Radar Basics and Estimating Precipitation Radar Basics and Estimating Precipitation

Jon W. ZeitlerJon W. Zeitler

Science and Operations OfficerNational Weather Service

Austin/San Antonio Forecast Office

Science and Operations OfficerNational Weather Service

Austin/San Antonio Forecast Office

Radar Beam BasicsRadar Beam Basics

As pulse volumes within the radar beam encounter targets, energy will be scattered in all directions. A very small portion of the intercepted energy will be backscattered toward the radar. The degree or amount of backscatter is determined by target:

size (radar cross section) shape (round, oblate, flat, etc.)

state (liquid, frozen, mixed, dry, wet) concentration (number of particles per unit volume)

We are concerned with two types of scattering, Rayleigh and non-Rayleigh. Rayleigh scattering occurs with targets whose diameter (D) is much smaller (D < /16) than the radar wavelength. The WSR-88D's wavelength is approximately 10.7 cm, so Rayleigh scattering occurs with targets whose diameters are less than or equal to about 7 mm or ~0.4 inch. Raindrops seldom exceed 7 mm so all liquid drops are Rayleigh scatters.

Potential problem: Nearly all hailstones are non-Rayleigh scatterers due to their larger diameters.  

As pulse volumes within the radar beam encounter targets, energy will be scattered in all directions. A very small portion of the intercepted energy will be backscattered toward the radar. The degree or amount of backscatter is determined by target:

size (radar cross section) shape (round, oblate, flat, etc.)

state (liquid, frozen, mixed, dry, wet) concentration (number of particles per unit volume)

We are concerned with two types of scattering, Rayleigh and non-Rayleigh. Rayleigh scattering occurs with targets whose diameter (D) is much smaller (D < /16) than the radar wavelength. The WSR-88D's wavelength is approximately 10.7 cm, so Rayleigh scattering occurs with targets whose diameters are less than or equal to about 7 mm or ~0.4 inch. Raindrops seldom exceed 7 mm so all liquid drops are Rayleigh scatters.

Potential problem: Nearly all hailstones are non-Rayleigh scatterers due to their larger diameters.  

Energy ScatteringEnergy Scattering

Probert-Jones Radar EquationProbert-Jones Radar Equation

Simplified Radar EquationSimplified Radar Equation

Since we technically don't know the drop-size distribution or physical makeup of all targets within a sample volume, radar meteorologists oftentimes refer to radar reflectivity as equivalent reflectivity, Ze.

The assumption is that all backscattered energy is coming from liquid targets whose diameters meet the Rayleigh approximation. Obviously, this assumption is invalid in those cases when large, water-coated hailstones are present in a sample volume. Hence, the term equivalent reflectivity instead of actual reflectivity is more valid.

Since we technically don't know the drop-size distribution or physical makeup of all targets within a sample volume, radar meteorologists oftentimes refer to radar reflectivity as equivalent reflectivity, Ze.

The assumption is that all backscattered energy is coming from liquid targets whose diameters meet the Rayleigh approximation. Obviously, this assumption is invalid in those cases when large, water-coated hailstones are present in a sample volume. Hence, the term equivalent reflectivity instead of actual reflectivity is more valid.

Equivalent Reflectivity (Ze)Equivalent Reflectivity (Ze)

                                  (Equation 5) Reflectivity (Z) vs.Decibels of Reflrectivity (dBZ)

Reflectivity (Z) vs.Decibels of Reflrectivity (dBZ)

dBZ = 10log10ZdBZ = 10log10Z

Beam-FillingBeam-Filling

Sending vs. ListeningSending vs. Listening

Sending vs. ListeningSending vs. Listening

99.843% of the time the WSR-88D is listening for signal returns. 99.843% of the time the WSR-88D is listening for signal returns.

A low PRF is desirable for target range and power, while a high PRF is desirable for target velocity. The inability to satisfy both needs with a single PRF is known as the Doppler Dilemma. The Doppler Dilemma is addressed by the WSR-88D with algorithms.

A low PRF is desirable for target range and power, while a high PRF is desirable for target velocity. The inability to satisfy both needs with a single PRF is known as the Doppler Dilemma. The Doppler Dilemma is addressed by the WSR-88D with algorithms.

The Doppler DilemnaThe Doppler Dilemna

Range FoldingRange Folding

Subrefraction: dry adiabatic, moisture increases with height. In addition to underestimated echo heights, this phenomenon tends to reduce ground clutter in the lowest elevation cuts.

Superrefraction: temperature inversion. In addition to overestimated echo heights, increases ground clutter in the lowest elevation cuts and is the cause of what we normally refer to as anomalous propagation or AP echoes.

Subrefraction: dry adiabatic, moisture increases with height. In addition to underestimated echo heights, this phenomenon tends to reduce ground clutter in the lowest elevation cuts.

Superrefraction: temperature inversion. In addition to overestimated echo heights, increases ground clutter in the lowest elevation cuts and is the cause of what we normally refer to as anomalous propagation or AP echoes.

The Earth is Round!The Earth is Round!

Each pulse has a volume with dimensions of ~ 500 meters (~ 1500 meters) in length by ~ 1° wide in short pulse (long pulse) mode. This means that two targets along a radial must be at least 250 (750) meters apart for the radar to be able to distinguish and display them as two separate targets (i.e., more than H/2 range separation distance).

Each pulse has a volume with dimensions of ~ 500 meters (~ 1500 meters) in length by ~ 1° wide in short pulse (long pulse) mode. This means that two targets along a radial must be at least 250 (750) meters apart for the radar to be able to distinguish and display them as two separate targets (i.e., more than H/2 range separation distance).

Storms Too Close!Storms Too Close!

Storms or Bats?Storms or Bats?

Strategies to Fix ProblemsStrategies to Fix Problems

Drop Size DistributionDrop Size Distribution

Drop Size DistributionDrop Size Distribution

Rainfall RateRainfall Rate

Rainfall RateRainfall Rate

Rainfall RateRainfall Rate

R(Z) Relationships (Battan 1973)

BREAK!BREAK!

Sends and receives horizontal & vertical polarized radiation

Sends and receives horizontal & vertical polarized radiation

Image courtesy Terry SchuurImage courtesy Terry Schuur

What is Dual Polarimetric Radar?What is Dual Polarimetric Radar?

Hydrometeor:Hydrometeor:

• ShapeShape

• OrientationOrientation

• Dielectric constantDielectric constant

• Distribution of sizesDistribution of sizes

Polarimetric Variables Depend Polarimetric Variables Depend on Several Thingson Several Things

Polarimetric Variables Depend Polarimetric Variables Depend on Several Thingson Several Things

•Rainfall Estimation (vast improvement)Rainfall Estimation (vast improvement)•Bright Band Detection (vast improvement)Bright Band Detection (vast improvement)•Clutter Filtering/Data Quality Improvement Clutter Filtering/Data Quality Improvement (vast improvement)(vast improvement)•Rain/Snow Discrimination (vast improvement)Rain/Snow Discrimination (vast improvement)•Hail Detection (some improvement)Hail Detection (some improvement)•Updraft Location (some improvement)Updraft Location (some improvement)•Tornado Detection (some improvement)Tornado Detection (some improvement)

•Rainfall Estimation (vast improvement)Rainfall Estimation (vast improvement)•Bright Band Detection (vast improvement)Bright Band Detection (vast improvement)•Clutter Filtering/Data Quality Improvement Clutter Filtering/Data Quality Improvement (vast improvement)(vast improvement)•Rain/Snow Discrimination (vast improvement)Rain/Snow Discrimination (vast improvement)•Hail Detection (some improvement)Hail Detection (some improvement)•Updraft Location (some improvement)Updraft Location (some improvement)•Tornado Detection (some improvement)Tornado Detection (some improvement)

Applications of Dual Applications of Dual Polarization RadarPolarization Radar

Applications of Dual Applications of Dual Polarization RadarPolarization Radar

Backscattering:Backscattering:ZZhh - reflectivity factor for horizontal polarization - reflectivity factor for horizontal polarization

ZZDRDR - differential reflectivity - differential reflectivity

||ρρhvhv(0)| - co-polar correlation coefficient(0)| - co-polar correlation coefficient

Propagation - forward scattering:Propagation - forward scattering:ΦΦDPDP - differential phase - differential phase

KKDPDP - specific differential phase (range derivative of - specific differential phase (range derivative of

ΦΦDPDP))

Backscattering:Backscattering:ZZhh - reflectivity factor for horizontal polarization - reflectivity factor for horizontal polarization

ZZDRDR - differential reflectivity - differential reflectivity

||ρρhvhv(0)| - co-polar correlation coefficient(0)| - co-polar correlation coefficient

Propagation - forward scattering:Propagation - forward scattering:ΦΦDPDP - differential phase - differential phase

KKDPDP - specific differential phase (range derivative of - specific differential phase (range derivative of

ΦΦDPDP))

Polarimetric VariablesPolarimetric VariablesPolarimetric VariablesPolarimetric Variables

Shapes of Large Drops in Equilibrium Shapes of Large Drops in Equilibrium

Differential Reflectivity (ZDR)

• Definition: the ratio of the power returns from the horizontal and vertical polarizations

• Units: decibels (dB)

vv

hhDR Z

ZZ 10log10

Simple ZDR Calculation for a Sample of Raindrop Sizes

Simple ZDR Calculation for a Sample of Raindrop Sizes

What does ZDR Mean?

• ZDR > 0 Horizontally-oriented mean profile

• ZDR < 0 Vertically-oriented mean profile

• ZDR ~ 0 Near-spherical mean profile

• ZDR > 0 Horizontally-oriented mean profile

• ZDR < 0 Vertically-oriented mean profile

• ZDR ~ 0 Near-spherical mean profile

Eh

Ev

Eh

Ev

Eh

Ev

-4-4 -3.5-3.5 -3-3 -2.5-2.5 -2-2 -1.5-1.5 -1-1 -0.5-0.5 00 0.50.5 11 1.51.5 22 2.52.5 33 3.53.5 44 4.54.5 55 5.55.5 66

                                                              

                     Small (Spherical) <<< RAIN >>> Large (Oblate)      

                     Dry <<< GRAUPEL >>> Wet               

            Dry (Prolate) <<<<< HAIL >>>>> Melting (Oblate)      

                  Aggregated/Low-Density <<< CRYSTALS >>> Pristine/Well-Oriented

                       Dry <<< SNOW >>> Wet               

GROUND CLUTTER / ANOMALOUS PROPAGATION                   

            BIOLOGICAL SCATTERERS

                     DEBRIS                   

                           CHAFF

                                                              

Differential Reflectivity (ZDR)Differential Reflectivity (ZDR)

1.1. median liquid drop sizemedian liquid drop size (ZDR↑,median drop diameter↑)

2.2. hail shaftshail shafts (ZDR ~ 0dB or negative coincident with high Zh)

3. areas of large rain drops or liquid-large rain drops or liquid-coated icecoated ice (ZDR ~3-6 dB)

4.4. convective updraftsconvective updrafts (ZDR ~1-5 dB) above 0oC level

5. tornado debris ball

ZDR is a Good Indicator of:ZDR is a Good Indicator of:

•Values are biased towards the larger hydrometeors (D6 dependence)•Tumbling/Random orientation will bias toward 0 ZDR

•Can be noisy if:-Low / Insufficient sampling (low SNR)

- Reduced correlation coefficient (CC)

•Values are biased towards the larger hydrometeors (D6 dependence)•Tumbling/Random orientation will bias toward 0 ZDR

•Can be noisy if:-Low / Insufficient sampling (low SNR)

- Reduced correlation coefficient (CC)

ZDR Limitations (Gotchas)ZDR Limitations (Gotchas)

May 9th tornadic supercell: Intense

ZDR Column

0oC level in-cloud ~17 kft

ρhv

Affected by:

• Hydrometeor types, phases, shapes,

orientations

• Presence of large hail

Correlation Coefficient (ρ hv): A correlation between the reflected horizontal and vertical power returns. It is a good indicator of regions where there is a mixture of precipitation types, such as rain and snow.

ρhv Usage

• Identify hail growth regions in deep moist convection (mixtures of hydrometeors)

• Reduce ground clutter/AP contamination (ρhv very low in these areas)

• Identify giant hail ???

ρhv

Correlation Coefficient Correlation Coefficient ((hvhv))

Reflectivity (ZReflectivity (Zhh))

SNOW~0.85-1.00

CLUTTER~0.5-0.85

CHAFF~0.2-0.5

Giant Hail, Protuberances, Mie Scattering: min Giant Hail, Protuberances, Mie Scattering: min ρρhvhv

ρhv Minimum…in Theory

Differential Phase Shift (ΦDP)

• Definition: the difference in the phase shift between the horizontally and vertically polarized waves

• Units: degrees (o)

VHDP

DP = h – v (h, v ≥ 0) [deg]

The difference in phase between the horizontally-and vertically-polarized pulses at a given range along the propagation path.

- Independent of partial beam blockage, attenuation, absolute radar calibration,

system noise

Differential Phase Shift DP

What Affects Differential Phase?

Forward Propagation has its Advantages

• Immune to partial (< 40%) beam blockage, attenuation, calibration, presence of hail

Gradients Most ImportantGradients Most Important

Specific Differential Phase Shift (KDP)

• Definition: range derivative of the differential phase shift

• Units: degrees per kilometer (o/km)

12

12

2)()(

rrrr

K DPDP

DP

• Provides a good estimate of liquid water in a rain/hail mixture

• Indicates the onset of melting

Specific Differential Phase (KDP): A comparison of the returned phase difference between the horizontal and vertical pulses. This phase difference is caused by the difference in the number of wave cycles (or wavelengths) along the propagation path for horizontal and

vertically polarized waves. This is the range derivative of DP,

typically calculated in 1-5 km increments along the radial.

Specific Differential Phase: KDP

Specific Differential Phase Shift (KDP)

*** Non-meteorological values not shown here because they are removed anywhere CC < 0.90 (or 0.85) ****** Non-meteorological values not shown here because they are removed anywhere CC < 0.90 (or 0.85) ***

-0.5-0.5 00 0.50.5 11 1.51.5 22 2.52.5 33 44 55

                             

   Small <<< RAIN >>> Large      

Dry <<< GRAUPEL >>> Wet            

Dry (Prolate) <<<<< HAIL >>>>> Melting (Oblate)

Dry/Aggregated <<< CRYSTALS >>> Pristine/Well-Oriented            

   Dry <<< SNOW >>> Wet            

                             

Kdp Usage

• To isolate the presence of rain from hail R(Z, Zdr, Kdp) much better than R(Z) Most sensitive to amount of liquid water

• To locate regions of drop shedding, “Kdp columns”• Drops are shed from melting or growing

hailstones near the updraft, forming a Kdp column

• To distinguish between snow/rain• Kdp in wet, heavy snow is almost always larger at

a fixed value of Zh than that observed for rain

KDP Limitations (Gotchas)• KDP values set to “No Data” at CC <

0.90, or 0.85)• Sensitive to non-uniform beam filling• Unreliable at far ranges

• KDP Smoothing techinque:KDP Smoothing techinque:

1. < 40 dBZ, KDP computed at each gate from 12 adjacent gates either side (6.25 km)

2. > 40 dBZ, KDP computed at each gate from 4 adjacent gates either side (2.25 km) to preserve heavy cores

1. < 40 dBZ, KDP computed at each gate from 12 adjacent gates either side (6.25 km)

2. > 40 dBZ, KDP computed at each gate from 4 adjacent gates either side (2.25 km) to preserve heavy cores

Compare Z and KDP fields at each gate

Marginally Severe Supercell

Beam Height ~ 4600 ft AGLBeam Height ~ 4600 ft AGL

ZZZDRZDR

ρHVρHVHCAHCA

5.25” diameter hail5.25” diameter hail

14 May 2003

Correlation Coefficient (CC)

• Definition: how similarly the horizontally and vertically polarized backscattered energy are behaving within a resolution volume for Rayleigh scattering

• Units: none (0-1.00)

2/122/12

*

)0(vvhh

hhvv

HV

SS

SS

ThinkThink Spectrum Width for HydrometeorsSpectrum Width for HydrometeorsTMTMThinkThink Spectrum Width for HydrometeorsSpectrum Width for HydrometeorsTMTM

Sij = An element of the backscatter matrix

Correlation Coefficient Values

•0.96 ≤ CC ≤ 1 Small hydrometeor diversity*

•0.80 ≤ CC < 0.96 Large hydrometeor diversity*

•CC < 0.70 Non-hydrometeors present

•0.96 ≤ CC ≤ 1 Small hydrometeor diversity*

•0.80 ≤ CC < 0.96 Large hydrometeor diversity*

•CC < 0.70 Non-hydrometeors present

* Types, sizes, shapes, orientations, etc.* Types, sizes, shapes, orientations, etc.

Correlation Coefficient (CC)

Non-Meteorological

Regime

Meteorological Regime

Overlap

0.20.2 0.30.3 0.40.4 0.50.5 0.60.6 0.70.7 0.80.8 0.850.85 0.90.9 0.910.91 0.920.92 0.930.93 0.940.94 0.950.95 0.960.96 0.970.97 0.980.98 0.990.99 11

                                                        

                                       Large <<< RAIN >>> SmallLarge <<< RAIN >>> Small

                                       Wet <<< GRAUPEL >>> DryWet <<< GRAUPEL >>> Dry   

               Wet / Large <<<<< HAIL >>>>> Dry / SmallWet / Large <<<<< HAIL >>>>> Dry / Small

                                       CRYSTALSCRYSTALS

         <<Melting Layer>> Wet <<< SNOW >>> Dry<<Melting Layer>> Wet <<< SNOW >>> Dry

GROUND CLUTTER / ANOMALOUS PROPAGATIONGROUND CLUTTER / ANOMALOUS PROPAGATION                              

        BIOLOGICAL BIOLOGICAL SCATTERERSSCATTERERS                                    

DEBRISDEBRIS                              

CHAFFCHAFF                                             

                                                        

What is CC Used for?

• Not-met targets (LOW CC < 0.70)

– Best discriminator

• Melting layer detection (Ring of reduced CC ~ 0.80 – 0.95)

• Giant hail? (LOW CC < 0.70 in the midst of high Z/Low ZDR)

Marginally Severe Supercell

What about the rest?All > 0.97

What about the rest?All > 0.97

InsectsInsectsPrecipPrecip

CC Limitations (Gotchas)

• High error in low signal-to-noise ratios (SNR)

• If low, errors increase in other dual-pol variables

One hour point measurements: Radar estimates vs. gages

R(Z) R(Z, KDP, ZDR)

Polarimetric Rainfall Algorithm vs. Conventional

Polarimetric Rainfall Algorithm vs. Conventional

Bias of radar areal rainfall estimates

Spring hail cases

Cold season stratiform rain

QPE Process in a NutshellStep 1

1. Hybrid scan the variables into Polar, 1 degree azimuth, 250 m bins

Hybrid Hydroclass

QPE Process in a Nutshell

2. Apply an instantaneous Rate: R(Z), R(KDP), and R(Z,ZDR)

But which one is accepted?

ZZR714.0

017.0)(

)(0.44)(882.0

KDPsignKDPR KDP

ZDRZZDRZR67.1770.0

0142.0),(

QPE Process in a Nutshell

3. Assign a variation of 1 of those 3 rates to each bin based on HCA precip type

Based on 43 events (179 hrs) of radar rainfall data

comparisons to a dense network of rain gauges in C. OK

Based on 43 events (179 hrs) of radar rainfall data

comparisons to a dense network of rain gauges in C. OK

Rate Designation TableR (mm/hr) Conditions Echo

Classes

Notcomputed

Nonmeteorological echo (Ground Clutter or Unknown) is classified GC ,UK

0 Classification is No Echo or Biological NE, BI

R(Z, ZDR) Light/Moderate Rain is classified RA

R(Z, ZDR) Heavy Rain or Big Drops are classified HR, BD

R(KDP) Rain/Hail is classified and echo is below the top of the melting layer RH

0.8*R(Z) Rain/Hail is classified and echo is above the top of the melting layer RH

0.8*R(Z) Graupel is classified GR

0.6*R(Z) Wet Snow is classified WS

R(Z) Dry Snow is classified and echo is in or below the top of the melting layer

DS

2.8*R(Z) Dry Snow classified and is echo above the top of the melting layer DS

2.8*R(Z) Ice Crystals are classified IC

QPE Output (all produced via hybrid scan)

• 4bit, 250 m Hybrid-scan Hydro Class• 8bit, 250 m Rate• 4 bit, 250 m 1hr Accum• 4 bit & 8bit versions of 250 m STP Accum (G-R

bias applied)• 8 bit, 250 m no G-R bias applied STP• 8 bit, 250 m User Selectable (will be used for any

and all accumulation time periods)• 8 bit, 250 m 1hr and STP Difference field vs.

Legacy

• Typical Radar sampling limitations (snow at 2000 ft AGL may not be snow at the surface)

• Verification

• “Fuzzy” Logic and cross over between types

• Differentiating between light rain and dry snow in weak echoes

Melting layer detection can help

• Typical Radar sampling limitations (snow at 2000 ft AGL may not be snow at the surface)

• Verification

• “Fuzzy” Logic and cross over between types

• Differentiating between light rain and dry snow in weak echoes

Melting layer detection can help

Hydrometeor Classification Algorithm Hydrometeor Classification Algorithm ChallengesChallenges

Hydrometeor Classification Algorithm Hydrometeor Classification Algorithm ChallengesChallenges

Melting Layer Detection

• Mixed phase hydrometeors: Easy detection for dual-pol!– Z typically increases

– ZDR and KDP definitely increase

– Coexistence of ice and water will reduce the correlation coefficient (CC ~0.95-0.85)

• Precipitation echoes – stratiform or convective regions – with high SNR

• Middle tilts (4°-10° elevation angles)

• Limitation: Overshoot precip

• “Project” results to other tilts in time and space

• Precipitation echoes – stratiform or convective regions – with high SNR

• Middle tilts (4°-10° elevation angles)

• Limitation: Overshoot precip

• “Project” results to other tilts in time and space

Melting Layer Detection Algorithm Melting Layer Detection Algorithm MethodologyMethodology

Melting Layer Detection Algorithm Melting Layer Detection Algorithm MethodologyMethodology

ML Product in AWIPS

Hail Detection• Dual-Pol Hail Signature

– High Z (> 45 dBZ)– Low ZDR (-0.5 to 1 dB), Low KDP (-0.5 to

1 o/km) if dry or mostly dry– Reduced CC (0.85 to 0.95)

• Limitations– Size detection?– Hail signatures may get diluted by

• Rain mixing with hail• Far range

Rain/Snow DiscriminationRAINRAIN SNOWSNOW

ZZ < 45 dBZ< 45 dBZ < 45 dBZ< 45 dBZ

ZZDRDR 0 to 2 dB0 to 2 dB -0.5 to 6 dB-0.5 to 6 dB

KKDPDP 0 to 0.6 deg/km0 to 0.6 deg/km -0.6 to 1 deg/km-0.6 to 1 deg/km

CCCC >0.95>0.95 >0.95 (can be less if >0.95 (can be less if wet)wet)

If the variables overlap so much, how can polarimetric radar discriminate between rain and snow???

Rain/Snow Discrimination: It’s all in trends with height

• Rain– Polarimetric signatures (ZDR and KDP) have a direct

dependence on Z– ZDR and KDP do not typically increase with height– Almost always a pronounced melting layer above rain

• Snow– Polarimetric signatures (ZDR and KDP) do not have

dependence on Z– ZDR and KDP typically increase with height– Differences between “warm” and “cold” snow

• “Cold” snow has higher polarimetric variables than “warm” snow

Warm vs. Cold vs. Wet Snow

• Temperature determines this– < -5oC = “Cold”– ~+1oC > T > -5oC = “Warm”– > +1oC = “Wet”

Crystals (plates, columns, needles)Crystals (plates, columns, needles)

Aggregate Crystals (Dry)Aggregate Crystals (Dry)

Aggregate Crystals (Wet)Aggregate Crystals (Wet)

Surface. Assume temperatures decrease steadily with heightSurface. Assume temperatures decrease steadily with height

Radar Cross Section Radar Cross Section CharacteristicsCharacteristics

Z/ZDR/CC Z/ZDR/CC CharacteristicsCharacteristics

High DensityHigh Density

High ConcentrationHigh ConcentrationOblate, Horizontal OrientationOblate, Horizontal Orientation

Small sizeSmall size

Z < 35 dBZZ < 35 dBZ

ZDR 0-6 dBZDR 0-6 dB

CC > 0.95CC > 0.95

Decreasing densityDecreasing density

Decreasing ConcentrationDecreasing Concentration

Less oblateLess oblate

Larger sizeLarger size

Z increasingZ increasing

ZDR decreasingZDR decreasing

0 > ZDR > 0.5 dB0 > ZDR > 0.5 dB

CC > 0.95CC > 0.95

Rapid increase in densityRapid increase in density

Rapid increase in oblatenessRapid increase in oblateness

Z increasing but < 45 Z increasing but < 45 dBZdBZ

ZDR rapidly increasingZDR rapidly increasing

0.50 > CC > 0.90.50 > CC > 0.9

Rain Snow Discrimination

Z ZDR

KDP CC

Snow

Rain

One Hour Later…

Z ZDR

KDP CC

-SN

Data Quality Improvement

• Ground clutter/Anomalous propagation– High reflectivity (Z) -- (> 35 dBZ)

– Near zero or slightly negative ZDR

– Noisy, lower correlation coefficient (CC) -- (< 0.90)

• Insects/Biological scatterers– Low reflectivity (Z) -- (< 35 dBZ)– Horizontally-oriented with elongated shape: very high

ZDR (> 2 dB up to 6 dB)

– Heterogeneity causes very low correlation coefficients (< 0.70)

Tornado Detection

• Tornado debris is large (from radar perspective), irregularly shaped and randomly oriented– Z > 45 dBZ– ZDR near 0 dB– CC very low (< 0.8)

• A good sign that a tornado is already in progress!– Diagnostic ONLY– Has only been verified for EF-1 or greater

tornadoes at relatively close ranges

Tornadic Debris Signature (TDS)

Z ZDR

CC

TDS!

Debris cloud near GM Plant