Weak Tornadoes in the Ohio Valley: A Pre-Storm
Environment Assessment (45)
Kristen Cassady, NOAA /
NWS, Wilmington, OH
Abstract:
The Ohio Valley is
susceptible to tornadoes and sits near the epicenter of the climatological
maxima in quasi-linear convective system-driven tornado frequency in the U.S.
(Smith et al. 2012, Thompson et al. 2012). These tornadoes are typically brief
and on the ground for only a few minutes (Smith et al. 2012), yielding many
instances of unwarned events (Brotzge and Erickson 2010). The short-lived
nature of the tornadoes provides a notable challenge to forecasters in the
warning decision process. Although research of the parent mesocyclones has been
performed from both a radar (Trapp et al. 2005, Hatzos 2016) and an
environmental parameters (Schaumann and Przybylinski 2012) perspective, this
project serves to further and expand upon this prior research and demonstrate
its applicability in the Ohio Valley. The objective of this study is to
identify key environment changes in the near-storm environment before tornadoes
develop.
Archived Rapid Refresh (RAP)
analysis data was studied in weak tornado environments in the National Weather
Service Wilmington, Ohio service area of southeastern Indiana, southwestern
Ohio, and northern Kentucky from 2009 to 2017. Pre-storm environments were
investigated utilizing 16 different RAP analyses stability, shear, and moisture
parameters in the hours prior to tornado development. Time trends of each
parameter were categorized by season and time of day and relationships were
identified for each variable through time. The data collection methodology
yielded over 6000 data points and parameters were assessed with regards to
positive or negative correlations to tornado development time. Trends of
individual stability and shear parameters were also compared to each other to
assess degree of correlation - both with and without respect to time. The study
identifies ingredient and parameter changes significant enough to support weak
tornado formation, and concludes with subsequent recommendations for better
real-time recognition of environments becoming increasingly favorable for such
events.