1. Precipitation Measurements using Radar
Precipitation Measurements using Radar
Prof. LEE, Dong-In
Pukyong National University
0505-i-am-cool ( )
Dr. Ronald E. Rinehart

2. Precipitation Rainfall has been measured for hundreds of years:
Prof. Lee, Dong-In

3.
Prof. Lee, Dong-In

4. Velocity measurements
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
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

5. Radar measurements of precipitation
Prof. Lee, Dong-In

6. Doppler radars Most modern radars are Doppler
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
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

7. Wind profilers Vertically pointing Doppler radars
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
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

8.
Distributed Targets
Meteorological targets consist of many(!) targets in the beam simultaneously.
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

9. 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
Prof. Lee, Dong-In

10. 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.
Prof. Lee, Dong-In

11. Snow Snow is also detectable by radar. An event from 1996: 2018-09-20
Prof. Lee, Dong-In

12. UND radar: rmax = 10(Z-C -MDS)/20
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( (-106.5))/20
= 1,188 km!
WSR-88D = 2,540 km!
Answer: Snow should be easily detected.
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

13. Then, why does radar have trouble seeing snow?
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.
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

14. 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:
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

15. Rainfall measurements from radar
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
Dr. Ronald E. Rinehart

16. Raindrop 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-l D
where N0 = 8000/(m3 mm), D is droplet diameter (mm) and l is given by
l = 4.1 R-0.21
where R is rainrate in mm/h
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

17. Marshall & Palmer DSD compared to data from Laws & Parsons
Marshall & Palmer DSD compared to data from Laws & Parsons
104
103
102
101
100
10-1
ND (m-3 mm-1)
R = 25 mm/h
R = 5 mm/h
R = 1 mm/h
Raindrop Diameter (mm)
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

18.
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
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

19. Z-R Relationships Source Relationship Marshall & Palmer 200 R Joss &
Z-R Relationships
Source
Relationship
Marshall & Palmer
200 R
1.6
Joss &
Waldvogel
300 R
1.5
Radar classes
259 R
1.50
Radar Class, 1994
429 R
1.59
Radar Class, 1997
263 R
1.51
Radar Class, 2000
258 R
1.28
Many more….
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

20. Z-R Relationships Z (dBZ) R (mm/h) Marshall-Palmer 100 Joss-Waldvogel
Z-R Relationships
Marshall-Palmer
100
Joss-Waldvogel 80
Cain-Smith (ND) 60
Laws-Parsons
Z (dBZ) 40 20
Sekhon-
Srivastava(water)
Sekhon-
Srivastava(ice)
0.1 1 10
100
1000
R (mm/h)
2000 Radar class
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

21. Hawaiian Radar Rainfall Measurements
Hawaiian Radar Rainfall Measurements
The importance of choosing the right Z-R relationship
An example.
Dr. Ronald E. Rinehart

22.
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

23.
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

24.
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 An algorithm tuned to the tropical drop size distribution does much better (red).
Prof. Lee, Dong-In
Dr. Ronald E. Rinehart

25. POSS (Precipitation Occurrence Sensor System)
Bistatic X-band(10.25GHz, 2.85cm)
CW Doppler radar
Doppler power density spectrum
Mesurements
Diameter
Fall velocity
Number density
DSD
Prof. Lee, Dong-In

26.
Prof. Lee, Dong-In

27. An example of POSS screen display
Number Density
Fall Velocity
DSD
Rainrate
An example of POSS screen display
Prof. Lee, Dong-In

28. POSS channel parameter
Prof. Lee, Dong-In

29. The Position of Radar, POSS and AWS in research area
Distance : 10 km
The Position of Radar, POSS and AWS in research area
Prof. Lee, Dong-In

30. Precipitation Observation Date
Prof. Lee, Dong-In

31. March – September (44 cases, 211hr 42min)
POSS measurement
March – September (44 cases, 211hr 42min)
POSS mesurement data (time resolution : 1 min)
- DSD, Number density, Rain amount
- Rainrate(R), Reflectivity(Z)
Prof. Lee, Dong-In

32. 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)
POSS
10km
10 km
Prof. Lee, Dong-In

33. AWS rain gauge measurement
Rain gauge data - Rain rate(mm/hr), Rain amount(mm)
POSS data – Rain rate(mm/hr), Rain amount(mm)
Comparison between rain rate and rain amount
obtained by POSS and rain gauge
Prof. Lee, Dong-In

34. Ex. Case studies M-P distribution
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
Prof. Lee, Dong-In

35. 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
Prof. Lee, Dong-In

36. Fig. The number frequency and percentage of each rain rate
Prof. Lee, Dong-In

37. Fig. The Z-R relationship obtained by POSS
ZP = 415 R 1.51
R2 =
Fig. The Z-R relationship obtained by POSS
Prof. Lee, Dong-In

38. 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)
Prof. Lee, Dong-In

39. Fig. The comparison of rainrate and rain amount obtained by POSS and rain gauge
Prof. Lee, Dong-In

40. Fig. Distribution of rainrate calculated using radar reflectivity
Prof. Lee, Dong-In

41. Fig. The reflectivity distribution of radar and POSS
11 April
ZC = 0.464ZR+4.654
30 April
ZC = 1.242ZR+5.395
30 August
ZC = 0.569ZR
Fig. The reflectivity distribution of radar and POSS
Prof. Lee, Dong-In

42. Fig. The comparison of rain rate by calibrated radar reflectivity
Prof. Lee, Dong-In

43. 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
Prof. Lee, Dong-In