1. Dual-Polarization Radars
Atmospheric Instrumentation
M. D. Eastin

2. Outline Dual-Polarization Radars Comparison (Single vs. Double)
Definitions and Notation
Parameters based on dual-polarimetric data
Differential reflectivity (ZDR)
Linear depolarization ratio (LDR)
Co-polar correlation (ρHV)
Differential phase shift (KDP)
Hydrometeor Classification Algorithm (HCA)
Improved Rainfall Estimation
Atmospheric Instrumentation
M. D. Eastin

3. Single vs. Dual Polarization Radars
Single-polarization radars:
Transmits and observes echoes using only horizontal polarization
Assumes ALL hydrometeors are spherical liquid drops
Estimates rain rates and storm total precipitation under these assumptions and constraints
HOWEVER
Large hydrometeors are NOT spherical
Upper-level hydrometeors are NOT liquid and are NOT spherical
Atmospheric Instrumentation
M. D. Eastin

4. Single vs. Dual Polarization Radars
Transmits and observes echo with both horizontal and vertical polarization
Unique hydrometer shapes and sizes more accurately determined from the two views
Can distinguish between large/small liquid drops, hail, graupel, and the ice crystal spectrum
Provides: More detailed information about storm structure and evolution
More accurate rain rate estimates → improved flash flood warnings
Atmospheric Instrumentation
M. D. Eastin

5. Single vs. Dual Polarization Radars
Backscatter Considerations:
Single Polarization
Radar transmits horizontal polarization
Radar receives only backscatter at horizontal polarization
Assumes all hydrometeors are spherical water drops
Radar cross section is “simple” and unique
Dual Polarization
Radar transmits horizontal and vertical polarization
Radar receives backscatter across all combinations:
Transmits Backscattered Power
Horizontal ↔ Horizontal or Vertical
Vertical ↔ Horizontal or Vertical
Radar cross-sections are not unique and are dependent on particle shape
Can only develop a unique radar equation for one polarization (horizontal → as before)
Otherwise: Dual-polarization parameters must be defined as “ratios” or “correlations” between the returned horizontal power and returned vertical power
Atmospheric Instrumentation
M. D. Eastin

6. Definitions and Notation
Observed Quantities:
Since dual-polarized radars can transmit and receive signals in both horizontal (H)
and vertical (V) polarization, the number of uniquely observed quantities increases
from two (single polarization) to eight (dual polarization)
Single: P HH / φ HH
Dual: P HH / P HV / P VV / P VH P H H
φ HH / φ HV / φ VV / φ VH
where: P = power of the signal (Watts)
often converted to ZE (dBZ)
φ = phase of the signal (radians)
often converted to VR (m s-1)
Co-polar: Signals transmitted and received
at the same polarization (PHH)
Cross-polar: Signals transmitted at one
polarization and received
at another (PHV)
Transmitted at horizontal polarization
Received at horizontal polarization
Atmospheric Instrumentation
M. D. Eastin

7. Definitions and Notation
Derived Parameters:
As a result of such additional information, a number of useful parameters have been
developed from combinations of these eight observations that can help distinguish
between hydrometeor type, shape, and size
1. Radar reflectivity (ZE) from PHH
2. Radial velocity (VR) from φHH
3. Spectral width (σ) from φHH
4. Differential reflectivity (ZDR) from PHH and PVV
5. Linear depolarization ratio (LDR) from PHH and PVH
6. Differential phase shift (KDP) from φHH and φVV
7. Co-polar correlation (ρHV) from PHH and PVV
Many more additional parameters have been developed (see Section in your text
and Cifelli and Chandrasekar 2010) but have not received as much attention due to their
narrow / limited use
Same as single
polarized radars
Unique to dual
polarized radars
Atmospheric Instrumentation
M. D. Eastin

8. Dual-Polarimetric Parameters
Differential Reflectivity (ZDR):
Used to identify hydrometeor shape / type
Depends on axis ratio:
Oblate particles ZDR > 0
Prolate particles ZDR < 0
Spherical particles ZDR ~ 0
Why use ZDR?
Hydrometeor shape can be easily inferred
Presence of hail and/or super-cooled drops can be easily inferred
4.0 mm mm mm
2.7 mm mm mm
ZVV
ZHH
Atmospheric Instrumentation
M. D. Eastin

9. Dual-Polarimetric Parameters
Differential Reflectivity (ZDR):
Liquid water drops
Shape ranges from spherical (small drops)
to oblate (large drops)
Drops fall with their major axis horizontal
ZDR = 0 to +5 dB
Ice Crystals
Shapes are highly variable
Typically fall with major axis horizontal
ZDR = +2 to +4 dB (columns)
ZDR = +3 to +6 dB (dendrites / plates)
ZDR = 0 to +1 dB (aggregates)
4.0 mm mm mm
2.7 mm mm mm
Atmospheric Instrumentation
M. D. Eastin

10. Dual-Polarimetric Parameters
Differential Reflectivity (ZDR):
Graupel and Hail
Graupel often has a “classic raindrop” shape
and falls with the major axis vertical
ZDR = –0.5 to +1 dB (graupel)
Hail is often spherical, but irregular, and it
tends to tumble while with falls but with its
major axis aligned vertically
ZDR = –1 to +0.5 dB (hail)
Atmospheric Instrumentation
M. D. Eastin

11. Dual-Polarimetric Parameters
Differential Reflectivity (ZDR):
Atmospheric Instrumentation
M. D. Eastin

12. Dual-Polarimetric Parameters
Differential Reflectivity (ZDR):
Heavy rainfall
(small drops)
ZE = 50 dBZ
ZDR < 2 dB
Hail-rain mix
(large drops)
ZE = 50 dBZ
ZDR = 3-5 dB
Atmospheric Instrumentation
M. D. Eastin

13. Dual-Polarimetric Parameters
Differential Reflectivity (ZDR):
Advantages:
Independent of radar calibration
Independent of hydrometeor concentration
Can easily identify hail/graupel
Can help identify melting layer
Easier to identify ground clutter
Limitations:
Susceptible to the same data quality
effects as traditional radar reflectivity
1. Attenuation
2. Second-trip echoes
3. Side lobes
Large Hail
Z > 45 dBZ
ZDR < 1 dB
Atmospheric Instrumentation
M. D. Eastin

14. Dual-Polarimetric Parameters
Linear Depolarization Ratio (LDR):
Used to identify hydrometeor shape / type
Detects tumbling, wobbling, canting angles,
phase, and irregular shaped hydrometeors
Small raindrops LDR < -30 dB
Large raindrops < LDR < -20 dB
Hail / raindrop mixture -20 < LDR < -10 dB
Wet snow < LDR < -13 dB
Why look at LDR?
Hydrometeor shape can better inferred
when combined with ZDR measurements
ZHV
ZHH
Atmospheric Instrumentation
M. D. Eastin

15. Dual-Polarimetric Parameters
Linear Depolarization Ratio (LDR):
Heavy rainfall
(small drops)
ZE = 50 dBZ
ZDR < 2 dB
LDR < -25 dB
Hail-rain mix
(large drops)
ZE = 50 dBZ
ZDR = 3-5 dB
LDR > -25 dB
Atmospheric Instrumentation
M. D. Eastin

16. Dual-Polarimetric Parameters
Linear Depolarization Ratio (LDR):
Advantages
Independent of radar calibration
Independent of hydrometeor concentration
Can help identify hydrometeor type
Limitations
Susceptible to the same data quality
effects as traditional radar reflectivity
1. Attenuation
2. Second-trip echoes
3. Side lobes
Susceptible to “noise” since cross-polar
signals are 2-3 orders of magnitude
smaller than co-polar signals
Large Hail
Z > 45 dBZ
ZDR < 1 dB
LDR > -20 dB
Atmospheric Instrumentation
M. D. Eastin

17. Dual-Polarimetric Parameters
Co-Polar Correlation (ρHV):
A measure of the linear correlation between
the co-polar horizontal backscatter (PHH)
and co-polar vertical backscatter (PVV)
within a pulse volume
Influenced by wobbling, canting angles,
phase, and irregular hydrometeors
Used to identify hydrometeor type,
mixed-phase precipitation, and
non-meteorological targets
Small diversity in hydrometeor type: < ρHV < 1.00
Large diversity in hydrometeor type: < ρHV < 0.95
Non-meteorological targets: ρHV < 0.85
Why use ρHV?
Clarify hydrometeor type when combined with ZDR and LDR
Identify other targets (insects, birds, debris, etc.)
PVV
PHH
Atmospheric Instrumentation
M. D. Eastin

18. Dual-Polarimetric Parameters
Co-Polar Correlation (ρHV):
Atmospheric Instrumentation
M. D. Eastin

19. Dual-Polarimetric Parameters
Co-Polar Correlation (ρHV):
Insects
(Low ρHV)
Atmospheric Instrumentation
M. D. Eastin

20. Dual-Polarimetric Parameters
Co-Polar Correlation (ρHV):
All Snow
(High ρHV)
Mixed Phase
(Low ρHV)
Atmospheric Instrumentation
M. D. Eastin

21. Tornado Debris Signature
Dual-Polarimetric Parameters
Co-Polar Correlation (ρHV):
Tornado Debris Signature
(TDS)
(Very low ρHV) ZE
ZDR
ρHV
Atmospheric Instrumentation
M. D. Eastin

22. Dual-Polarimetric Parameters
Co-Polar Correlation (ρHV):
Advantages
Independent of radar calibration
Independent of hydrometeor concentration
Limitations
Susceptible to the same data quality
effects as traditional radar reflectivity
1. Attenuation
2. Second-trip echoes
3. Side lobes
Affected by low signal to noise ratios
Large Hail
Z > 45 dBZ
ZDR < 1 dB
LDR > -20 dB
1.00 > ρHV > 0.85
Atmospheric Instrumentation
M. D. Eastin

23. Dual-Polarimetric Parameters
Differential Phase Shift (KDP):
Measure of the phase shift difference between
the co-polar horizontal (φHH) and the co-polar
vertical (φVV) returns due to both backscatter
and forward propagation
where:
Used to distinguish large drops from hail,
identify super-cooled drops above the
freezing layer, and estimate rain rate
Spherical hydrometeors KDP < 1.0
Oblate hydrometeors KDP > 1.0
Horizontal phase shift is often larger than
the vertical phase shift since large raindrops
are oblate → horizontal propagates slower
φDP = 0°
φDP = 10°
No phase
shift
Phase
No additional
phase shift
ΦDP = 0°
ΦDP = 25°
No phase
shift
Phase
No additional
phase shift
Atmospheric Instrumentation
M. D. Eastin

24. Dual-Polarimetric Parameters
Specific Differential Phase Shift (KDP):
Atmospheric Instrumentation
M. D. Eastin

25. Dual-Polarimetric Parameters
Specific Differential Phase Shift (KDP):
Atmospheric Instrumentation
M. D. Eastin

26. Large Drops (left edge)
Dual-Polarimetric Parameters
Specific Differential Phase Shift (KDP):
Advantages
Independent of radar calibration
Independent of drop concentration
Independent of attenuation
Can be used to distinguish large rain
rates (flash floods) from shafts of
large hail (severe hail)
Limitations
Noisy product → interpretation difficult
Less reliable at greater ranges
Large Hail (middle)
Large Drops (left edge)
Z > 45 dBZ
ZDR < 1 dB
LDR > -20 dB
1.00 > ρHV > 0.85
Atmospheric Instrumentation
M. D. Eastin

27. polarimetric variables
Hydrometeor Classification
Hydrometeor Classification Algorithm (HCA):
Algorithm runs in real-time on WSR-88D
Based on fuzzy logic technique
Total of 17 classification types
Five observed
polarimetric variables
[ ZHH ZDR LDR ρHV KDP ]
Temperature profile
Hydrometeor type at
each volume element
Fuzzy
Logic
Box
Atmospheric Instrumentation
M. D. Eastin

28. Hydrometeor Classification
Hydrometeor Classification Algorithm (HCA):
Atmospheric Instrumentation
M. D. Eastin

29. Hydrometeor Classification
Hydrometeor Classification Algorithm (HCA):
Atmospheric Instrumentation
M. D. Eastin

30. Hydrometeor Classification
Hydrometeor Classification Algorithm (HCA):
Atmospheric Instrumentation
M. D. Eastin

31. Hydrometeor Classification
Hydrometeor Classification Algorithm (HCA):
Atmospheric Instrumentation
M. D. Eastin

32. Improved Rainfall Estimation
Dual-Polarization Rain Rate Algorithms:
There are three dual-polarization quantities
that can be related to rain rate
ZHH over-sensitive to large drops
ZDR sensitive to the shape of medium
to large drops
KDP sensitive to both drop shape and
number concentration
WSR-88D Radars:
Uses the hydrometeor classification algorithm
to first identify the basic target type
Then applies the following three equations
Rain:
Mixed:
Snow:
Atmospheric Instrumentation
M. D. Eastin

33. Summary Dual-Polarization Radars Comparison (Single vs. Double)
Definitions and Notation
Parameters based on dual-polarimetric data
Differential reflectivity (ZDR)
Linear depolarization ratio (LDR)
Co-polar correlation (ρHV)
Differential phase shift (KDP)
Hydrometeor Classification Algorithm (HCA)
Improved Rainfall Estimation
Atmospheric Instrumentation
M. D. Eastin

34. References Atmospheric Instrumentation M. D. Eastin
Atlas , D., 1990: Radar in Meteorology, American Meteorological Society, 806 pp.
Cifelli, R. and Chandrasekar, V. , 2010: Dual-Polarization Radar Rainfall Estimation, American Geophysical Union, Washington, D. C.. doi: /2010GM000930
Crum, T. D., R. L. Alberty, and D. W. Burgess, 1993: Recording, archiving, and using WSR-88D data. Bulletin of the American Meteorological Society, 74,
Doviak, R. J., and D. S. Zrnic, 1993: Doppler Radar and Weather Observations, Academic Press, 320 pp.
Fabry, F., 2015: Radar Meteorology Principles and Practice, Cambridge University Press, 256 pp.
Joregensen, D. P., T. Matejka, and J. D. DuGranrut, 1996: Multi-beam techniques for deriving wind fields from airborne Doppler radars. Meteorology and Atmospheric Physics, 59,
Park, H. S., A. V. Ryzhkov, D. S. Zrnic, and K. E. Kim, 2009: The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS. Weather and Forecasting, 24,
Reinhart, R. E., 2004: Radar for Meteorologists, Wiley- Blackwell Publishing, 250 pp.
Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, et al., 2005: The joint polarization experiment: Polarimetric rainfall measurement and hydrometeor classification. Bulletin of the American Meteorological Society, 74,
Atmospheric Instrumentation
M. D. Eastin