Dual-Polarization Radars Atmospheric Instrumentation M. D. Eastin

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

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

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

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

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

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 6.2.5 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

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 3.7 mm 2.9 mm 2.7 mm 1.8 mm 1.4 mm ZVV ZHH Atmospheric Instrumentation M. D. Eastin

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 3.7 mm 2.9 mm 2.7 mm 1.8 mm 1.4 mm Atmospheric Instrumentation M. D. Eastin

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

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

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

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

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 -30 < LDR < -20 dB Hail / raindrop mixture -20 < LDR < -10 dB Wet snow -18 < LDR < -13 dB Why look at LDR? Hydrometeor shape can better inferred when combined with ZDR measurements ZHV ZHH Atmospheric Instrumentation M. D. Eastin

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

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

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: 0.96 < ρHV < 1.00 Large diversity in hydrometeor type: 0.85 < 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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: 10.1029/2010GM000930 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, 645-653. 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, 83-104. 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, 730-748. 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, 645-653. Atmospheric Instrumentation M. D. Eastin