Assimilation of Pacific Lightning Data into
a Mesoscale NWP Model
Antti Pessi, Steven Businger, and Tiziana Cherubini
University of Hawaii
K. Cummins, N. Demetriades, and T. Turner
Vaisala Thunderstorm Group Inc. Tucson, AZ
Outline
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Long-range lightning detection
PacNet
Relating lightning rates to rainfall rates
Assimilating lightning data into MM5
Case studies of impact of lightning data
Why lightning data?
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Very little conventional data
(soundings, SYNOPs) over the Pacific
Geostationary satellites:
IR images - difficult to distinguish
Radiosonde sites
between areas of active convection and anvil cloud.
Low-orbiting satellites: Passive and active sensors
provide high resolution data (AMSR-E, TMI, SSM/I)
but only ~twice daily coverage at lower latitudes.
Ground based radars: limited range (~300 km)
Lightning data: continuous, available in ~ real time,
long-range (~3-5000 km)
Long-range Lightning
Detection
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Ionosphere-earth wave guide allows VLF (5-25 kHz)
emissions (sferics) to propagate thousands of km
Best propagation during night and over ocean
LF/VLF Lightning Waveforms at Various Distances
Vertical electric field waveforms for cloud-to-ground return strokes
at three different distances. Note the increased complexity and lower
frequency content of the waveforms at longer distances.
The amplitude scale is not calibrated.
The time scale is in microseconds post digitizer trigger.
Pacific Lightning Detection Network (PacNet)
Motivation and goals
 PacNet is a network of long-range
lightning detectors in the Pacific
 Continuous, real-time monitoring of
convective storms over the Pacific
 Investigate the impact of data
assimilation of lightning derived
products on forecast accuracy in
regional NWP modeling (MM5,
WRF). In particular forecast
improvements for:
 Midlatitude, subtropical and
tropical cyclone intensity and
track
 Rainfall patterns and intensity
 Flash flood events
Antti Pessi and installation at Dutch Harbor, AK
PacNet Sensor Sites
IMPACT ESP Sensor in Lihue, Kauai
Currently 4 sensors installed at Dutch Harbor, Lihue, Kona and
Kwajalein. Sensors in North-America and Japan contributing
5 Days of Pacific Lightning Activity
PacNet Performance Projections
Detection Efficiency (DE) with NALDN
Day Time
Night Time
Diurnal Variation of PacNet Lightning
Average number of lightning strokes observed at each hour over
the N. Pacific (45 day average). 10 UTC is midnight HST.
Lightning - Rainfall Ratio
Warm Season Normalized Rain-Yields
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The lightning rainfall relationship
may vary significantly, depending
on air-mass characteristics and
cloud microphysics
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Over a particular climatic regime
and a limited geographic region,
lightning is well correlated to
convective rainfall (Zipser 1994).
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Specify typical lightning-rainfall
ratio for various storm systems
over the Pacific: extraropical
cyclones, kona lows, TUTTs,
tropical cyclones...
Warm season normalized CG flash density vs rainfall
Sloping black lines are contours of constant rain yield (kg/fl)
(Petersen and Rutledge 1998)
Methodology to determine
lightning vs rainfall ratio
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Domain divided into 0.5˚ x
0.5˚ grid
Lightning rates from LongRange Network
Rainfall rate from 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
Extratropical storm in the northeast Pacific
December 2002
Satellite rainfall measurements
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Aqua’s AMSR/E- Advanced Microwave Scanning Radiometer-EOS
Orbiting at 705 km, 70 degrees inclination
Cloud properties; precipitation (total, convective); radiative energy flux; land
surface wetness; sea ice; snow cover; SST; SS wind fields
12 channels at six discrete frequencies in the range of 6.9 to 89 GHz
Goddard Profiling Algorithm (GPROF) is used to calculate rainfall rates using
brightness temperatures
Convective part of total rainfall:
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measures of the local horizontal gradient of brightness temperatures
polarization of 85.5 GHz scattering signatures
TRMM’s TMI: lower resolution, inclination 40˚.
Aqua with its AMSR-E on top left
Cumulative Probability Distribution (Satellite Rainfall)
1
0.9
Cumulative
Probability
Matching
Technique
0.8
0.7
Cum. prob.
0.6
0.5
0.4
0.3
0.2
0.1
0
0
1
2
3
4
5
Convective Rainfall (mm/h)
Cumulative Probability Distribution (Lightning Strokes)
1
0.9
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0.8
0.7
Cum. prob.
0.6
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0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
Strokes
25
30
35
40
Take cumulative probability
for rainfall every 0.2 mm
The corresponding number
of flashes can be found
taking the same probability
for lightning strokes
6
Lightning - Convective Rainfall
Composite analysis of 15 storms in the central Pacific. Blue line is fitted
function R  2.2L0.52 where R is rainfall rate and L lightning rate.
Rainfall Assimilation into MM5
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Alexander et al. (1999) found relatively good correlation between
convective rainfall and lightning rates during the 1993 Superstorm.
They used a normalized parabolic heating profile, with heating max at
~500mb, to vertically distribute the total latent heating from the observed
rain through the temperature tendency equation
Resulted in improved numerical forecasts by assimilating latent heating
rates derived from lightning and satellite data. (rainfall from SSM/I,
lightning from NLDN and VLF networks).
p * T
L
 p * g liq R 0 Nh
t
Cp
R0 convective rainfall rate
 N normalized parabolic heating function
h
 T model predicted temp. from latent heating
 p*=p
sfc-ptop
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MM5 Model Description
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PSU/NCAR Mesoscale Model (MM5)
Limited-area, nonhydrostatic,
terrain-following, sigma-coordinate
model
27 km grid spacing, 39 vertical levels
Kain-Fritch convective
parameterization
FDDA
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Four Dimensional Data
Assimilation (FDDA)
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Lightning observations are mapped to vertical moisture profiles
(e.g. Papadopoulos et al. 2004)
Vertical moisture profiles are assimilated using MM5 FDDA
Newtonian nudging (or relaxation) nudges the model state
toward the observations by adding artificial tendency terms to
prognostic equations based on the difference between modeland observed states.
The following were defined:
- Obs nudging radius of influence in horizontal and vertical
- Time window of influence
- Nudging coefficient G
FDDA
N
p q
*
 F(q, x j ,t)  Gq  p
t
*
[W i (x j ,t)   i  (qo  qˆ ) i ]
2
i1
N
 W i(x j ,t )
i1
•
•
•
•
•
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F : model's physical forcing terms
G : nudging coefficient (relative magnitude of term)
W : 4-D weighting function (used to determine G)
 : observational quality factor (0-1)
qo : locally observed mixing ratio
qˆ : model mixing ratio interpolated to observation location
The model equations are written in "flux" form, where the prognostic
variables for horizontal wind, temperature, and mixing ratio are mass
weighted by p*. p* = ps - pt where ps is surface pressure and pt is
constant pressure at the top of the model
Experiment Design
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Initial conditions from GFS-model
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Boundary conditions every 6 hours
Initialization
00Z or 12Z
Model integration 60 h
Assimilation 8 h
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Horizontal radius of influence R=54 km, vertical
0.001 sigma
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Time window of influence ±15 min
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Model timestep 81 sec, nudging every second
timestep
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Nudging only if observed value is higher than
model computed value
R
Obs=lightning
gridpoint
Construction of Moisture Profiles
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Seven vertical moisture profiles typical for a range of rainfall
rates constructed using MM5 data:
- Go through each grid point over the storm
- Bin rainfall and corresponding moisture profile into
one of 7 categories
- Make a composite of all gridpoint values which results in 7 rainfall
and moisture profile categories
Compute lightning rates over 0.25º x 0.25º squares and 30
min time window during the whole assimilation period
Moisture profiles
Mixing Ratio (kg/kg)
0
0.002
0.004
0.006
0.008
0
5
10
Sigma Level
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15
20
25
30
35
40
No rain
1-3 mm/h
3-6 mm/h
>6 mm/h
Convert of Lightning Rate to
Moisture Profile
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Use lightning-rainfall relationship to relate lightning rate
with moisture profile. The relationship has been derived
by comparing lightning rates with rainfall rates from
TRMM and Aqua
Lightning rate => rainfall rate => moisture profile
Moisture profiles
Mixing Ratio (kg/kg)
0
0.002
0.004
0.006
0.008
0
5
10
Sigma Level
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15
20
25
30
35
40
No rain
1-3 mm/h
3-6 mm/h
>6 mm/h
Model Nudging
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Nudge MM5 initial and computed moisture profiles
Nudging only if observed value is higher than model
computed value
Model area
5 strokes btw 0:15 and 0:45=> obs. time 0:30
2 strokes btw 0:30 and 1:00=> obs. time 0:45
Model integration
3 strokes btw 0:00 and 0:30=> obs. time 0:15
Case Studies
Impact of PacNet Lightning Data
Timing of Squall Line Over Hawaii
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Lightning strikes
between 5-7 UTC
on 28 Feb. 2004.
Six-hour MM5 control forecast for rainband position was
off by ~150 km at 06UTC, 28 February 2004.
Timing of Squall Line Over Hawaii
L1000
Six-hour MM5 FDDA forecast improved surface pressure and
wind forecasts for 06UTC, 28 February 2004.
North-East Pacific Low 19 December 2002
983
972
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).
Lightning observations
09-12Z 12/19/2002
Reducing Forecast Error
over the Eastern Pacific
Assimilation of lightning data results
in a significantly improved forecast
of storm central pressure.
972
L 972
L 983
North-East Pacific Low 19 Dec. 2002
Lightning Strokes
06-09Z
(last assim. time 08Z)
Central sea-level pressure of simulated storm with
lightning nudging is 10 mb deeper than control run.
North-East Pacific Low 19 Dec. 2002
Discussion and future work
MM5 FDDA was used to assimilate vertical moisture profiles derived from
lightning observations
 Assimilating moisture profiles resulted in correct surface pressure vs. 11mb
error in CTRL run for Dec. 2002 storm.
 Assimilating moisture profiles also improved simulation of squall line over
Hawaii.
 Results are sensitive to nudging coefficient and to moisture profiles
 Increasing G (nudging coefficient) has the same effect as increasing
moisture values but is numerically unstable if G≥1/∆t. This experiment
proved to be unstable even at larger G (G=0.01)
FUTURE WORK
 Uncertainties remain in lightning-rainfall-moisture relationship
 Refine methods for finding and assimilating more realistic moisture profiles
 Investigate latent heating profiles using MM5 3/4D-Var
 Method can be made operational relatively easily by allowing
8 hours of assimilation in the beginning of the model run
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Acknowledgements
The authors would like to thank ONR
and NASA for support of PacNet.
Questions?
Moving Toward Operational Assimilation
 MM5 forecast initialized at 00Z gets its initial
conditions from ETA-model at 03-04Z
 LAPS is run to initialize the model and integration is
started ~08Z
 Lightning data has only ~1/2 hour delay => it can be
assimilated 00Z - 07Z operationally
MM5 Control/FDDA
Squall Line over Hawaii 28 Feb. 2004
MM5 Control/FDDA
Squall Line over Hawaii 28 Feb. 2004
MM5 Control/FDDA
NE Pacific Low 19 Dec. 2002