Assimilation of Pacific Lightning Data into Assimilation of Pacific Lightning Data into a Mesoscale NWP Modela Mesoscale NWP Model

Antti Pessi, Antti Pessi, Steven BusingerSteven Businger, and Tiziana Cherubini, and Tiziana Cherubini

University of HawaiiUniversity of Hawaii

K. Cummins, N. Demetriades, and T. TurnerK. Cummins, N. Demetriades, and T. Turner

Vaisala Thunderstorm Group Inc. Tucson, AZVaisala Thunderstorm Group Inc. Tucson, AZ

OutlineOutline

Long-range lightning detectionLong-range lightning detection PacNetPacNet Relating lightning rates to rainfall Relating lightning rates to rainfall

ratesrates Assimilating lightning data into MM5Assimilating lightning data into MM5 Case studies of impact of lightning Case studies of impact of lightning

datadata

Why lightning data?Why lightning data?

Very little conventional data Very little conventional data

(soundings, SYNOPs) over the Pacific(soundings, SYNOPs) over the Pacific Geostationary satellites: Geostationary satellites:

IR images - difficult to distinguish IR images - difficult to distinguish

between areas of active convection and anvil cloud.between areas of active convection and anvil cloud. Low-orbiting satellites: Passive and active sensors Low-orbiting satellites: Passive and active sensors

provide high resolution data (AMSR-E, TMI, SSM/I) provide high resolution data (AMSR-E, TMI, SSM/I) but only ~twice daily coverage at lower latitudes.but only ~twice daily coverage at lower latitudes.

Ground based radars: limited range (~300 km)Ground based radars: limited range (~300 km) Lightning data: continuous, available in ~ real time, Lightning data: continuous, available in ~ real time,

long-range (~3-5000 km)long-range (~3-5000 km)

Radiosonde sites

Long-range Lightning Long-range Lightning DetectionDetection

Ionosphere-earth wave guide allows VLF (5-25 kHz) Ionosphere-earth wave guide allows VLF (5-25 kHz) emissions (sferics) to propagate thousands of kmemissions (sferics) to propagate thousands of km

Best propagation during night and over oceanBest propagation during night and over ocean

Vertical electric field Vertical electric field waveforms for cloud-to-ground waveforms for cloud-to-ground return strokes at three return strokes at three different distances. Note the different distances. Note the increased complexity and increased complexity and lower frequency content of the lower frequency content of the waveforms at longer waveforms at longer distances.distances.

LF/VLF Lightning Waveforms at Various

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 SitesPacNet Sensor Sites

Currently 4 sensors installed at Dutch Harbor, Lihue, Kona and Kwajalein. Sensors in North-America and Japan contributing

IMPACT ESP Sensor in Lihue, Kauai

5 Days of Pacific Lightning 5 Days of Pacific Lightning ActivityActivity

PacNet Performance ProjectionsDetection Efficiency (DE) with NALDN

Day Time

Night Time

Diurnal Variation of PacNet LightningDiurnal 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 RatioLightning - Rainfall Ratio

The lightning rainfall relationship may vary significantly, depending on air-mass characteristics and cloud microphysics

Over a particular climatic regime and a limited geographic region, lightning is well correlated to convective rainfall (Zipser 1994).

Specify typical lightning-rainfall ratio for various storm systems over the Pacific: extraropical cyclones, kona lows, TUTTs, tropical cyclones...

Warm Season Normalized Rain-Yields

Warm season normalized CG flash density vs rainfall Sloping black lines are contours of constant rain yield (kg/fl)(Petersen and Rutledge 1998)

Domain divided into 0.5˚ x Domain divided into 0.5˚ x 0.5˚ grid0.5˚ grid

Lightning rates from Long-Lightning rates from Long-Range NetworkRange Network

Rainfall rate from AMSR-E Rainfall rate from AMSR-E and TMI sensors and TMI sensors

Lightning strokes occurring Lightning strokes occurring within ±15 min of satellite within ±15 min of satellite overpass time are counted overpass time are counted

Lightning count and average Lightning count and average rainfall are computed over rainfall are computed over each squareeach square

Methodology to determine Methodology to determine lightning vs rainfall ratiolightning vs rainfall ratio

Extratropical storm in the northeast PacificDecember 2002

Satellite rainfall measurementsSatellite rainfall measurements Aqua’s AMSR/E- Advanced Microwave Scanning Radiometer-EOSAqua’s AMSR/E- Advanced Microwave Scanning Radiometer-EOS Orbiting at 705 km, 70 degrees inclinationOrbiting at 705 km, 70 degrees inclination Cloud properties; precipitation (total, convective); radiative energy flux; land Cloud properties; precipitation (total, convective); radiative energy flux; land

surface wetness; sea ice; snow cover; SST; SS wind fieldssurface wetness; sea ice; snow cover; SST; SS wind fields 12 channels at six discrete frequencies in the range of 6.9 to 89 GHz12 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 Goddard Profiling Algorithm (GPROF) is used to calculate rainfall rates using

brightness temperaturesbrightness temperatures Convective part of total rainfall:Convective part of total rainfall:

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 Cumulative Probability Probability Matching Matching

TechniqueTechnique

Take cumulative probability Take cumulative probability for rainfall every 0.2 mmfor rainfall every 0.2 mm

The corresponding number The corresponding number of flashes can be found of flashes can be found taking the same probability taking the same probability for lightning strokes for lightning strokes

Cumulative Probability Distribution (Satellite Rainfall)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6

Convective Rainfall (mm/h)

Cum. prob.

Cumulative Probability Distribution (Lightning Strokes)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40

Strokes

Cum. prob.

Lightning - Convective RainfallLightning - Convective Rainfall

Composite analysis of 15 storms in the central Pacific. Blue line is fitted function where R is rainfall rate and L lightning rate.

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R = 2.2L0.52

Rainfall Assimilation into MM5

o Alexander et al. (1999) found relatively good correlation between convective rainfall and lightning rates during the 1993 Superstorm.

o 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

o 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).

R0 convective rainfall rate Nh normalized parabolic heating function T model predicted temp. from latent heating p*=psfc-ptop€

∂p * T

∂t= p *

L

Cpg ρ liq R0 Nh

PSU/NCAR Mesoscale Model (MM5) PSU/NCAR Mesoscale Model (MM5) Limited-area, nonhydrostatic, Limited-area, nonhydrostatic,

terrain-following, sigma-coordinate terrain-following, sigma-coordinate modelmodel

27 km grid spacing, 39 vertical levels27 km grid spacing, 39 vertical levels Kain-Fritch convective Kain-Fritch convective

parameterizationparameterization FDDA FDDA

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are needed to see this picture.

MM5 Model DescriptionMM5 Model Description

Four Dimensional Data Four Dimensional Data Assimilation (FDDA)Assimilation (FDDA)

Lightning observations are mapped to vertical moisture profiles Lightning observations are mapped to vertical moisture profiles (e.g. Papadopoulos et al. 2004)(e.g. Papadopoulos et al. 2004)

Vertical moisture profiles are assimilated using MM5 FDDAVertical moisture profiles are assimilated using MM5 FDDA Newtonian nudging (or relaxation) nudges the model state Newtonian nudging (or relaxation) nudges the model state

toward the observations by adding artificial tendency terms to toward the observations by adding artificial tendency terms to prognostic equations based on the difference between model- prognostic equations based on the difference between model- and observed states.and observed states.

The following were defined:The following were defined:

- Obs nudging radius of influence in horizontal and vertical- Obs nudging radius of influence in horizontal and vertical

- Time window of influence- Time window of influence

- Nudging coefficient G- Nudging coefficient G

FDDAFDDA

• : model's physical forcing terms: model's physical forcing terms• : nudging coefficient (relative magnitude of term): nudging coefficient (relative magnitude of term)• : 4-D weighting function (used to determine G): 4-D weighting function (used to determine G)• : observational quality factor (0-1): observational quality factor (0-1)• : locally observed mixing ratio: locally observed mixing ratio• : model mixing ratio interpolated to observation location: model mixing ratio interpolated to observation location

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∂p*q

∂t= F(q, x j , t) + Gq • p*

[∑i=1

N

W i2(x j , t) • γ i • (qo − ˆ q )i]

∑i=1

N

W i(x j ,t )

€

ˆ q

€

qo

€

γ€

F

€

G

€

W

The model equations are written in "flux" form, where the prognostic The model equations are written in "flux" form, where the prognostic variables for horizontal wind, temperature, and mixing ratio are mass variables for horizontal wind, temperature, and mixing ratio are mass weighted by p*. p* = pweighted by p*. p* = pss - p - ptt where p where pss is surface pressure and p is surface pressure and ptt is is

constant pressure at the top of the modelconstant pressure at the top of the model

Experiment Design

Horizontal radius of influence R=54 km, vertical 0.001 sigma

Time window of influence ±15 min Model timestep 81 sec, nudging every second

timestep Nudging only if observed value is higher than

model computed value

Model integration 60 hInitialization 00Z or 12Z

Initial conditions from GFS-model

Boundary conditions every 6 hours

R

Assimilation 8 h

Obs=lightninggridpoint

Construction of Moisture ProfilesConstruction of Moisture Profiles Seven vertical moisture profiles typical for a range of rainfall Seven vertical moisture profiles typical for a range of rainfall

rates constructed using MM5 data:rates constructed using MM5 data:

- - Go through each grid point over the stormGo through each grid point over the storm

- Bin rainfall and corresponding moisture profile into - Bin rainfall and corresponding moisture profile into

one of 7 categoriesone of 7 categories

- Make a composite of all gridpoint values which results in 7 - Make a composite of all gridpoint values which results in 7 rainfall rainfall and moisture profile categories and moisture profile categories

Compute lightning rates over 0.25Compute lightning rates over 0.25ºº x 0.25 x 0.25ºº squares and 30 squares and 30 min time window during the whole assimilation periodmin time window during the whole assimilation period

Moisture profiles

0

5

10

15

20

25

30

35

40

0 0.002 0.004 0.006 0.008

Mixing Ratio (kg/kg)

Sigma Level

No rain

1-3 mm/h

3-6 mm/h

>6 mm/h

Convert of Lightning Rate to Convert of Lightning Rate to Moisture ProfileMoisture Profile

Use lightning-rainfall relationship to relate lightning rate Use lightning-rainfall relationship to relate lightning rate with moisture profile. The relationship has been derived with moisture profile. The relationship has been derived by comparing lightning rates with rainfall rates from by comparing lightning rates with rainfall rates from TRMM and Aqua TRMM and Aqua

Lightning rate Lightning rate =>=> rainfall rate rainfall rate ==> moisture profile> moisture profile

Moisture profiles

0

5

10

15

20

25

30

35

40

0 0.002 0.004 0.006 0.008

Mixing Ratio (kg/kg)

Sigma Level

No rain

1-3 mm/h

3-6 mm/h

>6 mm/h

Model NudgingModel Nudging

Nudge MM5 initial and computed moisture profiles Nudging only if observed value is higher than model

computed value

3 strokes btw 0:00 and 0:30=> obs. time 0:15

5 strokes btw 0:15 and 0:45=> obs. time 0:30

2 strokes btw 0:30 and 1:00=> obs. time 0:45

Obs. nudging

Mo

de

l integra

tion

Model area

Case StudiesCase Studies

Impact of PacNet Lightning Impact of PacNet Lightning DataData

Timing of Squall Line Over Hawaii

Six-hour MM5 control forecast for rainband position was off by ~150 km at 06UTC, 28 February 2004.

Lightning strikes between 5-7 UTC on 28 Feb. 2004.

*

**

Six-hour MM5 FDDA forecast improved surface pressure and wind forecasts for 06UTC, 28 February 2004.

LL10001000

Timing of Squall Line Over Hawaii

Lightning observations Lightning observations 09-12Z 12/19/200209-12Z 12/19/2002

Observed Sea-level Pressure (left) and ETA 24-hr Observed Sea-level Pressure (left) and ETA 24-hr SLP and rainfall forecasts valid at 12 UTC 19 SLP and rainfall forecasts valid at 12 UTC 19 December 2002 (middle), show a December 2002 (middle), show a 11mb forecast 11mb forecast error in storm central pressureerror in storm central pressure (12 hr forecast (12 hr forecast shows 9mb error).shows 9mb error).

972972

983983

North-East Pacific Low 19 December 2002North-East Pacific Low 19 December 2002

972972

L 983L 972

Reducing Forecast Error over the Eastern PacificReducing Forecast Error over the Eastern Pacific

Assimilation of lightning data results Assimilation of lightning data results in a significantly improved forecast of in a significantly improved forecast of storm central pressure.storm central pressure.

North-East Pacific Low 19 Dec. 2002North-East Pacific Low 19 Dec. 2002

Lightning Strokes06-09Z (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. 2002North-East Pacific Low 19 Dec. 2002

Discussion and future workDiscussion and future work MM5 FDDA was used to assimilate vertical moisture profiles derived from MM5 FDDA was used to assimilate vertical moisture profiles derived from

lightning observationslightning observations Assimilating moisture profiles resulted in correct surface pressure vs. 11mb Assimilating moisture profiles resulted in correct surface pressure vs. 11mb

error in CTRL run for Dec. 2002 storm.error in CTRL run for Dec. 2002 storm. Assimilating moisture profiles also improved simulation of squall line over Assimilating moisture profiles also improved simulation of squall line over

Hawaii.Hawaii. Results are sensitive to nudging coefficient and to moisture profilesResults are sensitive to nudging coefficient and to moisture profiles Increasing G (nudging coefficient) has the same effect as increasing Increasing G (nudging coefficient) has the same effect as increasing

moisture values but is numerically unstable if G≥1/∆t. This experiment moisture values but is numerically unstable if G≥1/∆t. This experiment proved to be unstable even at larger G (G=0.01) proved to be unstable even at larger G (G=0.01)

FUTURE WORKFUTURE WORK Uncertainties remain in lightning-rainfall-moisture relationshipUncertainties remain in lightning-rainfall-moisture relationship Refine methods for finding and assimilating more realistic moisture profiles Refine methods for finding and assimilating more realistic moisture profiles Investigate latent heating profiles using MM5 3/4D-VarInvestigate latent heating profiles using MM5 3/4D-Var Method can be made operational relatively easily by allowingMethod can be made operational relatively easily by allowing

8 hours of assimilation in the beginning of the model run8 hours of assimilation in the beginning of the model run

AcknowledgementsAcknowledgementsThe authors would like to thank ONR The authors would like to thank ONR and NASA for support of PacNet.and NASA for support of PacNet.

Questions?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/FDDAMM5 Control/FDDASquall Line over Hawaii 28 Feb. 2004Squall Line over Hawaii 28 Feb. 2004

MM5 Control/FDDAMM5 Control/FDDASquall Line over Hawaii 28 Feb. 2004Squall Line over Hawaii 28 Feb. 2004

MM5 Control/FDDAMM5 Control/FDDANE Pacific Low 19 Dec. 2002NE Pacific Low 19 Dec. 2002