1. Tele-Conference with Lincoln Labs: Icing Hazard Level National Center for Atmospheric Research 29 April 2010

2. IHL Algorithm Approach Combine several existing microphysical algorithms – Melting level detection – Freezing drizzle detection – Particle identification (e.g., HCA, PID) First step: design a melting level detection algorithm based on PPI scans – Described in 10 January 2010 Report to LL

3. Melting Level Characteristics

4. Melting Level Detection The data difference between the center (green) region and the non-center (blue and red) regions are computed, and a derived value 'Ring(r,a)' is computed for that point

5. Example from Report ROHV

6. Example from Report Z

7. Zdr

8. Example from Report Three inputs Max. of three inputs

9. Smoothing Filter

10. Example from Report Clumping quality

11. Radar elevation angle comparison

12. Flow Chart

13. Next Step: Use PID Use the PID algorithm to – Identify clutter – Identify “wet snow” category which has been shown to mark the melting level First use previous melting level info. and sounding data to define a modified 0 deg. isotherm. This will be input to PID.

14. IHL Flow Chart NCAR Melting Level Detection Sounding Modified Sounding PID Dual-Pol Radar data Dual-Pol Radar data Is SLW likely? SLW Probability estimation (Spatial textures, other logic (?)) SLW Probability Field DQ/ CMD DQ/ CMD

15. A Data Example ROHV Vel Zdr Z

16. width PID

17. A Data Example RHOHV. Z.Z. Combo. Zdr

18. Data Example QualityCombined

19. Accompanying RHIs ROHV ZZdr Vel

20. RHIs PID Z Width

21. Next Steps PID will identify – Clutter, bugs, i.e., non-precip. Areas – Precip areas Places where icing probability is very low Concentrate on remaining areas Bring in texture computations – Ikeda et al. (FZDZ) – Plummer et al. – Koistinen (Radar Met. Conf., 2009) Texture could be a better particle metric than the dual pol. variables themselves

22. Frequency Histograms from Plummer et al. 2010

23. Frequency Histograms 2 from Plummer et al. 2010

24. SLW Probabilities Plummer et al.

25. SLW Probabilities Plummer et al.

26. Kdp and SLW Plummer et al.

27. IHL Implications For SLW Zdr is near zero and Kdp is near zero The frequency histograms indicate that the spatial textures of ice are greater than spatial textures of SLW These ideas will be integrated into NCAR’s IHL algorithm