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

7 Scans

5 Scans

Mitigation of Range Ambiguities

Uniform PRTs

Alternate batches of long (for Z) and short(for velocity) PRTS.

4 Scans

El = 19.5o

= 4.3= 5.25

= 2.4= 1.45= 0.5

Long PRTs (first PPI scan) for reflectivity ra>460 km;Short PRTs (second PPI scan) for velocity, ra <200km; typically 150 km

Reflectivity Field of

Widespread Showers

(Data displayed to

460 km)

Velocity Field: Widespread Showers (5dB overlaid threshold; data displayed to 230 km)

Spectrum Width Field: Widespread Showers (20 dB threshold)

Echoes from Birds leaving a Roost; Spectrum Width Field

Measurements of Rain

¶ R(Z) relations

¶ Error sources

¶ Procedure on the WSR-88D

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

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

DSDs, R(Z), and R(disdrometer)

Sep 11, 1999

Log(

N)

Log(

N)

DSD’s, R(Z), and R(disrometer)

Dec 3, 1999

Log(

N)

Locations of Z Data used in the WSR-88D for Rain Measurement

HEIG

HT

0 2035 230 km

RANGE

3.5°

2.4°

1.5°

0.5°

135

Applications of Polarization

♪ Polarimetric Variables

♪ Measurements of Rain

♪ Measurements of Snow

♪ Classification of Precipitation

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

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

Scatergrams: R(Z) and

R(KDP, ZDR)vs Rain Gauge

Sensitivity to Hail

R(g

au

ge

s)

R(g

au

ge

s)-

R(Z

)R

(ga

ug

es

)-R

(KD

PZ

DR)

Area Mean Rain Rate and Bias

R(gauges)-R(radar)

Fundamental Problems in Remote Sensing of Precipitation

♥Classification - what is where?

♥Quantification - what is the amount?

Weighting Functions

Partitions in the Zh, ZDR Space into Regions of Hydrometeor Types

Weighting Function forModerate Rain WMR(Zh, ZDR)

Scores for hydrometeor classes

M

ii

ij

M

ii

j

A

YZWAS

1

1),(

Ai = multiplicative factor 1Wj = 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

Florida

Florida

Florida

Florida

Fields of classified Hydrometeors - Florida

Fields of classified Hydrometeor - Florida

Fields of classified Hydrometeors - Florida

Suggestions

Data quality - develop acceptance testsAnomalous 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

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

• 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• 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

Investigate data “features”

• Feature distributions– AP Clutter

– Precipitation

• Best features have good separation between echo types

ClutterCluttermean V

WeatherWeathermean V

ClutterCluttertexture Z

WeatherWeathertexture Z

AP Weighting Functions

-30

a) Mean Radial Velocity(MVE)

b) Mean Spectrum Width(MSW)

c) Texture of SNR(TSNR)

d) Standard Deviation ofRadial Velocity (SDVE)

e) Vertical Difference ofReflectivity (GDZ)

-2.3 0 2.3 -50 50

0 3.2-30 30

0 0.7 30

0 45

-18 0

1

0

1

0

1

0

1

0

1

0

1000

-100 100

F) Spin

G) Sign

0 50 100

10

1

0

1

0

Median Spectrum Width

“Texture” of Reflectivity

Standard Deviation of Radial Velocity

“Reflectivity Spin”

“Reflectivity Sign”

Median Radial Velocity

-10 -0.6 0 0.6

Radial Velocity

AP Clutter

+3 m/s

Interest Field Radial Velocity

AP Clutter

+3 m/s

Field of Weights for AP ClutterWeighting functions areapplied to the feature field to create an “interest” field

Values scaled between 0-1

0-2.3 2.30

1

For median velocity field,the weighting function is:

Example of APDA using S-Pol data from STEPS

Polarimetric truth fieldgiven by the Particle Identification(PID) output

APDA is thresholded at 0.5

Good agreement between PID clutter and APDA

Reflectivity Radial Velocity

PID APDA

Clutter Rain

20 June 2000, 0234 UTC 0.5 degree elevation

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

6 pm 7 pm 8 pmR

adar

For

ecas

t w

/Rad

ar

2 hr 3 hr 4 hr

Fcs

t w

/o R

adar

6 pm 7 pm 8 pmR

adar

2 hr 3 hr 4 hr

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)

Z

KDP

ZDR

hv

Vertical Cross Sections

In Situ and Pol Measurements

T-28 aircraft