NEXRAD Dual-Polarization Product Review Kevin Scharfenberg OU-CIMMS and NOAA-NSSL

1. NEXRAD Dual-Polarization Product Review Kevin Scharfenberg OU-CIMMS and NOAA-NSSL 2nd Workshop on NWS Severe Weather Warning Technology -- 10 July 2007 -- Norman, OK

2. Dual-pol product field evaluation • Conducted Autumn 2006 • 8 WFOs (Duluth, Melbourne, Norman, Pittsburgh, Sacramento, St. Louis, Sterling, Tucson) • Several other agencies (RFC-Tulsa, Lincoln Labs, DoD, etc.) • Online training modules provided by WDTB • Used WDSS-II software to interrogate data

3. Dual-pol product field evaluation • Seven cases from KOUN prototype dual-pol WSR-88D • 14 May 2003: Widespread hail, heavy rain, considerable non-precipitation echoes • 10 Jun 2003: Squall line, heavy rain • 29 May 2004: Tornadic high-precipitation supercell • 22 Dec 2004: Light-moderate snow • 5 Jan 2005: Rain and freezing rain, some sleet • 28 Jan 2005: Heavy snow & light drizzle/mix • 2 May 2005: Light rain, low 0ºC level

4. Dual-pol product field evaluation • Online worksheets and questionnaires • Evaluation of how the products might be used in the office’s operations • Base products and algorithms • Also considered user factors (e.g., data displays, cursor readouts, etc.)

5. Dual-pol product field evaluation

6. Dual-pol product field evaluation

7. Dual-pol product field evaluation Norman working group experienced with KOUN data and technology transfer to operations concurrently evaluated products and analyzed field feedback Wrote basic findings & recommendations for final report Formed “needs assessment” and determined priorities

8. Base Products rhv ZDR KDP

9. Hydrometeor Classification Algorithm Rain/hail “Big drops” Biologicals AP/ground clutter Unknown Dry snow Wet snow Crystals Graupel No echo Light/moderate rain Heavy rain

10. Other products • Rainfall algorithms – Undergoing major revisions during field evaluation phase so could only be generic • Melting layer algorithm – no stand-alone output yet to evaluate, but critical to understanding hydrometeor classification algorithm (and later, rainfall algorithms) • “Filtered reflectivity” product – uses hydrometeor classification to filter out non-precip echoes from reflectivity product

11. Results • Use of base products to form a 4D conceptual model • Base products at highest-possible resolution, data precision • Broad-based training will be critical for success • Flexible, innovative display concepts needed • The “black box” algorithm problem

12. Results, continued • Hydrometeor classification – melting layer – rainfall algorithms intertwined, still in developmental stages • Need to keep users in the loop as algorithms are refined • Product color schemes need to be unique for quick identification • Product toggles, cursor readouts, etc. should be reexamined

13. Results, continued • Wide variety of end-users • Numerous software considerations (FFMP, clutter canceling, etc.) – some unknowns • Primary focus should be on highest-quality base data for users, software, algorithms, etc… • Downstream users can do the slicing & dicing • What about user-adaptable parameters and other NEXRAD initiatives (super-resolution, new VCPs, etc.)?

14. Setting an aggressive agenda for change • In my opinion, hydrometeor classification, rainfall accumulation, and other algorithms will be much more successful by incorporating multiple radars and other data sources • Must also coordinate operations concept with ongoing transition to “warn-on-forecast” models, gridded warnings, etc. • Also must keep in mind new technologies like FSI, AWIPS2, other radar upgrades, new radars, etc.

15. Setting an aggressive agenda for change • Discussion… • Push toward incorporation of multi-radar, multi-sensor products into routine NWS operations so we are prepared to take advantage of dual-pol capabilities • Think outside the box on visualization for complex data interrogation • Strive for the best possible training • Kevin.Scharfenberg@noaa.gov