1. A Spatial Analysis of Significant
Tornadogenesis Density and Risk in the
Complex Terrain of East Tennessee
Jeremy L. Buckles
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2. Goals of Project
• Analyze risk of tornado formation within 25
miles of a point.
• A detailed, local climatology of tornado
formation can assist with future research
projects.
• This can also assist emergency managers and
planners.
• Could potentially aid warning operations.
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3. Previous Research
• Davies-Jones (1984) first
really explored the role of
Storm-Relative Helicity
(SRH)—a representation
of streamwise vorticity—
in rotation of supercells.
• SRH shown to be an
important factor in
significant tornado
development (Rasmussen
and Blanchard, 1998)
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4. Previous Research – SRH
• Rasmussen (2003) and
Thompson, et al. (2003)
verified that boundary
layer inflow was
indicative of tornado
producing supercells.
• Thompson, et al. (2007)
– effective inflow layer
(generally lowest 2km).
Figure 11 from Thompson, et al. (2003) showing
0-1km SRH and associated tornado events.
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5. Importance of Effective Inflow Layer
• Helicity within the effective inflow layer
(approx. lowest 2 km) is extremely important
as shown by Thompson, et al. (2007).
• ESRH discriminates even better than 0-1 km or
0-3 km helicity.
• Terrain features such as mountains or valleys
can block or even accelerate flow.
• Could mountains disrupt effective inflow?
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6. East Tennessee Regional Terrain
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7. Local East Tennessee Research
• Concannon et al. (2000) – Tornado Risk decreased
from west to east across the Tennessee Valley.
• Gaffin and Parker (2006) – climatological
assessment of synoptic conditions favorable to
significant tornadoes in the Southern
Appalachians.
• Schneider (2009), Gaffin and Hotz (2011), and
Gaffin (2012) identified environments where
terrain likely influenced tornadogenesis.
• Gaffin (2012) noted instances of tornadogenesis
around mountain valleys.
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8. April 27, 2011 Tornado Tracks
Credit: Image is Figure 3 from Gaffin (2012)7/8/2015 Research in Applied Meteorology 8

9. Purpose in Tornadogenesis
Climatology Research
• Hypothesis: If terrain led
to tornado formation in
one instance, there
should be a climatological
record of similar events.
• Test it!
– Kernel Density Analysis
– Statistical Tests
• Standardized Anomalies
• Quantiles (Percentile Rank) Credit: Image is Figure 4 from Gaffin (2012)
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10. Past Density Studies
• Tornado density research is not new.
• Thom (1963) identified the probability of a
tornado striking a point in the United States
on a 1° x 1° grid.
• More recently, spatial analysis of tornadoes
has been conducted by Boruff et al. (2003),
Ashley (2007), and Dixon et al. (2011).
• No studies involving high-resolution analysis
of tornado start points around the East
Tennessee region.
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11. Methodology
• Data Collection
– Tornado database has inherent biases:
• Spatial & Temporal Biases (Doswell and Burgess, 1988)
– Different Verification Measures
– Modern communication
– Population Variability
– Technological enhancements to radar
• 1880s: MacFarlane (1884) noted evidence of unrecorded
tornadoes because the lack of verification.
– Grazulis (2001): Over past 100 years, tornado
frequency has increased, but number of EF2+
tornadoes have remained nearly constant.
• Greatest increase was in weak EF-0 and EF-1 tornadoes.
• Likely due to higher population and better verification.
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12. Methodology – Data Collection
• Data Collection – Tornado Database
– Dixon, et al. (2011) noted there is some
subjectivity to determining a dataset as long as it
is well-reasoned and acceptable for the
application.
– Dataset for this research focuses on EF-2 or
greater tornadoes within the NWS Morristown, TN
county warning area (CWA).
– No method removes all biases.
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13. Data Used
• EF-2+ tornado reports
from 9 NWS Databases
adjacent to and including
NWS Morristown.
– Data from adjacent offices
used to reduce boundary
error.
– Includes:
• SPC (1950-2014)
• Grazulis (1877-1950)
– 1124 Reports Used
• 8 excluded for obvious bad
data
– Plotted at Equator
• 91 points in MRX CWA
# of
Torn
MRX HUN OHX LMK JKL RLX RNK GSP FFC
F2-F5 91 165 215 189 50 32 35 109 238
-4 -1 -3
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14. Methodology - Analysis
• Kernel Density Estimation
(KDE)
– Density for discrete data;
Interpolation for continuous
data
– Widely-used, standard
method for spatial data
analysis (Dixon et al. 2011).
– Used by Brooks et al. (2003),
Boruff et al. (2003), Ashley
(2007), and Dixon et al.
(2011).
– Previous research shows that
kernel function is much less
important than kernel radius
(Silverman 1986).
Credit: ArcGIS 10.1 Help: How Kernel Density Works
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15. Methodology – Kernel Density
Estimation
• Kernel Density Estimation (KDE)
– KDE Function: ArcGIS uses a quadratic function
based on Silverman (1986, p.76, equation 4.5).
– KDE Radius: 40.2336 km or 25 miles
– Output Grid: 1609.344 meters or 1 mile
• Allows a smooth, detailed output which will show
small-scale variation in density distribution
– Resulting output is a continuous grid of point
density with units /km2.
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16. Tornado Start Point Density
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17. Narrow to Region of Interest
• Output grid is basically risk/km at each point.
– Interested in risk within 25 miles of a point.
– Must sum density within 25 cells around each cell.
• Next, extract cells only within the NWS
Morristown CWA for analysis.
– MRX CWA polygon shapefile used as a mask.
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18. Tornado Start Point Summed Density
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19. Statistics for MRX Region
Histogram Classification Statistics
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20. Analysis - Statistics
• The goal is to find areas of anomalously high
tornadogenesis density.
• Standardized anomalies are useful in
climatology and give a good estimate of
whether values are significantly above or
below the mean.
• Quantiles - percentile rank is useful for
showing where values rank in relation to the
median of the data.
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21. Standardized Anomalies
• Standardized Anomalies Found by:

Ν−𝑋
𝜎
; where N is value of a point, 𝑋 is the mean,
and 𝜎 is the standard deviation.
• Result can be positive or negative with the
mean having a standardized anomaly of zero
(0).
– For each data point, result shows how many
standard deviations above or below the mean.
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22. Tornado Density Standardized
Anomalies
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23. Quantiles
• Percentile positions within the ranked data.
– Median is the 50th percentile.
– Calculated: ; where, 𝑥 𝑛 is
value at percentile being calculated, 𝑝 is percentile
of interest, and 𝑥 𝑛+1is value one place above 𝑥 𝑛.
– Gives good analysis of spread of the data.
– Can show regions in the tails of the data.
• Based on histogram, this is a right/positive skewed
dataset (mean>median).
))(1( 1 nnnp xxpxx  
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24. Tornado Density Percentile Ranks
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25. Results and Conclusions
• Highest risk of tornadogenesis is across the
southern Tennessee valley.
• There is a higher risk of tornadogenesis near
and to the northeast of the French Broad River
Valley.
– No definitive conclusions can be made about the
cause of this.
– However, it does lend some additional credence
to results from Gaffin (2012).
• There is historical instances of tornadogenesis near
terrain features like large river valleys.
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26. Conclusions
• Recent Vortex II research by Markowski, et al.
(2012) showed that helicity is very important
to mid-level rotation.
– However, tornadogenesis is a product of
microscale baroclinic zones and rear flank
downdraft internal surges (RFDIS).
• Could explain higher tornadogenesis upstream
of river valley as storm rotation strengthened.
– Again, no definitive conclusion can be made on
the cause.
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27. Ideas for Future Research
• Mesoscale & microscale modeling studies on
terrain impacts:
– Boundary Layer Flow
– Helicity
– Microscale density gradients within storms and
around terrain
– Possible vortex stretching
• Computing power and microscale resolution is a
large obstacle.
• Similar local density climatology studies in other
regions can assist emergency management,
emergency planning, and potentially operations.
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28. Questions?
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