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Dual Polarization Radar and Rainfall Nowcasting

Dual Polarization Radar and Rainfall Nowcasting. by Mark Alliksaar. Dual Polarization can potentially improve rainfall nowcasting in three ways:.

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Dual Polarization Radar and Rainfall Nowcasting

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  1. Dual Polarization Radar and Rainfall Nowcasting • by Mark Alliksaar

  2. Dual Polarization can potentially improve rainfall nowcasting in three ways: 1. Radar attenuation can be corrected using the polarimetric parameter Φdp. This improves rain rate estimates and QPE derived from reflectivity factor Z. 2. Rain rate and QPE can also be derived directly from Kdp instead of Z. 3. Hail identification. In conventional QPE estimates, hail contamination is always a possibility in convective situations.

  3. Rain rate estimation from Kdp • R is rain rate in mm/hr • b,c are empirical constants • R derived from Kdp is more accurate because Kdp not subject to attenuation

  4. Attenuation correction using Φdp • ΔZ is attenuation correction • r is range along radial • α is an empirical constant

  5. August 2, 2005(day of Air France accident at YYZ)

  6. Corrected Radar Reflectivity0.4° PPI

  7. Enhanced View near YYZ Radar Reflectivity Corrected Radar Reflectivity

  8. Attenuation Calculation(Radial 200.5°) Zcorr Cloud Boundary Z φdp

  9. Validation with Buffalo NEXRADFrequency Histograms of Reflectivity near YYZ

  10. August 19, 2005(flash flood event in North Toronto)

  11. Radar Reflectivity Corrected Reflectivity ZCORR Reflectivity Z

  12. One Hour Precipitation Accumulation (Z)Enhanced View in North Toronto Rain Accumulation (Z)

  13. One Hour Precipitation Accumulation (Zc)Enhanced View in North Toronto Rain Accumulation (ZCORR)

  14. Improved QPE Using Zcorr (Location near MSC HQ in Downsview)

  15. Summer Applications: Hail Detection

  16. iParCA (interactive Particle Classification Algorithm) • developed by Environment Canada, King City research group • input: 6 polarimetric radar products (Zh, Zdr, ρHV, Kdp as well as standard deviations of Zh and Zdr) • output: hydrometeor type at each range gate determined by fuzzy logic routines

  17. an example of a fuzzy logic membership function for moderate rain

  18. iParCA GUI interface

  19. URP Hail Algorithm: Related to storm structure Based on vertical integration of cell’s reflectivity profile Disadvantages: Difficult to quantify Exact hail location not specified iParCA Hail Algorithm: Measurements directly related to hail properties iParCA Fuzzy Logic Thresholds: Z : 50 – 75 dBZ ZDR : 0 – 1 dB ρHV : 0.80 – 0.90 φDP : abrupt changes Comparison of URP and iParCA Hail Algorithms

  20. Grimsby HailstormJuly 23rd 2008 – 0140 Z Radar Reflectivity Enhanced View

  21. Grimsby HailstormJuly 23rd 2008 – 0140 Z ρHV φdp VIL

  22. 1 2 1 2 Grimsby HailstormJuly 23rd 2008 – 0140 Z Hail Pixel Map : iParCA : URP

  23. Grimsby HailstormJuly 23rd 2008 – 0230 Z Hail Pixel Map : iParCA : URP

  24. Detection Statistics of URP vs iParCA Hail Algorithms • 74 cells examined for 21 days during the summers of 2005-2008. • Cases were selected by meteorologist M. Leduc targeting those cells which may contain high impact weather based on reflectivity patterns.

  25. Summary Statistics of URP vs iParCA Hail Algorithms • 30 cells on 10 of the study days were examined in depth to assess the physical reasoning for the differences in the algorithm performance. • In 21/30 cells iParCA was subjectively determined to be better in terms of the quality of the information • Reasons for iParCA superiority: • Geometry/Timing 4 cases • Attenuation Correction 5 cases • Dual Polarization Discrimination 11 cases • Location 5 cases • iParCA Hail Product superior for 70% of cells studied

  26. Questions?

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