Operational Applications of Polarimetric Radar

Steven A. RutledgeDepartment of Atmospheric ScienceColorado State University

Acknowledgements—All current and former staff and students of the Radar Meteorology Group, CSU-CHILL staff, Profs. Bringi and Chandra from CSU/ECE, funding agencies especially NSF, NASA and NOAA, andmany colleagues in the community.

Benefits of Polarization Diversity….(based on several decades of research)

• Identification of anomalous propagation and clutter (non-meteorological echo)---data quality control

• Improved rain rate estimation, especially in presence of ice

• Remotely sense cloud and precipitation processes, especially when combined with Doppler measurements

• Detection of severe weather including hail• Attenuation correction, especially critical at sub S-band

wavelengths

Radar[ ]

TMatrixn Propagatio

S

⎥⎦

⎤⎢⎣

⎡Matrix

Scattering

H

V

The observations are a combination of backscatter and propagation characteristics of precipitation. Use various polarization schemes to remotely sense the precipitation medium.

Polarimetric Radar Variables

Horizontal (Vertical) Reflectivity (ZH,V) Size, concentration

Differential Reflectivity (ZDR) ZDR = 10 log (ZH/ZV)

Basic measure of mean shape; median volume diameter (D0) can be retrieved. N0 fixed by Z.ZDR ~ 3 dB rainZDR ~ 0 dB hail/graupel

Differential Phase (ΨDP) ϕdp = ϕh - ϕv

Specific Differential Phase (KDP)Filtered, range derivative of ϕdpLWC, oblateness; isotropic vs. anisotropic scatterers--- KDP very different between rain/hail

Oblate Raindrop

Small RaindropHail/Graupel

Polarimetric Radar Variables

Linear Depolarization Ratio (LDR)Orientation, canting, melting—not possible withNEXRAD polarimetric configuration

Correlation Coefficient (ρHV); measure of correlation between estimates of ZH and ZV.Mixed phase, melting—strong function of howdiverse the particle shapes are in pulse volume. Clutter or AP have very low correlations—useful!

Z ∝ Σ [N(D) • D6 ]

R ∝ Σ [N(D) • D3.67 ]

Kdp ∝ Σ [N(D) • D4.24 ]

Advantages of using Kdp for rainfall estimationLess sensitive to variations in DSD than Z (4.24th moment of DSD is closer

to 3.67th than 6th !)Independent of power calibration-phase measurementLess sensitive to beam blockingImmune to attenuation—provided enough signal!

Issues regarding Kdp

Trade-off between accuracy and spatial resolution (rain estimation). Filtering required from a noisy field ϕdp

Backscatter differential phase (Mie targets), range effects, gradient regions, choice of drop shape model

Issues regarding ZDR

Reflectivity gradients—power received through sidelobes that are mismatched can produce spurious values of ZDR in low reflectivity regions next to strong cores

Antenna performance is critical, main beam H/V matching

Three body scattering

Differential attenuation

Presence of hail---GOOD and BAD

NCAR S-pol

CSU-CHILL

Intense storm, rain and hailas viewed by the CSU-CHILLradar

Polarimetric conversion of NEXRAD

• Single transmitter, simultaneous transmit, two receivers

Seliga and Bringi, 1976 (JAM)---first discussion of single transmitter, dual-receiver configuration---now known as STSR

REMOVING NON-METEOROLOGICAL ECHOUSING POLARIMETRIC THRESHOLDS

Original ZV ρHV

σ(ΦDP) Filtered ZH

Basic thresholding of data can eliminate most non-meteorological echo

σ(ΦDP) – Clutter/AP, Noise

ρHV – Clutter/AP, Noise

ZH & ZDR – Biological Scatterers

LDR – Second Trip

(e.g., Ryzhkov & Zrnic 1998)

Ryzhkov et al. (2005)

REMOVING NON-MET ECHO:FUZZY LOGIC CLASSIFICATION (FHC)

Clutter/AP

Rain

Insects/Birds

Example from JPOLE of rain embedded in clutter/AP and biological scatterers

Remove using flags on FHC category Also simple thresholding techniques used

Mean clear-air power overseveral hours by azimuth0.8° elev.; clutter flagged white(from S-Pol, NAME 2004)

Major Blocks

MountainClutter

Ocean

MinorBlock

Invoke self-consistency of ZH and KDP in rain (Scarchilli et al. 1996)

ZH scatter plot for KDP between 1 and 2° km-1 by azimuth (~1 week’s data)

Depressions in ZHscatter denote blocks;Median dBZ reductionis +dBZ correction needed

OceanMtns

USING POLARIMETRIC RADAR TO CORRECT BLOCKAGE

Application for NEXRADin areas of rough terrain

Examples of rainfall estimation methods for polarimetric radar

1. R(KDP,ZDR) = 90.8 * (KDP)0.93 *10(0.169*ZDR) mm hr-1

2. R(ZH,ZDR) = 6.70 x 10-3 * (ZH)0.927 * 10(0.1*-3.433*ZDR)

mm hr-1

3. R(KDP) = 40.5 * (KDP)0.85 mmhr-1

4. R(ZH) = (ZH/300)0.7143 mm hr-1

• (1) and (2) assume Beard and Chuang equilibrium model (includes oscillations)

• Coefficient in (3) assumes equilibrium model of Pruppacher and Beard (no oscillations)

• (4) is the default NEXRAD Z-R

Equations Used in CSU Blended Algorithm

Intense storm over DIA

Fort Collins Flood Rain Gauge Total Map (inches)28 July 1997

Comparison of Radar-Derived Rainfall Totals

Cheyenne NEXRAD Z-R Rain Total CSU-CHILL Polarimetric Rain Total

• Storm totals using standard NEXRAD Z-R only 50-65% of gauge max

• Totals using polarimetric variables (Z, ZDR, KDP) are 95% of gauge max

• Polarimetric radar max location within ~0.5 km of gauge max

For rain events in Colorado……..

Need for good Z, ZDRcalibrations

NSSL Synthetic Rainfall Algorithm

R = R(ZH)/f1(ZDR), if R(ZH) < 6 mm hr-1

R = R(KDP)/f2(ZDR), if 6 < R(ZH) < 50 mm hr-1

R = R(KDP) , if R(ZH) > 50 mm hr-1

Where,R(ZH) = (ZH/300)0.714

R(KDP) = 44.0|KDP|0.822 sign(KDP)

f1(ZDR) = 0.4 + 5.0|Zdr-1|1.3 ; Zdr is in linear unitsf2(ZDR) = 0.4 +3.5|Zdr-1|1.7

• R(ZH), R(KDP) and ZDR are averaged over 1 km X 1º area prior to calculating point estimates

• Negative KDP is used to compensate spurious large positive KDP’s in regions of high reflectivity gradients

• f1 and f2 attempt to compensate for DSD variability in a mean sense• Exhibited best performance among all polarimetric (16) and non

polarimetric (1) relations used in JPOLE

Performance of NSSL Synthetic Algorithm

Ryzhkov et al. 2005

Ryzhkov et al. 2005

• Vast improvement over R(ZH)• Performance needs to be evaluated for other locations

To what fraction of total rainfall will polarimetric rainfall retrievals apply?

Steve Nesbitt, CSU

Winter Spring

Summer Fall

Fraction of rainfall exceeding 35 dBZ

Nw, Dm for stratiformrain

Derive these quantitiesfrom polarimetricobservations

Tune Z-R

Nw, Dm for convective

rain

Derive these quantities from polarimetric observations

Tune Z-R

⎥⎦

⎤⎢⎣

⎡= 4

467.3

oww D

WNπρ

Estimation of rainfall via polarimetric tuning technique—derive Gamma DSD parameters from polarimetric data

Bringi, Gorgucci and colleagues

Fuzzy-Logic Hydrometeor ID

Hydrometeor ClassesLarge Hail (D > 2 cm; LH)Small Hail (D < 2 cm; SH)Rain (R)High-Density Graupel (HG)Low-Density Graupel (LG)Drizzle (Drz)Wet snow (WS)Dry Snow (DS)Vertical Ice (VI)

1. Examine polarimetric parameters and temperature at each grid point

2. Score each hydrometeor category based on observations relative to known range of values for each hydrometeor class (determined from field obs, scatter modeling)

3. Highest score wins

Algorithm produces “dominant” hydrometeor type---thiscan be summed to provide storm volumes of hydrometeortype-crude information can be derived on mixing ratios.

June 29: STEPS Lim et al. (2005)C

B

Polarimetric dataused to diagnosehail and graupelcontents and relate toelectrical properties

K. Wiens and S. Tessendorf

Polarimetricradar as a cloud physicstool

Ryzhkov et al. 2005, J. Appl. Met.

PolarimetricTornado Detection

LoweredDiff ReflectivityandCorrelationCoefficient

Brandes and Ikeda, JAM, 2004

Mapping of meltinglevel

March 2003 blizzard, microphysical processesrevealed by polarimetric datacollected by the CSU-CHILLFacility—courtesy Pat Kennedy

CSU-CHILL, Z Rain-snow, Vr

ZDR LDR

Conclusions• Much research has been done to date using polarimetric radars

and much has been learned. More work is needed to investigate application of polarimetric rain estimators to broad spectrum ofrain regimes

• Use of polarimetric S-band radars for cool season precipitation is relatively unexplored compared to warm season precipitation—this work needs to be accelerated in advance of the NEXRAD polarimetric upgrade

• Also need to further explore role of surface based measurements such as disdrometers, profilers, mesonets, etc to enhance polarimetric NEXRAD network

• Need more study of short wavelength polarimetric radars combined with S-band data

S-Band and X-Band Polarimetric Radars Complement One Another

Example: Mesoscale Convective System

Trailing Stratiform

LeadingConvection

•X-band allows greater accuracy in light rain events (e.g., stratiform)•S-band provides excellent rainfall estimates in heavy rain (convective)

X-band S-band

Matrosov and Martner

© 1998 Prentice-Hall, Inc. -- From: Lutgens and Tarbuck, The Atmosphere, 7th Ed.

Collaborative Adaptive Sensing of the Atmosphere

• Network of small radars• Low to the ground• Adaptive• Low cost

$$$$$$$$$$$$$$

$ $ $0-3 km

Courtsey J. Brotzge, OU

The Future• Polarimetric NEXRAD’s on line---improved rain and snow

estimation for a wide range of hydrological applications, improved data quality, better detection of convective and mesoscale phenomena

• NASA GPM mission launches in 2010-2011 and provides 3 hourly, 4 km rain estimates as backdrop to the finer scale measurements

• NEXRAD network supplemented by smaller, shorter wavelength radars in key areas for detailed wind and precipitation information

• Widespread use of 3-D lightning mapping networks for aid in storm nowcasting and basic research; integration of other surface-based sensors

• Massive data assimilation effort to couple with forecast models—serious challenge