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In-flight icing hazard detection

In-flight icing hazard detection with dual and single-polarimetric moments from operational NEXRADs. David Serke a , Scott Ellis b , John Hubbert b David Albo a , Christopher Johnston a , Charlie Coy a , Dan Adriaanson a and Marcia Politovich a

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In-flight icing hazard detection

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  1. In-flight icing hazard detection with dual and single-polarimetric moments from operational NEXRADs David Serke a, Scott Ellis b, John Hubbert b David Albo a, Christopher Johnstona, Charlie Coya, Dan Adriaansona and Marcia Politovich a a NCAR- National Center for Atmospheric Research Research Applications Laboratory Boulder, Colorado b NCAR- National Center for Atmospheric Research Earth Observing Laboratory Boulder, Colorado

  2. Icing detection with S-band introduction • In-flight icing an important factor in accidents and is a focus of the FAA • No single instrument can remotely and unambiguously detect IFI conditions • Combinations of sensors (ex: NIRSS) and/or numerical weather prediction models (ex:CIP) • NCAR work for MIT-LL and FAA (Smalley et al., 2009) to develop Icing Hazard (IH) algo • In '11-'12, NIRSS moved to Colorado to compare output to IH from research radars (Ellis et al., Serke et al.) • In '12-'13, NCAR began an FAA project for IH using several NEXRAD cases (Albo et al., Serke et al.) • Determine quantifiable benefit to dual-polarization (DP) over single-polarization (SP) for IH • Compare significant number of icing PIREPs to IH versions and NIRSS Motivations

  3. Icing algorithms (S-band radar) IH DP = all dual-pol components IH SP-n = single-pol only IH SP-y = single-pol, but added TEMP & Z mixed phase algorithm Freezing Drizzle (Ikeda et al., 2008) Mixed Phase (Plummer et al., 2010) removed Zdr and ρHV removed altered removed

  4. Method • Match many MOG and Null severity PIREPs to output from four icing algorithms • PIREP shortcomings are known (Brown et al., 1996) but are best available, good over large case set • For the three IH s: • 900 azimuth quadrant centered on PIREP, closest in time - 10% or more of volume warned 'yes icing' for MOG is a match - 90% or more volume warned as 'no or maybe icing' for Null is a match • PODY is fraction of total cases each algorithm properly detects as 'yes icing', (similar for PODN)

  5. Sample case studies Sample case studies Yes Maybe no REFL IHDP IHSP-y IHSP-n Webpage for each of the 75 cases is available for viewing at: https://wiki.ucar.edu/display/icinghazardlevel/Home

  6. Results • Relate aircraft icing to synoptic-scale atmospheric forcing mechanisms (Bernstein et al., 1997) • 5 'developing low' cases had graupel at surface. Will explore further (Evaristo et al., 2013) • 5 'ahead warm front' cases were freezing rain. All IH detected since FRZRA algo is single pol • Overall, IHDP and NIRSS had high icing detection. NIRSS > IHDP for 'Null' detection rate • Adding back SLW (REFL and temperature) module for IHSP-y made difference, but still not good

  7. Summary Future Work • Over a statistically significant number of cases over a wide variety of weather conditions … * IHDP had > 0.90 PODY * IHSP-n had low skill at detecting icing * TEMP and Z weighting functions in IHSP-y was much better than without, but ½ PODY as dual-pol • Determine frequency of low SNR, small-drop cases • Test NSSL's ρHV low SNR filter • Build and test DP graupel detection and high-Zdr (Williams et al. 2010) modules for IH • Expand geographical diversity of IH cases • NASA research flight campaign '14-'15?

  8. Thank you! • Questions?

  9. Backup slides

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