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Analysis & Diagnosis 1Radar Palette Home Radar ArtifactsRadar Palette Home Radar Artifacts

Radar Interpretation Problems

Reflectivity


Acknowledgments

• Phil Chadwick’s Radar Palette Course• MetEd courses- stole a few slides• Polarimetric presentation- staff at King City• Dave Patrick.. Radar and snow rates.• K-stream


Outline Radar Interpretation Problems-Reflectivity

• Attenuation– By Intervening Precipitation– Wet Radome

• Radar Beam filled or not filled • Low topped precip

– Drizzle

• Virga• Bright Band • The sometimes invalid assumptions of the z-r

relationship • Second Trip Echoes


Radar Sources of Error

Attenuation


Attenuation: What lies beyond?

Radar Palette Home Radar ArtifactsRadar Palette Home Radar Artifacts

Attenuation

• The All time best Canadian Example


Foreground Storm

Wedge of attenuation

Example of Attenuation in Precipitation


Attenuation

Warm FrontalSupercell


Attenuation

• north • end• of • derecho?


Correcting Attenuation

Polarimetric Radar


Heavy Rain QPE (cont’d)

Rain Accumulation (Z)


Heavy Rain QPE (cont’d)

Rain Accumulation (ZCORR)


Sources of Error - Radar Dome Wetting


Bright Band

• Melting snowflakes are large bright radar targets

• Reflectivity from melting snow is larger than that of the rain below or the snow above as falling snow passes through the melting layer

• Huge impact on quantitative precipitation estimates


Bright band shows up as ring (or partial ring) of high Z centred on the radar

Bright band


Bright Band

• Freezing Level ~2.5km AGL

• Either associated with Rain or Freezing Rain

Melting of Snow Flakes starts at 2.5 km height


Melting layer signature:- Z


Melting layer signature:- ρHV

Bright Band Ring


Vertical Bright Band over WKR January 21st, 2006

rainsnow

0°C


Radar Beam Overshooting

What real weather the radar can miss


PPI and CAPPI

1.5km CAPPI

4.0 km CAPPI

0.3 Degree PPI

0.3 Degree PPI


North East South West North

North East South West North

Approximate current lowest beam location BALD

Approximate proposed lowest beam location LOALA


Radar Beam Undershooting

Weather that’s not hitting the ground


Sources of Error - Virga

Typical of strong overrunning WCB with equally strong and dry CCB


Cross section to the southwest


Low-elevation angle Doppler Z PPI scan

Let’s look at the same case observed by the Doppler scans

Low level reflectivities to the west of the radar – rain?


3.5 Degree Doppler Z PPI scan

Echoes at higher altitude precedeprecipitation at low Levels – virga?


What the radar misses close in


Algorithms that fail very close to Radar…Hail

• As you approach the cone of silence the top part of the storm is above the highest scan angle of the radar.

• Height of MAXR and VIL values are unrealistically low.

• URP hail algorithm fails.


June 16th 2008- 1900Z


June 16th 2008- 1900Z

150.25/3.25 161/5.75

Z 51.16 55.72

Zcorr 49.52 52.47

Zdr 2.52 2.67

Zdrcorr 2.17 0.53

Rhohv 0.98 0.76

21

1

2


Sources of Error - ECHO TOP - Beamwidth and Beamfilling


Echo Tops


Partial Beam Filling


Radar beam widths and sensitivity

• Not all radars are equal• In Canada• Many are retrofitted Enterprise radars with 1.1°

beam width and 4 m dishes• Some (10) are Andrews radars with 0.65° beam

width and 6 m dishes• Many have obvious issues

– differences in sensitivity between each other

• But many have less obvious, but significant problems that only become apparent with long-term precipitation accumulations


Beam Spreading

At 240 km

3 km diameter

Radar beam

At 30 km

0.5 km diameter

Radar beam


Partial Beam Filling


One storm 2 radars with different beamwidths


Second Trip Echoes

to a distant target...first pulse

returning first pulse

second pulse


Second Trip Echo

actual position of echo beyond range

Echo within range

mapped position of 2nd trip echo

Pulse OnePulse Two


Second Trip Echo

Elong

ated R

adial

Weaker

Far AwayRandon

Doppler

Changing the PRF changes Location

Check the Satellite


Purple Haze


Reflectivity…Rain Rates and Snow Rates

Snow being depicted using the Rain Z-R Relationship

Snow depicted using the Snow Z-S Relationship


Snow Reflectivity & Snowfall Rate

• Value of a depends on– whether the snow is dry or rimed– snowflake size distribution y-intercept constant

• Large N0 => large number of small flakes compared to large– radar dark matter ??

• Small N0 => small number of small flakes compared to large – snowflake terminal velocity

• dense flakes fall twice as fast

67.13/53/20, )]/([ SVNkZ trimeddry

67.1SaZ



• For a typical range of values of N0, and dry

snow falling at 100 cm/s and wet snow falling at 200 cm/s

– Here is a graph of Z vs. S in mm/hDry airy snow falling slowly; 0.75 mm/h

Rimed dense snow falling rapidly; 3 mm/h

Factor of 4 difference in snow rate for

same Z !!



• Plot these empirical Z-S relationships on the theoretical graph …

Spatial dendrites Z = 3300SZ = 3300S1.71.7

Standard Z = 1780SZ = 1780S2.212.21 Plates & columns

Z = 400SZ = 400S1.61.6

Standard eq’n underestimates almost all high

snow rate situations


Percentage Underestimate of Snow Water Equivalent by Standard Z-S Equation - 10+cm Events

-70

-60

-50

-40

-30

-20

-10

0

YVR YXS YYC+4dBZ

YWG YYZ+4dBZ

YOW YFC YQM YHZ YYT Ave All33

Stations

Per

cen

tag

e S

WE

Un

der

esti

mat

e

Pct UnderEst SWE by Std Z-S eq'n - 10+cm Events



Another hypothesis ZDR vs Z scatter plotsand observed precipitation (snow) • Red squares

– Radar estimate close

• Green– Radar estimate slightly

underdone

• Purple triangles– Radar low by factor of 2-3.


Z and Rainrate


• Reflectivity Z is empirically related to rain rate R in the form :

Z = aRb

• In Canada we use Marshall-Palmer (stratiform rain) :

Z = 200RZ = 200R1.61.6

• U.S. NEXRAD :

Z = 300RZ = 300R1.4 1.4

• U.S. Tropical :

Z = 250RZ = 250R1.2 1.2

Empirical Z-R Equations


• Z proportional to drop diameter to 6th power.

• If more big drops than standard DSD radar overestimates rainfall – Attenuation often cancels

this out.

• If more small drops Z underestimates rainfall.


Observed precipitation


Radar Accumulations


Conclusions

• Radar returns are not always what they seem.• Before making conclusions think about

– -How high the beam is above the ground– How wide is it where you are looking.– What other echoes did it have to go through to get to

the storm you are interested in. – What are the chances the drop size (snowflake)

distribution is different from the “standard”– Does the beam go through a melting layer– Could there be second trip echoes.

• Despite all the above radar does quite a remarkable job of detecting weather.


THE END

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