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Dual-Polarization Quantitative Precipitation Estimation Group Discussion Report Edward A. Brandes

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Dual-Polarization Quantitative Precipitation Estimation Group Discussion Report Edward A. Brandes

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  1. Dual-Polarization Quantitative Precipitation Estimation Group Discussion Report Edward A. Brandes National Center for Atmospheric Research

  2. Dual-Polarization Radar Impacts: ● Data quality ● Hydrometeor classification ● Microphysics retrieval/understanding of precipitation processes ● Microphysics parameterization in numerical models ● Precipitation quantification

  3. Group Discussion Objectives • Identify science issues related to quantitative precipitation estimation with polarimetric radar • Facilitate the transfer from research to operations

  4. Discussion Topics ● Measurement accuracy/Calibration ● Drop axis ratios/DSD model ● Role of KDP ● Hail ● Estimator variable mix/functional form ● Algorithm testing and verification criteria ● Default algorithms ● New techniques/hardware

  5. Polarimetric Variables • Radar Reflectivity: • Specific Differential Propagation Phase: • Differential Reflectivity: _______________ • Correlation Coefficient:

  6. Measurement Accuracy Parameter Error Error in R Z 0.5 dB 10% ZDR 0.1−0.2 dB 5−25% KDP 0.2o km−1 5−100% Model Error Axis ratio ZDR error of 0.2−0.3 dB DSD form Algorithm Simulation or observations Canting angle

  7. KDP Advantages ● Immune to calibration problems ● Unaffected by attenuation ● Unaffected by beam blockage ● Insensitive to dry tumbling hail

  8. 12 June 1997 Rainfall Accumulation 0600-1030 UTC R(Z ) R(K ) H DP 60 30 30 60 60 90 90 120 120 40 40 150 150 60 60 80 80 (a) (b)

  9. KDP Potential problems ● Reflectivity gradients ● Differential backscatter phase shift ● Sensitivity to mismatched sidelobes ● DSD sensitivity ● High noise level in ΦDP measurements ● Reduced spatial resolution in rainfall estimates

  10. Estimator variable mix/functional form • Enhanced Z−R Relations • Power-law/Polynomial Relations • Composite Algorithms • Drop-Size Distribution Retrieval

  11. Florida-Tuned Power-Law Estimators(ZH and KDP in linear units, ZDR in dB) Empirical axis ratios: r = 0.9951 + 0.02510D – 0.03644D2 + 0.005303D3 – 0.0002492D4  Radar Reflectivity:  Specific Differential Phase:  Specific Diff. Phase/Diff. Reflectivity: Reflectivity/Diff. Reflectivity:

  12. Summary Results:Tuned Power-Law EstimatorsEmpirical Axis Ratios(388 radar−gauge comparisons)

  13. NSSL Composite Algorithm(linear units) R(Z) < 6 mm h−1 6 < R(Z) < 50 mm h−1 R(Z) > 50 mm h−1 Where:

  14. Polarimetric Rainfall Estimation Point Estimates Areal Estimates

  15. Drop-Size Distribution Retrieval with Polarimetric Radar Measurements Gamma drop size distribution: N(D)=N0Dμexp(-ΛD) N0 drop concentration parameter m distribution shape term L slope factor Solution:

  16. Spatial Distribution of Retrieved DSD Parameters 17 September 1998 Convective rain: 192646 UTC Stratiform rain: 222154 UTC

  17. Comparison: Physical ProcessesConstrained-Gamma and Marshall−Palmer DSD Models

  18. Recommendations ● Endorse the NSSL composite algorithm as an initial product ● Update and enhance methods for radar calibration ● Conduct a community-wide inter- comparison study to improve algorithms

  19. Questions! Comments!