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Bias and random errors in radar measurements of precipitation and their scale dependence. Aldo Bellon, Isztar Zawadzki, and GyuWon Lee McGill University, Montreal Canada

McGill University, Montreal Canada

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Bias and random errors in radar measurements of precipitation and their scale dependence. Aldo Bellon, Isztar Zawadzki, and GyuWon Lee. McGill University, Montreal Canada. Each source of error in precipitation estimates by radar is evaluated separately. Radar calibration. AP+GE removal. - PowerPoint PPT Presentation

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Page 1: McGill University, Montreal Canada

Bias and random errors in radar measurements of precipitation and their

scale dependence.

Aldo Bellon, Isztar Zawadzki, and GyuWon Lee

McGill University, Montreal Canada

Page 2: McGill University, Montreal Canada

Strong attenuation by precipitation at

C-band

Z R

Wet-radome

Attenuation

Each source of error in precipitation Each source of error in precipitation estimates by radar is evaluated separatelyestimates by radar is evaluated separately

Beam broadening

Height increase

How to get Z at How to get Z at groundground !

Radar calibration

AP+GE removal

Optimum Surface Precipitation (OSP)

Page 3: McGill University, Montreal Canada

High Resolution Sector:15-35 km & 120-320 az.

Page 4: McGill University, Montreal Canada

Projection of near data into far ranges

h=1.1 1.5

1.9 3.1

40

80

120

160

200

Page 5: McGill University, Montreal Canada

When observedat a farther range

ERRORS DUE TO EXTRAPOLATION FROM HEIGHT OF MEASUREMENT TO GROUNDERRORS DUE TO EXTRAPOLATION FROM HEIGHT OF MEASUREMENT TO GROUND

BW=0.2 0.5

1.72.0

To determine the errors,values at the measurementheight are compared with the low level ground truth

Page 6: McGill University, Montreal Canada

Projection of near

range VPRs into far ranges

250 hr of data,

21 events

Page 7: McGill University, Montreal Canada

Errors in non-

corrected

Errors in corrected

data

Height of lowest elv. angle

ref

ref

R

RRn

NRMS

2/1

2)(1

Page 8: McGill University, Montreal Canada

Climatological Vertical Profiles

Page 9: McGill University, Montreal Canada

Errors before Correction with Correction with

correction inner VPR climatological VPR

* * *

Page 10: McGill University, Montreal Canada

Total Error Summary: Stratified by BB height(Data from 0.50 elevation for r>100km and from 1.5 km CAPPI for r<100km)

Page 11: McGill University, Montreal Canada

Spatial-scale dependence of errorsSpatial-scale dependence of errors

range

Normalized RMS error after VPR correction as a function of the verification area.(For 1-hr accumulations at the “lowest default height”).

Page 12: McGill University, Montreal Canada

Time-scale Time-scale dependence dependence

of errorsof errorsNormalized RMS error atthe lowest default height after correction by the VPRas a function of accumulationtime at 10-km resolution.

Page 13: McGill University, Montreal Canada

Representativeness of VPRRepresentativeness of VPR

Increased NRMS errors for 1-hr accumulations generated with VPR correctionfactors that are appropriate for a different time interval (in order to simulatea different VPR at farther ranges). The uncorrected NRMS and that from theclimatological correction are provided as reference. (At the “lowest default height” and at 10-km resolution).

Page 14: McGill University, Montreal Canada

ConclusionsConclusionsErrors depend: Height of BB, Verification area,

Length of accumulation, Range

Errors < 20 % if: BB height > 2.5 km, Area > 100 km2

Acc. Time > 45 min. Range < 130 km

Errors due to Ground clutter, DSD variability (R-Z uncertainty) Calibration, Attenuation (C-band) must be added to the VPR errors.

Essential to differentiate between Stratiform/Convection, particularly at far ranges (> 150 km)

The non-homogeneity of the VPR adds may substantiallyincrease the error.