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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
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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
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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.
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One-polarization Radar
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Dual-polarization Radar
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Polarimetric radar variables
1. Differential reflectivity ZDR
2. Total differential phase ΦDP
3. Specific differential phase KDP
4. Cross-correlation coefficient ρhv
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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
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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
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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
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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)
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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
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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
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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
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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
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Bright band detection. January 5, 2005. El = 5.18º
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Bright band detection. January 5, 2005. El = 0.43º
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A tower
Partial beam blockage of the radar beam
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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
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Data Quality: Correction of Radar Reflectivity for Attenuation
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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)
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Point Estimates
Polarimetric Rainfall EstimationAreal Estimates
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Spring hail cases
Cold season stratiform rain
The bias in areal rain rates estimated from radar using conventional and polarimetric algorithms
Polarimetric Rainfall Estimation
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The Quality of Rainfall Estimation as a Function of Range
“Cold season” events “Warm season” events
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Sensitivity of rainfall estimate to DSD variations
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• 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