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Weather Radar Data. Doppler Spectral Moments Reflectivity factor Z Mean Velocity v Spectrum width  v Polarimetric Variables Differential Reflectivity Z DR Specific Differential Phase Correlation Coefficient  hv Linear Depolarization Ratio L DR.

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Weather radar data
Weather Radar Data

  • Doppler Spectral Moments

    • Reflectivity factor Z

    • Mean Velocity v

    • Spectrum width v

  • Polarimetric Variables

    • Differential Reflectivity ZDR

    • Specific Differential Phase

    • Correlation Coefficient hv

    • Linear Depolarization Ratio LDR


Contributors to Measurement Errors*1) Widespread spatial distribution of scatterers (range ambiguities)*2) Large velocity distribution (velocity ambiguities)3) Antenna sidelobes 4) Antenna motion*5) Ground clutter (regular and anomalous propagation)*6) Non meteorological scatterers (birds, etc.)*7) Finite dwell time 8) Receiver noise*9) Radar calibration *--- these can be somewhat mitigated


Mitigation of Range Ambiguities

Uniform PRTs

Alternate batches of long (for Z) and short

(for velocity) PRTS.

El = 19.5o

Long PRTs (first PPI scan) for reflectivity ra>460 km;

Short PRTs (second PPI scan) for velocity, ra <200km; typically 150 km

7 Scans

= 5.25

= 4.3

5 Scans

= 2.4

= 1.45

4 Scans

= 0.5


Reflectivity Field of Widespread Showers(Data displayed to 460 km)





Measurements of Rain data displayed to 230 km)

  • R(Z) relations

  • Error sources

  • Procedure on the WSR-88D


Reflectivity Factor data displayed to 230 km)

Rainfall Rate Relations

Marshall-Palmer:

Z = 200 R1.6

Z(mm6 m-3); R(mm h-1)

For WSR-88D:

Z = 300 R1.4 - convective rain

Z = 200 R1.2 - tropical rain


Rain Rate Error Sources data displayed to 230 km)

  • *1) Radar calibration

  • 2) Height of measurements

  • *3) Attenuation

  • 4) Incomplete beam filling

  • *5) Evaporation

  • *6) Beam blockage

  • 7) Gradients of rain rate

  • 8) Vertical air motions

  • *9) Variability in DSD


DSDs, R(Z), and R(disdrometer) data displayed to 230 km)

Sep 11, 1999

Log(N)

Log(N)


DSD’s, R(Z), and R(disrometer) data displayed to 230 km)

Dec 3, 1999

Log(N)



Applications of Polarization data displayed to 230 km)

  • Polarimetric Variables

  • Measurements of Rain

  • Measurements of Snow

  • Classification of Precipitation


Polarimetric Variables data displayed to 230 km)

  • Quantitative - Zh, ZDR, KDP

  • Qualitative - |hv(0)|, , LDR, xv, hv

  • Are not independent

  • Are related to precipitation parameters

  • Relations among hydrometeor parameters allow retrieval of bulk precipitation properties and amounts


Rainfall Relation R(K data displayed to 230 km)DP, ZDR)

R(KDP, ZDR) = 52 KDP0.96 ZDR-0.447

- is least sensitive to the variation of the median drop diameter Do

- is valid for a 11 cm wavelength


Scatergrams: R(Z) and R(K data displayed to 230 km)DP, ZDR)vs Rain Gauge


Sensitivity to Hail data displayed to 230 km)


R(gauges) data displayed to 230 km)

Area Mean Rain Rate and BiasR(gauges)-R(radar)

R(gauges)-R(Z)

R(gauges)-R(KDPZDR)


Fundamental Problems in Remote Sensing of Precipitation data displayed to 230 km)

  • Classification - what is where?

  • Quantification - what is the amount?


Weighting Functions data displayed to 230 km)


Partitions in the Z data displayed to 230 km)h, ZDR Space into Regions of Hydrometeor Types


Weighting Function for data displayed to 230 km)Moderate Rain WMR(Zh, ZDR)


Scores for hydrometeor classes data displayed to 230 km)

Ai= multiplicative factor 1

Wj = weighting function of two variables assigned to the class j

Yi= a variable other than reflectivity (T, ZDR, KDP, hv, LDR)

j = hydrometeor class, one the following: light rain,

moderate rain, rain with large drops, rain/hail mixture,

small hail, dry snow, wet snow, horizontal crystals,

vertical crystals, other

Class j for which Sj is a maximum is chosen as the correct one


Florida data displayed to 230 km)


Florida data displayed to 230 km)


Florida data displayed to 230 km)


Florida data displayed to 230 km)


Florida data displayed to 230 km)





Suggestions
Suggestions data displayed to 230 km)

  • Data quality - develop acceptance tests

  • Anomalous Propagation - consider “fuzzy logic” scheme

  • Classify precipitation into type (snow, hail, graupel, rain, bright band) even if only Z is available

  • Calibrate the radar (post operationally, use data, gauges, ..anything)


Specific Differential Phase at short wavelengths (3 and 5 cm)

  • Overcomes the effects of attenuation

  • Is more sensitive to rain rate

  • Is influenced by resonant scattering from large drops


Suggestions for polarimetric measurements at 3 and 5 cm
Suggestions for Polarimetric measurements at cm)=3 and 5 cm

  • Develop a classification scheme

  • Develop a R(KDP, ZDR) or other polarimetric relation to estimate rain

  • Correct Z for attenuation and ZDR for differential attenuation (use DP)

  • Use KDP to calibrate Z


Radar Echo Classifier cm)

  • Uses “fuzzy logic” technique

  • Base data Z, V, W used

  • Derived fields (“features”) are calculated

  • Weighting functions are applied to the feature fields to create “interest” fields

  • Interest fields are weighted and summed

  • Threshold applied, producing final algorithm output


AP Detection Algorithm cm)

  • Features derived from base data are:

    • Median radial velocity

    • Standard deviation of radial velocity

    • Median spectrum width

    • “Texture” of the reflectivity

    • Reflectivity variables “spin” and “sign”

      • Similar to texture

  • Computed over a local area


Clutter cm)

mean V

Clutter

texture Z

Weather

mean V

Weather

texture Z

Investigate data “features”

  • Feature distributions

    • AP Clutter

    • Precipitation

  • Best features have good separation between echo types


AP Weighting Functions cm)

Median Radial Velocity

Median Spectrum Width

1

1

“Texture” of Reflectivity

Standard Deviation of Radial Velocity

0

0

“Reflectivity Spin”

F) Spin

100

0 50

G) Sign

“Reflectivity Sign”

-10 -0.6 0 0.6

10


Field of Weights for AP Clutter cm)

AP Clutter

AP Clutter

For median velocity field,

the weighting function is:

1

Interest Field

Radial Velocity

0

Radial Velocity

+3 m/s

+3 m/s

-2.3

0

2.3

Weighting functions are

applied to the feature field

to create an “interest” field

Values scaled between 0-1


Reflectivity cm)

Radial Velocity

Example of APDA

using S-Pol data

from STEPS

Polarimetric truth field

given by the

Particle Identification

(PID) output

APDA is thresholded

at 0.5

Good agreement between PID clutter

and APDA

PID

APDA

Clutter

Rain

20 June 2000, 0234 UTC 0.5 degree elevation


Storm-Scale Prediction cm)

  • Sample 4-hour forecast from the Center for Analysis and Prediction of Storms’ Advanced Regional Prediction System (ARPS) – a full-physics mesoscale prediction system

  • For the Fort Worth forecast

    • 4-hour prediction

    • 3 km grid resolution

    • Model initial state included assimilation of

      • WSR-88D reflectivity and radial velocity data

      • Surface and upper-air data

      • Satellite and wind profiler data


7 pm cm)

8 pm

6 pm

4 hr

3 hr

2 hr

Forecast w/Radar

Radar


7 pm cm)

8 pm

6 pm

Radar

4 hr

3 hr

2 hr

Fcst w/o Radar


R(Z) for Snow and Ice Water Content cm)

Snow fall rate:

Z(mm6m-3) =75R2 ; R in mm h-1 of water

Ice Water Content:

IWC(gr m-3)= 0.446 (m)KDP(deg km-1)/(1-Zv/Zh)


Vertical Cross Sections cm)

Z

ZDR

KDP

hv



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