92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Wednesday, 25 January 2012
Climatology of Severe Weather Outbreaks
Hall E (New Orleans Convention Center )
Chad M. Shafer, Univ. of South Alabama, Mobile, AL; and J. G. Hollingsworth, C. A. Doswell III, A. E. Mercer, L. M. Leslie, and M. B. Richman
Manuscript (2.5 MB)

Poster PDF (4.5 MB)

A climatology of 4057 severe weather outbreaks from 1979-2008 is developed. Each event is ranked using a linear-weighted index, after assessing the values of several severe weather report variables. This permits developing a climatology of the outbreaks, with respect to their perceived relative severity.

Each event is associated with a particular region, as identified by two-dimensional kernel density estimation, using an 18-km horizontal grid encompassing the conterminous United States. For each grid point within the region associated with a particular outbreak, the magnitude of a synoptic variable or severe weather diagnostic variable is determined at the valid times of the outbreaks. The mean (or median) value of the variable over all the selected grid points is used in the climatology. Synoptic variables include geopotential heights, temperature, dew point, and wind speed and direction at selected pressure levels and at the surface. Severe weather diagnostic variables include CAPE, CIN, bulk shear, the lifting condensation level, storm-relative helicity, the energy-helicity index, the supercell composite parameter, and the significant tornado parameter.

Preliminary findings suggest the most significant severe weather outbreaks (i.e., major tornado outbreaks) have a tendency to feature the strongest midlevel flow, lowest sea-level pressures, strongest vertical wind shear, and highest low-level storm-relative helicity. The climatological analysis demonstrates the similar capability of certain synoptic variables to discriminate outbreaks based on their severity, compared to severe weather diagnostic variables. Finally, there is clearly a false alarm problem, with a substantial number of less significant cases exhibiting similar tendencies, no matter what variable is selected.

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