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Enhancing Icing Hazard Detection Algorithm for National Center for Atmospheric Research Tele-Conference 29 April 2010

Learn about innovative techniques in detecting icing hazards using a combination of existing algorithms and texture computations. Explore the Icing Hazard Level (IHL) approach for precise identification of weather conditions. Detailed insights from January 2010 report to Lincoln Labs will be shared.

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Enhancing Icing Hazard Detection Algorithm for National Center for Atmospheric Research Tele-Conference 29 April 2010

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

  15. A Data Example Z Zdr Vel ROHV

  16. width PID

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

  18. Data Example Combined Quality

  19. Accompanying RHIs Z Zdr ROHV Vel

  20. RHIs Z PID 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 Histogramsfrom Plummer et al. 2010

  23. Frequency Histograms 2from 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

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