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Mesoscale Atmospheric Systems
Radar meteorology (part 2)
10 March 2015
Heini Wernli
Today‘s topics
-  Stratiform precipitation & the „bright band“
-  Doppler radar ! wind measurements & fall speed
-  Polarimetric radar ! detailed microphysical information
Weather Radars in Europe
September 2013
202 weather radars:
184 have Doppler
48 have dual-polarization
http://www.eumetnet.eu/opera
Useful radar webpages
"  MeteoSwiss » www.meteoschweiz.ch
"  nowcasting of precipitation (ETH spinoff) » www.meteoradar.ch
"  tornadic phenomena around Switzerland » www.tordach.org/ch
"  European composite » www.meteox.com
Stratiform precipitation and the “bright band”
" 
" 
" 
" 
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vertical wind < ~ 1 m/s
horizontal scale ~ 100 km
stable stratification
external lifting through, e.g., fronts, orography
in polar regions and in midlatitudes in winter
The bright band
Circular symmetry implies a reflectivity field that is horizontally stratified.
Close to the radar, the signal comes from a region where T>0°C (rain)
Far away from the radar, the signal comes from a region where T<0°C (snow).
The “bright band” indicates the region where snow flakes are melting.
The bright band
RHI scan shows a fairly uniform structure in the horizontal on scales
of ≈100 km
snow
bright band
rain
The physics of the bright band
As snowflakes fall into air warmer than 0°C, their radar reflectivity
increases for several reasons:
1. The dielectric constant of water exceeds that of ice by about 5 dB
2. In its initial stage, melting produces distorted snowflakes with
higher reflectivity compared to spherical drops of the same mass
3. Raindrops fall faster than snowflakes (6 ms-1 vs. 1.5 ms-1), which
reduces their concentration in space. This dilution effect accounts in
part for the decrease in reflectivity in the lower part of the melting
layer
4. If large rain drops break up further reduction in reflectivity occurs
Bright band in a convective situation?
reflectivity
Doppler velocity
Doppler velocity
"  The echo of a moving particle from a pulse 1 has the phase
Φ1 = Φ 0 +
2r1 2π
λ
"  For the succeeding pulse 2 will, the particle will have moved thus
the new phase for pulse 2 is
Φ2 = Φ0 +
2r2 2π
λ
"  From the distance the particle has moved in the meantime we can
calculate its velocity along the radar beam:
Δr r2 − r1 (Φ 2 − Φ1 )⋅ λ ⋅ PRF ΔΦ ⋅ λ ⋅ PRF
v=
=
=
=
Δt 1 / PRF
4π
4π
PPI of the Doppler velocity in westerly wind situation
Doppler field of cyclonic circulation (tornado?)
Doppler field of strong horizontal convergence
Doppler velocity: Nyquist velocity
"  We run into trouble if the phase shift reaches ±π. The situation
then is ambiguous and we cannot tell whether the particle moves
towards or away from the radar.
vmax
πλ ⋅ PRF λ ⋅ PRF
=
=
4π
4
"  vmax is the so-called Nyquist velocity. Together with the
maximum range we can write
c
rmax =
2 ⋅ PRF
λ ⋅c
rmax ⋅ vmax =
8
Doppler velocity – aliasing (folding)
"  Aliasing happens where the
radial velocity exceeds the
Nyquist velocity (in amplitude).
"  The human eye can identify
(and correct) such regions very
well.
Why Doppler and polarization?
Dynamics and Microphysics of precipitation
Precipitation formation is directly related to
atmospheric motion.
# Hydrometeors are displaced
(advection plus sedimentation)
# Doppler shift of radar waves
Cloud and precipitation particles have different
shape, phase, size, and falling behaviour
# scattering properties
# polarization
Shape of falling raindrops
Raindrops falling with their terminal velocity are oblate.
a
Drops can be described as rotational
ellipsoids with the axis a and b
Observations in a
b
a
b
Deq = 2.6 mm 3.4 mm
5.8 mm
Deq = 7.4 mm
8.0 mm
vertical wind tunnel
Polarimetric Radar Observations
"  The polarization of an electromagnetic wave is defined by the
orientation of the electrical field vector E
"  Conventional Doppler radars use horizontal linear polarization only
Polarimetric Radar Observations
"  Dual polarization mode
simultaneous H and V
transmit and receive
Rain
Graupel
Hail
Dual-polarization measures
ZHH
Reflectivity
~ rain rate
ZDR
Differential reflectivity
ZH - ZV
~ shape
~ orientation
KDP
specific differential propagation
phase
~ ice / water content
LDR
Linear depolarisation ratio
ZVH - ZH
~ shape
~ melting
ρHV
Correlation coefficient
~ shape
~ oscillation
~ wobbling
~ canting
Differential Reflectivity (ZDR)
Differential reflectivity is the ratio between horizontal and vertical
reflectivity factor
ZDR = 10 log⎛⎜ zzVH ⎞⎟
⎝
⎠
or
ZDR = ZH − ZV
unit dB
H
V
using zH, zV in mm6 m3, or ZH, ZV in dBZ.
"  positive ZDR is caused by oblate
particles.
ZH
ZV
"  ZDR depends on particle shape, orientation and falling behaviour.
"  Note: Polarimetric quantities are only available for rainfall rates above
a certain value, since small raindrops are spherical.
Differential Reflectivity (ZDR)
Indication for
oblate
particles falling
horizontally
orientated
Differential Reflectivity (ZDR)
POLDIRAD at Waltenheim-sur-Zorn
"  ZDR can be used to identify insects and birds in clear air echoes
Z
" 
Rain: ZDR 0 – 5 dB
" 
Insects ZDR 5 – 10 dB
ZDR
Linear Depolarization Ratio (LDR)
The linear depolarization ratio LDR describes the ratio of cross-polar
reflectivity to co-polar reflectivity
⎞
⎟
LDR = 10 log⎛⎜ zzVH
H
⎝
⎠
or
LDR = ZVH − ZH
"  using zVH, zH in mm6 m-3 or ZVH, ZH in dBZ.
"  LDR is caused by particles which are
rotated to the polarization plane.
"  LDR is weighted by reflectivity.
"  LDR depends on the shape of the particles,
their orientation and their falling behaviour.
(unit dB)
Linear Depolarization Ratio (LDR)
Indication
for oblate
particles
falling
irregularly
or canted
Classification of Hydrometeors
Forecasters want to see this and not that
14
10
8
6
4
2
R eflectivity (dBZ )
0
12
Height (km)
Height (km)
12
14
R eflectivity
10
8
6
4
2
40 55 60
8
6
4
2
14
H eig ht (k m)
Height (km)
D epolariz ation R atio
10
L D R (dB )
-­‐35 -­‐28 -­‐19 -­‐13 60 65 70 75 80
R ange (km)
-­‐3 -­‐0.5 +0.5 4.5 R ange (km)
R ang e (km)
12
Z D R (dB )
60 65 70 75 80
60 65 70 75 80
14
D ifferential R eflectivity
12
10
8
6
4
2
Hydrometeor T ype
S
S S now
G G raupel
G
H Hail
HW Wet Hail
HLW L arge Wet H ail
r S mal R aindrops
r
R L arge R aindrops
S
H HW
G
HW
H
R
HLW
60 65 70 75 80
R ange (km)
Classification of Hydrometeors
From observations and theoretical or practical considerations we know:
Z
(dBZ)
ZDR
(dB)
KDP
(°/km)
ρHV(0)
LDR
(dB)
Rain
10 – 55
0–5
0 – 10
≈ 1.0
< -30
Ice crystals
< 15
0–2
0
≈ 0.99
< -30
Snow
aggregates
< 25
0–2
0
≈ 0.99
< -30
Graupel
up to 40
≈0
≈0
> 0.95
< 0.95 melting
< -30
< -25 melting
Hail
up to 70
≈0
≈0
0.9 – 0.95
< 0.9 melting
> -25
-25 – -15 melting
Insects
<5
5 – 10
?
0.9 – 1.0 ?
< -30 ?
Birds
<5
3–6
?
0.9 – 1.0 ?
< -30 ?
Ground
clutter
any
noisy
noisy
1 stopped
< 0.6 rotating
antenna
> -20
Classification of Hydrometeors
Based on fuzzy logic
(Vivekanandan et el., 1999)
LDR
Based on thresholds
(Höller et al., 1994)
Each manufacturer (researcher) has her/his own algorithm,
display, hydrometeor classes, parameters to adjust
Classification
based on thresholds
(Höller et al., 1994)
Classification using new Monte Lema radar data
1840 UTC
9 June 2012
Master thesis Pascal Graf