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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
slide2
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
slide3
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

slide5
Velocity Field: Widespread Showers (5dB overlaid threshold; data displayed to 230 km)
slide8
Measurements of Rain
  • R(Z) relations
  • Error sources
  • Procedure on the WSR-88D
slide9
Reflectivity Factor

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

slide10
Rain Rate Error Sources
  • *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
slide11
DSDs, R(Z), and R(disdrometer)

Sep 11, 1999

Log(N)

Log(N)

slide14
Applications of Polarization
  • Polarimetric Variables
  • Measurements of Rain
  • Measurements of Snow
  • Classification of Precipitation
slide15
Polarimetric Variables
  • 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
slide16
Rainfall Relation R(KDP, 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

slide20
R(gauges)

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

R(gauges)-R(Z)

R(gauges)-R(KDPZDR)

slide21
Fundamental Problems in Remote Sensing of Precipitation
  • Classification - what is where?
  • Quantification - what is the amount?
slide25
Scores for hydrometeor classes

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

suggestions
Suggestions
  • 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)
slide35
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 =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
slide37
Radar Echo Classifier
  • 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
slide38
AP Detection Algorithm
  • 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
slide39
Clutter

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
slide40
AP Weighting Functions

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

slide41
Field of Weights for AP Clutter

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

slide42
Reflectivity

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

slide43
Storm-Scale Prediction
  • 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
slide44
7 pm

8 pm

6 pm

4 hr

3 hr

2 hr

Forecast w/Radar

Radar

slide45
7 pm

8 pm

6 pm

Radar

4 hr

3 hr

2 hr

Fcst w/o Radar

slide46
R(Z) for Snow and Ice Water Content

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)

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