national s&t center for disaster reduction rainfall estimation by bmrc c-pol radar icmcs-v...
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National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Rainfall estimation by BMRC Rainfall estimation by BMRC C-Pol radarC-Pol radar
ICMCS-V 2006.11.03
1Lei FengLei Feng and 1,2Ben Jong-Dao JouBen Jong-Dao Jou
( 鳳雷 ) ( 周仲島 ) .
1National S&T Center for Disaster Reduction 2National Taiwan University
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
ObjectivesObjectives
• To illustrate the ability of rainfall estimation using Areal R(ΦDP) and R(KDP) by BMRC C-Pol radar. Radar-Raingauge comparisons in three different sizes of area :
– Multi-beam Multi-beam (Area ~100 km2) Areal R(ΦDP)
– Single-beamSingle-beam (Area ~ 25 km2) Areal R(ΦDP)
– PointPoint (Area ~ 2 km2) R(KDP)
• Try to correct the wind drift effectwind drift effect when comparing with single raingauge.
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
2
1
2
1
),(y
y
x
xdxdyyxRAR
2
1
2
1
)],([y
y
x
x
bDP dxdyyxKc
n
j
bjDPjDP
bj rrrr
rrc)],(),([)](2[
2
)(
2 121
1212
NSSL Ryzhkov and Zrnic(1998)
CSU Bringi (2001)
j
n
ji
m
ijiDPjDPjDP rrrrrr
c
),(),(),(
2 1122
2
1
2
1
)],([y
y
x
x DP dxdyyxKcAR
Two Areal Rainfall schemesTwo Areal Rainfall schemes
Notice the difference:Gate area ↑ with range ↑ but NSSL scheme without area weighting
Keep the area weighting, but need ΦDP information at each range gate
1
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
-30
-35
-40
-45
-5015 20 25 30 35
C-Pol radar at (0,0)
In Darwin
18 rain gauges in the 10 x 10 km2 area
BMRC C-PolBMRC C-Pol rain gaugerain gauge networknetwork
5 single-beams, the area of 5 single-beams, the area of each beam is ~ 25 kmeach beam is ~ 25 km22
1 multi-beam, the area of 1 multi-beam, the area of each beam is ~ 100 kmeach beam is ~ 100 km22
18 raingauges, the radar c18 raingauges, the radar coverage of each gauge is ~ overage of each gauge is ~ 2 km2 km2 2 (radius 0.8 km)(radius 0.8 km)
RD-69
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Case A - 15 Jan 1999
Case A, Time series plot (100 km2)
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Case B - 01 Mar 1999
Case B, Time series plot (100 km2)
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Case C - 17 Mar 1999
Case C, Time series plot (100 km2)
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Multi-beam results
• Area size: ~100 km2
• Very high correlation coefficient: 0.97
• Small standard deviation: 1.99 mm/hr
• Little underestimate• Sample number: 108
case (A+B+C)
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Single-beam results
• Area size: ~25 km2
• High correlation coefficient: 0.94
• Small standard deviation: 3.43 mm/hr
• Little underestimate• Sample number: 590
case (A+B+C)
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Point results
• Area size: ~2 km2
• Low correlation coefficient: 0.86
• Large standard deviation: 6.38 mm/hr
• Under estimation• Sample number: 1091
case (A+B+C)
• The result is getting worse as the verification area getting smaller. Why ?
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Point Comparison ProblemsPoint Comparison Problems• Inherence difference of the measurements: Rain
gauge accumulates continuously rainfall on a point while radar samples almost instantaneously a volume averaged rainfall rate.
2
2
2
22
),,(1
),,(L
L
L
Lradar dxdytvyuxRL
tyxR
• Zawadzki (1975) already described Radar-Gauge comparison problems:
2
2
),,(1
),,(t
tgauge dttyxRt
tyxR
Wind drift effect
Time lag effect
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Can we correct the wind drift effect ?Can we correct the wind drift effect ?
from DLOC
Strong horizontal wind Overestimate or Underestimate ?
How about the wind drift effect ?How about the wind drift effect ?
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
OptimalOptimal offsetoffset vectorvector
• An area of radar data which covering all surface rain gauges is moved around the original point in a square window (8km x 8km) with 200 m interval in X and Y direction.
• The cross-correlation coefficient is calculated between the time lagged (1.5 minute) surface rain rates of the gauges and the space shifted radar rain rates.
• A two-dimensional correlation field is produced. The distance from the point of the maximum correlation to the original point was defined as “optimal offset” of the horizontal displacement.
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
OptimalOptimal offset vectoroffset vector
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reductionoptimal offset vector
-4
-2
0
2
4
-4 -2 0 2 4
X(km)
Y(k
m)
15-J an
1-Mar
17-Mar
Only 39/89 volumes can be easily found out the offset vectors, most of them are convective type rain.
Case B
Case A
Case C
Is these two reasonable ?
2 km
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Checking the optimal vector far from system moving velocity case
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No wind drift correctionAfter wind drift correction
Case A Case B
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If the coefficient of R(KDP) estimator increase 50%, it look better.
Can we do this change for this case ?
No wind drift correctionafter wind drift correctionBut significant underestimation
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Rainfall with smaller raindrops need to use higher coefficient in R(KDP) estimator
Adopted from L. D. Carey ATMO 689
big Zdr ~ big Do
small Zdr ~ small Do
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Averaged Volume median diameter using Zdr(D0)
CASE
gauge rain rate radar rain rate
> 5 mm/h >10 mm/h > 20 mm/h > 5 mm/h > 10 mm/h >20 mm/h
A 1.38 1.44 1.54 1.38 1.46 1.55
B 1.42 1.46 1.53 1.44 1.46 1.51
CC 1.09 1.09 1.10 1.10 1.14 1.14 1.11 1.11 1.20 1.20 --
In case C, D0 is significant lower than case A and B
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Storm motion
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Disdrometer observation Radar estimation
Volume median diameter DVolume median diameter D0 0 estimationestimation
Note: the comparison here is not the same case, but are similar squall line type precipitation in Darwin.
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
No wind drift correctionAfter wind drift correction
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
summary (1)summary (1)• It’s very important to consider the wind drift
effect when doing single point radar-gauge comparison. In this study, the normalized error has 17% improvement.
rainrate A rainrate B rainrate C Rainrate ABC Do
No Wind Drift correction 0.78 0.85 0.74 0.81 0.74
Wind drift Wind drift correctioncorrection 0.93 0.93 0.96 0.96 0.87 0.87 0.93 0.93 0.93 0.93
Correlation Coefficient of radar-raingauge comparison
N
igauge
N
igaugeradar
RN
RRNNE
1
1
1
1
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
summary (2)summary (2)
• Use the BMRC C-Pol radar phase base estimator to estimate rain rate is very accurate , especially on convective rainfall.
• For accurate rain rate estimation, it needs to consider the DSD variability such as stratiform rainfall, orographic rainfall, shallow convective warm rain and so on when using R(KDP) estimator.
National S&T Center for Disaster ReductionNational S&T Center for Disaster Reduction
Lag 3 min Lag 4 min
Optimal vector Finding, 06:40 15-Jan-1999 (C-Pol at Darwin)
Lag 0 min Lag 1 min Lag 2 min
Lag 5 min
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