Rainfall Monitioring Using Rainfall Monitioring Using Dual-polarization RadarsDual-polarization Radars

Alexander RyzhkovAlexander Ryzhkov

National Severe Storms National Severe Storms Laboratory / University of Laboratory / University of

Oklahoma, USAOklahoma, USA

Polarimetric weather radar has enormous potential for

- Accurate rainfall estimation

- Classification of different types of radar echo including

- hail detection,

- rain / snow discrimination,

- identification of nonmeteorological scatterers (ground /clutter / AP, insects , birds, chaff, etc.),

- tornado detection

- Data quality improvement

- Microphysical parametrization of the storm-scale numerical models

Benefits of a dual-polarization weather radar

Upcoming polarimetric upgrade of the NEXRAD radars

Based on the results of two decades of research studies and the demonstration project referred to as Joint POLarization Experiment (JPOLE), the US National Weather Service made a decision to add polarimetric capability to all existing operational WSR-88D radars starting in 2008 – 2009.

Similar upgrade is planned by national weather services of Canada and several European countries.

One-polarization Radar

Dual-polarization Radar

Polarimetric radar variables

1. Differential reflectivity ZDR

2. Total differential phase ΦDP

3. Specific differential phase KDP

4. Cross-correlation coefficient ρhv

Differential reflectivity ZDR

Zv

Zh

)dB(Z)dB(Z)dB(Z vhDR

• ZDR is a measure of the median raindrop diameter

• ZDR is efficient for discrimination between rain and snow

ZDR depends on the particle size, shape, orientation, and density

time

H

H

V

V

time

ΦDP

ΦDP

Differential phase ΦDP

)]Φftπ2(jexp[AH hh

)]Φftπ2(jexp[AV vv

vhDP ΦΦΦ

ΦDP is not affected by radar miscalibration, attenuation, and

partial beam blockage

Specific differential phase KDP

dr

Φd

2

1K DP

DP - radial derivative of differential phase

bDPKAR b = 0.75 – 0.85

• KDP is less affected by DSD variations at higher end of the raindrop spectrum than Z

• KDP is less affected by the presence of hail than Z

• KDP is immune to radar miscalibration, attenuation, and partial beam blockage

• KDP can be used for calibration of Z according to consistency between Z, ZDR, and KDP in rain

Cross-correlation coefficient ρhv

vh

*

hvPP

VHρ

H and V are complex voltages and Ph and Pv are powers of radar signals at orthogonal polarizations

• ρhv is an important parameter for data quality assessment and classification of radar echoes

• ρhv is high (close to 1) for rain and dry snow, moderately low for hail and wet snow in the melting layer, and very low for nonmeteorological scatterers (ground clutter /AP, biological scatterers, chaff, and tornado debris)

Radar echo classification and data quality control

Accurate rainfall estimation is contingent upon reliable classification of radar echoes and unbiased measurements of key radar variables

Ten classes of radar echo will be identified with the classification algorithm implemented on the polarimetric prototype of the WSR-88D radar

1. Ground clutter / AP

2. Biological scatterers

3. “Big” drops

4. Light and moderate rain

5. Heavy rain

6. Rain / hail

7. Graupel

8. Wet snow (melting layer)

9. Dry snow

10. Snow crystals

INSECTS

BIRDS

Data Quality: Identification & Filtering of Nonmeteorological Echo

AP – Ground Clutter / Anomalous Propagation

BS – Biological Scatterers (insects, birds)

RA – Rain

Classification Legend

99% of nonweather echo is correctly identified if SNR > 10 dB

Hydrometeor Classification: Hail Detection

14 May 2003Classification Legend

HA – Hail / Rain

HR – Heavy Rain

MR – Moderate Rain

LR – Light Rain

BD – ‘Big Drops’

BS – Biological Scatterers

AP – Ground Clutter/ Anomalous Propagation

Hail Detection: A Summary of Validation during JPOLE

• Hail Detection Statistics

- Conventional Hail Detection Algorithm

POD=88%, FAR=39%, CSI=0.56

- Polarimetric Hail Detection Algorithm

POD=100%, FAR=11%, CSI=0.89

• Conventional method provides probability of hail in a storm, whereas polarimetric algorithm determines location of hail within the storm

Bright band detection. January 5, 2005. El = 5.18º

Bright band detection. January 5, 2005. El = 0.43º

A tower

Partial beam blockage of the radar beam

BBAA

R(Z)R(Z) R(KDP)R(KDP)30 km30 km 30 km30 km

Lake TexomaLake

Texoma

Data Quality: Partial Beam Blockage

48-hour rain accumulation map from

A. conventional R(Z) relation

B. polarimetric R(KDP) relation

18 – 20 October 2002

Data Quality: Correction of Radar Reflectivity for Attenuation

KTLXKOUN

CIM

KINX

KFDR

KVNX

KOUNKOUN 100 km range ring

ARS rain gauge micronet

Polarimetric radars

WSR-88D radars

EVAC rain gauge piconet

Mesonet Gages

JPOLE Instrumentation and Dataset

• 98 events have been observed during JPOLE

• 24 rain events (50 hours) are validated with the ARS micronet (42 gages)

• 22 rain events (83 hours) are validated with the Mesonet (108 gages)

Point Estimates

Polarimetric Rainfall EstimationAreal Estimates

Spring hail cases

Cold season stratiform rain

The bias in areal rain rates estimated from radar using conventional and polarimetric algorithms

Polarimetric Rainfall Estimation

The Quality of Rainfall Estimation as a Function of Range

“Cold season” events “Warm season” events

Sensitivity of rainfall estimate to DSD variations

• Conventional and polarimetric rainfall estimation algorithms have been validated using 108 Oklahoma Mesonet and 42 ARS Micronet gages during JPOLE.

• The polarimetric algorithm outperforms the conventional one in terms of bias and RMS error. The RMS error of the one-hour total estimate is reduced 1.7 times for point measurements and 3.7 times for areal rainfall estimates.

• Most significant improvement is achieved in areal rainfall estimation and in measurements of heavy precipitation (often mixed with hail).

• The polarimetric method is more robust with respect to radar calibration errors, beam blockage, attenuation, DSD variations, and presence of hail than the conventional R(Z) method.

Polarimetric Rainfall Estimation: Summary