
lightning
discharges
to
identify
intensifying
thunderstorms
and
tropical
cyclones
(Goodman
et
al.
2013).
Studies
have
shown
lightning
activity
trends
are
related
to
severe
storm
development
and
can
be
used
as
predictors
of
tornados,
excessive
winds,
and
hail
events
(Schultz
et
al.
2011,
Thompson
et
al.
2014,
Medici
et
al.
2017).
Earlier
studies
in
the
1990s
claimed
that
rapid
increases
in
lightning
flash
rates
occur
before
the
onset
of
severe
weather.
These
abrupt
increases
in
rates
have
been
acceptably
named
“lightning
jumps”
within
the
scientific
community.
These
studies
used
CG
lightning
data
due
to
the
ease
of
availability,
high
detection
efficiency,
and
broad
coverage
across
the
United
States
and
other
regions.
With
the
advent
of
newer
lightning
detection
technologies,
more
recent
studies
have
demonstrated
that
trends
in
total
and
IC
lightning
are
more
robustly
correlated
to
severe
weather
occurrences
than
previously
stated
CG
trends
(Medici
et
al.
2017).
A
study
by
Schultz
et
al.
(2011)
found
that
average
lead
times
prior
to
severe
weather
occurrence
were
higher
using
total
lightning
as
compared
with
CG
lightning
(20.6
min
vs.
13.5
min).
2.
Project
Motivation
Previous
studies
such
as
Molinie
et
al.
(2004),
Mecikalski
et
al.
(2010)
Harris
et
al.
(2010),
Sieglaff
et
al.
(2011),
and
Mecikalski
et
al.
(2013)
have
provided
insight
into
the
usefulness
of
relationships
between
satellite
imagery
and
convection/lightning
initiation.
These
studies
investigated
cloud
top
temperatures
Haegele
13
and
cooling
rates
in
connection
to
the
onset
of
thunderstorm
development
(convection)
or
lightning
in
order
to
better
predict
the
timing
and
location
of
either
convective
storm
initiation
or
lightning
initiation.
Mecikalski
et
al.
(2010)
used
results
to
formulate
an
algorithm
called
Satellite
Convection
Analysis
and
Tracking
(SATCAST)
system,
which
uses
spatial
temporal
and
spectral
information
from
Geostationary
Operational
Environmental
Satellites
(GOES)
to
identify,
track,
and
monitor
growing
convective
clouds
in
their
pre-‐convective
state
to
nowcast
convective
initiation.
These
studies
used
older
and
now
outdated
satellite
imagers
including
GOES-‐
8,
10,
and
12
and
Meteosat-‐9.
Mecikalski
et
al.
(2010)
in
regards
to
newer
and
more
capable
forthcoming
imagers
stated,
“Therefore,
developing
an
understanding
that
guides
the
proper
use
of
the
‘new’
IR
channel
information
to
improve
applications
such
as
CI
&
LI
nowcasting
will
be
needed.”
GOES-‐R,
the
newest
series
of
satellites
(GOES-‐16
launched
in
November
2016,
operational
in
December
2017)
provides
new
instruments
capable
of
better
understanding
convection
and
lightning
development
processes.
The
onboard
Advanced
Baseline
Imager
(ABI)
improves
every
product
from
previous
GOES
imagers
and
introduces
a
host
of
new
products.
The
new
ABI
views
Earth
with
16
spectral
bands
(compared
to
5
on
previous
satellites),
including
two
visible,
four
near-‐infrared
and
ten
infrared
channels.
In
addition
to
the
16
available
channels,
25
derived
products
and
RGB
composites
are
newly
available.
The
GOES-‐R
series
satellites
provide
3x
more
spectral
information,
4x
better
spatial
resolution
and
are
Haegele
14
5x
faster
than
previous
GOES
imagers
(NOAA-‐NASA
2017).
Figure
2.0
compares
the
GOES-‐R
Series
ABI
to
the
current
(now
previous)
GOES.
Figure
2.0
Comparison
Table
of
GOES-‐R
ABI
capabilities
vs.
previous
GOES
(NOAA-‐NASA
2017)
All
of
these
previous
studies
also
used
either
ground-‐based
lightning
networks
that
were
capable
of
measuring
cloud-‐to-‐ground
strikes
only
or
used
lightning
mapping
arrays
capable
of
detecting
cloud
flashes,
but
very
limited
in
spatial
coverage.
The
GOES-‐R
series
satellites
carry
a
revolutionary
instrument
called
the
Geostationary
Lightning
Mapper
(GLM),
an
optical
transient
detector
capable
of
detecting
momentary
changes
in
an
optical
scene
indicating
the
presence
of
lightning.
The
GLM
is
the
first
operational
lightning
mapper
in
geostationary
orbit
and
is
able
to
measure
and
discern
IC
and
CG
lightning
flashes.
GLM
observable
trends
in
IC,
CG,
and
total
lightning
provide
critical
information
to
forecasters,
Haegele
15
allowing
them
to
focus
on
initial
thunderstorm
development
and
intensifying
severe
storms
before
these
storms
produce
damaging
winds,
hail
or
even
tornados
(NOAA-‐
NASA
2017).
The
advent
of
GOES-‐16
(GOES-‐R
series)
provides
potential
for
new
answers
regarding
the
difficult
operational
meteorology
challenge
of
lightning
prediction.
With
newly
available
instruments
and
capabilities,
this
study
aims
to
determine
any
features
or
trends
in
GOES-‐16
ABI
imagery,
specifically
cloud-‐top
temperature,
that
might
help
predict
the
onset
of
lightning
in
the
cloud.
With
GOES-‐16
launching
in
November
2016
and
the
study
period
being
May-‐July
2017,
this
study
serves
as
an
initial
research
assessment
of
the
usefulness
and
capabilities
of
GOES-‐16
products
in
providing
new
insight
for
operational
meteorology.
3.
Data
&
Methods
a. GOES-‐R
Satellite
Imagery
Using
a
standard
National
Weather
Service
(NWS)
AWIPS
II
(Advanced
Weather
Interactive
Processing
System)
workstation,
GOES-‐16
ABI
infrared
(IR)
channels
were
examined
to
determine
the
strongest
signal
for
cloud-‐top
cooling.
IR
channels
are
preferred
because
they
are
not
limited
by
the
amount
of
sunlight
and
therefore
provide
accurate
measurements
both
during
the
day
and
night.
Previous
literature
(Harris
et
al.
2010,
Mecikalski
et
al.
2013)
found
the
legacy
GOES
ABI
“clean
window
IR”
channel
to
be
best.
After
reviewing
all
10
IR
available
channels
from
GOES-‐16,
it
was
determined
the
best
signals
were
found
in
“long
wave
window”
channels
11,
13,
14,
15,
with
channel
13
(10.3
μm)
“clean
window”
Haegele
16
producing
the
strongest
signal.
Channel
13
was
thus
used
for
this
study
as
the
cloud-‐
top
temperature
measurement
for
all
interested
cells.
Figure
3.0
shows
the
10
IR
channels
responses
to
cloud-‐top
temperatures
in
respect
to
lightning
initiation
(t=0).
Although
all
10
IR
channels
were
investigated,
the
four
longwave
IR
channels
(shown
in
Figure
3.0
as
blue
lines)
yield
the
most
accurate
cooling
trend
for
cloud-‐
top
temperatures.
Figure
3.0
Temperature
trends
around
lightning
initiation
for
the
10
GOES-‐16
IR
channels.
The
four
long-‐wave
window
channels
(blue)
show
the
greatest
response.
Channel
13
was
used
for
this
study
Following
similar
previous
studies
(Molinie
et
al.
2004,
Sieglaff
et
al.
2011,
Harris
et
al.
2010),
this
study
focused
on
only
isolated
thunderstorm
cells.
Any
embedded
or
multi-‐cell
thunderstorms
such
as
mesoscale
convective
systems
Haegele
17
(MCS),
squall
lines
and
other
linear
features
were
not
included
for
data
quality
purposes.
Multi-‐cell
and
other
embedded
systems
were
proven
to
disturb
lightning
flash
data
and
made
it
difficult
to
decipher
which
cell
was
responsible
for
the
production
of
lightning.
Isolated
single
cells
made
this
process
much
easier
and
more
accurate.
The
temporal
scale
for
this
study
was
30
minutes
prior
and
after
lightning
initiation
(defined
at
t=0).
GOES-‐16
ABI
imagery
is
available
for
the
Continental
United
States
(CONUS)
(5000km
x
3000
km)
every
5
minutes
and
at
either
1
minute
or
30
seconds
for
smaller
viewing
regions
known
as
“mesoscale
sectors”
(1000km
x
1000km).
The
mesoscale
viewing
sectors
are
able
to
be
re-‐positioned
to
focus
on
various
interest
areas
across
the
CONUS
and
even
the
hemisphere.
At
the
time
of
this
study,
GOES-‐16
was
still
in
preliminary
stage
and
therefore
mesoscale
sector
imagery
often
had
data
blackouts
and/or
were
not
available
due
to
frequent
viewing
jumps
during
instrumentation
tests.
This
study
took
place
from
May-‐July
2017
and
during
this
time
period
the
mesoscale
sector
was
often
positioned
over
tropical
storms
and/or
severe
storms
of
special
interest.
However,
the
sector
was
re-‐positioned
frequently,
making
data
collection
for
1
hour
very
unreliable.
For
these
reasons,
the
CONUS
5-‐
minute
imagery
was
used
because
it
provided
the
most
constant
and
reliable
data
for
cloud-‐top
temperature
measurements.
Figure
3.1
shows
a
comparison
of
the
cloud
top
temperatures
using
the
legacy
GOES-‐13
15-‐minute
(green
triangles)
and
newly
available
GOES-‐16
5-‐minute
(red
squares)
and
1-‐minute
(blue
diamonds)
imagery
during
a
preliminary
investigation.
Note
the
increase
in
data
points
with
Haegele
18
the
GOES-‐16
imagery
and
therefore
the
more
detailed
cooling
trend
as
compared
to
the
legacy
GOES-‐13
imagery
every
15
minutes.
Figure
3.1
Comparison
example
of
1-‐min
and
5-‐min
(GOES-‐16)
and
legacy
15-‐
min
(GOES-‐13)
cloud-‐top
temperatures
for
one
storm.
b. Lightning
Data
Earth
Networks
Total
Lightning
Network
(ENTLN)
intra-‐cloud
(IC)
pulses
were
used
for
the
lightning
initiation
data.
Preliminary
investigations
showed
that
IC
pulses
preceded
both
cloud
flashes
and
CG
strikes
and
were
best
proxy
signal
for
initial
cloud
electrification
and
lightning
initiation.
ENTLN
boasts
greater
than
89%
total
lightning
detection
efficiency
for
CONUS
(Liu
and
Heckman
2011).
Combined
with
linked
temporal
scales
to
the
GOES-‐16
ABI
imagery
and
the
unavailability
of
Haegele
19
GOES-‐16
GLM
data,
ENTLN
is
the
best
alternate
IC
lightning
detection
method
for
the
study.
ENTLN
provides
1-‐min,
5
minute,
and
15-‐
minute
lightning
data.
The
5-‐
minute
data
were
used
because
it
allowed
for
real-‐time
comparison
of
lightning
data
and
the
GOES-‐16
ABI
data.
Figure
3.2
shows
an
example
of
sampled
cloud-‐top
temperatures
and
various
lightning
counts
(using
AWIPS
II)
for
a
cell
near
Baltimore,
MD.
Note
the
equal
time
stamps
for
both
the
ENTLN
and
GOES-‐16
data.
Also
note
the
offset
in
location
of
the
cloud-‐top
temperatures
and
lightning
due
to
parallax
from
the
satellite-‐viewing
angle.
Figure
3.2
Sampled
Cloud-‐Top
Temperature
of
a
cell
located
near
Baltimore,
MD
and
time
correlated
lightning
data
Haegele
20
c. Soundings
Investigations
into
further
understanding
lighting
initiation
relationships
beyond
cloud-‐top
temperatures
led
to
examining
soundings
for
possible
atmospheric
parameter
relationships.
A
Rapid
Refresh
(RAP)
model
analysis
sounding
corresponding
to
the
approximate
time
and
location
of
each
cell
was
obtained
from
a
freely
available
source
at:
http://mtarchive.geol.iastate.edu.
RAP
soundings
provide
spatially
and
temporarily
efficient
sounding
parameters
easily
displayed
in
BUFKIT
software.
METAR
(Meteorological
Terminal
Aviation
Routine
Weather
Report)
stations
were
displayed
on
AWIPS
II
during
the
sampling
of
cloud-‐
top
temperatures
and
lightning
data,
making
it
easy
to
determine
close
proximity
sounding
stations.
This
study
selected
sounding
parameters
including
CAPE,
Lifted
Index,
Equilibrium
Level,
Precipitable
Water,
LCL,
LFC,
WBZ
and
500-‐700mb
Lapse
Rates.
Figure
3.3
shows
an
example
of
a
sounding
used
in
the
study
using
BUFKIT.
Note
the
measured
sounding
parameters
(in
orange)
in
the
black
table
to
the
left.
These
values
were
then
used
for
analyzing
relationships
with
cloud-‐top
temperature
at
lightning
initiation
and
sounding
indices.
Haegele
21
Figure
3.3
Example
of
a
sounding
used
in
BUFKIT
and
measured
indices
d. Procedures
Cloud-‐top
temperatures
using
GOES-‐16
ABI
Channel
13
(10.3
μm)
CONUS
5-‐
minute
imagery
were
recorded
for
isolated
cells
30
minutes
prior
and
after
the
first
IC
pulse(s)
as
determined
by
corresponding
ENTLN
5-‐minute
lightning
data.
Selected
cells
from
June-‐July
2017
were
classified
into
four
CONUS
regions:
Northeast,
Southeast,
Midwest,
and
Plains-‐Rockies.
Figure
3.4
shows
the
area
of
study
displaying
all
cells
divided
into
corresponding
regions.
Blue
circles
mark
Northeast
cells,
green
mark
Midwest
cells,
purple
mark
Southeast
cells,
and
red
mark
Plains-‐Rockies
cells.
In
total,
60
cells
and
corresponding
data
were
collected.
Haegele
22
Figure
3.4
Map
of
area
study
displaying
all
cells
for
each
region
Linear
regressions
and
simple
statistics
were
used
to
determine
trends
in
cloud-‐top
temperatures
in
relationship
to
lightning
initiation
and
total
lightning
across
the
various
regions.
Further
investigation
into
possible
relationships
between
lightning
initiation
and
atmospheric
parameters
(i.e.
CAPE,
LI,
LCL,
LFC,
WBZ,
etc.)
were
then
examined
using
RAP
model
analysis
soundings
for
nearest
(in
relation
to
the
cell
position)
and
time
approximate
METAR
stations.
Linear
regressions
and
simple
statistics
were
used
to
determine
relationships
between
lightning
initiation
and
sounding
indices.
4.
Results
a.
Cloud-‐Top
Temperature
at
Lightning
Initiation
Cloud-‐top
temperatures
at
lightning
initiation
vary
from
region
to
region
in
storms
over
the
over
the
United
States.
Table
4.0
shows
the
minimum,
mean,
and
Haegele
23
maximum
cloud-‐top
temperatures
at
lightning
initiation
as
well
as
standard
deviation.
Northeast
and
Midwest
storms
reflect
similar
temperature
profiles
with
cloud
temperatures
in
the
range
of
-‐20°C
to
-‐40°C,
averaging
about
-‐30°C.
Cloud-‐top
temperatures
for
Northeast
storms
range
from
-‐19.8°C
to
-‐40.7°C,
averaging
-‐30.0°C.
Midwest
storms
occur
at
temperature
ranges
from
-‐19.7°C
to
–42.0°C,
averaging
-‐30.1°C.
Storms
in
the
Southeast,
Great
Plains
and
Rockies
share
similar
temperature
profiles
with
colder
temperatures
and
broader
ranges
than
those
in
Northeast
and
Midwest
storms.
Lightning
initiation
in
Southeast
storms
tend
to
occur
in
clouds
with
temperatures
ranging
from
-‐17.7°C
to
-‐55.3°C,
averaging
-‐38.5°C.
Lightning
initiation
occurs
in
the
Great
Plains
and
Rockies
in
cloud
temperatures
ranging
from
-‐19.6°C
to
-‐55.5°C,
averaging
-‐42.7°C.
Figure
4.0
shows
the
trends
in
average
cloud-‐top
temperature
for
cells
in
each
region
from
30
minutes
prior
to
lightning
initiation
(t=-‐30),
through
lightning
initiation
(t=0),
to
30
minutes
after
lighting
initiation
(t=30).
Figure
4.1
is
a
box
and
whisker
diagram
to
visually
display
the
distribution
of
cloud-‐top
temperatures
at
lightning
initiation
for
each
region.
Northeast
Midwest
Southeast
Plains-‐
Rockies
Max
Temp
(°C)
-‐19.8
-‐19.7
-‐17.7
-‐19.6
Mean
Temp
(°C)
-‐30.0
-‐30.6
-‐38.5
-‐42.7
Min
Temp
(°C)
-‐40.7
-‐42.0
-‐55.3
-‐55.5
Standard
6.78
7.08
11.45
9.98
Deviation
Table
4.0
Regional
Maximum,
Mean,
Minimum
Temperatures
at
Lightning
Initiation
(LI)
and
Standard
Deviation
Haegele
24
Figure
4.0
1-‐hr
Regional
Average
Cloud-‐Top
Temperature
Trends
Centered
at
Lightning
Initiation
(LI)
Figure
4.1
Regional
Cloud-‐Top
Temperatures
at
Lightning
Initiation
Box
and
Whisker
Diagram
Haegele
25
b.
Cloud-‐top
Temperature
Cooling
Rates
During
the
1-‐hour
period,
centered
at
lightning
initiation,
various
cooling
trends
occur
in
cloud-‐top
temperature
across
the
four
regions.
Table
4.1
shows
starting
(t=
-‐30
min)
temperatures,
ending
(t=
30
min)
temperatures,
total
cooling,
maximum
cooling
rate
(°C/5-‐minutes),
and
the
maximum
cooling
rate
time
in
relation
to
lightning
initiation.
Cells
in
the
Southeast
have
the
greatest
total
cooling
of
-‐56.7°C,
followed
by
cells
in
the
Midwest
with
total
cooling
of
-‐50.0°C,
Great
Plains
and
Rockies
with
total
cooling
of
-‐46.1°C,
and
cells
in
the
Northeast
have
the
least
amount
of
total
cooling
of
-‐41.4°C.
Maximum
cooling
rates
(°C/5-‐minutes)
are
greatest
in
Southeast
cells
at
-‐12.6,
followed
by
Midwest
cells
at
-‐8.9,
Northeast
cells
at
-‐8.3,
and
cells
in
the
Great
Plains
and
Rockies
have
the
weakest
cooling
rates
at
-‐7.3.
Cells
in
all
regions
show
greatest
cooling
rates
at
or
near
the
time
of
lightning
initiation
except
for
cells
in
the
Great
Plains
and
Rockies
where
maximum
cooling
rates
occur
ten
minutes
prior
to
lightning
initiation.
Figure
4.2
shows
average
regional
cloud-‐top
5-‐minute
cooling
trends
from
30
minutes
prior
to
30
minutes
after
lightning
initiation.
Start
(°C)
End
(°C)
Northeast
Midwest
Southeast
Plains-‐
Rockies
-‐8.2
-‐0.6
-‐2.2
-‐9.3
-‐49.6
-‐50.6
-‐58.9
-‐55.4
Total
Cooling
(°C)
-‐41.4
-‐50.0
-‐56.7
-‐46.1
Max
Cooling
Rate
(°C/5min)
-‐8.3
-‐8.9
-‐12.6
-‐7.3
Time
in
Relation
to
LI
(min)
0
0
0
-‐10
Table
4.1
Regional
Cloud-‐Top
Temperature
Endpoints
and
Cooling
Trends
Haegele
26
Figure
4.2
Regional
Average
Cloud-‐Top
Temperature
Cooling
Trends
c.
Cloud-‐Top
Temperature
at
LI
and
Total
IC
Lightning
Linear
regressions
of
cloud-‐top
temperatures
at
LI
and
storm
total
IC
lightning
yielded
moderately
correlated
relationships.
Figure
4.3
shows
these
regressions
and
r-‐values.
In
Northeast
cells
there
was
a
moderate
negative
relationship
between
LI
temperature
and
total
storm
IC
with
an
r-‐value
of
0.64.
Thus,
cooler
temps
at
LI
tend
to
lead
to
higher
amounts
of
total
storm
IC
lightning.
Cells
in
the
Southeast
and
Midwest
had
similar
moderate
negative
relationships
with
r-‐values
of
0.65
and
0.64
respectively.
Cells
in
the
Great
Plains
and
Rockies
had
a
slightly
weaker,
but
still
moderate,
positive
relationship
with
an
r-‐value
of
0.54.
In
this
region,
unlike
the
others,
cooler
temps
at
LI
did
not
correlate
with
greater
amounts
of
total
IC
lightning.
Haegele
27
Figure
4.3
Linear
Regressions
of
Regional
Cloud-‐Top
Temperature
&
Total
IC
Lightning
d.
Sounding
Indices
Linear
regressions
of
cloud-‐top
temperatures
at
lightning
initiation
and
sounding
indices
of
special
interests
(i.e.
CAPE,
PW,
WBZ,
LCL,
LFC,
LI,
EL,
and
500-‐
700
mb
lapse
rate)
yield
various
results
with
no
region-‐to-‐region
correlation
signatures.
Most
regressions
yielded
very
weak
(r
<
0.3)
correlations
except
for
a
few
“stand
out”
moderate
correlations
between
cloud-‐top
temperature
and
sounding
indices.
Figure
4.4
shows
the
only
four
correlations
with
r
>
0.50.
Cloud-‐
top
temperatures
at
lightning
initiation
in
Southeast
cells
had
moderate
negative
relationships
between
precipitable
water
(PW)
and
also
wet
bulb
zero
(WBZ)
heights
with
respective
r-‐values
of
0.68
and
0.58.
Cloud-‐top
temperatures
at
lightning
initiation
in
Northeast
cells
had
moderately
negative
correlation,
r
=
0.55,
Haegele
28
with
the
height
of
the
level
of
free
convection
(LFC).
Cloud-‐top
temperatures
at
lightning
initiation
in
Great
Plains
and
Rockies
cells
had
moderately
negative
correlation,
r
=
0.51,
with
the
convective
available
potential
energy
(CAPE).
Figure
4.4
Regressions
of
Cloud-‐Top
Temperature
at
Lighting
Initiation
and
Sounding
Parameters
5.
Discussion
Thunderstorm
cloud-‐top
temperature
relative
to
lightning
initiation
(LI)
varies
considerably
from
region
to
region
and
even
from
storm
to
storm
within
the
studied
regions.
However,
storms
in
the
Northeast
and
Midwest
showed
quite
similar
temperature
trends
especially
from
around
the
time
of
LI
to
30
minutes
after.
Average
cloud-‐top
temperatures
at
LI
for
the
Northeast
were
-‐30.0°C
and
-‐30.6°C
for
Midwest
cells.
Minimum
(warmest)
and
maximum
(coldest)
cloud-‐top
temperatures
were
also
quite
similar
for
cells
in
these
two
regions.
The
maximum
Haegele
29
temperature
for
lightning
initiation
is
-‐19.8°C
in
the
Northeast
and
-‐19.7°C
in
the
Midwest.
Minimum
temperatures
at
LI
were
-‐40.7°C
in
the
Northeast
and
-‐42.0°C
in
the
Midwest.
Cells
in
the
Northeast
and
Midwest
also
have
less
spread
in
temperatures
at
LI.
Storms
in
the
Southeast,
Great
Plains
and
Rockies
had
colder
cloud-‐top
temperatures
at
LI
and
also
much
larger
spread
than
storms
in
the
Northeast
and
Midwest.
Average
cloud-‐top
temperature
for
Southeast
cells
were
-‐38.5°C
and
in
the
Great
Plains
and
Rockies
-‐42.7°C.
Maximum
temperatures
at
LI
were
-‐17.7°C
in
the
Southeast
and
-‐19.6°C.
Minimum
temperatures
at
LI
were
nearly
identical
for
both
regions
with
-‐55.3°C
for
Southeast
cells
and
-‐55.5°C
for
cells
in
the
Great
Plains
and
Rockies.
Despite
variation
in
average
and
even
minimum
cloud-‐top
temperatures
at
LI
for
various
regions,
the
maximum
cloud-‐top
temperatures
at
LI
were
very
similar
for
all
regions.
In
all
regions
the
warmest
temperatures
at
LI
were
-‐17.7
to
-‐19.8°C.
Maximum
temperatures
at
LI
for
cells
in
the
Northeast,
Midwest,
and
the
Southeast
were
0.1-‐0.2°C
different
from
one
another
and
1.9-‐2.1°C
different
from
maximum
temperatures
at
LI
for
cells
in
the
Southeast.
These
results
reinforce
the
fact
that
glaciation
in
cloud
tops
generally
begins
around
-‐15
to
-‐20°C
and,
therefore,
since
ice
particles
play
a
significant
role
in
charge
distribution
within
a
cloud,
LI
does
not
occur
earlier
or
at
warmer
temperatures.
The
overall
maximum
cloud-‐top
cooling
and
cooling
rate
occurred
in
storms
in
the
Southeast,
especially
along
the
Gulf
Coast.
Maximum
cooling
rates
occurred
at
or
very
near
the
time
of
lightning
initiation
for
cells
in
the
Northeast,
Southeast,
and
Haegele
30
Midwest
while
cells
in
the
Great
Plains
and
Rockies
had
maximum
cooling
rates
about
10-‐minutes
prior
to
lightning
initiation.
There
was
moderate
correlation
between
cloud-‐top
temperature
at
LI
and
the
amount
of
IC
lightning
over
the
next
30
minutes
in
storms
over
all
four
regions.
Storms
in
the
Northeast,
Southeast,
and
Midwest
had
greater
amounts
of
cloud
lightning
when
lightning
initiation
occurred
at
colder
temperatures.
Therefore
the
cloud-‐top
temperature
at
lightning
initiation
can
possibly
determine
the
intensity
of
storms,
in
terms
of
IC
lightning.
Opposingly,
storms
in
the
Great
Plains
and
Rockies
had
greater
amounts
of
cloud
lightning
at
warmer
temperatures.
However,
more
samples
from
this
region
would
help
determine
the
strength
of
the
correlation.
As
shown
in
Figure
4.3,
it
appears
the
direction
of
the
trend
line
is
highly
influenced
by
two
data
points
near
-‐20°C
that
show
very
high
lightning
totals.
Larger
sample
sizes
would
help
determine
if
this
opposing
relationship,
compared
to
the
other
regions,
is
valid
or
not.
Further
efforts
to
relate
lightning
initiation
temperature
to
atmospheric
parameters
measurable
by
soundings
have
been
generally
inconclusive.
There
were
no
significant
region-‐to-‐region
correlations
between
RAP
sounding
parameters
and
cloud-‐top
temperatures
and
lightning
activity.
There
were
some
“stand
out”
moderate
correlations
including
cloud-‐top
temperature
at
LI
and
precipitable
water
(PW)
and
also
wet
bulb
zero
(WBZ)
heights
for
storm
in
the
Southeast.
Colder
cloud-‐
top
temperatures
coincided
with
greater
PW
values.
PW
values
ranged
from
approximately
1.5
to
2.25”
with
values
around
2-‐2.25”
coinciding
with
colder
cloud-‐
top
temperatures
and
lesser
amounts
of
cloud
lightning.
Wet
bulb
zero
heights
for
Haegele
31
storms
in
the
Southeast
occurred
from
approximately
10,000
to
13,600
feet
with
higher
WBZ
heights
coinciding
with
colder
temperatures
and
lesser
amounts
of
cloud
lightning.
In
the
northeast,
storms
had
level
of
free
convection
(LFC)
heights
ranging
from
approximately
2500
to
6500
feet
with
higher
LFC
heights
generally
coinciding
with
colder
cloud-‐top
temperatures
at
lightning
initiation
and
lesser
amounts
of
cloud
lightning.
In
the
Great
Plains
and
Rockies,
storms
in
environments
with
more
convective
available
potential
energy
(CAPE)
coincided
with
colder
cloud-‐top
temperatures
and
higher
amounts
of
cloud
lightning.
Subsequent
work
with
larger
sample
sizes,
including
GOES-‐16
ABI
derived
products
and
1-‐minute
imagery,
and
especially
optical
lightning
data
from
the
Geostationary
Lightning
Mapper
(GLM),
would
be
beneficial
in
confirming
results
described
herein.
Increasing
the
sample
size
to
include
more
cells
for
each
region
is
critical
in
the
accuracy
of
the
statistics
determined
in
the
results.
Including
storms
occurring
throughout
the
year
or
a
least
including
some
seasonal
variation
would
be
beneficial
in
determining
if
there
are
any
inter-‐seasonal
signatures
as
well
in
cloud-‐
top
temperatures
and
lightning
activity.
Increasing
temporal
resolution
by
using
1-‐
minute
satellite
imagery
may
allow
for
greater
detection
of
cloud-‐top
temperature
signatures.
The
most
anticipated
supplement
would
be
incorporating
GLM
data.
Using
GLM
data,
would
allow
for
validation
of
ground-‐based
lightning
detection
systems,
such
as
Earth
Networks
used
in
this
study,
and
also
validate
the
enhanced
usefulness
of
lightning
detection
that
the
GLM
promises.
Haegele
32
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