The Utility of Long-Range
Lightning Networks
A UH view
Steven Businger
Outline
! Pacific Lightning Detection Network (PacNet)
! Lightning, cloud ice, aerosol, rainfall relationships
! Lightning in Hurricanes
! Lightning data assimilation
Long-range Lightning Detection
Ionosphere-earth wave guide allows VLF (5-25 kHz) emissions (sferics) from cloud to ground strikes to propagate thousands of km. Best propagation is over ocean and at night.
PacNet Sensor Sites
Currently 4 sensors installed at Dutch Harbor, Lihue, Kona and Kwajalein. Sensors in North-America and Japan contribute.
IMPACT ESP Sensor in Lihue, Kauai
Synoptic View
Like GOES, long-range lightning data provide a synoptic view over data-sparse oceans (left). Lightning strikes match broad pattern of rainfall from AMSR-E (right).
Synoptic View
Like GOES, long-range lightning data provide a synoptic view over data-sparse oceans.
Synoptic View
Time series of daily total lightning strikes
Synoptic View
Histogram of monthly mean lightning strikes.
GOES 12 IR Image 9-21-05 1545Z; Lightning Overlaid from 15Z - 16ZMinimum Central Pressure 945 mb; 3 hours into Rapid Intensification
(30 Kts increase in M.S.W. in 24 hrs - John Kaplan HRD)
Rita
Lightning Signatures in
Tropical Cyclones
Katrina
Animation of lightning distribution in Katrina
Detection
Efficiency
The detection
efficiency (%) of the
long-range lightning
network must be
taken into account in
quantitative
applications of the
lightning strike data.
2005 Hurricanes Were
Unusually Active in Lightning
0
200
400
600
800
1,000
1,200
0-25 25-50 50-75 75-100 100-125 125-150 150-175 175-200 200-225 225-250 250-275 275-300
0130Z
0300Z
0730Z
0900Z
1330Z
1500Z
1630Z
1745Z
1930Z
2100Z
2230Z
Hurricane Jerry had highest previously published lightning rate with ~ 600 strikes within 60 km of eyewall
Rita had >1300 strikes within 50 km of eyewall
Rita
Central Sea-Level Pressure vs Lightning Rate
Rita
Aircraft radar reflectivity and lightning strikes
1513 UTC – 1533 UTC 21 September.
Rita
TRMM 87 GHz and Ice Product w/ lightning strikes
1506 UTC – 1617 UTC 21 September.
RitaAircraft radar reflectivity
TRMM ice product
and lightning strikes
1506 UTC – 1617 UTC 21
September.
Rita
Aircraft radar reflectivity and lightning strikes
1452 UTC 22 September.
Conceptual Model
During high lightning rates: eye wall is steep and eye is
small, vertical velocities and ice concentrations are large.
During low lightning rates: eye is large, eye wall is
sloped, vertical velocities are modest, and ice
concentrations are low.
" Domain divided into 0.5˚ x 0.5˚ grid
" Lightning rates from Long-Range Network
" Rainfall rate from NASA AMSR-E and TMI sensors
" Lightning strokes occurring within ±15 min of satellite overpass time are counted
" Lightning count and average rainfall are computed over each square
Methodology to Determine Relationship
of Lightning to Rainfall
28 February 2004
Lightning - Convective Rainfall Relationship
Composite analysis of 15 storms in the central Pacific. Blue line is fitted function where R is rainfall rate and L lightning rate. Data points were obtained using cumulative probability matching technique (Calheiros and Zawadski 1987)
Lightning Data Assimilation
into a NWP Model
Two approaches:Four-Dimensional Data Assimilation (FDDA) of Moisture ProfilesLatent heating assimilation
NWP Model Description
PSU/NCAR Mesoscale Model (MM5) 40 vertical levels and 27-km grid spacingKain-Fritch convective parameterizationReisner graupel explicit moisture scheme
Model integration 60 hInitialization 00Z or 12Z
Initial conditions from GFS-model
Boundary conditions every 6 hours
Assimilation 8 h
Moisture profiles
0
5
10
15
20
25
30
35
40
0 0.002 0.004 0.006 0.008
Mixing Ratio (kg/kg)
Sig
ma L
evel
No rain
1-3 mm/h
3-6 mm/h
>6 mm/h
Assimilation of Lightning Data" Compute lightning rates over 0.25º x 0.25º squares and 30 min time
window during the whole assimilation period" Use lightning-rainfall relationship to match lightning rate with moisture
profile." Radius of influence of observations in horizontal = 54 km" Time window of influence = ±15 min" Nudging every second time step if observed value is higher than
model computed value.
Lightning observations 09-12Z 12/19/2002
Observed Sea-level Pressure (left) and ETA 24-hr SLP and rainfall forecasts valid at 12 UTC 19 December 2002 (middle), show a 11mb forecast error in storm central pressure (12 hr forecast shows 9mb error).
972
983
North-East Pacific Low 19 December 2002
972
L 983L 971
Reduced Forecast Error over the Eastern Pacific
Assimilation of lightning data results in a significantly improved forecast of storm central pressure.
Latent Heating Approach
Sample convective latent heating profiles over the NE Pacific storm for 1-2 mm/h and 5-6 mm/h rain rates.
Latent Heating (convective)
0
5
10
15
20
25
30
35
40
-200 -150 -100 -50 0 50 100 150
K/day
Mo
del Level
1-2 mm/h
5-6 mm/h
Rainfall rates derived from lightning observations are used to scale model’s latent heating profiles
972
L 983
Reduced Forecast Error over the Eastern Pacific
Assimilation of lightning data results in a significantly improved forecast of storm central pressure.
L 976
Sea-Level Pressure 19-20 Dec. 2002
968
970
972
974
976
978
980
982
984
986
0 6 12 18 24
Time
SLP
(m
b)
FDDA
CTRL
OBS
LAT
Time Series of Storm Central
Sea-Level Pressure
Assimilation 8h
AcknowledgementsThe authors would like to thank ONR and NASA for support of PacNet.
References
Pessi, A.T., S. Businger, T. Cherubini, K. L. Cummins, and T. Turner, 2005: Toward the assimilation of lightning data over the Pacific Ocean into a mesoscale NWP model. 85th Annual AMS Meeting held in San Diego, CA.
Squires, K. and S. Businger, 2006: The Morphology of Eyewall Cloud to Ground Lightning Outbreaks in Two Category Five Hurricanes. MWR, in review.