14B.4 The Development, Display, and Preliminary Evaluation of Conditional Supercell Tornado Probabilities

Thursday, 10 November 2016: 11:00 AM
Pavilion Ballroom West (Hilton Portland )
Bryan T. Smith, NOAA/NWS/NCEP/SPC, Norman, OK; and R. L. Thompson and A. R. Dean

The Storm Prediction Center (SPC) developed conditional probabilities of tornado damage rating from near-storm environment data and radar-based storm-scale characteristics, from a 7-year (2009-2015) sample of tornadoes from supercells (4700+ events), and a 2-year (2014-2015) sample of supercells (5400+ events) with documented large hail (>1 inch in diameter) or severe/damaging wind gusts (>50-kt) in the contiguous United States (CONUS).  The probabilities are derived from both filtered tornado-segment data by the maximum EF-scale tornado event per hour, and hail/wind data by the maximum report magnitude per hour— with additional hail/wind filtering of > 10 mi and + 15 min of a start/end point of a tornado—on a 40-km horizontal grid.  Near-storm environment data, consisting primarily of supercell-related convective parameters from hourly objective mesoscale analysis calculated at the SPC, accompanied each grid-hour event.  Convective mode was assigned to each event by manually examining full volumetric WSR-88D data at the beginning time of each event and only right-moving (RM) supercell and right-moving marginal (MRGL) supercell grid-hour events are included. 

Conditional probabilities of RM/MRGL supercell-mode tornado occurrence were calculated from this database of observed events and are available for display on SPC workstations.  A preliminary subjective evaluation of the conditional probabilities is currently ongoing and initial findings will be shared.  From this work, a proposed approach for the diagnosis and short-term prediction of tornado intensity using a combination of observations with numerical weather prediction (NWP)–based objective analyses and one-hour forecasts will be discussed.  A few examples highlighting the strengths and weaknesses of applying this conditional-probability technique will be discussed.

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