1. Severe Hazards Analysis & Verification Experiment (SHAVE) Kevin Scharfenberg OU-CIMMS & NOAA-NSSL 2 nd Workshop on NWS Severe Weather Warning Technology -- 11 July 2007 -- Norman, OK

2. SHAVE 2006 Goal: Collect high temporal and spatial resolution data that describe the distribution of hail sizes in hail swaths produced by thunderstorms Verification and validation of multi-radar/multi-sensor hail algorithms Severe HAil & Verification Experiment 2006

3. SHAVE 2006 More SHAVE 2006 goals: Use high-resolution verification data in the development of techniques for probabilistic warnings of severe thunderstorms Associate changes in the hail size distribution with storm evolution Enhance climatological information about hail in the United States

4. SHAVE 2006 Data sources: Google Earth (business locations and phone numbers) Rural phone directories (selected counties with plat maps)

5. SHAVE 2006 results Data collection days83 Total phone calls13854 “Good” data points4880 “Good” except time658 Hail w/ questionable location42 Hail w/ questionable size371 Busy / intercept operator777 Wrong location47 No answer or machine5485 Disconnected / Do Not Call1286 Other307 Areal resolution: ~ 1 point / 59 km 2 Temporal resolution: ~ 1 point / 3.1 minutes

6. Storm Data problems SHAVE verification calls during summer 2006

7. Storm Data problems Storm Data reports: 1 tornado, 1.75” hail SHAVE hail reports (~35)

8. Storm Data problems

10. SHAVE 2007 Severe Hazards Analysis & Verification Experiment 2007 + Expand effort to include wind and tornado damage swaths + Focus on verification for Oklahoma resources (PAR, CASA, KOUN, etc.) New resources: Online media (streaming local TV coverage, local newspapers, newswires) SpotterNetwork.org Delorme Street Atlas 2007 residential phone database Digital locators (county assessor databases, 411.com)

11. SHAVE 2007

12. Setting an aggressive agenda for change Argument: Change is needed  Existing storm database resolution and associated verification methods are incompatible with planned resolution of “warn-on-forecast” models and gridded threat- based warnings  Our ability to resolve features is outpacing our ability to document them

13. Setting an aggressive agenda for change For discussion:  Gridded, probabilistic verification - Probability of exceedance - Initialized by computer model/algorithm - Calibrated by nearby reports & human analysis - Reports still catalogued - Multimedia, online, collaborative, near-real-time data portal (e.g., wiki) Kevin.Scharfenberg@noaa.gov