GyuWon Lee, Aldo Bellon, and Isztar Zawadzki
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Transcript of GyuWon Lee, Aldo Bellon, and Isztar Zawadzki
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Error structure in rain estimation by radar1. Radar Calibration.2. Attenuation.3. Variability of drop size distribution.
GyuWon Lee, Aldo Bellon, and Isztar Zawadzki
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Radar Calibration Error in the current calibration by gages
Gage calibration
34%
Disdrometer calibration
7%
Perfect gage
Per
fect
rad
ar
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Radar Calibration Error in the polarimetric calibration
KDP: 1 dB
KDP & ZDR: 0.3 dB
The (specific) differential phase shift, DP (KDP) is immune to the radar calibration error whereas the reflectivity (Z) is affected by the calibration error.
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Radar Calibration Calibration by disdrometer and polarimetry
by a disdrometer by polarimetry
Bias=1.5
Lee and Zawadzki (2002) Submitted to J. Hydrology
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Polarimetric radar calibration Sensitivity to the drop deformation
date KDP calibration (dB) Disdrometric calibration
(dB)Pruppacher and Beard (1970)
Illingworth and Johnson
(1999)
Goddard et al. (1995)
Andsager et al. (1999)
2001. 9. 13. -1.92 -3.16 -4.27 -4.10 -1.73 2001. 9. 25.
1.72 1.24 0 -0.21 1.51
2001.10. 25.
-1.58 -2.14 -3.34 -3.58 -1.15
KDP = (3.5 ~ 8.8)x10-5 D4.6~5.2
Zh D6
Zh=(3.9 ~ 6.5)x105 KDP(1.0~1.2)
Ex: Due to the drop deformation,
dZh~2 dB at KDP=1 deg/km
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Quantification of Wet-radome Attenuation Method 1 : Monitoring ground echoes with time
King City C-band radar
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Quantification of wet-radome Attenuation Method 2 : Natural variability over a large area
< the variability caused by the precipitation at the radar
site
Franktown C-band radar
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Attenuation & Calibration error.
H-B Correction
Gage Simulation
+ Random error
Minimization of
Cost function.
ATTENUATION SIMULATIONAND CORRECTION
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How many parameters are needed to describe the variability of DSDs?
N D N D( ) exp( ) 0 R
N D N D D( ) exp( ) 0
2/12)(1
R
RR
NT
R-Z
R-(Z,M2)
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Scale dependence of the variability of drop size distribution
1. Climatological variability
A single Climatological Z-R relationship
SDfe ~ 37 %
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Scale dependence of the variability of drop size distribution
2. Day-to-Day variability
A single Climatological Z-R relationshipSDfe= 34%
3. Variability within a day
Daily Z-R relationships
SDfe~ 31 %
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Scale dependence of the variability of DSD
UHF profiler Reflectivity
Between different physical processes
Within a quasi-homogeneous
physical process
SDfe= 30%
SDfe= 10%Collocated disdrometer
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Conclusion and Discussion
1. Three different methods of radar calibration
- accuracy
- consistency between methods
2. Quantification of attenuation & Correction method.
- QPE in C-band (?)
3. The variability of drop size distribution
- scale dependence of DSD variability
- categorization of different physical processes
: using morphological characteristics
& polarimetric information
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Error structure in rain estimation by radar4. AP & Ground Echoes5. Range effects6. VPR & Optimal Surface Precipitation (OSP)
Aldo Bellon, GyuWon Lee, and Isztar Zadwadzki
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Eliminate GE and AP
a) Radial velocity at one or more elevation angles
b) Vertical gradient of reflectivity c) Horizontal gradient of reflectivity
d) Preferred location of AP echoes.
AP
GE
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AP & GE Elimination
From Polarimetric Information
a) SD of ZDR
b) SD of ΦDP
c) SD of Z
d) Radial Velocity
Radial velocity & Reflectivity
(1km x 1deg.)
Polarization (150 m)
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VPR CORRECTION ===> SURFACE RAINFALL
1. Accumulations based on CAPPIs at a pre-defined height (1.5 to 2.5 km) (Correction applied to the 1-h accumulations as a function of range)
a) dBZ = dBZ [ CAPPI height ] - dBZ [ Ref. height ]
b) Beyond 110 km, apply Gaussian smoother (vertical) to last profile
dBZ = dBZG [ H(range) ] – dBZLast VPR [ Ref. height ]
c) Interpolate dBZ at every km and convert it into a rainfall rate factor
- multiply uncorrected 1-h accumulations and integrate for total rainfall
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2. Accumulations based on OSP maps(Optimum Surface Precipitation)
(Correction performed at every radar cycle and for every pixel)
a) Lowest pixel (~1.0 km) above the 3-D average ground echo mask
- Radial velocity used to reach any precip. above stationary targets - If this height is close to echo top, perform horizontal interpolation ELSE
- dBZ is obtained as in (1) to modify reflectivity at selected height
b) Automatic VPR identification and correction
- Avoids rather than corrects for bright band Data is taken from height sufficiently below BB bottom or above BB top c) No dBZ adjustment for convective pixels ( > 30 dBZ 1.5 km above BB height)
- Separate Z-R for stratiform and convective pixels
d) Knowledge of 0º isotherm would prevent correction in cases of low bright band - identifies low level growth due to warm rain or snow
3. Simulations of high resolution volume scan (3-D) data
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Rainfall Accumulation Correction
Original 1hr accum.
Optimal 1hr accum.
BB contamination
VPR correction
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Range Effects
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CAPPI SimulationH= 1.1 km H= 1.9 km
H= 2.7 km H= 3.7 km
120 km
240 km
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Error Structure: Instant. F(range, height)
Before Correction
Aft
er C
orr
ecti
on
1x1 km2
9x9 km2
2/12)(1
R
RR
NSDfe E
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Range-Dependent Errors
Bias
BIAS
Measurement in snow
Contamination by the BB
Random error after bias correction
A stratiform case with a weak bright band at 3.5 km
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Animation of 1-hr Accum.
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Error Structure: 1-hr Accum. F (range, height)Before Correction After Correction
1 km x 1 km
9 km x 9 km
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CONVECTIVE
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A Convective Case Error Structure: 1-hr Accum. F (range, height)
1 km x 1 km 9 km x 9 km
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A Convective Case Error Structure: 1-hr Accum. F (range, height)
Before Correction After Correction
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A Snow Case
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A Snow Case Error Structure: 1-hr Accum. F (range, height)
Before Correction After Correction
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Snowfall Correction
Before Correction After Correction
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Thank you!
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Low Bright Band (Worst Case Scenario)
Uncorrected Erroneously Corrected
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Radar Composite