GyuWon Lee, Aldo Bellon, and Isztar Zawadzki

1. Error structure in rain estimation by radar1. Radar Calibration. 2. Attenuation. 3. Variability of drop size distribution. GyuWon Lee, Aldo Bellon, and Isztar Zawadzki

2. Disdrometer calibration 7% Radar CalibrationError in the current calibration by gages Gage calibration Perfect radar 34% Perfect gage

3. Radar CalibrationError in the polarimetric calibration The (specific) differential phase shift, DP (KDP) is immune to the radar calibration error whereas the reflectivity (Z) is affected by the calibration error. KDP: 1 dB KDP & ZDR: 0.3 dB

4. Radar CalibrationCalibration by disdrometer and polarimetry by a disdrometer by polarimetry Bias=1.5 Lee and Zawadzki (2002) Submitted to J. Hydrology

5. Polarimetric radar calibrationSensitivity to the drop deformation KDP= (3.5 ~ 8.8)x10-5 D4.6~5.2 Zh  D6 Zh=(3.9 ~ 6.5)x105KDP(1.0~1.2) Ex: Due to the drop deformation, dZh~2 dB at KDP=1 deg/km

6. Quantification of Wet-radome Attenuation Method 1 : Monitoring ground echoes with time King City C-band radar

7. 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

8. ATTENUATION SIMULATION AND CORRECTION Gage Simulation + Random error Minimization of Cost function. Attenuation & Calibration error. H-B Correction

9. R-(Z,M2) R-Z How many parameters are needed to describe the variability of DSDs?

10. Scale dependence of the variability of drop size distribution • Climatological variability • A single Climatological Z-R relationship SDfe ~ 37 %

11. Scale dependence of the variability of drop size distribution 2. Day-to-Day variability A single Climatological Z-R relationship SDfe= 34% 3. Variability within a day Daily Z-R relationships SDfe~ 31 %

12. Between different physical processes SDfe= 30% Within a quasi-homogeneous physical process SDfe= 10% Scale dependence of the variability of DSD UHF profiler Reflectivity Collocated disdrometer

13. 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

14. Error structure in rain estimation by radar4. AP & Ground Echoes5. Range effects6. VPR & Optimal Surface Precipitation (OSP) Aldo Bellon, GyuWon Lee, and Isztar Zadwadzki

15. 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

16. AP & GE Elimination Radial velocity & Reflectivity (1km x 1deg.) From Polarimetric Information a) SD of ZDR b) SD of ΦDP c) SD of Z d) Radial Velocity Polarization (150 m)

17. 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

18. 2. Accumulations based on OSPmaps (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 correctsfor 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

19. Rainfall Accumulation Correction VPR correction BB contamination Original 1hr accum. Optimal 1hr accum.

20. Range Effects

21. CAPPI Simulation H= 1.9 km H= 1.1 km 120 km 240 km H= 3.7 km H= 2.7 km

22. Error Structure: Instant. F(range, height) 1x1 km2 Before Correction After Correction 9x9 km2

23. Random error after bias correction BIAS Measurement in snow Contamination by the BB Range-Dependent Errors A stratiform case with a weak bright band at 3.5 km Bias

24. Animation of 1-hr Accum.

25. Error Structure: 1-hr Accum. F (range, height) Before Correction After Correction 1 km x 1 km 9 km x 9 km

26. CONVECTIVE

27. A Convective Case Error Structure: 1-hr Accum. F (range, height) 1 km x 1 km 9 km x 9 km

28. A Convective Case Error Structure: 1-hr Accum. F (range, height) Before Correction After Correction

29. A Snow Case

30. A Snow Case Error Structure: 1-hr Accum. F (range, height) Before Correction After Correction

31. Snowfall Correction After Correction Before Correction

32. Thank you!

33. Low Bright Band (Worst Case Scenario) Uncorrected Erroneously Corrected

34. Radar Composite