Mesoscale Atmospheric Systems Radar meteorology (part 2) 11 March 2014 Heini Wernli

Today‘s topics -  Stratiform precipitation & the „bright band“

-  Doppler radar à wind observation

-  Polarimetric radar à microphysical information

Weather Radars in Europe

202 weather radars:

173 have Doppler

46 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”

§  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 reflectivities than those of 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?

Doppler velocity

§  The echo of a moving particle from a pulse 1 has the phase

§  For the succeeding pulse 2 will, the particle will have moved thus the new phase for pulse 2 is

§  From the distance the particle has moved in the meantime we can calculate its velocity along the radar beam:

( )πλ

πλ

4PRF

4PRF

PRF/11212 ⋅⋅ΔΦ

=⋅⋅Φ−Φ

=−

=Δ

Δ=

rrtrv

λπ22 1

01r

+Φ=Φ

λπ22 2

02r

+Φ=Φ

PPI of the Doppler velocity

Real Doppler field of circulation

Real Doppler field of convergent flow

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 is the so-called Nyquist velocity. Together with the maximum range we can write

4PRF

4PRF

max⋅

=⋅

=λ

ππλv

PRF2max ⋅=

cr

8r maxmax

cv ⋅=⋅λ

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.

Precipitation formation is directly related to atmospheric motion. è Hydrometeors are displaced (advection plus sedimentation) è Doppler shift of radar waves

Cloud and precipitation particles have different

shape, phase, size, and falling behaviour

è scattering properties è Polarization

Why Doppler and Polarization?

Dynamics and Microphysics of precipitation

Shape of Falling Raindrops Raindrops falling with their terminal velocity are oblate.

Drops can be described as rotational

ellipsoids with the axis a and b

Observations in a

vertical wind tunnel

(Pruppacher & Klett)

baa

b

Deq = 2.6 mm 3.4 mm 5.8 mm

Deq = 7.4 mm 8.0 mm

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

- simple technical realization

- possible contamination by

strong depolarization in

melting layer

Rain Graupel Hail

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

Dual-polarization measures

Differential Reflectivity (ZDR)

Differential reflectivity is the ratio between horizontal and vertical

reflectivity factor

using zH, zV in mm6 m3, or ZH, ZV in dBZ.

§  positive ZDR is caused by oblate

particles.

§  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.

dB unit = orlog VHzz ZZZDR10=ZDRVH −⎟⎠

⎞⎜⎝

⎛

Z H Z V

V

H

Differential Reflectivity (ZDR)

Indication for

oblate

particles falling

horizontally

orientated

Differential Reflectivity (ZDR)

§  ZDR can be used to identify insects and birds in clear air echoes

§  Rain: ZDR 0 – 5 dB

§  Insects ZDR 5 – 10 dB

Z ZDR

POLD

IRAD

at

Wal

tenh

eim

-sur

-Zor

n

Linear Depolarization Ratio (LDR) The linear depolarization ratio LDR describes the ratio of cross-polar

reflectivity to co-polar reflectivity

§  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.

dB) unit( = LDRorlog HVHzz ZZ10 = LDR

HVH −⎟

⎠

⎞⎜⎝

⎛

Indication for oblate particles falling irregularly or canted

Linear Depolarization Ratio (LDR)

Forecasters want to see this and not that

Differential  R eflectivity

R ange  (km)

Heigh

t  (km

)

60                        65                      70                      75                      80

2

46

8

10

12

14

40      55      600

Heigh

t  (km

)

2

46

8

10

12

14

R ange  (km)60                        65                      70                      75                      80

ZDR  (dB)-­‐3      -­‐0.5    +0.5    4.5              

Heigh

t  (km

)

2

46

8

10

12

14

60                        65                          70                          75                          80R ange  (km)

LDR  (dB)

-­‐35        -­‐28      -­‐19        -­‐13              

R eflectivity

Depolarization  R atio

R                  Large  Raindrops

G

S S

rR

GH

H

HW HW

HLW

Hydrometeor  Type

2

46

8

10

12

14

Heigh

t  (km

)

S                  S nowG                  GraupelH                  HailHW            Wet  HailHLW        Large  Wet  Hailr                      Smal  RaindropsR

R eflectivity  (dBZ )

R ange  (km)60                        65                      70                      75                      80

Classification of Hydrometeors

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

Z (dBZ)

ZDR (dB)

KDP (°/km)

ρHV(0) LDR (dB)

Insects < 5 5 – 10 ? 0.9 – 1.0 ? < -30 ?

Birds < 5 3 – 6 ? 0.9 – 1.0 ? < -30 ?

Chaff < 5 0 – 6 ? < 0.3 > -20

Ground clutter

any noisy noisy 1 stopped < 0.6 rotating antenna

> -20

Classification of Hydrometeors

From observations and theoretical or practical considerations we know:

Based on thresholds (Höller et al., 1994)

Based on fuzzy logic (Vivekanandan et el., 1999)

LDR

Each manufacturer (researcher) has her/his own algorithm, display, hydrometeor classes, parameters to adjust

Classification of Hydrometeors

Classification based on thresholds (Höller et al., 1994)

Classification using new Monte Lema radar data Master thesis Pascal Graf

1840 UTC 9 June 2012