Assimilation of Pacific Lightning Data into a Mesoscale NWP Model
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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. Long-range lightning detection PacNet

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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
Outline Model

  • 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
Why lightning data? Model

  • Very little conventional data

    (soundings, SYNOPs) over the Pacific

  • Geostationary satellites:

    IR images - difficult to distinguish

    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)

Radiosonde sites


Long range lightning detection
Long-range Lightning Detection Model

  • 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 Model

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) Model 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 Model

PacNet Sensor Sites

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



Pacnet performance projections detection efficiency de with naldn
PacNet Performance Projections ModelDetection Efficiency (DE) with NALDN

Day Time

Night Time


Diurnal variation of pacnet lightning
Diurnal Variation of PacNet Lightning Model

Average number of lightning strokes observed at each hour over the N. Pacific (45 day average). 10 UTC is midnight HST.


Lightning rainfall ratio
Lightning - Rainfall Ratio Model

Warm Season Normalized Rain-Yields

  • 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 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 0.5˚ grid Model

Lightning rates from Long-Range 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

Methodology to determine lightning vs rainfall ratio

Extratropical storm in the northeast Pacific

December 2002


Satellite rainfall measurements
Satellite rainfall measurements Model

  • 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:

    • 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 matching technique
Cumulative Probability Matching Technique Model

  • Take cumulative probability for rainfall every 0.2 mm

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


Lightning - Convective Rainfall Model

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


  • R Model0 convective rainfall rate

  • Nh normalized parabolic heating function

  • T model predicted temp. from latent heating

  • p*=psfc-ptop

Rainfall Assimilation into MM5

  • 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).


Mm5 model description
MM5 Model Description Model

  • 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


Four dimensional data assimilation fdda
Four Dimensional Data Assimilation (FDDA) Model

  • 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 model- and observed states.

  • The following were defined:

    - Obs nudging radius of influence in horizontal and vertical

    - Time window of influence

    - Nudging coefficient G


FDDA Model

  • : model's physical forcing terms

  • : nudging coefficient (relative magnitude of term)

  • : 4-D weighting function (used to determine G)

  • : observational quality factor (0-1)

  • : locally observed mixing ratio

  • : 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 Model

  • 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

  • Initial conditions from GFS-model

  • Boundary conditions every 6 hours

Initialization

00Z or 12Z

Model integration 60 h

Assimilation 8 h

R

Obs=lightning

gridpoint


Construction of moisture profiles
Construction of Moisture Profiles Model

  • 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


Convert of Lightning Rate to Moisture Profile Model

  • 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


Model nudging
Model Nudging Model

  • Nudge MM5 initial and computed moisture profiles

  • Nudging only if observed value is higher than model computed value

Model area

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

Obs. nudging

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


Case studies

Case Studies Model

Impact of PacNet Lightning Data


Timing of Squall Line Over Hawaii Model

*

*

*

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 Model

L1000

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


North east pacific low 19 december 2002
North-East Pacific Low 19 December 2002 Model

983

972

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).


L Model983

L972

Reducing Forecast Error over the Eastern Pacific

972

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


North east pacific low 19 dec 2002
North-East Pacific Low 19 Dec. 2002 Model

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.



Discussion and future work
Discussion and future work Model

  • 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


Acknowledgements ModelThe authors would like to thank ONR and NASA for support of PacNet.


Questions? Model


Moving Toward Operational Assimilation Model

  • 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 ModelSquall Line over Hawaii 28 Feb. 2004


Mm5 control fdda squall line over hawaii 28 feb 20041
MM5 Control/FDDA ModelSquall Line over Hawaii 28 Feb. 2004


MM5 Control/FDDA ModelNE Pacific Low 19 Dec. 2002


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