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Future QPE: Dual-Pol and Gap-Filler Radars

Future QPE: Dual-Pol and Gap-Filler Radars. Kevin Scharfenberg University of Oklahoma/CIMMS and NOAA National Severe Storms Laboratory. Quantitative Precipitation Estimation. Dual-polarization in one slide. Current state: linear horizontal E pulses: — — — …

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Future QPE: Dual-Pol and Gap-Filler Radars

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  1. Future QPE: Dual-Pol and Gap-Filler Radars Kevin Scharfenberg University of Oklahoma/CIMMS and NOAA National Severe Storms Laboratory

  2. Quantitative Precipitation Estimation

  3. Dual-polarization in one slide • Current state: linear horizontal E pulses: — — — … • Original WSR-88D contract specified capability for later upgrade to dual-pol • After upgrade, WSR-88D will transmit simultaneous horizontal/vertical pulse (“slant 45º”): ∕ ∕ ∕ ∕ … • Separate receivers will listen for horizontal and vertical backscatter

  4. Early dual-pol QPE results Areal (basin) Estimates Point Estimates

  5. Early dual-pol QPE results Bias of radar areal rainfall estimates Spring hail cases Cold season stratiform rain

  6. Differential reflectivity (Zdr) Reflectivity (Zh) Similar reflectivity – very different differential reflectivity! Northeast – Mostly large rain drops Southwest – Mostly hail

  7. Differential reflectivity (Zdr) Reflectivity (Zh) Similar reflectivity – very different differential reflectivity! Northwest – relatively large number of relatively small drops Southeast – relatively small number of relatively large drops

  8. Quantitative Precipitation Estimation Warm rain case – A very unusual DSD!

  9. Quantitative Precipitation Estimation Hail case – Z-R relations break down!

  10. Hydrometeor Classification Z ZDR RHI in stratiform rainfall rhv KDP

  11. No Echo Lgt/mod rain Heavy rain Hail “Big drops” Graupel Ice crystals Dry snow Wet snow Unknown AP or Clutter Biological Hydrometeor classification algorithm

  12. Dual-pol QPE Algorithm Operational strategy Where HCA detects Use R= Ground clutter / AP / biologicals 0 Rain R(Z, Zdr) Possible hail below melting layer R(KDP) Wet snow 0.6R(Z) Graupel/hail above melting layer 0.8R(Z) Dry snow / ice crystals 2.8R(Z) R(Z) is from standard WSR-88D R(Z) equations.

  13. Dual-pol and partial attenuation NCAR SPOL radar ; From Vivekanandan et al. 1999, JTech 16, 837-845 Partial terrain blockage

  14. WSR-88D coverage at 3 km AGL

  15. “Gap-Filler” Boundary Layer Radars Courtesy CASA project

  16. “Gap-Filler” Boundary Layer Radars CASA radars Nearest WSR-88D

  17. Radar-based QPE: The Future • Dual-pol WSR-88D upgrade • Dual-pol, low-power “gap-filler” radars • Multiple-radar data mergers incorporating NWP • Corrections for dual-pol radar QPE using rain gages • Incorporation of dual-pol base data vertical profiles • Incorporate corrections for partial beam attenuation (including partial terrain blockage!)

  18. Questions? Kevin.Scharfenberg@noaa.gov

  19. Quantitative Precipitation Estimation R(Z) on a 2 km x 2 km grid

  20. Quantitative Precipitation Estimation Dual-pol QPE on a 2 km x 2 km grid

  21. Hydrometeor Classification * * Height  * Increasing value 

  22. Quantitative Precipitation Estimation • Operational QPE algorithm • Significant improvement over R(Z), particularly inside 150 km and in heavy rain (and possible hail) • Measurable improvement 150-230 km • Measurable improvement over adjusted R(Z) using vertical Zh profiles/mean-field bias (MFB) corrections • Later work to incorporate multiple radars, corrections using MFB, vertical dual-pol profiles, beam attenuation

  23. Differential Reflectivity (Zdr)

  24. Differential Reflectivity (Zdr) • Indicates the presence of larger liquid drops • Hail shafts without a lot of liquid water

  25. Differential Reflectivity (Zdr) Differential reflectivity Zdr = 10 log (Eh/Ev) = Zh - Zv [dB] The reflectivity-weighted mean axis ratio of scatterers in a sample volume Zdr > 0  Horizontally-oriented mean profile Zdr < 0 Vertically-oriented mean profile Zdr ~ 0 Near-spherical mean profile Ev Eh

  26. Differential Phase Shift (fDP) Differential Phase Shift fDP = fh – fv (fh, fv≥ 0) [deg] The difference in phase between the horizontally- and vertically-polarized pulses at a given range along the propagation path. - Two-way process - Independent of partial beam blockage, attenuation - Independent of absolute radar calibration - Immune to propagation effects on calibration - Independent of system noise

  27. Specific Differential Phase Shift (KDP) Specific Differential Phase Shift fDP(r2) – fDP(r1) KDP = [deg/km] 2 (r2 – r1) The range derivative of differential phase shift - Identify areas with significantly non-spherical scatterers (usually, rain) - Can estimate rain amount in rain/hail mixture

  28. Specific Differential Phase Shift (KDP)

  29. Specific Differential Phase Shift (KDP) Result: The KDP dilemma - Using a long-distance derivative for calculating KDP can oversmooth heavy rain features but reduces noise - Using a short-distance derivative for calculating KDP retains features in heavy rain but is also noisy

  30. Specific Differential Phase Shift (KDP) Calculating KDP: current practice - If Z > 40 dBZ, use a KDP calculation range of 9 gates (2 km). - Otherwise, use a range derivative of 25 gates (6 km) - Filter the final KDP product at 0.9 rhv

  31. Outline Differential phase shift (fDP)

  32. Outline Specific differential phase shift (KDP)

  33. Quantitative Precipitation Estimation Rainfall estimation using polarimetric variables R(Z, ZDR) = 0.0142 Z0.77 ZDR-1.67 [mm/h] R(KDP) = 44|KDP|0.822 sign(KDP) [mm/h]

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