Lecture3 Jon Radar

8/10/2019 Lecture3 Jon Radar

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Radar Basics and Estimating Precipitation

Jon W. Zeitler

Science and Operations OfficerNational Weather Service

Austin/San Antonio Forecast Office


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Radar Beam Basics


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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 areconcerned with two types of scattering, Rayleigh and non-Rayleigh.

Rayleigh scattering occurs with targets whose diameter (D) is much smaller (D 0Horizontally-oriented mean profile

ZDR< 0Vertically-orientedmean profile

ZDR~ 0Near-sphericalmean profile

Eh

Ev

Eh

Ev

Eh

Ev


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-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Small (Spherical) > Large (Oblate)

Dry > Wet

Dry (Prolate) > Melting (Oblate)

Aggregated/Low-Density > Pristine/Well-Oriented

Dry > Wet

GROUND CLUTTER / ANOMALOUS PROPAGATION

BIOLOGICAL SCATTERERS

DEBRIS

CHAFF

Differential Reflectivity (ZDR)


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1. median liquid drop size(ZDR,median

drop diameter)

2. hail shafts(ZDR ~ 0dB or negative

coincident with high Zh)

3. areas oflarge rain drops or liquid-

coated ice(ZDR~3-6 dB)

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

5. tornado debris ball

ZDRis a Good Indicator of:


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Values are biased towards the largerhydrometeors (D6dependence)

Tumbling/Random orientation will bias

toward 0 ZDRCan be noisy if:

-Low / Insufficient sampling (low

SNR)

- Reduced correlation coefficient (CC)

ZDRLimitations (Gotchas)


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M 9th t di


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May 9thtornadic

supercell: Intense

ZDRColumn

0oC level in-cloud ~17 kft


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hv

Affected by:

Hydrometeor types, phases, shapes,

orientations

Presence of large hail

Correlation Coefficient (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.


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hvUsage

Identify hail growth regions in deep moist

convection (mixtures of hydrometeors)

Reduce ground clutter/AP contamination(hvvery low in these areas)

Identify giant hail ???


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Minimum in Theory


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Giant Hail, Protuberances, Mie Scattering: min hv

hvMinimumin Theory

Diff ti l Ph Shift ( )


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Differential Phase Shift (DP)

Definition: the difference in the phase shift

between the horizontally and vertically

polarized waves

Units: degrees (o)

VHDP


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fDP= fhfv (fh, fv 0) [deg]

The difference in phase between the horizontally-

and vertically-polarized pulses at a given rangealong the propagation path.

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

Differential Phase Shift fDP

What Affects Differential Phase?


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What Affects Differential Phase?

Forward Propagation has its


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Forward Propagation has its

Advantages

Immune to partial (< 40%) beam

blockage, attenuation, calibration,

presence of hail

Gradients Most Important

Specific Differential Phase Shift


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(KDP)

Definition: range derivative of the differential phaseshift

Units: degrees per kilometer (o

/km)

12

12

2

)()(

rr

rr

K DPDP

DP

ff


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

Specific Differential Phase: KDP



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(KDP)

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

-0.5 0 0.5 1 1.5 2 2.5 3 4 5

Small > Large

Dry > Wet

Dry (Prolate) > Melting (Oblate)

Dry/Aggregated > Pristine/Well-Oriented

Dry > Wet

K U


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KdpUsage

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, Kdpcolumns Drops are shed from melting or growing

hailstones near the updraft, forming a Kdpcolumn

To distinguish between snow/rain

Kdpin wet, heavy snow is almost always largerat a fixed value of Zhthan that observed for rain


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KDP Limitations (Gotchas)

KDP values set to No Data at CC 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


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Marginally Severe Supercell

14 May 2003


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Beam Height ~ 4600 ft AGL

ZZDRHVHCA

5.25 diameter hail

14 May 2003

Correlation Coefficient (CC)


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Definition: how similarly the horizontally andvertically polarized backscattered energy arebehaving within a resolution volume for Rayleighscattering

Units: none (0-1.00)

2/12

2/12

*

)0(

vvhh

hhvv

HV

SS

SSr

Think Spectrum Width for Hydrometeor

Sij= An element of the backscatter matrix


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Correlation Coefficient Values

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.



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Non-

Meteorological

Regime

Meteorological

RegimeOverlap

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.85 0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1

Large > Small

Wet > Dry

Wet / Large > Dry / Small

CRYSTALS

Wet > Dry

GROUND CLUTTER / ANOMALOUS PROPAGATION

BIOLOGICAL

SCATTERERS

DEBRIS

CHAFF

Wh t i CC U d f ?


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What is CC Used for?

Not-met targets (LOW CC < 0.70)

Best discriminator

Melting layer detection (Ring of reducedCC ~ 0.800.95)

Giant hail? (LOW CC < 0.70 in the midst

of high Z/Low ZDR)

M i ll S S ll


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Marginally Severe Supercell

What about the r

All > 0.97

InsectsPrecip

CC Limitations (Gotchas)


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CC Limitations (Gotchas)

High error in low signal-to-noiseratios (SNR)

If low, errors increase in otherdual-pol variables


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Polarimetric Rainfall Algorithm vs.


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Polarimetric Rainfall Algorithm vs.

Conventional

Bias of radar areal rainfall estimates

Spring hail

cases

Cold seasonstratiform rain

QPE Process in a Nutshell


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Step 1

1. Hybrid scan thevariables into

Polar, 1 degree

azimuth, 250 mbins

Hybrid Hydroclass


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QPE P i N t h ll


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

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

Rate Designation Table


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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 andecho is belowthe top of the melting layer RH

0.8*R(Z) Rain/Hail is classified andecho is abovethe 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 andecho is in or below the top of the meltinglayer

DS

2.8*R(Z) Dry Snow classified andis echo abovethe top of the melting layer DS

2.8*R(Z) Ice Crystals are classified IC

QPE Output (all produced via


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Q p ( p

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-Rbias applied)

8 bit, 250 m no G-R bias applied STP

8 bit, 250 m User Selectable (will be used for anyand all accumulation time periods)

8 bit, 250 m 1hr and STP Difference field vs.

Legacy

Hydrometeor Classification AlgorithmCh ll


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Typical Radar sampling limitations (snow at2000 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

Challenges

Melting La er Detection


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Melting Layer Detection

Mixed phase hydrometeors: Easy

detection for dual-pol!

Z typically increases

ZDR and KDPdefinitely increase

Coexistence of ice and water will reduce the

correlation coefficient (CC ~0.95-0.85)

Melting Layer Detection Algorithm


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Precipitation echoesstratiform or

convective regionswith high SNR

Middle tilts (4-10elevation angles)

Limitation: Overshoot precip

Project results to other tilts in time and

space

Melting Layer Detection Algorithm

Methodology

ML Product in AWIPS


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ML Product in AWIPS

Hail Detection


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Hail Detection

Dual-Pol Hail Signature High Z (> 45 dBZ)

Low ZDR (-0.5 to 1 dB), Low KDP (-0.5 to1 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 Discrimination


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Rain/Snow Discrimination

RAIN SNOWZ < 45 dBZ < 45 dBZ

ZDR 0 to 2 dB -0.5 to 6 dB

KDP 0 to 0.6 deg/km -0.6 to 1 deg/km

CC >0.95 >0.95 (can be less if

wet)

If the variables overlap so much, how can polarimetric radar

discriminate between rain and snow???

Rain/Snow Discrimination: Its all


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in trends with height

Rain Polarimetric signatures (ZDRand KDP) have a direct

dependence on Z

ZDRand KDPdo nottypically increase with height

Almost always a pronounced melting layer above rain

Snow Polarimetric signatures (ZDRand KDP) do nothave

dependence on Z

ZDRand KDPtypically increase with height Differences between warm and cold snow

Cold snow has higher polarimetric variables than warmsnow

Warm vs. Cold vs. Wet Snow


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a s Co d s e S o

Temperature determines this

< -5oC = Cold

~+1oC > T > -5oC = Warm

> +1oC = Wet

Crystals (plates, columns, needles)

Aggregate Crystals (Dry)

Aggregate Crystals (Wet)

Surface. Assume temperatures decrease steadily with height

Radar Cross Section

Characteristics

Z/ZDR/CC

Characteristics

High DensityHigh Concentration

Oblate, Horizontal Orientation

Small size

Z < 35 dBZ

ZDR 0-6 dB

CC > 0.95

Decreasing density

Decreasing Concentration

Less oblateLarger size

Z increasing

ZDR decreasing

0 > ZDR > 0.5 dBCC > 0.95

Rapid increase in density

Rapid increase in oblateness

Z increasing but < 45

dBZ

ZDR rapidly increasing

0.50 > CC > 0.9

Rain Snow Discrimination


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Rain Snow Discrimination

Z ZDR

KDP CC

Snow

Rain

One Hour Later


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One Hour Later

Z ZDR

KDP CC

-SN

Data Quality Improvement


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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


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Tornado Detection

Tornado debris is large (from radar perspective),irregularly shaped and randomly oriented

Z > 45 dBZ

ZDRnear 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)


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Tornadic Debris Signature (TDS)

Z ZDR

CC

TDS!

Debris cloud near GM Plant


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Debris cloud near GM Plant

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