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Identifying and Ranking Multi-Day Severe Weather Outbreaks

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Monday, 5 November 2012
Symphony III and Foyer (Loews Vanderbilt Hotel)
Chad M. Shafer, Univ. of South Alabama, Mobile, AL; and C. A. Doswell III
Manuscript (1.0 MB)

Handout (2.8 MB)

Previous studies have developed techniques to identify severe weather outbreaks in the conterminous United States (CONUS) using kernel density estimation (KDE) and to rank the events using a multivariate, linear-weighted index. These techniques used separate 24-h periods (1200 UTC on the nominal date to 1159 UTC the following day). Forecast experience suggests that, although most events occur during a single 24-h period, some outbreaks occur for much longer. Therefore, the techniques developed in previous work are expanded to identify severe weather outbreaks using both their spatial and temporal extent for all reported severe weather from 19602011. To meet this objective, regions associated with clusters of severe weather reports are identified via KDE for overlapping time periods. Any intersecting KDE regions for the overlapping time periods are identified as the same severe weather event. The cases then are ranked using the aforementioned index developed in previous studies.

The technique identifies approximately 4000 severe weather outbreaks during the 52-y period, with fewer than 25% of the events occurring for more than 24 hours. Characteristics and rankings of the multi-day outbreaks are presented for three overlapping time periods (6-h, 3-h, and 1-h) and various thresholds of the KDE-approximated probability density function. Preliminary findings suggest multi-day outbreaks commonly are associated with three scenarios: seasonably strong progressive troughs sustaining severe convection traversing the CONUS, quasi-stationary amplified longwave troughs with multiple shortwave troughs initiating severe convection repeatedly in the same general region, and quasi-stationary initiating boundaries underneath midlevel flow oriented parallel to the boundaries.