Transcript Slide 1

Radars in Helsinki
Testbed
Elena Saltikoff, FMI
17.7.2015
Tutkia tutkia – Radars to find out
Where’s
precipitation
How much ?
5 min, 1 km res.
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A radar does not measure precipitation,
just scattering of microwaves
P= Measured reflected power (watts 10-13)
Clutter cancellation
Z=Reflectivity by precipitation (dBZ)
Assume precipitation
type
Z=aRb, assume a and b
R=Rainfall intencity (mm/h)
Integrated rainfall in N hours (mm)
Rainfall in river catchment area (m3)
Dual Pol can improve QPE
by improving these
Not so easy for gauges either
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Reflectivity and velocity
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Clutter can be defined as
Microwaves scattered by unwanted objects
Total dBZ
Hills
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Velocity
Hill speed
zero m/s
Filtered dBZ
No hills
Rho
Sea clutter
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The bad news: we live on a spherical globe.
For FMI standard products, we compensate as much as we can...
Profile measurement volume
h
r
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1(2)
Weather Radars in Helsinki Testbed
•
Ready made datasets – gif images
•
dBZ: Reflectivity images every 15 minutes are composites of 4
FMI radars: Vantaa in the centre, Ikaalinen in Northwest, Korpo
in Southwest and Anjalankoski in east
•
vpr: Vertical profiles of reflectivity separately from each above
mentioned individual radar. Also as text files.
•
Rho and ZDR: dual polarization parameters hourly from Kumpula
radar at Helsinki University Campus
•
Data archived as IRIS raw files (typically 2-8 Mb)
•
At FMI process in Jordan or in Harry
•
At University, process in Analysis
•
Possible to convert to other formats
Radar data parameters
• FMI data is the same from all radars, all campaigns
• Kumpula radar data is different for each campaign
• August 2005: 5 tasks repeated every 10 minutes
• Nov 2005 and May 2005: two different schedules
alternating, some tasks the same all month
• Tasks described in a pdf at the testbed website
• Inventory of datasets available too
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Excercise: What is this ?
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Radar scanning geometry in 3D
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Kumpula August 2005
The task schedule consists of 5 subtasks, repeated every 10 minutes (10 minute interval longest
acceptable for convection research)
Task
Main purpose
Rang
e/km
Elevations
/deg
Mod
e
Moments
PRF/H
z
Max
wind
PRO_A
Good dBZ
150
0.8 1.7 2.7
FFT
Z, T, V,W, SQI
1000
13.3
m/s
PRO_B
Dual pol low part
150
0.3 1.2 3.6
7.0 16
PPP
Z, T, V,W, SQI
ZDR, KDP,
RhoHV, PhiDP
1000
13.3
m/s
PRO_C
Dual pol upper
air
120
2.2 4.6 11.0
22 45 90
PPP
As above
1200
16 m/s
D_PRO
Horizontal
transmission,
H+V receiving
(for LDR)
120
2.2 4.6 45
(top to
down)
PPP
Z,T,V,W,SQI
LDR, RhoH,
PhiH
1200
16 m/s
E_PRE
Dual PRF, 8-bit
(for
mesocyclone
winds)
120
3.0 8.0
FFT
Z, T, V, W
1200/
800
32 m/s
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FMI tasks 1991-2006
The task schedule consists of 3 subtasks, repeated every 5 minutes during campaigns
Task
Main purpose
Rang
e/km
Elevations
/deg
Mod
e
Moments
PRF/H
z
Max
wind
VOL_A
Good dBZ
250
0.3 0.8 1.7
2.7
PPP
Z, T, V
570
7m/s
VOL_B
Middle part
120
4
5.5
8
PPP
As above
13
25
PPP
VOL_C
Upper air
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80
22 m/s
850/567
Hz
As above
1200/80
0
32 m/s
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Hydrometeor classification is not possible with dBZ only
-20 -10
DBZ
rain
hail
snow
sleet
insects
birds
clutter
ZDR -5
rain
hail
snow
sleet
insects
birds
clutter
0
10
20
30
40
50
60
Overlap of
hail and
heavy rain
Overlap of
snow and
insects
…
0
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3
5
RHO
rain
hail
snow
sleet
insects
birds
clutter
0.2
0.4
0.8
0.9
0.95
0.99
1
Help from
dual pol
parameters
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Three ways to collect dual-pol data
• Alternating H and V ”Old-fashioned mode”
• Transmit H, receive H and V ”LDR mode”
• Z,V and LDR Linear Depolarization Ratio
• More sensitivity
• Transmit H and V, receive H and V ”Star mode”
• Z, V and ZDR, Rho, KdP, PhiDP
•
ZDR - Differential Reflectivity
•
Rho - Correlation Coefficient
•
PhiDP - Differential Phase
•
KDP - Specific Differential Phase
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ZDR=10Log(Zh/Zv)
V
%Zv
ZDR < 0
ZDR > 0
ZDR > 0
ZDR > 0
%Zh
ZDR > 0
ZDR=0
H
courtesy of Timo Puhakka, HU
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ZDR=10Log(Zh/Zv)
•
•
•
•
•
•
•
•
•
•
generally, for hydrometeors ZDR -3..+3 dB (ratio 1:2)
Increases with the sizes of liquid drops
Small with dry snow
Positive with horizontally oriented plate-crystals
Negative with vertically oriented ”needles”
Small or negative with hail
Indicates presence of frozen precipitation
Indicates super cooled water in updrafts
Indicates the onset of melting
With Zh can detect hail
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ZDR in showers, sea clutter and birds
Non-met
Weather
Non-met
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Correlation coefficient rhv
V
V
H
H
Correlation coefficient = 1 for spheres and oriented spheroids
courtesy of Timo Puhakka, HU
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Decrease of correlation Rho indicates
• Variety of hydrometeor types
• Mixture of liquid and frozen hydrometeors (”Snöblandat regn”)
• Hydrometeors with irregular shape
• Wide distribution of hydrometeor orientation
• Presence of large hail
• Correlation coefficient <0.95 for hail, hail/rain mixture and for wet
aggregates
courtesy of Timo Puhakka, HU
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RHO sea clutter and birds:
pink > 0.94 precipitation
Interference
Birds
Sea
clutter
Anaprop
Showers
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RHO in elevation 7 deg
- melting layer 0.94-0.99
Ice and
snow
Melting
snow
Water
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Linear Depolarization Ratio LDR
LDR vh  10 Log ( s hv
Shv=0
Shv=0
2
 /  s hh
2
)
Shv > 0
LDR  
LDR  
LDR  
courtesy of Timo Puhakka, HU
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Linear Depolarization Ratio LDR
•
Dry snow
LDR<-30 dB
•
Rain
LDR<-27 dB
•
Dry aggregates, small hail,graupel
LDR<-20 dB
•
Wet aggregates, small hail,graupel
-20<LDR<-10 dB
•
Hail, rain/hail mixture
LDR>-20 dB
courtesy of Timo Puhakka, HU
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High resolution RHI’s of melting layer
dBZ,
Rho,
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LDR
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PhiDP
The anisotropy of the medium leads to phase difference
between horizontal and vertical waves
(when horizontal waves go through more water)
The detection of this phase difference is the basis for
PhiDP.
More often, range derivative of PhiDP known as KDP, is
used.
•
courtesy of Timo Puhakka, HU
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Kdp example
• Attenuation !
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Attenuation visible in ZDR
• horizontal waves more attenuated
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Datasets are huge - Recommended procedure
• Select situation from Weather Diary
• Browse ready-made images
• Select and limit the dataset you want
• Read readme.files
• Get data
• Process
• Make conclusions
• For reporting, consider whether you want to use
ready-made images or draw your own
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Lake effect snow last Tuesday evening
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