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Structural and Environmental Characteristics of Extratropical Cyclones that CauseTornado Outbreaks in the Warm Sector: A Composite Study
EIGO TOCHIMOTO AND HIROSHI NIINO
Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan
(Manuscript received 9 January 2015, in final form 27 November 2015)
ABSTRACT
The structural and environmental characteristics of extratropical cyclones that cause tornado outbreaks
[outbreak cyclones (OCs)] and that do not [nonoutbreak cyclones (NOCs)] are examined using the Japanese
55-year Reanalysis dataset (JRA-55). Composite analyses show differences between OCs and NOCs: for
OCs, storm relative environmental helicity (SREH) and convective available potential energy (CAPE) are
notably larger, and the areas in which these parameters have significant values are wider in the warm sector
than they are for NOCs. The larger CAPE inOCs is due to larger amounts of low-level water vapor, while the
greater SREH is due to stronger low-level southerly wind.
The composite analyses for environmental fields defined by 20-day means suggest that environmental
meridional flows have the potential to advect large amounts of warm and moist air northward, creating at-
mospheric instability in the troposphere that contributes to the occurrence of a tornado outbreak.A piecewise
potential vorticity (PV) diagnosis shows that low- to midlevel PV anomalies are the main contributor to the
difference in the low-level winds between OCs and NOCs, whereas upper-level PV anomalies make only a
minor contribution.
An examination of the structures of the extratropical cyclones and the upper-level jet stream suggests that
the difference in the low-level winds between OCs and NOCs is due to differences in the structure of the jet
stream. The OCs develop when the jet stream displays larger anticyclonic shear. This causes a more merid-
ionally elongated structure of OCs, resulting in stronger low-level winds in the southeastern quadrant of the
cyclones.
1. Introduction
Typical springtime tornado outbreaks in the United
States (e.g., Carr 1952; Fujita et al. 1970; Galway 1975,
1977; Grazulis 1993) are associated with several synoptic
features: extratropical cyclones (ECs), frontal systems,
and upper-level troughs together with their associated
jet stream. For instance, from 3 to 11 May 2003, an ex-
tended outbreak occurred across the central and eastern
United States in association with ECs and caused 41
fatalities, 642 injuries, and ;$829 million (U.S. dollars)
in damage (Hamill et al. 2005). This outbreak occurred
in the warm sector of the ECs where atmospheric in-
stability and vertical shear are strong. Despite the
recent availability of long-term reanalysis data and
sophisticated numerical simulations, it is still not fully
understood how synoptic environments cause tornado
outbreaks.
Numerous studies have investigated the relationships
between synoptic fields and tornado outbreaks (e.g.,
Miller 1972; Johns and Doswell 1992; Stensrud et al.
1997; Roebber et al. 2002; Gaffin and Parker 2006;
Corfidi et al. 2010). Uccellini and Johnson (1979)
performed a case study of a severe weather outbreak
over Indiana and Ohio on 10–11 May 1973. They
pointed out that the coupling between upper-level and
low-level jet streaks influenced the development of se-
vere convective storms. Using a mesoscale model,
Stensrud et al. (1997) examined the effectiveness of
convective weather parameters in distinguishing torna-
dic from nontornadic thunderstorms for nine severe
weather episodes in the United States. They concluded
that storm-relative environmental helicity (SREH;
Davies-Jones et al. 1990) is useful for determining re-
gions where supercell storms are likely to occur.
Corresponding author address: Eigo Tochimoto, Atmosphere
and Ocean Research Institute, The University of Tokyo, 5-1-5,
Kashiwanoha, Kashiwa, Chiba 277-8564, Japan.
E-mail: [email protected]
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DOI: 10.1175/MWR-D-15-0015.1
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Roebber et al. (2002) showed that upper-level potential
vorticity (PV) influenced the occurrence of a tornado
outbreak event on 3–4 May 1999. These previous stud-
ies, however, are mainly individual case studies.
In recent studies, statistical analyses have been per-
formed on the relationships between tornado out-
breaks and synoptic features (Mercer et al. 2009, 2012;
Shafer et al. 2009, 2010b). Shafer et al. (2009) per-
formed numerical simulations of 50 tornadic outbreaks
(TOs) and 50 primarily nontornadic outbreaks (NTOs)
associated with a few tornadoes, convective wind gusts,
and hail to investigate the differences in prevailing
synoptic features between TOs and NTOs. They
showed that dynamical parameters, including vertical
wind shear and SREH, are most useful for dis-
tinguishing between the outbreak types. Mercer et al.
(2012) classified synoptic patterns for outbreaks into
several types and identified synoptic-scale differences
between TOs and NTOs: TOs are accompanied by
stronger upper-level troughs and low-level thermal
advection than NTOs. They also emphasized the im-
portance of SREH and vertical shear in the develop-
ment of TOs. These studies demonstrated that
synoptic-scale conditions play a substantial role in the
occurrence of tornado outbreaks.
The present study focuses on ECs, which are one of
the primary synoptic features leading to tornado out-
breaks. Figure 1 shows a synoptic pattern that typically
results in tornado outbreak (e.g., Newton 1967). This
pattern is similar toMiller’s (1972) type-B pattern and is
characterized by strong progressive ECs. An upper-level
jet streak, located near the center of the EC, strengthens
vertical shear and advects cool, dry air into the upper
andmiddle troposphere, while low-level southerly winds
advect warm and moist air into the warm sector,
creating a region of convective instability. As a result,
the warm sector becomes a favorable region for tornado
outbreak (e.g., Johns and Doswell 1992). Although
earlier studies have pointed out that many tornado
outbreaks are accompanied by ECs with an upper-level
trough and a jet streak (e.g., Roebber et al. 2002; Lee
et al. 2006; Corfidi et al. 2010), not all ECs produce
tornado outbreaks. Thus, the differences in the structure
and environment of ECs that cause and do not cause
tornado outbreaks remain poorly understood.
In the present study, we use composite analyses of
reanalysis data between April and May from 1995 to
2012 in the United States to investigate differences in
the structure and environment between ECs that cause
tornado outbreaks and those that do not, and we con-
sider the physical mechanisms for the differences. The
remainder of this paper is organized as follows. The
analysis methods are described in section 2, the results
are outlined in section 3, a discussion is presented in
section 4, and finally a summary is given in section 5.
2. Methodology
a. Dataset and detection of ECs
We used Japanese 55-year Reanalysis data (JRA-55;
Ebita et al. 2011; Kobayashi et al. 2015) that provides
6-hourly gridpoint values with a resolution of 1.258 3 1.258at 37 levels. The vertical grid spacing is 25 hPa from 1000
to 750 hPa, 50 hPa from 750 to 250hPa, and 25hPa from
250 to 100 hPa. To calculate SREH, meridional and
zonal components of winds are interpolated linearly to
height levels at 250-m intervals.
The ECs are detected by applying the tracking algo-
rithm of Hodges (1994, 1995, 1999) to 6-hourly relative
vorticity at 900 hPa, where the relative vorticity is
truncated to T42 horizontal resolution to focus on
synoptic-scale cyclones (Jung et al. 2012; Yanase et al.
2014). Hodges’s tracking algorithm is used in many
studies of ECs (e.g., Hoskins and Hodges 2002;
Bengtsson et al. 2006; Hodges et al. 2011).Moreover, the
algorithm using 900-hPa vorticity can detect ECs rea-
sonably well (Yanase et al. 2014). The tracking method
is applied to the region 308–47.58N, 1208–408W. Tornado
data are taken from the Severe Weather Database
produced by the StormPredictionCenter at NOAA.We
only use the data after the year 1995 in the majority of
the present analyses as enhanced Fujita (EF0) tornadoes
drastically increased after 1995 and there is no obvious
trend from 1995 to 2012 (not shown).
b. Categorization of tornadic ECs
In what follows, a tornadic EC (TEC) is defined as
an EC that is accompanied by a tornado within 158 inlongitude and latitude from the center of the EC, and
within 3 h of the 6-hourly analysis time of JRA-55. We
will be focusing on ECs that cause many tornadoes in
the warm sector, because the structural and environ-
mental characteristics of ECs that cause many tor-
nadoes in the frontal zones may differ. Thus, we
exclude tornadoes occurring in regions with a tem-
perature gradient exceeding 2K (100 km)21. If ECs
and their associated fronts are assumed to have
speeds of;20–50 km h21, they move 60–150 km in 3 h.
Thus, the locations of tornadoes would not depart
much from the frontal zones defined by the present
study in the JRA-55, which has a horizontal resolu-
tion of 120 km. Note that the above criterion may not
exclude tornadoes associated with drylines or wind
shift lines that are not accompanied by large tem-
perature gradients.
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The TECs obtained based on the above criteria are
then categorized into two groups according to the
number of associated (nonfrontal) tornadoes occur-
ring between 0900 UTC on one day and 0900 UTC on
the following day. If the number is $15, the TEC is
called an outbreak cyclone (OC). If it is #5, the TEC
is called a nonoutbreak cyclone (NOC). These two
categories of TECs will be studied in detail. The
strength of tornadoes is not considered in the present
study; the purpose of our study is to identify differ-
ences in the structure and environment between ECs
that cause many tornadoes and those that cause a
small number of tornadoes regardless of the tornado
strength. It may be argued that the choice of 15 as a
threshold is somewhat arbitrary. However, we have
checked the sensitivity to the threshold by changing
the criterion to .10 and .20 for OCs and have con-
firmed that the results do not change qualitatively. In
appendix C we show that the 14 EC cases fall below
the 15-tornado OC criterion solely because of the
exclusion of tornadoes in the frontal zones. The tor-
nadoes associated these cases occur mainly in the
warm frontal zones and the warm sector, with very
few along the cold fronts. Although the composite
structures of the 14 ECs that cause tornadoes in the
warm frontal zones exhibit interesting features that
are different from those for OCs, their detailed study
will be left for future work.
c. Composite analysis
Composite analysis is performed to identify differ-
ences in the structural and environmental fields between
OCs and NOCs. In the analysis, physical variables are
superposed with respect to the TEC center, which is
defined by the vorticity maximum at 900 hPa. The time
at which the largest number of tornadoes occurred
within 24h is defined as the key time (KT), where the
time will be given in units of hours. To exclude weak
ECs, only TECs with a 900-hPa relative vorticity
of $3 3 1025 s21 at KT are analyzed. In addition, only
TECs with a central sea level pressure (SLP) lower than
1005hPa are superposed to investigate the differences
betweenOCs andNOCs, as it is desirable to examine the
differences for TECs of similar intensity. The sensitivity
of the results to the vorticity and SLP criteria is dis-
cussed in appendix A.
The numbers of OCs and NOCs thus selected are 55
and 41, respectively. The composite analysis is per-
formed fromKT2 12 to KT. Although we use a simple
FIG. 2. Geographical distribution of the centers of outbreak cyclones (OCs; red circles) and
nonoutbreak cyclones (NOCs; blue circles) at key time (KT) defined in the text.
FIG. 1. A typical synoptic pattern for tornado outbreaks
(Newton 1967).
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average technique to obtain the composite field, it will
be shown that common and important characteristics
of OCs and NOCs are obtained. Permutation testing
(Efron and Tibshirani 1993), which is a statistical
technique to determine if the means of two distribu-
tions are different, is performed on the meteorological
variables. In what follows, p values of 0.1, 0.05, and
0.01 correspond to the 90%, 95%, and 99% confidence
levels, respectively. This test is superior to the t test in
the present study as the distribution of the variables is
unknown and the t test requires the variable to
follow a normal distribution (Mercer et al. 2009).
d. Mesoscale environmental parameters
Of the many dynamic and thermodynamic parame-
ters that characterize tornadic storm environments,
SREH, convective available potential energy (CAPE),
and energy helicity index (EHI) are used in the present
study. SREH gives a potential for rotational charac-
teristics of convective storms (Davies-Jones et al.
1990), and is defined as
SREH5
ðh0
k3›V
›z� (V2 c) dz , (2.1)
whereV is the environmental wind vector, c is the storm
motion vector, h is an assumed inflow depth, and k is a
unit vertical vector. We adopt h5 1 km and estimate c
using a method developed by Bunkers et al. (2000).
CAPE is defined as the positive buoyant energy
available to a parcel that rises from its initial height level
to the level of neutral buoyancy (LNB), as follows:
CAPE5 g
ðLNB
LFC
u(z)2 u(z)
u(z)dz , (2.2)
where u(z) is the potential temperature of the parcel,
u(z) is the potential temperature of the environment, g is
the acceleration due to gravity, and LFC is the level of
free convection.
Furthermore, EHI (Hart and Korotky 1991) defined
by
EHI5SREH3CAPE
1:63 105(2.3)
is used as an environmental parameter. Rasmussen
(2003) suggested that a formulation of EHI as a combi-
nation of the 0–1-km SREH and CAPE is the best dis-
criminator between tornadic and nontornadic storms.
Shafer et al. (2009) also found that EHI is a useful pa-
rameter for distinguishing outbreak types.
TABLE 1. Mean latitude and longitude of the locations of OCs
and NOCs centers at KT, and standard deviation of their latitude
(slat) and longitude (slon).
Mean lat slat Mean lon slon
OC 40.5 3.5 268.3 5.6
NOC 40.7 4.3 269.5 8.2
FIG. 3. Distribution of tornadoes (red dots) for (a) OCs and (b) NOCs at KT. Grayscale shows the composite vorticity
(s21) and contour lines show geopotential height (m) at 900 hPa. The centers of the TECs are located at (0, 0).
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e. Piecewise PV inversion
A piecewise PV inversion (Davis and Emanuel 1991)
is used to investigate the relative contribution of indi-
vidual components of the PV anomaly to the TEC
structure. Ertel (1942) defines PV as
q51
rh � =u , (2.4)
where q is PV, r is the density of air, h is the absolute
vorticity, u is potential temperature, and = is the three-
dimensional vector differential operator.
A nonlinear balance equation, derived by Charney
(1955), is used to invert Ertel’s PV. Assuming a hydro-
static balance and the irrotational wind component to be
much smaller than the nondivergent component, the
divergence equation and Ertel’s PV can be rewritten in
spherical coordinates as
=2F5= � f=C12
a4 cos2f
"›2C
›l2
›2C
›f22
�›2C
›l›f
�2#,
(2.5)
and
q5gkp
p
�( f 1=2C)
›2F
›p22
1
a2 cos2f
›2C
›l›p
›2F
›l›p
21
a2›2C
›f›p
›2F
›f›p
�, (2.6)
respectively, where F is the geopotential, C is the non-
divergent streamfunction, l is longitude, f is latitude, a is
the radius of the earth,k5R/cp is thePoisson constant, f is
theCoriolis parameter,p is pressure, andp5 (p/p0)k is the
Exner function. This study assumes homogeneous lateral
boundary and Neumann-type upper and lower boundary
conditions (Davis and Emanuel 1991). At each grid point,
PV anomalies are defined as deviations from the basic PV
field, which is defined by the 20-day running mean.
Equations (2.5) and (2.6) are linearized with respect to PV
field anomalies as described byDavis andEmanuel (1991).
In the present study, PV anomalies are divided into
three components: the upper-level component (UPV)
between 500 and 1hPa, the lower boundary component
(LBT) at 987.5 hPa, and the lower to midtroposphere
perturbation component (MPV) between 975 and
550 hPa. Potential temperature anomalies in the upper
and lower boundaries are included in UPV and LBT,
respectively, because they are equivalent to PV anom-
alies (Bretherton 1966).
3. Results
a. Distribution of TECs
Most OCs and NOCs occur between the central plains
and the East Coast. However, OCs are concentrated
more in the central plains (Fig. 2). The difference is
presented quantitatively in Table 1. Although the mean
latitude and longitude of OCs are similar to those of
FIG. 4. SREH (color shading; m2 s22) at KT for (a)OCs and (b) NOCs, and (c) p values for difference in SREHbetweenOCs andNOCs
(color shading). Black solid lines in (a) and (b) indicate contours of geopotential height (m) at 900 hPa, and those in (c) indicate the
differences in SREHbetweenOCs andNOCs. The contour interval in (a) and (b) is 20m, and that in (c) is 20m2 s22. Only contours above
20m2 s22 are shown in (c).
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NOCs, the standard deviation is larger in NOCs. Thus,
the locations of NOCs are more dispersed than those of
OCs, especially meridionally.
b. Composite analysis
1) DISTRIBUTION OF TORNADOES
Composite geopotential height and vorticity fields for
OCs andNOCs, together with tornado locations relative
to the cyclone center at KT, are shown in Fig. 3. Most
tornadoes associated with OCs occur in the southeast
quadrant, corresponding to the warm sector, and par-
ticularly within 58 latitude and longitude from the OC
center (Fig. 3a). Conversely, tornadoes associated with
NOCs appear to occur more sporadically (Fig. 3b).
However, a kernel density estimate analysis (Wilks
2006) indicates that there is no notable difference in the
spatial density distribution of tornadoes between OCs
FIG. 5. Horizontal distribution of CAPE (m2 s22) for (left) OCs, (middle) NOCs, and (right) p values for the difference between OCs
and NOCs at (a)–(c) KT 2 12, (d)–(f) KT 2 6, and (g)–(i) KT. Contours in (a) and (b) indicate geopotential height at 900 hPa (m), and
those in (c) differences in CAPE between OCs and NOCs. The contour interval in (a) and (b) is 20m, and in (c) it is 200m2 s22.
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and NOCs (not shown). Thus, the apparently sporadic
distribution is due to the small number of tornadoes
associated with NOCs, which reflects a relatively less
favorable environment for tornadogenesis, as wewill see
in the next section in more detail. The area of vorticity
exceeding 1 3 1025 s21 extends farther southward for
OCs than for NOCs.
2) ENVIRONMENTAL PARAMETERS
Mesoscale environmental parameters from the com-
posite fields are examined to identify significant envi-
ronmental conditions that lead to tornado outbreaks.
Significant differences exist between OCs and NOCs in
both dynamic and thermodynamic parameters. A region
of large SREH, exceeding 150m2 s22 and extending at
least 500km to the south, exists in the east-southeast
quadrant of theOC centers (Fig. 4a). SREH for NOCs is
notably smaller (Fig. 4b). Furthermore, the area in
which the SREH exceeds 100m2 s22 is narrower for
NOCs. The differences in SREH values between OCs
and NOCs are 100m2 s22 at most and the area of sta-
tistical significance exceeding the 95% or 99% confi-
dence level extends over the east-southeast quadrant of
the OC centers (Fig. 4c). Thus, OCs provide a dynami-
cally more favorable environment for supercell and
tornado formation than NOCs. These results are con-
sistent with studies using the areal coverage of envi-
ronmental parameters (e.g., Hamill et al. 2005; Shafer
et al. 2009, 2010a).
Notable differences also exist in the distribution of
CAPE between OCs and NOCs (Fig. 5). For OCs, at
KT 2 12 the area in which CAPE exceeds 600m2 s22
extends southward from the center (Fig. 5a), while for
NOCs there is no region with significant CAPE ex-
ceeding this value (Fig. 5b). CAPE for both OCs and
NOCs increases with time. Since KTs in most of the
OCs and NOCs occur at 1800 or 0000 UTC, this sig-
nificant increase in CAPE from KT 2 12 to KT 2 6 or
KT is likely to be caused by daytime solar radiative
heating. At KT the maximum value of CAPE is
;1000m2 s22 for OCs but ,400m2 s22 for NOCs. The
difference is statistically significant (exceeding 99%
confidence level) in the south-southeast region of the
cyclone center from KT 2 12 to KT. The values of the
difference exceed 500m2 s22 at KT 2 6 and KT. Thus,
regions of tornadogenesis in OCs are thermodynami-
cally unstable and have the potential for strong con-
vection to develop.
Differences in CAPE betweenOCs andNOCs appear
to be partly explained by low-level water vapor fields
(Fig. 6). In OCs, low-level specific humidity of 0.012–
0.014 kgkg21 intrudes 200km south of the cyclone cen-
ter. In NOCs this value of low-level humidity occurs
1000km south of the cyclone center. Although more
water vapor is found between 2000 and 2500 km south of
the cyclone center in OCs, the CAPE is largest at
;1000km south of the center of OCs. Figures 7a and 7b
show vertical cross sections of specific humidity and
FIG. 6. Specific humidity (color shading; kg kg21) at 950 hPa and CAPE (contours; m2 s–2) at KT for (a) OCs and
(b) NOCs. The lines A–A0 and B–B0 show the location of the cross sections presented in Fig. 7.
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temperature at;1000 and 2500km south of the cyclone
center (lines A–A0 and B–B0 in Fig. 6), respectively.
Specific humidity near the surface along B–B0 exceeds0.014 kgkg21, which is higher than along A–A0. Thenear-surface temperature 200–1000km west of the cy-
clone center is 300K for both cross sections, whereas the
temperature at 500 hPa along A–A0 is ;5K colder than
along B–B0. Thus, the largest CAPE is located approx-
imately 1000km south of the cyclone center near the line
A–A0.The difference in CAPE between OCs and NOCs
could also be caused by differences in temperature
stratification. To explore this possibility, vertical cross
sections along the same latitude as A–A0 (Fig. 7c) areplotted of the difference in temperature between OCs
and NOCs, and the temperature for NOCs. The tem-
perature at midlevel is warmer for OCs than for NOCs.
Thus, the larger CAPE for OCs near A–A0 is not ex-
plained by the temperature stratification, which is more
unstable for NOCs.
The overlap of regions with large SREH andCAPE in
OCs (Figs. 4 and 6) suggests that OCs are indeed more
favorable than NOCs for severe storm development.
This may also be explained by the difference in distri-
butions of EHI between OCs and NOCs. The region
with EHI. 0.1 is much wider for OCs than for NOCs; in
fact a region with EHI. 0.4 for OCs exists southeast of
the cyclone center (Fig. 8).
3) STRUCTURE OF TECS
Composite structures of TECs are now examined to
understand the differences in convective parameters
between OCs and NOCs (Fig. 9). The geopotential
height and PV for OCs are more meridionally elongated
at both KT2 12 and KT than those for NOCs, resulting
in a stronger zonal pressure gradient and resultant me-
ridional geostrophic wind. The low-level PV for OCs
differs from that for NOCs at a statistically significant
level (exceeding 95%) in the south-southeast region of
the cyclone center. Although the distributions of p
FIG. 7. Vertical–zonal cross sections of specific humidity (color shading; kg kg21) and temperature (contours; K) along (a) A–A0 and(b) B–B0 (Fig. 6) at KT. (c) The vertical–zonal cross section of differences in temperature between OCs and NOCs, and temperature for
NOCs along the latitude of A–A0.
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values are noisy, we think that those around the warm
sector are meaningful since they correspond to the region
in which the PV associated with the EC is large, while the
distributions of p values in other regions do not show a
good correspondence. Similar differences in the warm
sector are also found at other low levels (e.g., 875 and
850hPa).
Meridional winds at 900hPa for KT 2 12 and KT are
presented in Fig. 10. A low-level southerly wind de-
velops to the southeast of the OC’s center, with maxi-
mum speeds in excess of 15ms21 at KT, which is
reflected in the distribution of SREH (Fig. 4). In con-
trast to OCs, NOCs are characterized by weaker winds
of about 10–12m s21 with a narrower meridional extent
in the south to east-southeast region. The difference in
the southerly wind exceeds 4ms21 and the difference is
statistically significant at the 99% confidence level
over a wide area (Fig. 10c). This suggests that OCs have
stronger low-level vertical shear, which contributes to
larger SREH, and could potentially transport a larger
amount of water vapor from the south.
To understand the relationship between low-level
meridional wind and SREH, the equation for SREH is
reconsidered. The right-hand side (rhs) of Eq. (2.2) can
be written as follows:
SREH5
ðh0
�(y2 c
y)du
dz2 (u2 c
x)dy
dz
�dz , (3.1)
where cx(cy) is the zonal (meridional) component of the
storm motion. The first (second) term on the rhs of Eq.
(3.1) is associated with vertical shear of the zonal (me-
ridional) wind. Figure 11 shows the magnitude of each
term for OCs and NOCs. For both OCs and NOCs, the
term associated with vertical shear of the meridional
wind is the primary contributor to the SREH, indicating
that the differences in SREH between OCs and NOCs
are primarily due to meridional wind speeds at low
levels.
Now we examine upper-level features of TECs
(Fig. 12). At KT 2 12, high PV (.3.5PVU) is located
slightly to the west of the OCs’ center. With time, PV
increases to 4PVU and intrudes south-southeastward.
For NOCs, in contrast, high PV (.4PVU) is located
approximately 1000km to the northwest of the center at
KT 2 12. The high PV approaches the NOCs’ surface
center with time, but is still located several hundred
kilometers west of the center at KT, which is again
farther than in OCs. The vertical cross sections of the
difference of PV between OCs and NOCs along the line
C–C0 at KT2 12 and KT are shown in Figs. 12c and 12f,
respectively. The region in which statistical significance
exceeds 95% is found west of the cyclone center in the
upper levels.
Upward motion associated with TECs extends from
the northeast to the southeast region of their centers
(Fig. 13). The peak vertical pressure velocity is located
FIG. 8. EHI (color shading) at KT for (a) OCs and (b) NOCs. Contours indicate geopotential height. The contour
interval is 20m.
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to the east of the center for both OCs and NOCs, but
vertical motion in OCs is slightly stronger than in
NOCs. Furthermore, the upward velocity is equal to or
stronger than 20.1 Pa s21 over a wider area for OCs.
The stronger and wider synoptic-scale upward motion
may contribute to destabilization of the lower tropo-
sphere (Markowski and Richardson 2010). As these
differences persist from KT 2 12 to KT (not shown),
OCs have a larger potential for severe convective storm
formation over a wider area. The differences have
statistical significance exceeding the 95% or 99%
confidence level within areas in the warm sector of the
cyclone (Fig. 13c). These areas of upward motion may
be related, in part, to the location of the upper-level
disturbances shown in Fig. 12. As described in Hoskins
et al. (1985), an upper-level PV disturbance induces
upward motion in front of its direction of movement.
Since the upper-level PV is located closer to the warm
sector in OCs than in NOCs, stronger upward motion is
likely to occur over a wider area.
4) ENVIRONMENTAL FIELDS
To examine the environmental fields for both OCs
and NOCs, a 20-day running mean was calculated. A
large-scale high pressure system covers the southeastern
quadrant for both OCs and NOCs (Figs. 14a,b). In the
southwestern quadrant, a large-scale trough extends
southward. Strong south-southwesterly flows are evi-
dent along the western edge of the high and the eastern
edge of the trough; these are stronger forOCs (Figs. 14a,b).
There is more low-level moisture to the south of the
cyclone center for OCs than for NOCs (Figs. 14c,d).
Low-level moist air with humidity exceeding 0.01kgkg21
intrudes from the south to about 350km to the south of
the center for OCs, while it is located about 500km to the
south for NOCs.
FIG. 9. Horizontal distribution of PV (potential vorticity units; 1 PVU 5 1026 K kg21m2 s21) for (left) OCs and (middle) NOCs, and
(right) p values for the difference in PV betweenOCs and NOCs. Contours in (a) and (b) indicate geopotential height at 900 hPa (m), and
those in (c) indicate differences in PV betweenOCs and NOCs (PVU). The contour interval in (a) and (b) is 20m, and in (c) it is 0.1 PVU.
(a)–(c) KT 2 12 and (d)–(f) KT.
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The horizontal structure of the jet stream at the upper
levels also shows some difference between OCs and
NOCs (Fig. 15). Both OC and NOC centers are located
in the north of the jet streak at KT 2 12; however, the
OCs center is located slightly closer to the exit region of
the jet streak. Between KT2 12 and KT, the OCs move
toward the exit region, whileNOCs remain in themiddle
part of the jet streak. Another important difference
between OCs and NOCs is that the jet streak for OCs
displays greater meridional shear. In particular, anticy-
clonic shear on the southern side of the jet streak is
stronger. The relationship between the meridional
structure of the jet streak and the TECs is discussed in
section 4.
c. Piecewise PV inversion
A piecewise PV inversion (Davis and Emanuel
1991) is performed to identify the relative contribu-
tions of PV anomalies in the UPV, LBT, and MPV
(see section 2e) to the low-level southerly winds that
affect the SREH. Figure 16 compares the ‘‘total
southerly winds,’’ defined as the sum of recovered
meridional winds induced by all of the PV anomalies,
with the southerly wind from JRA-55. Total meridi-
onal winds in the range 10–14m s21 are found in the
east-southeast region of the OCs center and those in
the range 8–10m s21 extend over the east-southeast
region of the NOCs center. Although the total me-
ridional winds are 20%–30% weaker than those in
JRA-55, their horizontal distribution and the quali-
tative differences between OCs and NOCs are similar
to those for JRA-55. Thus, we consider it would be
meaningful to examine the winds induced by each
component of the PV anomaly. It seems that the dif-
ferences in magnitude of total wind recovered from
the PV anomalies and that from JRA-55 are due to the
assumption of the nonlinear balance equation [Eq.
(2.5)], in which irrotational winds are assumed to be
smaller than the nondivergent winds. This nonlinear
balance assumption may become less accurate in cases
when irrotational winds are stronger and they are not
negligible compared with the nondivergent winds.
FIG. 10. As in Fig. 9, but for meridional winds (color shading; m s21) at 900 hPa and the difference in meridional winds (contours; m s21).
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The significant differences in the total southerly winds
induced by the total of UPV, MPV, and LBT between
OCs and NOCs are found in the east-southeast region of
the cyclone center (Figs. 17a,b). The southerly winds in
OCs are larger than NOCs and the difference exceeds at
least 3m s21. The differences are statistically significant
at the 95% or 99% confidence level (Fig. 17c).
UPV-induced meridional winds extend from north-
east to southeast of the low center for both OCs and
NOCs (Figs. 17d,e). Induced southerly winds in OCs are
stronger throughout the period KT 2 12 to KT. The
difference is 1–1.5m s21 in the east-southeast region of
the cyclone center. Although there are small areas in this
region with statistical significance exceeding the 95%
level (Fig. 17f), this difference explains only a minor
fraction of the differences in total southerly winds be-
tween OCs and NOCs.
The sum of meridional winds induced by MPV and
LBT is presented in Figs. 17g–i. For OCs, the
southerly winds extending from south to north on the
east side of the center are stronger and cover a me-
ridionally wider area than for NOCs. These stronger
southerly winds are due to the southward elongation
of positive PV at low levels (Fig. 9). The positive PV
anomaly seems to be associated with the cold front
extending south-southwest from the cyclone center.
A considerable fraction of the differences in the
southerly wind seems to be explained by
MPT 1 LBT.
4. Discussion
The present composite analysis indicates that sig-
nificant differences exist between the structures of OCs
FIG. 11. SREH (m2 s22) for (top)OCs and (bottom)NOCs.Only contours above 100m2 s22 are drawn. Color shading
shows the contribution of vertical shear of (a),(c) meridional and (b),(d) zonal winds.
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and NOCs, which result in larger SREH for OCs and
smaller SREH for NOCs.Mercer et al. (2009, 2012) and
Shafer et al. (2009, 2010b) also discussed synoptic-scale
structure of dynamic and thermodynamic fields. How-
ever, they were mainly interested in the identification
of the most distinguishing parameters that determine
tornado outbreaks. We have contributed to explicitly
and quantitatively revealing the connection between
EC structures and important parameters for tornado
outbreaks.
Our analyses also identify CAPE as a significant fac-
tor in distinguishing OCs and NOCs, which was not
identified in these previous studies (Monteverdi et al.
2003; Mercer et al. 2009; Shafer et al. 2009, 2010b). The
previous studies such as Mercer et al. (2009, 2012) and
Shafer et al. (2009, 2010b) primarily compared tornadic
and nontornadic outbreaks without concentrating on
the EC environment. As the present study focuses
specifically on the differences between ECs that cause
outbreaks of tornadoes and those that do not, the find-
ings of our study are not necessarily inconsistent with the
previous results.
We would like to emphasize that the differences in
the environmental parameters for composite analyses
between OCs and NOCs are mainly due to the struc-
ture of ECs and depend less strongly on the strength of
the ECs (although we note that all ECs considered in
this study meet a minimum threshold). Although OCs
tend to have lower central pressure than NOCs, the
differences in EC strength between OCs and NOCs
have little impact on the composite fields. This is con-
firmed by a more sophisticated composite analysis in
which weighted averages are used (see appendix A for
more details).
Our results suggest that strong low-level south-
erly winds cause larger SREH and also increase
FIG. 12. Horizontal distributions of PV (PVU) at 250 hPa for (left)OCs and (middle)NOCs, and (right) the vertical–zonal cross sections
of p values for the difference in PV between OCs and NOCs along the latitude of the EC center. Contours indicate (a),(b) geopotential
height at 900 hPa (m) and (c) the vertical cross section of differences in PV between OCs and NOCs (PVU). The contour interval in
(a) and (b) is 20m, and that in (c) is 0.1 PVU. (a)–(c) KT 2 12 and (d)–(f) for KT.
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atmospheric instability, through moisture transport.
The piecewise PV inversion indicates that the UPV
disturbances are not the main contributor to the dif-
ferences in the low-level southerly flow (Fig. 4) be-
tween OCs and NOCs. Instead, the difference is caused
mainly by the sum of MPV and LBT anomalies asso-
ciated with EC structure and their accompanying warm
and cold fronts.
These structural differences between OCs and
NOCs are influenced by the upper-level jet stream.
Two paradigms exist regarding the relationship be-
tween the structure of ECs and the jet stream. The first,
suggested by Schultz et al. (1998) and Schultz and
Zhang (2007), is that ECs developing at the exit region
of a jet streak display meridionally elongated struc-
tures, while ECs developing at the entrance region
display zonally elongated structures. Background dif-
fluent (confluent) flow at the exit (entrance) stretches
the ECs and associated fronts meridionally (zonally).
The second paradigm is suggested by several authors
(e.g., Davies et al. 1991; Wernli et al. 1998) who ex-
amined the sensitivity of ECs and surface frontal
structures to background barotropic shear of the jet
stream. When the background shear is anticyclonic,
ECs develop with strong cold fronts, and are meridi-
onally elongated. When the shear is cyclonic, ECs
develop with strong warm fronts having a northwest–
southeast axis.
Clear differences are identified in EC structure and
associated cold fronts between OCs and NOCs.
Figure 18 shows equivalent potential temperature
and frontogenesis associated with deformation fields
at 950 hPa. The OCs display meridionally elongated
pressure fields (Fig. 3) and cold fronts (Fig. 18a),
while those of NOCs display less meridional elon-
gation (Fig. 18b). In contrast, there is no notable
difference between the magnitudes of the de-
formation fields for warm fronts associated with OCs
and NOCs. These features are consistent with pre-
vious studies on the relationship between the struc-
ture of ECs and that of the jet stream (Wernli et al.
1998), which is notably different for OCs and NOCs
in this study. For OCs, the jet stream displays a
meridionally sharper structure with stronger merid-
ional anticyclonic shear on the southern side of the
jet streak. In contrast, in NOCs the jet stream dis-
plays significantly weaker meridional anticyclonic
shear, suggesting that the meridionally elongated
structure and stronger southerly winds in OCs are
caused by the stronger anticyclonic shear of the
jet stream.
There is a possibility that, in the case of stronger
cyclonic shear, a warm front oriented from north-
west to southeast plays an important role in the
formation of severe convection: near the front there
is strong vertical shear and warm moist air is
advected. In addition, low-level convergence at the
front contributes to the initiation of convection. In
the present study, however, the cases associated with
the warm front are not studied in detail since we
focus on the tornadoes in the warm sector (see
appendix C).
FIG. 13. As in Fig. 4, but for (a),(b) vertical pressure velocity at 850 hPa (color shading; Pa s21) at KT and (c) the difference in the vertical
pressure velocity between OCs and NOCs (contours).
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Large-scale circulation may also influence the occur-
rence of tornado outbreaks. The south-southwesterly
wind along the western edge of the high pressure system
advects warm moist air northward, forming an unstable
layer in the troposphere.MostOCspropagate through the
southern central plains, while the tracks of NOCs are
more dispersed (Fig. 19). This difference suggests that
most OCs propagate through the region where large
amounts of low-level water vapor intrude from the south
along the high pressure system, resulting in larger CAPE.
5. Summary
The structures and environmental fields of ECs that
affect tornado outbreaks were identified by a composite
analysis of JRA-55 data. The ECs accompanied by 15 or
FIG. 14. Environmental fields of the cyclones: (left) OCs and (right) NOCs. (a),(b) Southerly winds at 900 hPa
(color shading; m s21) and (c),(d) specific humidity at 950 hPa (color shading; kg kg21). Contours drawn at 10-m
intervals indicate the environmental geopotential height (m) at 900 hPa.
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more tornadoes were defined as OCs, and those ac-
companied by 5 or fewer as NOCs. Between 1995 and
2012, 55 OCs and 41 NOCs of similar strengths were
selected from ECs in April and May in the United
States. We also examined the relationship between EC
structure and convective parameters.
The structures of OCs and NOCs exhibit clear dif-
ferences: OCs display meridionally elongated struc-
tures, while the NOCs are less meridionally elongated.
Low-level southerly winds are stronger in OCs, facili-
tating the transport of warm moist air into the warm
sector of the EC. Indeed, the highest low-level specific
humidity is found in the south-southeast region of
the OC.
We found significant differences in convective pa-
rameters between OCs and NOCs. In OCs, values of
SREH and CAPE are both larger and significant over a
wider portion of the warm sector than in NOCs. It is
suggested that this larger CAPE associated with OCs is
due to larger amounts of low-level water vapor, while
the larger SREH is due to stronger low-level southerly
winds. Accordingly, EHI, which is a combination of
SREH and CAPE, is also larger for OCs. It is noted that
daytime solar radiative heating may also contribute to
increase the CAPE for both OCs and NOCs.
Upper-level disturbances may also influence the
occurrence of a tornado outbreak by inducing upward
motion in front of the disturbance. The UPV for OCs
is located closer to the center of the surface cyclone
than in NOCs. As a result, the upward motion for OCs
is induced over a wider area in the east-southeast
sector and is likely to play an important role in pro-
viding an environment favorable for severe convec-
tive storms.
FIG. 15. As in Fig. 9, but for environmental wind speed at 250 hPa (color shading; m s21).
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We also showed that the large-scale circulation as-
sociated with a high pressure system over the Atlantic
Ocean affects the occurrence of tornado outbreaks.
The large-scale meridional flow transports water va-
por northward, creating unstable layers in the tro-
posphere. This large-scale system has a time scale
longer than that of a typical EC life cycle. Thus, a
tornado outbreak is associated not only with EC
structure but also with large-scale circulation such as
the meridional flows along the edge of the high
pressure system.
A piecewise PV inversion suggested that low-level
southerly flows are associated with EC structures and
accompanying fronts. The sum of MPV and LBT is
the main contributor to stronger southerly winds for
OCs but not for NOCs. The MPV anomalies display
meridionally elongated structures in OCs, consistent
with the meridionally elongated low-level southerly
winds, while UPV anomalies contribute little to dif-
ferences in low-level southerly winds between OCs
and NOCs.
The structures of OCs and NOCs appear to be
related to the strength of anticyclonic shear in the
jet stream. Since the anticyclonic shear in the jet
stream is stronger for OCs than for NOCs, the
structure of the OCs is meridionally more elon-
gated, resulting in stronger southerly wind in the
southeastern quadrant. To assess the relationship
between the EC structure and the meridional shear
of the jet stream in more detail, an idealized nu-
merical experiment is under way, and will be re-
ported elsewhere.
FIG. 16. Total southerly winds at 900 hPa (color shading; m2 s22) and total geopotential height anomaly (con-
tours; m) at 900 hPa for (a) OCs and (b) NOCs. Southerly winds (color shading; m s21) and geopotential height
anomaly (contours; m) for JRA-55 at 900 hPa for (c) OCs and (d) for NOCs.
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The present study clarifies important relationships
between the structure of ECs that cause tornado out-
breaks in April and May in the United States and the
environmental fields. Since environmental factors
such as the upper-level jet stream, low-level water
vapor, and temperature fields change seasonally, the
structural characteristics of ECs that cause outbreaks
may also have seasonal variability. A study to examine
the seasonal variability of the structural and environ-
mental characteristics of ECs that cause a tornado
outbreak is being undertaken and will also be reported
elsewhere.
FIG. 17. Southerly winds (m s21) induced by (a) the sum of UPV, MPV, and LBT; (d) by UPV; and (g) the sum of MPV and LBT for
OCs. (b),(e),(h) As in (a),(d),(g), but for NOCs. (c),(f),(i) The p values for difference between OCs and NOCs. Contours indicate
(left),(middle) geopotential height and (right) differences in the southerly winds between OCs and NOCs.
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Acknowledgments. We thank Dr. K. I. Hodges of
Analysis of Weather Systems in Climate Models and
Numerical Weather Prediction, University of Read-
ing for providing his tracking tools, and Prof. C. Davis
of MMM, the Earth and Sun Systems Laboratory at
NCAR for providing the program for the piecewise
PV inversion technique. We are also grateful to
Dr. W. Yanase of the Atmosphere and Ocean Re-
search Institute, the University of Tokyo and Dr.
A. Kuwano-Yoshida of the Earth Simulator Center,
Japan Agency for Marine–Earth Science and Tech-
nology for their useful comments. We also would like
to thank Dr. Y. P. Richardson and three anonymous
reviewers for helpful comments, which have greatly
improved the manuscript. This study was supported
in part by JSPS KAKENHI Grant 24244074, and
Field 3, Strategic Programs for Innovative Research,
Ministry of Education, Culture, Sports, Science and
Technology.
APPENDIX A
Sensitivity of the Results to the SLP and VorticityCriteria
As is evident from Fig. 3, the composite central SLP
for OCs is lower than that for NOCs. In this appen-
dix, we first examine the impact of the difference in
FIG. 18. Deformation fields [color shading; K (100 km)21 s21] and equivalent potential temperature (contours; K) at
950 hPa for (a) OCs and (b) NOCs at KT 2 12.
FIG. 19. The EC tracks for (a) OCs and (b) NOCs. Red and blue circles indicate the distributions of ECs at KT.
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the composite central SLP between OCs and NOCs
on the overall results in the present study by using the
following weighted-average technique. The OCs and
NOCs are classified into 5 bins based on their central
SLP (980–985, 985–990, 990–995, 995–1000, and
1000–1005 hPa; see Fig. A1). The numbers of OCs
and NOCs contained in bin i (i 5 1, 2, . . . , 5) are
denoted by nOi and nNi, respectively. If a three-
dimensional field of a particular physical variable
around an OC or NOC is denoted by qOij(x, y, z) or
qNik(x, y, z), the composite field of q for OC or NOC
is calculated by
FIG. A1. Scatter diagram between 900-hPa vorticity and sea level pressure (SLP) for the EC
centers. Red diamonds indicate OCs and blue squares NOCs.
FIG. A2. SREH (color shading; m2 s22) and geopotential height (contours; m) for (a) OCs and (b) NOCs obtained by
composite analysis using a weighted-average technique.
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qO(x, y, z)5
�4
i51
nOi1 n
Ni
2nOi
�nOi
j51
qOij(x, y, z)
�4
i51
nOi1 n
Ni
2nOi
,
qN(x, y, z)5
�4
i51
nOi1 n
Ni
2nNi
�nNi
k51
qNik
(x, y, z)
�4
i51
nOi1 n
Ni
2
,
where j5 1, 2, . . . , nOi and k5 1, 2, . . . , nNi.
The composite fields of the SREH obtained by this
weighted-average technique for OCs and NOCs
(Fig. A2) turn out to be quite similar to those from the
simple average technique used in section 3 (Fig. 4). Note
that the composite central SLPs of OCs and NOCs in
Fig. A2 are nearly equal. Therefore, the differences in
the SLP do not explain the differences in environmental
parameters between OCs and NOCs.
Figure A1 also indicates that OCs tend to have stronger
vorticity at 900hPa, especially for lower SLP (less than
995hPa), which seems to be one of the statistically impor-
tant differences in the cyclone structure between OCs and
FIG. A3. (top) SREH (color shading; m2 s22) for (a) OCs and (b) NOCs with central SLP between 995 and 1005 hPa and vertical
vorticity exceeding 53 1025 s21, and (c) p values for the difference between OCs and NOCs (color shading). Contours in (a),(b) indicate
geopotential height (m), and those in (c) indicate the difference in SREH between OCs and NOCs. (bottom) As in (top), but for CAPE
(color shading; m2 s22).
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NOCs. We performed a t test to examine whether or not
the averages of vorticity and SLP are different for OCs and
NOCs. The results of the t test show that differences in
vorticity and SLP between OCs and NOCs are statistically
significant, exceeding the 95% confidence level. Thus, the
vorticity and SLP anomalies for OCs are significantly
stronger and deeper, respectively. The difference in the
distributions ofOCs andNOCs in terms of vertical vorticity
and central SLP motivates an examination of the differ-
ences in cyclone structure between OCs and NOCs that
FIG. C1. (a) SREH (color shading; m2 s22) and (b) CAPE (color shading; m2 s22) for composite ECs that are
accompanied by 15 or more tornadoes only when tornadoes near frontal zones are included. Contours indicate
geopotential height (m). Blue dots indicate the distribution of tornadoes.
FIG. B1. (a) SREH (color shading; m2 s22) and (b) CAPE (color shading; m2 s22) for composite ECs associated with
only F/EF2 or stronger tornadoes. Contours indicate geopotential height (m).
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have similar intensity in both vorticity and central SLP. For
this purpose, we pick out the OCs and NOCs that have
central SLP between 995 and 1005hPa and vorticity ex-
ceeding 5 3 1025 s21, and perform a composite analysis.
The results of the composite analysis (Fig.A3) are basically
similar to Figs. 4, 5, andA2: there are significant differences
in SREH and CAPE between OCs and NOCs. For OCs,
the SREH is larger and the area in which its value exceeds
100m2s22 is wider in the southeast region of the cyclone
center (Figs. A3a,b), and the CAPE is also larger in the
south-southeast region of the cyclone center (Figs. A3d,e).
Areas in which differences of SREH and CAPE between
OCs and NOCs are statistically significant also exist
(Figs. A3c,f), although they are somewhat narrower than in
Figs. 4 and 5.
We also performed a composite analysis in which only
strong ECs (central SLP is lower than 1000hPa) are
considered. The results indicated that the differences
betweenOCs and NOCs are similar to those described in
section 3 (not shown). Furthermore, another composite
analysis without the upper SLP threshold of 1005hPawas
performed. The results show that the differences in the
environments between OCs and NOCs increase (not
shown), because NOCs include a larger number of
weaker ECs with central SLP of higher than 1005hPa. In
conclusion, the differences in the characteristics of OCs
and NOCs in the present composite analysis are robust
and depend onlyweakly on the choice of the thresholds of
central SLP and vertical vorticity.
APPENDIX B
Composite for ECs Associated with the Strong(F/EF2) Tornadoes
We selected ECs that are accompanied by F/EF2 or
stronger tornadoes (hereafter, F2C) from April to May be-
tween 1995 and 2012 and made a similar composite analysis
for their structure and environment.Overall features such as
the spatial structure of ECs and distributions of SREH and
CAPE (Fig. B1) are similar to those for OCs (Figs. 4 and 5).
The maxima of SREH and CAPE for F2C are ;200 and
800m2s22, respectively. It is rather unexpected that these
values are slightly smaller than those for OCs.
APPENDIX C
Characteristics of ECs that Cause a TornadoOutbreak Including Tornadoes near Frontal Zones
In the present study, there are 14 ECs that are ac-
companied by 15 or more tornadoes but are not
categorized as OCs, because the number of the torna-
does drops below 15 when tornadoes that occur near the
frontal zones are excluded. The tornado distributions
with respect to the cyclone center, the composite cy-
clone structures, and the composite fields of environ-
mental parameters for these 14 ECs are shown in
Figs. C1a and C1b. The contours of geopotential height
show that a low pressure trough associated with a warm
frontal zone extends eastward from the cyclone center,
and most of the tornadoes associated with this category
of ECs are located near the warm frontal zone or the
warm sector. Note that few tornadoes are located near
the cold frontal zone in these 14 cases, indicating that,
for ECs that spawned more than 15 tornadoes, most
tornadoes associated with the cold frontal zone occur
when a sufficient number also occur in the warm sector
to meet OC criterion.
Values of SREH exceeding 150m2 s22 are found to
the east of the cyclone center, while the SREH in the
warm sector is smaller than for OCs (Fig. 4). Values of
CAPE exceeding 600m2 s22 intrude farther north-
ward than for OCs (Fig. 5). Although these results
may indicate interesting features of ECs that cause
tornadoes near the warm frontal zone, the sample size
(14) may be somewhat small to generalize the results
statistically, so their detailed study will be left for
the future.
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