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Severe Hazards Analysis & Verification Experiment
(SHAVE)
Kevin ScharfenbergOU-CIMMS & NOAA-NSSL
2nd Workshop on NWS Severe Weather Warning Technology -- 11 July 2007 -- Norman, OK
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
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
SHAVE 2006
Data sources:
Google Earth (business locations and phone numbers)
Rural phone directories (selected counties with plat maps)
SHAVE 2006 results
Data collection days 83
Total phone calls 13854
“Good” data points 4880
“Good” except time 658
Hail w/ questionable location 42
Hail w/ questionable size 371
Busy / intercept operator 777
Wrong location 47
No answer or machine 5485
Disconnected / Do Not Call 1286
Other 307
Areal resolution:
~ 1 point / 59 km2
Temporal resolution:
~ 1 point / 3.1 minutes
Storm Data problemsSHAVE verification calls during summer 2006
Storm Data problems
Storm Data reports:1 tornado, 1.75” hail
SHAVE hail reports (~35)
Storm Data problems
Storm Data problems
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)
SHAVE 2007
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
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)
