Journal
April 2019of the Meteorological Society of Japan,
H. FUDEYASU
97(2), 439−451,
and R.
2019.
YOSHIDA
doi:10.2151/jmsj.2019-025
Special Edition on Tropical Cyclones in 2015 − 2016

439

Statistical Analysis of the Relationshipbetween Upper Tropospheric Cold Lows and Tropical Cyclone Genesis
over the Western North Pacific
Hironori FUDEYASU
Yokohama National University, Yokohama, Japan

andRyuji YOSHIDACIRES, University of Colorado Boulder, Colorado, USA
NOAA Earth System Research Laboratory, Colorado, USA
RIKEN Center for Computational Science, Kobe, Japan
Research Center for Urban Safety and Security, Kobe University, Kobe, Japan(Manuscript received 23 March 2018, in final form 24 December 2018)

Abstract
This study examined the statistical characteristics of tropical cyclones (TCs) for which the cyclogenesis (TCG)
process was modulated by upper tropospheric cold lows (UCLs) over the western North Pacific during the 38
years from 1979 to 2016. Among the 965 TCs, 90 TCs (9 %, 2.4 per year) were defined as having TCG influenced by UCLs in the northwest quadrant of the TC region (UL-TCs). Most UL-TCs occurred in the summer,with large variability in the annual occurrence rate of UL-TCs during June to October, ranging from 0 to approximately 30 %. The annual variation was related to the activity of the Tibetan high and the summer temperature
anomaly over Japan. The extremely hot summer of 2016 was partly enhanced by the intense Tibetan high, when
4 UL-TCs also occurred. The average location of UL-TCs at the time of TCG and tropical storm formation (TSF)
was significantly farther to the north than the average location of TCs not formed under the influence of UCL(N-UL-TCs). Many UL-TCs occurred in lower tropospheric environments associated with the shear line or confluence regions. The UL-TCs tended to move northward, and the occurrence rate of UL-TCs that made landfall
in Japan was approximately double that of other countries. The atmospheric environmental parameters around
UL-TCs at the time of TCG were more favorable for the development of TCs than those around N-UL-TCs. In
contrast, the atmospheric and oceanic environmental parameters around UL-TCs at the time of TSF were lessfavorable for the development of TCs, such that UL-TCs tended to remain at weak intensity.
Keywords

tropical cyclone; genesis process; upper tropospheric cold lows

Citation Fudeyasu, H., and R. Yoshida, 2019: Statistical analysis of the relationship between upper troposphericcold lows and tropical cyclone genesis over the western North Pacific. J. Meteor. Soc. Japan, 97, 439–451, doi:
10.2151/jmsj.2019-025.
Corresponding author: Hironori Fudeyasu, Faculty of Education, Yokohama National University, 79-2 Tokiwadai,
Hodogaya-ku, Yokohama, 240-8501, Japan
E-mail: fude@ynu.ac.jp
J-stage Advance Published Date: 13 December 2018
©The Author(s) 2019. This is an open access article published by the Meteorological Society of Japan undera Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0).

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1.

Journal of the Meteorological Society of Japan

Introduction

Tropical cyclones (TCs) form under the large-scale
environmental conditions favorable for cyclogenesis(TCG) (e.g., Gray 1968, 1998). The lower tropospheric flow patterns that contribute to TCG over thewestern North Pacific (WNP) can be grouped into fivecategories (Ritchie and Holland 1999): shear line (SL),confluence region (CR), monsoon gyre (GY), easterly
wave (EW), and the Rossby wave energy dispersion
from a preexisting TC (PTC).
The large-scale environmental conditions in theupper troposphere also strongly affect TCG over the
WNP (e.g., Briegel and Frank 1997). Most previous
studies of TCG associated with upper troposphericforcing focused on tropical upper tropospheric troughs(TUTTs). Colon and Nightingale (1963) have reported
that upper tropospheric southwesterlies on the easternand southern sides of a TUTT provide favorable environmental conditions for the development of low-level
disturbances. Sadler (1976) showed that a TUTT has
three possible mechanisms for the development of
TCs over the WNP: (1) the accompanying subequatorial ridge on the south side of a TUTT can decrease
the vertical shear; (2) divergent flow on the southeast
side of the cyclonic circulation (TUTT cell) in a TUTT
can increase the ventilation aloft to aid developmentof TCs; and (3) channels to large-scale westerlies
on the south side of a TUTT may be established for
efficient outflow of released heat. Many previous statistical analyses (Briegel and Frank 1997; Ritchie andHolland 1999; Fu et al. 2007; Chen et al. 2008; Wang
and Wu 2016) demonstrated that the climatologicalfeatures of the environmental conditions favorable for
TCG coexisted with a TUTT over the WNP. Briegel
and Frank (1997) analyzed 41 TCG cases in 1988 and1989 and found that 25 TCs formed when a TUTT
was present, and 16 TCs formed when a TUTT was
located in the northwest region, within 2,500 km of
the TCG location prior to the time of TCG.
Other previous studies (Japan Meteorological
Agency 1990; McTaggart-Cowan et al. 2013) haveproposed upper tropospheric cold lows (UCLs) as
another possible upper tropospheric factor contributing to TCG over the WNP. UCLs are generally defined
as a depression with a cold air center in the upper
troposphere and are related to a cutoff low in either
mid-latitude westerlies (Molinari and Vollaro 1989;
Postel and Hitchman 1999; Sakamoto and Takahashi2005) or a TUTT cell (Sadler 1976; McTaggart-Cowan
et al. 2013). A cutoff low is recognized as a meso-αscale-isolated low-pressure system that moves without

Vol. 97, No. 2

merging with an upper tropospheric trough. A TUTT
cell is a deep upper tropospheric trough that forms a
low-pressure center. Here, following previous studies,
UCLs refer to both cutoff lows and TUTT cells.
Japan Meteorological Agency (1990) reported thattropical depressions were well developed on the southand southeast sides of UCLs, where the convective
instability was enhanced by the UCLs. The upper
tropospheric divergence and weak vertical shear in
the convective instability regions on the southeast
side of UCLs appear to facilitate lower level liftingand convective development in the core regions of theTCs (Shi et al. 1990; Rappin et al. 2011; Sears andVelden 2014). It has been hypothesized that the upper
tropospheric divergence and ascending motion associated with a UCL may help induce low-level mass andmoisture convergence, which facilitate the generationof deep convection and the bottom-up growth mechanism of TCG (Kelly and Mock 1982; Hendricks et al.
2004; Montgomery et al. 2006). In a previous study
by Nonaka (2005), a statistical analysis of 118 TCG
events over the WNP from 1999 to 2003 found thatTCG cases were associated with UCLs.
Another previous study by Wei et al. (2016) investigated changes in TC track and intensity due to
UCLs over the WNP during the 13 years from 2000
to 2012. The results of this study showed that 73 % of
TCs coexisted with UCLs that included an upper tropospheric low originating from TUTT cells and cutoff
lows in the mid-latitudes, and 44 % of the TCs hadinteracted with UCLs during their lifetimes. A greaternumber of UCLs appeared in the western sectors of
TC circulations within a 10° radius. A previous study
presented the global climatology of baroclinically
influenced TC developments using an objective classification strategy. This study showed that a troughinduced environment during TC-development, whichis generally associated with a TUTT cell, provided a
relatively efficient pathway for TCG over the WNP
and North Atlantic basins.
Although Wei et al. (2016) have revealed the statistical characteristics of TCs interacting with UCLs
throughout the TC lifecycle, few have examined thecharacteristics of TCs where TCG was influenced by
UCLs in the northwest quadrant of the TCG region(hereinafter referred to as UL-TCs). In addition, theanalysis period of the statistical study described above
was only 4 years, and annual changes in UL-TCs were
not investigated. Thus, the objective of this study was
to examine the annual and seasonal changes in UL-TCs
over the WNP over a period of 38 years (1979 – 2016).
In particular, during the summers of 2015 and 2016,

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there were several differences in the upper tropospheric environmental conditions that may have contributed
to TCG. This study also investigated the statisticalcharacteristics of UL-TCs over the WNP and theenvironmental conditions around them, including both
atmospheric and oceanic physical parameters.
2.

Data and definition

This study examined 965 TCs occurring over
the WNP during the 38 years from 1979 to 2016
with tropical storm (TS) intensity or greater, i.e., amaximum sustained wind speed of at least 35 knots,
as derived from the best-track data archives of theRegional Specialized Meteorological Center. Since
the focus of this study was the early phase of thecyclogenesis process, the location of the first besttrack record for each TC was taken as the location ofTCG. The location of TCG for each TC was generally
identified from the tropical depression or low before
TS intensity was reached. Following Yoshida andIshikawa (2013), the time of TCG was defined as 69
hours prior to the time of the first best-track record foreach TC.
As in several previous studies (e.g., Wei et al.
2016), UCLs were detected as strong cyclonic circulations at 200-hPa height with a cold anomaly at 300hPa height using global reanalysis data, namely theJapanese 55-year reanalysis project dataset (JRA55;
Kobayashi et al. 2015) (details are available online at
http://jra.kishou.go.jp/JRA-55/index_en.html). The
JRA55 dataset contains 6-hourly data with a horizontal resolution of 1.25° for both longitude and latitude,
and data are available from 1958 to the present day. To
detect UCLs, we computed the area-averaged 200-hPa
height horizontal wind and 300-hPa height temperature fields derived from JRA55 at the time of TCG.
The target area in our study was the northwest quadrant within 1,500 km from the location of TCG at thetime of TCG. The following quantitative definitions
were used to detect UCLs that may have contributed
to TCG:(1) The UCL in the target area at the location closest
to TCG had a relative vorticity greater than 5.0 ×
10−5 s−1 at 200 hPa.
(2) The divergence and north–south component of thehorizontal wind above TCG (200 hPa) were positive.
(3) The temperature of the UCL at 300 hPa was lower
than the average of the temperatures 3.75° eastand 5.0° west of the UCL.
Although some previous studies (e.g., Wernli andSprenger 2007; Wei et al. 2016) defined UCLs by441

cutoff lows only, some of the UCLs detected by ourdefinitions were not cutoff lows. Upper troposphericlows that did not have the typical structure of a UCL
were removed by this subjective check. Figure 1
shows the general features of UCLs detected by theabove definitions. UCLs decreased the vertical shear
in the north–south direction and enhanced the upper
tropospheric divergence on their southeastern side,which provides favorable environmental conditions
for TCG.
This study compared the statistical characteristicsof the environmental conditions around UL-TCs withthose of TCs that had formed without UCLs (N-ULTCs). Atmospheric physical parameters were calculated from the JRA55, and oceanic physical parameters
were derived from the three-dimensional variationalbased parent domain output from the 30-year fourdimensional variational ocean reanalysis dataset for
the WNP (FORA-WNP30; Usui et al. 2017) (details
are available online at http://synthesis.jamstec.go.jp/
FORA). It should be noted that the present study did
not use the nested domain output of the FORA-WNP30
dataset with a horizontal resolution of 0.1° because thenested domain was limited in its coverage of the study
area from 1982 to 2015. To compare the features ofthe environmental conditions around UL-TCs, we also
investigated the environmental parameters around TCs
whose TCG was influenced by UCLs in the northeast
quadrant of the TCG region (hereafter referred to as
NE-UL or NE-UL-TC), using the northeast quadrant
within 1,500 km of the location of TCG and the above
definitions, namely (1) and (3).
Using the JRA55 and FORA-WNP30, this study
evaluated the following physical parameters: upper
tropospheric divergence, lower tropospheric relative
vorticity, magnitude of vertical shear, lower- andmiddle-tropospheric relative humidity, maximum potential intensity (MPI), convective available potential
energy (CAPE) derived from the MPI equation, seasurface temperature (SST), tropical cyclone heatpotential (TCHP), and depth of the 26°C oceanic
isotherm. The relative humidity was calculated as theaverage over the height range of 850 – 700 hPa (RHLO)
and 500 – 300 hPa (RHHI). The magnitude of the vertical shear was defined as the difference in horizontal
wind between heights of 200 and 850 hPa (SHRD),
and between heights of 500 and 850 hPa (SHRS). The
850-hPa relative vorticity (VOR850) and the 200-hPa
divergence (DIV200) were also calculated. This study
used two area averages: the average for the circulararea with an 800-km radius from the location of TCG,
and an annulus of 200 – 800 km from the TC center.

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Fig. 1. Composites of 200-hPa horizontal winds (m s−1): (a) vertical shear between 200 and 850 hPa (m s−1, contour)
and vertical shear in the north–south direction (m s−1, shading), and (b) 200-hPa divergence (× 10−5 s−1, contour)
and divergence in the north–south direction (× 10−5 s−1, shading). The composite was made with respect to 87
UCLs detected over the 38-year analysis period between June and October. The contour intervals are (a) 2 m s−1and (b) 0.5 × 10−5 s−1, respectively. The regions with (a) a vertical shear less than 4 m s−1 and (b) a divergence morethan 0.8 × 10−5 s−1 are heavily shaded, while the regions with (a) a vertical shear of 4 – 8 m s−1 and (b) a divergenceof 0.0 – 0.8 × 10−5 s−1 are lightly shaded. The locations of UL-TCs relative to UCLs at the time of TCG are indicated by closed circles.

The former area average was applied to evaluate theatmospheric conditions during TCG, while the latterarea average was applied after TS formation (TSF) toremove the TC structure, as in several previous studies(e.g., Knaff et al. 2005). Beneath the TC center, theoceanic physical parameters, SST, TCHP, and depth of
the 26°C oceanic isotherm were evaluated to stratify
distinct features. TCHP was estimated by summing
the ocean heat content from the surface to the depth of
the 26°C isotherm (Leipper and Volgenau 1972; Wada
2015).
This study examined the differences in MPI and
CAPE between UL-TCs and N-UL-TCs using theatmospheric conditions derived from the JRA55 andthe SST derived from the FORA-WNP30. The revised
MPI equation presented in Bister and Emanuel (1998)
was used in this study. To investigate atmosphericstability, this study used E-CAPE, a version of CAPE
calculated from the simple equation presented in MPI
theory, which is an approximate form.
This study also examined the relationships between
UCLs and five relevant lower tropospheric flow
patterns to TCG as identified by Ritchie and Holland(1999). To categorize the five TCG flow factors, weused the objective method developed by Yoshida andIshikawa (2013) along with best-track data and the

JRA55. Details of the objective algorithm are provided in Yoshida and Ishikawa (2013) and Fude­yasu andYoshida (2018).
3.

Results

3.1 Annual and seasonal variation in UL-TC
Among the 965 TCs, 90 UL-TCs and 875 N-ULTCs were detected using our definitions. The occurrence rate of UL-TCs (9 %) was very small, similar to
the results of Nonaka (2005) and McTaggart-Cowan(2013). Nonaka (2005) detected 4 TCs associated witha UCL, out of a total of 118 TCs (occurrence rate of
3 %), while McTaggart-Cowan et al. (2013) reported
an average of 2.3 trough-induced TC developments
per year over the WNP.
Among all the TCs, 672 TCs did not meet definition(1). In other words, a positive relative vorticity within
1,500 km of the northwest quadrant at the time of
TCG was seen in only 293 TCs. Of these 293 TCs,
179 TCs were excluded since neither the southerlies
nor the divergence at a height of 200 hPa occurred
above the TCG location. Of the remaining 114 TCs,
24 did not exhibit a distinct cold core in the upper
troposphere.
Figure 2 shows the total number of UL-TCs foreach month of the analysis period, and the rates ofApril 2019

H. FUDEYASU and R. YOSHIDA

Fig. 2. Monthly total number of UL-TCs over the
38-year analysis period. The percentages are therates of UL-TC occurrence divided by the monthly total of TCs.

UL-TC occurrence divided by the monthly total ofTCs. There was large seasonal variability in the num­
ber of UL-TCs; most occurred in the summer, while
only a few occurred in the winter. It should be notedthat no UL-TCs occurred from January to May. The
seasonality of UL-TCs is consistent with the results of
Nonaka (2005) and McTaggart-Cowan (2013).
Figure 3 shows the annual number of UL-TCs andtheir rate of occurrence over the WNP from 1979 to2016. The average was 2.4 per year, with 1992, 1999,
and 2001 experiencing 5 UL-TCs each, which was
the highest number among all the years, followed
by 1979, 1982, 1983, 1984, 1988, 1990, 2004, 2009,2012, and 2016 with 4 UL-TCs each. In contrast, no
UL-TCs occurred in 1995, 1997, 2003, and 2015. As
shown in Fig. 3b, the occurrence rate of UL-TCs, i.e.,
the number of UL-TCs divided by the number of TCs

443

from June to October, varied widely from 0 to approximately 30 %.
This study examined changes in the annual variability of UL-TCs related to the El Niño Southern
Oscillation (ENSO), based on the JMA definition (details are available online at http://www.data.jma.go.jp/
gmd/cpd/db/elnino/learning/faq/elnino_table.html).
The average annual number (2.6) of UL-TCs and theiroccurrence rate (12 %) in La Niña years were almost
the same as in El Niño years (2.2, 9 %) and neutral
years (i.e., when neither phenomenon occurred) (2.4,
9 %). Therefore, the annual variability in UL-TCs was
not related to the ENSO.
It is interesting to note that the annual variability
in UL-TCs was related to the temperature anomaly
during summer over Japan, resulting from the midtropospheric geopotential height in the mid-latitudes.
Figure 4a shows a scatter plot of the number of
UL-TCs and the temperature anomaly over Japanaveraged from July to September, as provided by the
JMA. The number of UL-TCs tended to increase withthe temperature anomaly, where regression analysis
yielded a correlation coefficient of 0.32. Figure 4b
shows the composite of the 500-hPa geopotential
height averaged for the 8 years in which the July to
September Japan temperature anomaly was more than
0.7ºC (1994, 1999, 2000, 2004, 2010, 2012, 2013,
and 2016) as well as the difference in the 500-hPa
geopotential height between these 8 years and otheryears. The positive regions of this difference extended
from the Asian continent to the sea east of Japan. A
possible reason for the correlation between the annual
variability in UL-TCs and the temperature anomaly
over Japan is that the mid-tropospheric geopotential
height over the northwestern part of the WNP is partly

Fig. 3. Annual variation in (a) UL-TC occurrence over the 38-year analysis period and (b) occurrence rate of ULTCs normalized by the total number of TCs from June to October.

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Fig. 4. (a) Scatter plot of the number of UL-TCs against the temperature anomaly over Japan from July to September. (b) The 500-hPa geopotential height (contour) averaged for the 8 years during which the temperature anomaly
over Japan was more than 0.7°C (1994, 1999, 2000, 2004, 2010, 2012, 2013, and 2016), and the difference in the
500-hPa geopotential height between these 8 years and the other years (shading). The contour interval is 50 m,
while regions greater than 0 m are shaded.

enhanced by an upper tropospheric high, namely the
Tibetan high. The UCLs frequently moved southwestward from the mid-latitudes when the anticyclonic
circulation associated with the Tibetan high extended
eastward and acted as a steering flow, leading tohotter summers over Japan. In fact, the extremely hotsummer of 2016 was partly enhanced by the intense
Tibetan high, when 4 UL-TCs also occurred. In mostof the UL-TC cases, a UCL originating from cutoff
lows in the mid-latitudes moved around the intense
Tibetan high.
3.2 Statistical characteristics of UL-TCs
As discussed in the previous section, there was large
seasonal variability in the occurrence of UL-TCs. To
compare the statistical characteristics of UL-TCs and
N-UL-TCs, this study considered the 743 TCs in thesummer and autumn seasons (June to October), whenboth UL-TCs and N-UL-TCs can occur. A total of 87
UL-TCs (12 %) and 656 N-UL-TCs were detectedduring this analysis period.
Table 1 shows the average locations (namely, theclimatological mean locations) of UL-TCs, N-ULTCs, and all TCs between June and October over the
38-year analysis period. Differences in the structureand environmental parameters between UL-TCs and
N-UL-TCs were assessed using a t-test. The ratios inTable 1 were obtained by normalizing each variable,
by subtracting the sample mean and dividing by thestandard deviation. The average location of TCG
was farther to the north for UL-TCs compared with

N-UL-TCs, and this difference was significant at the
5 % level. Figure 5 shows the locations of UL-TCs
and of the UCLs influencing TCG. The locations of
the UCLs were concentrated at latitudes near 20°N.
The UL-TCs appeared in the zonal area farther to thenorth than the average location of N-UL-TCs and theclimatological mean. Figure 5b shows the geographical location of UCLs with respect to the location of
TCG; the distance between UCLs and TCG locations
ranged from 133 to 1,450 km, with an average of 883
km, and all were located in the northwest quadrant of
the TCG region.
As the location of UL-TCs at the time of TCG wasusually farther north than the climatological mean,
the location of UL-TCs at TSF and maturity (maturetime) tended to be in the northern part of the WNP(Table 1). The time of TSF was defined as that whenthe minimum TS intensity threshold (35 knots) was
reached, while the mature time was defined as the time
when the maximum wind speed occurred. It should be
noted that the average duration between TCG and TSF
was significantly shorter for UL-TCs than for N-ULTCs. Although the time of TCG, i.e., the time of thefirst best-track record for each TC, depends on thelimitations of the dataset (temporal resolution and observational capabilities), the statistical characteristics
of UL-TCs that have a shorter duration between TCG
and TS compared with the climatological mean areimportant for the prediction of TCs.
This study also calculated two other durations, i.e.,the duration of the TC developmental stage (from theApril 2019

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H. FUDEYASU and R. YOSHIDA

Table 1. Statistical summary of the characteristics of UL-TCs and N-UL-TCs, and the climatological mean
for all TCs (ALL), between June and October over the 38-year analysis period. Areas for which a t-test
indicated statistical differences with a 5 % level of significance (10 % for maximum wind) are presented inbold, italic font. The percentages correspond to normalized variables, obtained by subtracting the sample
mean and dividing by the sample standard deviation.
UL-TC

N-UL-TC

ALL

Time of TCG

NumberAve. lon. (°E)
Ave. lat. (°N)

87140.5 ( 9 %)16.8 ( 28 %)656
138.9 (−1 %)15.2 (−4 %)

743
139.2
15.4

Time of TSF

Ave. lon. (°E)
Ave. lat. (°N)
Duration from TCG (day)137.8 ( 15 %)19.2 ( 30 %)
5.4 (−20 %)135.3 (−2 %)17.4 (−4 %)5.8 ( 3 %)

135.6
17.7
5.8

One day after time of TSF

Δ lon. (°)Δ lat. (°)−1.7 ( 13 %)
2.3 ( 25 %)−2.2 (−2 %)1.8 (−3 %)

−2.2
1.9

Mature time

Ave. lon. (°E)
Ave. lat. (°N)
Duration from TSF (day)Maximum wind (knot)
Minimum pressure (hPa)131.3 ( 14 %)25.3 ( 22 %)3.0 ( −7 %)66.2 (−13 %)967.6 ( 16 %)128.9 (−2 %)23.7 (−3 %)3.2 ( 1 %)69.7 ( 2 %)962.7 (−2 %)

129.2
23.9
3.2
69.3
963.3

Decaying time

Duration from TSF (day)

5.2 ( −6 %)5.4 ( 1 %)5.4

Fig. 5. Locations of (a) UL-TCs (closed circles) and UCLs (crosses), and (b) UCLs relative to UL-TCs at the timeof TCG, over the 38-year analysis period.

TSF time to the mature time) and the lifetime of the
TC (from the TSF time to the decay time) (Table 1).
The decay time was defined as the point at which thesystem fell below the TS intensity threshold. Neitherof these durations differed significantly between ULTCs and N-UL-TCs.
The statistical characteristics of the UL-TC movements were also investigated. The direction of movement at the time of TSF was defined in terms of thechange in location of the TC from the time of TSF
to the following day. The UL-TCs tended to movefarther north than the N-UL-TCs (Table 1). Since it ispossible that environmental flows influenced not only
TCG but also the direction of movement of TCs, theupper-level southerlies associated with UCLs (Fig. 1)
may have enhanced the northward movement of the
UL-TCs after TSF.
Along the locations of each TC track, the numberof landfalls for different countries in the WNP (Japan,
China, Vietnam, and the Philippines) was determined.
In this study, we defined a TC landfall when a TC
track intersected with a coastline (Fudeyasu et al.

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Journal of the Meteorological Society of Japan

Fig. 6. Number of TCs that made landfall in Japan,
China, the Philippines, and Vietnam between Juneand October over the 38-year analysis period.
The filled bars indicate UL-TCs, while the percentages correspond to the UL-TC occurrence
rates divided by the total number of TCs thatmade landfall in each country.

2014). Over the 38-year study period, a total of 107
TCs resulted in landfalls in Japan between June andOctober (Fig. 6). Among these, UL-TCs were the mostfrequent, with 21 landfalls and an occurrence rate(i.e., the number of UL-TC landfalls divided by the
total number of TC landfalls) of 20 %. The occurrence
rate of UL-TC landfalls in Japan was approximately
double that of other countries.
At the mature time, there were significant differences in average intensity among the different cyclone
types (Table 1): the minimum central pressure of
the UL-TCs was significantly higher than that of the
N-UL-TCs at the 5 % significance level, while themaximum wind speed of the UL-TCs was significantly lower than that of the N-UL-TCs at the 10 %significance level. This study also examined the occurrence of different TC intensity categories based on
the Saffir–Simpson scale (Simpson 1974). The rates
of occurrence, stratified by class and divided by the
total number of occurrences for UL-TCs, N-UL-TCs,
and all TCs, are shown in Fig. 7. The occurrence rateof the TS class for UL-TCs was about 50 %, which
is higher than that for N-UL-TCs (45 %) and the climatological mean (45 %). By contrast, the occurrence
rate for major TCs exceeding category 3 on the Saffir–
Simpson scale (> 96 knots) was 10 % for UL-TCs,
which is lower than that for the climatological mean
(15 %) and N-UL-TCs (15 %). This means that the
UL-TCs tended to remain at weak intensity, rarely
developing into intense TCs.
3.3 Environmental parameters around UL-TCs
Tables 2 and 3 show the atmospheric and oceanic

Vol. 97, No. 2

Fig. 7. Occurrence rate by TC intensity class divided by the number of UL-TC, N-UL-TC, and
all TC occurrences between June and Octoberover the 38-year analysis period. Classes are defined by the TS and Saffir–Simpson scales.

physical parameters for UL-TCs, N-UL-TCs, and allTCs, at the time of TCG and TSF. It should be notedthat the oceanic physical parameters, namely E-CAPE
and MPI, were calculated over 34 years from 1982
to 2015, which corresponds to the period of available
FORA-WNP30.
At the time of TCG, in addition to the higher
DIV200 around UL-TCs than that around N-ULTCs, there were significant differences in some of theother atmospheric physical parameters. The SHRS
around UL-TCs was significantly weaker than thataround N-UL-TCs, while the RHLO and RHHI
around UL-TCs were significantly higher at the 5 %significance level. This means that the atmosphericenvironment around UL-TCs at the time of TCG was
more favorable for TCG than that around N-UL-TCs,
which is consistent with the results of previous studies(e.g., Japan Meteorological Agency 1990) reportingthat UCLs may help induce moisture convergence,
facilitating the generation of deep convection andthus favoring TCG. However, it should be noted that
the VOR850, E-CAPE, MPI, and oceanic physicalparameters (Table 3) at the time of TCG did not differ
between UL-TCs and N-UL-TCs.
Regarding the atmospheric physical parametersat the time of TSF, the relative humidity and vertical
shear did not differ between UL-TCs and N-UL-TCs,
but the DIV200, VOR850, E-CAPE, and MPI around
UL-TCs were significantly lower than those around
N-UL-TCs. There were also significant differences in
the oceanic physical parameters; the SST, TCHP, andApril 2019

447

H. FUDEYASU and R. YOSHIDA

Table 2. As in Table 1, for the atmospheric environmental parameters between June and October during the 38-year
analysis period. The E-CAPE and MPI were evaluated over the 34 years from 1982 to 2015, for which FORAWNP30 were available.
UL

N-UL

ALL

Time of TCG

RHLO (%)
RHHI (%)
VOR850 (× 10−5 s−1)
DIV200 (× 10−5 s−1)
SHRS (m s−1)
SHRD (m s−1)
E-CAPE (J kg−1)
MPI maximum wind (m s−1)

71.5 ( 31 %)44.0 ( 38 %)
4.1 (   2 %)4.6 ( 42 %)
3.1 (−27 %)
8.7 (−12 %)14281.1 (   0 %)120.8 (   0 %)70.3 (−3 %)40.6 (−4 %)4.0 ( 0 %)2.9 (−5 %)3.8 ( 2 %)9.6 ( 1 %)14278.2 (−0 %)120.8 ( 0 %)

70.5
41.0
4.0
3.1
3.7
9.5
14278.5
120.8

TS formation time (TSF)

RHLO (%)
RHHI (%)
VOR850 (× 10−5 s−1)
DIV200 (× 10−5 s−1)
SHRS (m s−1)
SHRD (m s−1)
E-CAPE (J kg−1)
MPI maximum wind (m s−1)

72.2 ( −7 %)45.4 ( −4 %)8.9 (−21 %)
6.5 (−21 %)
3.1 (   5 %)
7.1 (   1 %)
13950.6 (−17 %)119.0 (−18 %)72.4 ( 0 %)45.8 ( 1 %)11.0 ( 3 %)7.4 ( 3 %)3.0 (−1 %)7.0 (−0 %)14398.0 ( 3 %)121.1 ( 3 %)

72.4
45.8
10.8
7.3
3.0
7.1
14348.0
120.8

Table 3. As in Tables 1 and 2, for the oceanic physical parameters below the TC center between
June and October during the 34-year analysis period from 1982 to 2015, for which FORAWNP30 were available.
UL

N-UL

ALL

Time of TCG

SST (°C)
TCHP (kJ cm−2)26-°C-depth (m)29.3 (   6 %)94.9 ( −8 %)92.9 (−13 %)29.3 (−1 %)98.3 ( 1 %)99.8 ( 2 %)

29.3
97.9
99.0

TS formation time (TSF)

SST (°C)
TCHP (kJ cm−2)26-°C-depth (m)28.9 (−14 %)77.4 (−15 %)80.2 (−23 %)29.1 ( 3 %)84.5 ( 2 %)91.3 ( 3 %)

29.1
83.7
90.0

depth of the 26°C isotherm for UL-TCs were smaller
than those for N-UL-TCs at the 5 % significance level.
Therefore, the TC-development efficiency of the atmospheric and oceanic environments around UL-TCs
was lower than that around N-UL-TCs at the time of
TSF. This seems to be due to the average location of
UL-TCs at the time of TSF being farther north. The
lower efficiency of these atmospheric and oceanic conditions means that the UL-TCs tend to remain at weakintensity. There were significant differences in theatmospheric and oceanic physical parameters between
the mature and decay times (not shown).
To compare the features of the environmental para­
meters around UL-TCs, Tables 4 and 5 show the physical parameters for NE-UL-TCs, i.e., those influenced
by UCLs in the northeast quadrant of the TCG region.
Over the 38-year analysis period, there were 743 TCs

between June and October, of which 79 were classified as NE-UL-TCs. At the time of both TCG and
TSF, most of the physical atmospheric parameters did
not differ between NE-UL-TCs and TC environments
without northeastern UCLs (N-NE-ULs or N-NE-ULTCs), with the exception of E-CAPE and MPI. The
E-CAPE and MPI around NE-UL-TCs were lower
than those around N-NE-UL-TCs. In terms of oceanic
physical parameters, the SST (TCHP) of NE-UL-TCs
was smaller than that of N-NE-UL-TCs at the time ofTCG (TSF) at the 5 % significance level. Therefore,
the TC-development efficiency of the atmospheric andoceanic environments around NE-UL-TCs was lower
than that around N-NE-UL-TCs, in contrast to theenvironments around UL-TCs at the time of TCG.

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Journal of the Meteorological Society of Japan

Vol. 97, No. 2

Table 4. As in Table 2, for the atmospheric environmental parameters around NE-UL and N-NE-UL between Juneand October during the 38-year analysis period.
NE-UL

N-NE-UL

ALL

Time of TCG

RHLO (%)
RHHI (%)
VOR850 (× 10−5 s−1)
DIV200 (× 10−5 s−1)
SHRS (m s−1)
SHRD (m s−1)
E-CAPE (J kg−1)
MPI maximum wind (m s−1)70.0 (−14 %)41.5 ( 6 %)4.6 ( 9 %)3.4 ( 9 %)3.6 ( −2 %)9.1 ( −8 %)13003.4 (−23 %)114.7 (−23 %)70.5 ( 2 %)41.0 (−1 %)3.9 (−1 %)
3.1 (−0 %)3.7 ( 0 %)9.6 ( 1 %)
13464.2 ( 3 %)116.7 ( 2 %)

70.5
41.0
4.0
3.1
3.7
9.5
13417.5
116.5

TS formation time (TSF)

RHLO (%)
RHHI (%)
VOR850 (× 10−5 s−1)
DIV200 (× 10−5 s−1)
SHRS (m s−1)
SHRD (m s−1)
E-CAPE (J kg−1)
MPI maximum wind (m s−1)

72.6 ( 6 %)45.2 ( −6 %)12.0 ( 16 %)
7.5 ( 4 %)
3.2 ( 10 %)
7.2 ( 3 %)13973.2 (−17 %)119.2 (−17 %)72.3 (−0 %)45.8 ( 1 %)10.6 (−1 %)7.3 (−1 %)2.9 (−1 %)7.0 (−0 %)14308.0 ( 2 %)120.7 ( 2 %)

72.4
45.8
10.8
7.3
3.0
7.1
14271.0
120.5

Table 5. As in Table 3, for the oceanic environmental physical parameters around NE-UL and
N-NE-UL during the 38-year analysis period between June and October.
NE-UL

N-NE-UL

ALL

Time of TCG

SST (°C)
TCHP (kJ cm−2)26-°C-depth (m)29.2 (−18 %)92.2 (−14 %)97.4 ( −4 %)29.3 (2 %)98.4 (2 %)99.3 (0 %)

29.3
97.7
99.1

TS formation time (TSF)

SST (°C)
TCHP (kJ cm−2)26-°C-depth (m)29.1 ( −1 %)76.0 (−23 %)80.8 (−13 %)29.1 (0 %)84.6 (3 %)90.8 (1 %)

29.1
83.7
90.0

3.4 Lower tropospheric flows contributing to TCGs
around UL-TCs
This study examined the relationship between
UL-TCs and the lower tropospheric environmental
flows contributing to TCG classified in Ritchie andHolland (1999), namely the TCG factors SL, CR, GY,
EW, and PTC. When the major TCG factor could not
be determined using the objective detection method
developed in Yoshida and Ishikawa (2013), it was
denoted as unclassified flow (UCF). Figure 8 shows
the distribution of TCG factors among 90 UL-TCs
over the 38-year study period. Of the 90 UL-TCs,
those related to the SL and CR were the most frequent(28 UL-TCs), followed by those related to the EW (12UL-TCs) and then to the GY (10 UL-TCs). Our results
are consistent with those of Ritchie and Holland(1999), in which it was found that for the many TCG
cases influenced by a UCL, a CR was also present in
the lower tropospheric environment.

As shown in Fig. 9, there was a large difference inthe number of TCs related to the five TCG factors. To
compare the effects of the different environments on
UL-TC formation, the rate of UL-TC occurrence wasdivided by the number of TCs for each TCG factor,
shown as percentages in Fig. 9. The occurrence rate of
the UL-TCs was similar among the TCG factors, withthe exception of UCF and CR. The occurrence rateof UL-TCs related to the CR (15 %) was larger thanthose for the other known TCG factors.
Fudeyasu and Yoshida (2018) revealed the statistical characteristics of TCs stratified by TCG factors.
The occurrence rate of TCs that made landfall in Japanwas highest for the CR, followed by SL. Figure 10
shows the tracks of 28 TCs that formed in an environment associated with both SL and a UCL (SL-UL-TC),and both CR and a UCL (CR-UL-TC). Of the 28 SLUL-TCs, 7 TCs made landfall in Japan, corresponding
to an occurrence rate of 25 %. On the other hand, 10

April 2019

H. FUDEYASU and R. YOSHIDA

449

Fig. 9. Number of TCs and UL-TCs stratified by
TCG factors over the 38-year analysis period. The
filled bars indicate UL-TCs, and the percentages
are the UL-TC occurrence rates divided by the
total number of TCs for each of the TCG factors.
Fig. 8. Distribution of UL-TCs stratified by TCG
factors over the 38-year analysis period. Note:shear line, SL; confluence region, CR; monsoon
gyre, GY; easterly wave, EW; Rossby wave energy dispersion, PTC; unclassified flow, UCF.

CR-UL-TCs made landfall in Japan, corresponding toan occurrence rate of 36 %. Although the number ofTCs that made landfall is not sufficient to perform arobust statistical analysis, the trends in the movementof CR-UL-TCs and SL-UL-TCs are important fordisaster prevention in Japan.
It should be noted that 7 of the 40 UCF-TCs were
UL-TCs. This means that 7 TCs were formed in anenvironment with upper tropospheric forcing but

without lower tropospheric forcing. No clear evidenceof upper tropospheric forcing having a direct impact
on TCG was found. This means that a UCL provides
favorable environmental conditions for the development of low-level disturbances on its southeasternside, but it is not directly responsible for the low-level
disturbances. Because the purpose of this study was
to investigate the characteristics of TCs affected by a
UCL, clarification of the impact of upper troposphericforcing on TCG is reserved for future studies.
4.

Conclusion

This study statistically examined the characteristics
of TCs for which TCG was modulated by UCLs overFig. 10. TC tracks (lines), and locations at TCG (closed circle) and TSF (crosses) of (a) 28 SL-UL-TCs and (b) 28
CR-UL-TCs. The dashed lines correspond to tracks of TCs below TS intensity.

450

Journal of the Meteorological Society of Japanthe WNP, during the 38 years from 1979 to 2016.
Among the 965 TCs, 90 TCs (9 %, 2.4 per year) were
defined as having UCL contributions to TCG (ULTCs). Regarding the geographical locations of the
UCLs with respect to the locations of TCG, the distance between the location of a UCL and TCG ranged
from 133 to 1,450 km, with an average distance of 883
km, where the UCLs were situated to the northwest of
the location of TCG. Analysis of the seasonal changes
in TCG cases influenced by UCLs showed that most
UL-TCs occurred in the summer. The annual variationin the occurrence rate of UL-TCs during June to
October exhibited substantial variability, ranging from0 to approximately 30 %. The annual variation in
UL-TCs was related to the activity of the Tibetan highand the summer temperature anomaly over Japan. The
extremely hot summer of 2016, during which four
UL-TCs occurred, was enhanced by the Tibetan high.
The statistical characteristics of UL-TCs and theenvironmental parameters around them, including
both atmospheric and oceanic features, were revealed.
Our results, comparing the characteristics of UL-TCs
and N-UL-TCs between June and October over the
38-year analysis period, are summarized below:• The average location of UL-TCs at the time of TCG
was farther north than that of N-UL-TCs.
• The average duration from TCG to TSF was significantly shorter for UL-TCs than for N-UL-TCs.
• The UL-TCs tended to move northward at the timeof TSF.
• The occurrence rate of UL-TCs that made landfall
in Japan was approximately double that in othercountries.
• UL-TCs tended to remain at weak intensity andrarely developed into intense TCs.
• At the time of TCG, the lower- and middletropospheric relative humidity around UL-TCs
was higher, and the vertical shear around UL-TCs
was weaker, in comparison to N-UL-TCs. The atmospheric environment around UL-TCs was morefavorable for the development of TCs than thataround N-UL-TCs. However, the E-CAPE, MPI,
and oceanic physical parameters did not differ between UL-TCs and N-UL-TCs.
• At the time of TSF, the relative humidity andvertical shear did not differ between UL-TCs and
N-UL-TCs, but the upper tropospheric divergence,lower tropospheric relative vorticity, E-CAPE, MPI,
SST, TCHP, and depth of the 26°C isotherm around
UL-TCs were lower than those around N-UL-TCs.
The atmospheric and oceanic environments around
UL-TCs were less suitable for the development ofVol. 97, No. 2

TCs than those around N-UL-TCs.
• At the mature time, there were significant differences in the atmospheric and oceanic physical para­
meters between UL-TCs and N-UL-TCs.
• Many UL-TCs in lower tropospheric environments
were associated with the SL or CR.
• The TCs that formed in an environment associatedwith both the CR and a UCL (CR-UL-TCs) tended
to move northward, resulting in a higher occurrence
rate of landfalls in Japan.
The direct impact of upper tropospheric forcing
from UCLs on TCG remains unclear. However, therelationship between UCLs that influenced TCG andthe statistical characteristics of TCs shown here canbe used in future cyclogenesis studies. In addition, thestatistical characteristics of UL-TCs are important forthe prediction of TCG; in particular, understanding
the tendency for UL-TCs to move northward will be
important for disaster prevention in Japan.
Acknowledgments
The authors would like to thank Dr. A. Wada for his
helpful support of data analysis. This study utilized
the dataset of JRA55 provided by the JMA. This study
also utilized the dataset of FORA-WNP30, which
was kindly provided by the Japan Agency for MarineScience and Technology (JAMSTEC) and the Meteorological Research Institute of Japan Meteorological
Agency (JMA/MRI). This work was supported by the
Ministry of Education, Culture, Sports, Science andTechnology (MEXT) KAKENHI Grants 17H02956
and 17K14398. Joint Usage/Collaborative ResearchCenter for Multidisciplinary Disaster Prevention Studyof the Disaster Prevention Research Institute (DPRI)at Kyoto University (29G-05), Collaborative Research
Project on Computer Science with High-Performance
Computing at Nagoya University (2017). This research was also supported by the Integrated Research
Program for Advancing Climate Models (TOUGOU
program) from the MEXT, Japan.
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