Transcript Precipitation Measurements using Radar

Precipitation Measurements
using Radar
Prof. LEE, Dong-In
Pukyong National University
[email protected]
0505-i-am-cool (0505-4-26-2665)
Precipitation
• Rainfall has been measured for
hundreds of years:
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Velocity measurements
• Obtained by tracking echoes and
knowing the time between
measurements
• Doppler shift - moving targets change
the frequency of the returned signal
• transmit known frequency and measure
the frequency shift of returned signal
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Radar measurements
of precipitation
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Doppler radars
• Most modern radars are Doppler
• NEXRAD (next generation radar, USA)
• WSR-88D
• routinely measure velocities
• used to detect tornadoes, mesocyclones,
wind speeds
• “Doppler radar” used as gimmick but not
often shown by TV weather people
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Wind profilers
• Vertically pointing Doppler radars
• use 3 beams, one vertical, one 15° toward east,
one 15° toward north
• Measurements at 500 m intervals every 5
min, 24 h a day
• Limitations:
• antenna sidelobe problems
• near-by moving targets
• nocturnal bird migrations
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Distributed Targets
• Meteorological targets
consist of many(!)
targets in the beam
simultaneously.
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Example - cloud
• Continental clouds have 200 cloud
droplets/cm3
• For 1° beamwidth radar at range of 57 km,
beam will be 1 km in diameter.
• If radar uses 1 ms pulselength, radar will
illuminate effective volume of 150 m length.
• So, radar sample volume will illuminate
more than 2•1016 cloud droplets
simultaneously.
1 km
57 km
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Example - rain
• There will be fewer raindrops, but still 109
to 1012 raindrops in typical sample volume.
• 2000 Lab Reports at PKNU on drop-size
distributions had the following total number of
drops per m3 per mm diameter interval:
• 2116, 2017, 26314, 992, 7219, 8677, 224, 816, 7470,
6600, 6296, 2841, 1947, giving an average of 5700
±6900 m-3 mm-1
• That’s 6.7 • 1011 raindrops in a radar sample volume.
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Snow
• Snow is also detectable by radar.
• An event from 1996:
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Snow
• “Snow is not very detectable by radar.”
• Is this often-quoted comment true or false?
• If Z from snow = 20 dBZ, how far away can a
radar detect it?
Z = C + Pr + 20 log(r/1 km)
UND radar: rmax = 10(Z-C -MDS)/20
= 10(20 + 64.55 +(-106.5))/20
= 1,188 km!
WSR-88D = 2,540 km!
• Answer: Snow should be easily detected.
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Then, why does radar have
trouble seeing snow?
• The height of snow storms is typically
< 5 km but often < 3 km
• Echo will extend only to 164 km
before 0.5° antenna elevation beam is
above storm.
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Radar reflectivity factor
• We define radar reflectivity factor as
z = SD6
where the summation is carried out over a
unit volume, not the radar sample volume.
• The final, slightly simplified version of
the radar equation is:
 pt g h K z
pr 
2 2
1024 ln 2  r
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Rainfall measurements from
radar
• Rainfall is one of the major uses of radar
• To determine rainrate from radar
reflectivity factor data, we use a Z-R
relationship
• Z-R relationships can be determined from
• radar and raingage data
• drop size distributions
Raindrop size distributions
• J. S. Marshall and W. McK. Palmer (1948)
measured raindrop size spectra at Ottawa.
• Found exponential size distribution of the
form:
ND = N0 e- D
where N0 = 8000/(m3 mm), D is droplet
diameter (mm) and  is given by
 = 4.1 R-0.21
where R is rainrate in mm/h
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Marshall & Palmer DSD compared
to data from Laws & Parsons
104
ND (m-3 mm-1)
103
102
101
100
10-1
0
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2
3
Prof. Lee,
Dong-In (mm)
Raindrop
Diameter
4
5
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Z-R Relationships
• To convert radar measurable Z to
hydrologically useful parameter R, we
need a relationship to convert
between these.
• Convenient, empirical relationship is a
power-law relationship:
Z = ARb
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Z-R Relationships
Source
Relationship
Marshall & Palmer
200 R1.6
Joss & Waldvogel
300 R1.5
Radar classes
259 R1.50
1.59
Radar Class, 1994
429 R
Radar Class, 1997
263 R1.51
Radar Class, 2000
258 R1.28
Many more….
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Z-R Relationships
Marshall-Palmer
100
Joss-Waldvogel
Z (dBZ)
80
Cain-Smith (ND)
60
Laws-Parsons
40
20
0
0.1
SekhonSrivastava(water)
1
10
100
R (mm/h)
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1000
SekhonSrivastava(ice)
2000 Radar class
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Hawaiian Radar Rainfall
Measurements
The importance of choosing the
right Z-R relationship
An example.
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•
Fig. The performance of the WSR-88D's precipitation algorithms in a tropical
environment is demonstrated by comparing observed six/twelve hour cumulative rainfall
amounts from LARC rain gauges to computed values from radar algorithms. Results show
that the standard algorithm (orange) seriously underestimates the rainfall rate (blue) on
Oahu for the flood event of 25 January 1996. An algorithm tuned to the tropical drop siz
distribution does much better (red).
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POSS
(Precipitation Occurrence Sensor System)
• Bistatic X-band(10.25GHz, 2.85cm)
CW Doppler radar
• Doppler power density spectrum
S( f )  
Dmax
Dmin
N ( D) V ( D)  S ( f , D)  dD
• Mesurements
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•
•
•
Diameter
Fall velocity
Number density
DSD
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Number Density
Fall Velocity
DSD
Rainrate
An example of POSS screen display
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POSS channel parameter
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Distance : 10 km
The Position of Radar, POSS and AWS in research area
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Precipitation Observation Date
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• POSS measurement
March – September (44 cases, 211hr 42min)
POSS mesurement data (time resolution : 1 min)
- DSD, Number density, Rain amount
- Rainrate(R), Reflectivity(Z)
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
V f  9.6 1  e
0.5674 D
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
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• Radar measurement
Radar raw data (Busan)
– UF format
- Polar grid data(transfering)
– Cartesian grid data
(Using by Sprint)
- dBZ value
– 10 x 10 km around POSS
– averaged dBZ
- ZR(ave. value) - ZP(POSS)
- ZC (calibrated value)
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POSS
10km
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• AWS rain gauge measurement
Rain gauge data - Rain rate(mm/hr), Rain amount(mm)
POSS data – Rain rate(mm/hr), Rain amount(mm)
Rain rate (mm/hr)
Rain amount (mm)
120.0
40
100.0
POSS
AWS
POSS
30 80.0
AWS
20 60.0
40.0
10
20.0
930
914
911
914
913
909
905
814
906
731
716
713
716
710
701
701
625
625
622
614
614
605
618
528
530
521
507
501
510
428
414
330
414
411
301
301
328
0 0.0
Time
Time
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Comparison between rain rate and rain amount
obtained by POSS and rain gauge
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Ex. Case studies
• M-P distribution
N ( D)  N0e
3
 D
1
1
N0 (mm mm )  8000 (mm )  4.7R
0.21
D(mm) : diameter, R(mm/hr) : rain rate,
N0 : the value of ND for D=0
N(D) : the number of drops of diameter
between D and D + dD
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Fig. Comparison between
Observed Nd (POSS
measurement) and M-P’s Nd
Black line : M-P’s Nd (0.5, 1, 2, 4,
8, 16, 32, 64, 128 (mm/hr))
Red line : Observed Nd
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7000
50
45
40
5000
35
4000
30
25
3000
20
2000
15
(%)
Number of Frequency
6000
10
1000
5
0
0
0<R<0.5
0.5<R<1
1<R<2
2<R<3
3<R<5
5<R<10
10<R<20
20<R<100 100<R<200
R (mm/hr)
Fig. The number frequency and percentage of each
rain rate
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814
905
906
814
905
906
Fig. The Z-R relationship
obtained by POSS
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930
914
914
913
911
910
930
914
914
913
911
910
909
731
731
909
716
716
712
706
701
624
714
0.5
714
1
714
1.5
714
2
712
Time
712
712
706
701
624
622
614
613
530
522
521
507
ZP = 415 R
622
614
613
530
522
521
400
501
800
507
428
424
411
328
301
a Values
1000
501
428
424
411
328
301
b Values
1400
1200
1.51
600
R2 = 0.8749
200
0
Time
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Converting ZP-R relationship
• POSS Z-R relationship calculation
• Correction coefficient:
• Radar reflectivity(ZR) and POSS
reflectivity(ZP)
• Rainfall rate estimation from corrected
radar reflectivity (Zc)
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Rain amount (mm)
20
16
12
POSS
AWS
8
4
0
411
430
830
Time
4
POSS
R (mm/hr)
3
AWS
2
1
0
411
430
830
Time
Fig. The comparison of rainrate and rain amount
obtained by POSS and rain gauge
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2001.
08.
11
30
2001.04.
4. 30
5
8
3
RP (mm/hr)
RP (mm/hr)
4
6
2
1.6
1.6
ZR = 200 R1.6
1.51
ZR = 415 R1.51
ZR = 200 R
ZR = 415 R
RP (mm/hr)
3
4
2
1
2
1
0
0
0
300
200
1600
400
500
400
1800
600
700
600
2000
800
8009002200 1000 10001100
Time
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Fig. Distribution of rainrate calculated using radar
reflectivity
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Reflectivity (dBZ)
50
Radar
POSS
40
11 April
30
ZC = 0.464ZR+4.654
20
10
0
1600
1630
1700
1730
1800
1830
1900
1930
2000
2030
2100
2130
2200
2230
2300
2330
Time
Radar
50
POSS
30 April
Reflectivity (dBZ)
40
30
ZC = 1.242ZR+5.395
20
10
0
340
410
440
510
540
610
640
710
740
810
840
910
940
1010
1040
Time
Radar
Reflectivity (dBZ)
50
POSS
40
30 August
30
ZC = 0.569ZR+21.764
20
10
0
210
240
310
340
410
440
510
540
610
640
710
740
810
840
910
940
1010
Time
Prof. Lee, Dong-In
Fig. The reflectivity distribution
of radar and POSS
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2001. 4.
08.30
2001.
1130
2001.
30
2001.08.
11
2001.
4.4.30
84
10
4
810
RP (mm/hr)
RP (mm/hr)
RP (mm/hr)
ZC = 200 R1.6 1.6
ZC = 200 R
8
63
ZC = 200 R1.6
8
63
R
RPP (mm/hr)
(mm/hr)
R
RPPP (mm/hr)
(mm/hr)
C
ZC = 415 R1.51
ZC = 415 R1.51
6
6
42
4
21
42
4
21
2
2
00
0
00
0
1600
200
300
RP (mm/hr)
RP (mm/hr)
(mm/hr)
Z R
= P415
R1.51
400
1800
400
500
600
600
7002000 800
800
900 2200 1000 1000
1100
-1
-2
1600
300
200
400
1800
500
400
600
7002000 800
600
900 22001000 1000
1100
800
Time
Time
Time
Time
Time
Time
Fig. The comparison of rain rate by calibrated
radar reflectivity
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Conclusions
• Reflectivity can be converted to rainrate R using a
Z-R relationship.
• Rainrates could be obtained from many rainfall
observation experiments at Busan area.
• Z-R relation ( Z = 415 R 1.51 ) was calculated by
drop size distribution (POSS disdrometer, directly)
at all Busan rainevents.
• Correction coefficients were obtained from Radar
ZR and POSS ZP.
• Keep on observing rainfall events for better Z-R
relationship and rainfall estimation
• By cloud type and rainfall system
• By topographic and orographic characteristics
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