FEBRUARY 2018

FUDEYASU AND YOSHIDA

435

Western North Pacific Tropical Cyclone Characteristics Stratified by Genesis Environment
HIRONORI FUDEYASU
Yokohama National University, Yokohama, Japan

RYUJI YOSHIDARIKEN Advanced Institute for Computational Science, and Research Center for
Urban Safety and Security, Kobe University, Kobe, Japan(Manuscript received 25 April 2017, in final form 27 November 2017)
ABSTRACTThe characteristics of tropical cyclones (TCs) in the summer and autumn seasons over the western North
Pacific that are associated with different environmental factors that influence TC genesis (TCG) were studied.
The authors objectively categorized factors into the five TCG factors classified by Ritchie and Holland:monsoon shear line (SL), monsoon confluence region (CR), monsoon gyre (GY), easterly wave (EW), and the
Rossby wave energy dispersion from a preexisting TC (PTC). The GY-TCs tended to develop slowly, and thehighest rates of occurrence of rapid intensification (RI) were found for the CR-TCs, whereas the GY-TCs
rarely experienced RI. The average storm size of the GY-TCs at the time of formation was the largest of theaverages among the TC types, while the EW- and PTC-TCs were smaller, although these differences disappeared at the mature time. There were no significant differences in the sea surface temperature (SST)
beneath the TCs, but the tropical cyclone heat potential (TCHP) of the PTC-TCs was higher. The PTC-TCs
tended to develop as intense TCs and exhibited favorable environmental characteristics, such as high TCHP,
high convective available potential energy, and weak vertical shear. The occurrence rate of the PTC-TCs thatmade landfall in the Philippines was higher than the averages of the other TC types, whereas those of the
EW-TCs (PTC-TCs) that made landfall in Japan (China) were lower. These results provide important information for use in disaster prevention.

1. Introduction
A tropical cyclone (TC) forms under large-scale environmental conditions favorable for cyclogenesis(TCG), which are generally found where sea surface
temperatures (SSTs) exceed 268C and supportive largescale flow patterns coexist (Gray 1968, 1998). A large
body of research on the large-scale environmental conditions favorable for TCG has been conducted (e.g.,
Emanuel and Nolan 2004; Camargo et al. 2007;
McGauley and Nolan 2011; Korty et al. 2012).
Ritchie and Holland (1999, hereafter RH99) statistically investigated the environments of 199 TCG casesthat occurred from 1984 to 1992 (excluding 1989) over
the western North Pacific (WNP). They classified largescale flow patterns in TCG into five factors: monsoon
shear line (SL), monsoon confluence region (CR),monsoon gyre (GY), easterly wave (EW), and the
Rossby wave energy dispersion from a preexisting TC
Corresponding author: H. Fudeyasu, fude@ynu.ac.jp(PTC). The SL is enhanced by the horizontal cyclonicshear associated with the monsoon trough over the
WNP. The CR is mainly the zonal confluence zone between easterly trade winds and westerly monsoon winds
at the eastern extremity of the monsoon trough. The GY
has been recognized as a synoptic-scale gyre embedded
within a developed monsoon trough (Lander 1994; Chenet al. 1996). These three factors (SL, CR, and GY) areclosely related to Asian monsoons over the WNP. Gray
(1968, 1998) reported that a monsoon trough is the mostfavorable environment for TCG.
The EW is related to the synoptic-scale trade easterly
wind system. The trough of easterly trade winds provides an environment favorable for TCG (Heta 1990,
1991). The PTC is associated with a preexisting TC. A
mature TC disperses its energy as a Rossby wave in asoutheastward direction, and one of the low pressure
areas of the wave train sometimes develops into a TC
(McDonald 1998; Li and Fu 2006; Li et al. 2006). RH99
reported that the rate of occurrence of TCs controlled by
the five TCG factors were as follows: SL accounts for

DOI: 10.1175/MWR-D-17-0110.1
Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

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42%, CR for 29%, GY for 3%, EW for 18%, and PTC
for 8%.
Yoshida and Ishikawa (2013; hereafter YI13), building on the results of RH99, developed a method of detecting the environmental factors that contribute to TCG.
The TCG detection method could objectively categorize
TCG environments for each TC as one of the five factors
defined by RH99. YI13 further extended the analysis period of TCG cases to 30 years, from 1979 to 2008. The
results of their detection were similar to those of RH99.
Differences in the environmental factors that contribute to TCG could be considered as key determinantsof the characteristics of TCs. For example, the tracks of
TCs are greatly influenced by large-scale atmosphericcirculations in the lower–midtroposphere over the TC
life cycle, consisting of the Asian monsoon, midlatitude
westerly winds, and easterly trade winds. It is, thus,
possible that environmental factors influence not only
TCG, but also the direction of movement of TCs, affecting the destination of the TC track. However, YI13
did not investigate the characteristics of TCs or thedifferences in environmental conditions stratified by the
TCG environment.
This study revisited the dataset presented in YI13,
extended through 2013, and statistically investigated thecharacteristics of TCs associated then with the environmental factors that influence TCG using information frombest track data. The differences in the environmental
physical parameters of the factors that influence TCG andaffect the characteristics of TCs were also examined. The
purpose of this study was to investigate how the different
environmental factors contributing to TCG are related tothe characteristics of the resulting TC.
The remainder of this paper is organized as follows.
Section 2 introduces the methods of analysis. The statistical results are described in section 3. The influence ofenvironmental factors that contribute to TCG on thecharacteristics of the resulting TCs are further discussed
in section 4. The major results are summarized in the
final section.

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In YI13, the SL is identified as the location where thesign of the 850-hPa zonal wind component changes fromnegative on the northern side of the grid to positive on
the southern side. The score for SL is calculated asfollows: ›uexp(B 3 dist) ,(1)
SCRSL 5 A
›y avewhere u is the zonal wind at the 850-hPa level, ‘‘dist’’means the distance between the center grid of SL andthe location of TCG, and A and B are arbitrary constants, with A 5 21.0 and B 5 21.0 3 1025 for SCRSL.
The location of each TCG was taken from the first record of the Joint Typhoon Warning Center (JTWC) best
track data (Chu et al. 2002). An upper limit of 1500 km is
set for the distance, and the SL score is not calculated
when ‘‘dist’’ exceeds the upper limit.
The contribution scores for the CR and EW patterns
are defined as the border between westerly and easterly
zonal winds and as the intensity of the west–east shear of
the meridional winds near the trough, respectively.
These scores are calculated using the equations below: ›uexp(B 3 dist) ,›x ave ›yexp(B 3 dist) ,SCREW 5 A
›x aveSCRCR 5 A

(2)
(3)

where y is the meridional wind at the 850-hPa level,
and A and B are arbitrary constants, with A 5 21.0 and
B 5 21.0 3 1025 for SCRCR and A 5 2.0 3 1021 andB 5 21.0 3 1022 for SCREW. An upper limit of 1500 km
is set for the distance for SCRCR.
The GY pattern is evaluated using the similarity of the
850-hPa wind field to a composite of typical GY cases
reported by Lander (1994), with the score for GY andthe degree of pattern matching M determined as follows:
SCRGY 5 exp(2M) 3 (z 2 zstd ) ,nj qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ(C(0 i,j) 2 data0(i,j) )2 ,M5
i50 j50

(4)

ni

åå

(5)

2. Methodology
a. TCG detection method
To separate TCG cases into categories influenced by
the different environmental factors defined by RH99,
the TCG detection method developed in YI13 was usedin this study. Using reanalysis data, YI13 defined the SL,
CR, GY, EW, and PTC with ‘‘contribution scores’’ forthe flow patterns as an objective algorithm. This paper
briefly reviews contribution scores SCRSL, SCRCR,SCRGY, SCREW, and SCRPTC.

where z is the relative vorticity, which is expressed as theexcess above the standard value zstd 5 21.0 3 1025. For
M [Eq. (5)], C0 is the model GY pattern sea level pressure (SLP) field, data0 is the corresponding SLP field of agiven TCG case, i and j represent the horizontal grid,and ni and nj are the width and length, respectively.
Based on the previous discussion (Li and Fu 2006; Liet al. 2006), the contribution of PTC is estimated according to the amplitude of the wave closest to the PTC
after searching for a preexisting TC northwest of the

FEBRUARY 2018

TCG location. Once a preexisting TC is found, we
compute the amplitude of the first Fourier component
2of SLP along the line connecting the preexisting TC
Cwv1
and TCG locations with the score for PTC, as follows:
2.
SCRPTC 5 Cwv1

(6)

A lower limit of 1.01, derived by the empirical method, is2
set for Cwv1.
Each score (SCRx) is finally normalized by its
maximum value [Max(SCRx)] and minimum value[Min(SCRx)] derived in the latest 30 years, as follows:
SCRx 5437

FUDEYASU AND YOSHIDASCRx 2 Min(SCRx ).
Max(SCRx ) 2 Min(SCRx )(7)

Along with the final scores after normalization, themaximum contribution score is determined as the major
flow pattern contributing to the TCG, namely, the SL, CR,
GY, EW, and PTC. When the major TCG factor could not
be determined by the TCG detection method, it was considered to be unclassified flow (UCF). See YI13 for the
specific details of the TCG detection method.
For the TCG detection method, this study used 850hPa wind and SLP derived from the Japanese 25-year
Reanalysis Project (JRA-25; Onogi et al. 2007) (http://
jra.kishou.go.jp/JRA-25/index_en.html). The JRA-25
was collected every 6 h between 1979 and 2013, with ahorizontal resolution of 1.258 for both longitude andlatitude. The analysis period of this study was 35 years,
from 1979 to 2013, which is the same as the available
period of JRA-25. We extended the analysis period of
the TCG cases used in YI13, which investigated theperiod of 1979 to 2008.

b. Data and environmental physical parameters
Although YI13 analyzed the TCG factors from allTCs, including tropical depressions (TDs), this study
focused only on TCs of tropical storm (TS) or greater
intensity, that is, maximum 1-min sustained wind speed
in excess of 17.2 m s21 or 34 kt. This study analyzed thecharacteristics of TCs associated with each factor that
influences TCG using TC data derived from the International Best Track Archive for Climate Stewardship(IBTrACS; Knapp et al. 2010) at the time of TS formation (TSF) and mature time. The time of TSF was
defined as the time when minimum TS intensity wasreached, and the mature time was defined as the time
when the maximum wind speed occurred. It should be
noted that the time of TSF was usually different from thetime of TCG, which is the first record in the JTWC best
track data in YI13. This study focused on the time of
TSF and the mature time for two reasons: the characteristics and conditions of TCs at both of these times areimportant for disaster prevention and mitigation, andboth times were suitable for comparing the characteristics of a TC among the different TCG factors.
For each factor that influences TCG, the relationshipbetween the average influence due to each factor andthe other factors was assessed at the 5% significancelevel using a t test that indicates significant differences
versus all systems. For example, the average of the SL
was compared to the average influence of the otherfactors, that is, the CR, GY, EW, PTC, and UCF.
This study also investigated the differences in theenvironmental physical parameters among factors that
influence TCG: the magnitude of the vertical shear,
lower- and midtropospheric humidity, maximum potential intensity (MPI; Emanuel 1986), convective
available potential energy (CAPE) derived from the
MPI equation, SST, tropical cyclone heat potential(TCHP), depth of the 268C isotherm in the ocean, andsea surface 100-m depth-averaged ocean temperature.
The magnitude of the vertical shear was defined as thedifference in horizontal winds between the 850- and200-hPa levels, which were the averages of a 58 3 58 box
relative to the center of the TC, using the JRA-25. The
lower- and midtropospheric humidities were defined as
the mixing ratio at the 850- and 500-hPa levels, respectively, which were averages of a 58 3 58 box relative
to the TC center, using the JRA-25.
The information on the ocean was derived from thethree-dimensional variational-based parent domain
output from the Four-dimensional Variational Ocean
Reanalysis for the Western North Pacific (FORAWNP30; Usui et al. 2017; http://synthesis.jamstec.go.jp/
FORA/). The data have a horizontal resolution of 0.58
for both longitude and latitude, with 54 vertical levels
from 0.5- to 6000-m depth and a 6-h time interval from1982 to present. Using FORA-WNP30, the TCHP was
calculated by summing the ocean heat content from thesurface down to the depth of the 268C isotherm (Leipper
and Volgenau 1972; Wada 2015) as follows:
Z26

Q 5 Cp å ri (Ti 2 26)Dzi ,

(8)

z50

where Q is the TCHP value (J cm22), Cp is specific heatat constant pressure, Ti is water temperature (8C), Dz iswater layer thickness, and r is the density of water. The
parameter Z26 is the depth (m) of the 268C isotherm.
Using the JRA-25 and FORA-WNP30, this study
examined the differences in the MPI and CAPE. The
MPI was proposed originally by Emanuel (1986), andthe MPI and CAPE revised by Bister and Emanuel(1998) were used to account for dissipative heating. The
MPI and CAPE were calculated as averages of a 58 3 58

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box relative to the TC center. Note that the SST wasused at the TC center to create distinct features stratified
by TCG environment.
The equivalent blackbody temperatures TBB, derived
from the merged IR channels from five geostationary
satellites of the Climate Prediction Center (http://www.
cpc.ncep.noaa.gov/products/global_precip/html/wpage.
merged_IR.shtml), were used to describe the cloudfeatures of TCs for each TCG factor. These data were
defined on a 4-km horizontal grid for every 1-h period.

3. Resultsa. Genesis number and seasonal changes
The total number and percentage of TCs that reached
TS or greater intensity influenced by each environmental factor over the analysis period from 1979 to 2013
were as follows: among the 874 TCs, the most frequent
influencing factor was SL (396 TCs, 46% of the total),followed by CR (157 TCs, 18%) and EW (119 TCs,14%). PTC (83 TCs) and GY (78 TCs) accounted for 9%each, and UCF (41 TCs) accounted for 4%.
The seasonal changes in the formation of TCs influenced by the five environmental factors and UCF werealso examined. The SL- and EW-TCs (hereafter, SL-TC,
EW-TC, and similar terms indicate a TC that formed
in a large-scale flow mainly associated with an SL, EW,
or similar factor) occurred throughout the year, whereas
the CR-, GY-, and PTC-TCs rarely occurred in winterand spring. The number of the CR- and GY-TCs, which
are related to Asian monsoon activity, tended to increase in the summer, while the PTC-TCs tended only tooccur in the summer and autumn, when many TCs wereformed. Seasonal changes in the number of the SL- and
EW-TCs, therefore, were less significant than changes in
the numbers of the CR-, GY-, and PTC-TCs. The seasonality of the TCs of TS or greater intensity was similar
to that reported by YI13, who considered environmental
factors for all TCs, including TDs.

b. Tropical storm formation location
As shown in the previous section, seasonality in TCG
is different for TCs influenced by different environmental factors. To compare the characteristics of a TC
among the different TCG factors correctly, this study
considered 677 TCG cases in the summer and autumn
seasons from June to November (Table 1), when any of
the influencing factors could occur frequently. Even
when the analysis period was limited in this way, theproportional occurrence of the factors that influenced
TCG was almost the same as the result obtained over thewhole period.

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Table 1 shows the average location of TSF for eachfactor that influenced TCG over the 35-yr analysis period between June and November. There were significant differences in the average location of TSF for theSL, EW, and PTC. EW- and PTC-TCs occurred, on
average, farther to the east than other types of TCs, andthis difference was significant at the 5% level. In contrast, the location of TSF of the SL-TCs was more biased
to the west than that of other types of TCs. Additionally,
the EW-TCs (PTC-TCs) occurred, on average, farther
to the north (south) than other types of TCs.
If this study examines the distribution of the locationof TSF in detail, it is clear that the situation for eachenvironmental factor was different. Figure 1 shows thelocation of TSF classified into the factors that influenced
TCG over the 35-yr analysis period between June andNovember. The location of TSF of the SL-TCs extended
from the west to the east, where the SL was formed by
the westerly monsoon and eastward winds. Many locations of TSF of the CR-TCs were located over theeastern Philippine Sea, where a confluence area is likely
formed. The EW-TCs were generally located in theeastern part of the WNP, most likely due to the easterly
winds coming from the eastern North Pacific. Because
the PTC-TCs form on the southeastern side of a preceding TC, it is rare for them to form in the northern partof the WNP and the South China Sea, resulting in significant differences in the average location between the
PTC-TCs and the other types of TCs.

c. Intensity
There were no differences in the average intensity ofTCs generated by different environmental factors at thetime of TSF because this was the time when all TCs
reached TS intensity. However, the rate of development, defined by the increase in the maximum windspeed 1 day after the time of TSF, was significantly different. The rate of development of the GY-TC was
slower than the rate of the other types of TCs, with thedifference being significant at the 5% level (Table 1).
Rapid intensification (RI) for a TC is defined here as
an increase in wind speed of 17.2 m s21 or more in 24 h.
Over the 35-yr analysis period between June andNovember, 96 TCs (14% of the total) experienced RI.
Among the TCs generated by different environmental
factors, the occurrence of RI for the SL-TCs was thelargest (43 TCs), followed by the CR-TCs (25 TCs) andthe EW- and PTC-TCs (10 TCs). Only six GY-TCs experienced RI. To compare environments favorable for
RI correctly, rates of RI occurrence were divided by the
number of occurrences of each environmental factor
that influence TCG, as shown in Fig. 2. The rate of occurrence of RI for the SL (14%) was the same as the

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439

FUDEYASU AND YOSHIDATABLE 1. Statistical summary of the parameters associated with the environmental factors that influenced TCG over the 35-yr analysis
period between June and November. Areas where a t test indicates significant differences with a 5% level of significance are presented inboldface font.

Time of TSF

One day after
time of TSF
Mature time

Decaying timeNo.
Avg lat (8N)
Avg lon (8E)
Radius of 30-kt wind(n mi, 1 n mi 5 1.852 km)
RMW (km)
Dlon (8)Dlat (8)Dwind (kt)
Duration from TSF (day)Max wind (kt)
Min pressure (hPa)Radius of 30-kt wind (n mi)
RMW (km)Duration from TSF (day)

SL

CR

GY

EW

PTC

UCF

ALL

300 (44%)
16.8
134.2
115.2

130 (19%)
17.4
133.5
111.6

61 (9%)
16.4
133.4
129.5

92 (14%)
17.9
138.3
104.7

66 (10%)
13.9
140.8
102.3

28 (4%)
19.8
132.2
125.5

677
16.9
135.1
113.6

38.4
22.2
1.8
13.4
3.2
69.7
959.6
226.5
24.0
5.3

37.9
21.9
2.0
13.0
3.0
67.5
963.3
220.5
26.9
5.2

43.1
22.0
1.7
11.4
3.0
68.9
961.5
246.4
22.3
5.3

36.7
22.7
1.7
15.0
2.9
69.9
960.5
241.0
24.9
5.3

39.2
23.4
1.3
13.7
3.9
77.0
953.1
255.0
18.5
6.5

39.1
21.6
1.8
9.8
3.0
63.0
968.1
220.4
26.7
4.7

38.1
22.3
1.8
13.3
3.2
69.7
960.3
231.7
24.5
5.4

average (the climatology of all TCs; 14%). The rate ofoccurrence of RI for the GY-TCs (9%) was smaller than
the rate of occurrence of RI among all TCs, meaningthat the GY-TCs experienced RI more rarely than theother types of TCs. The low rate of RI for the GY-TCs isrelated to their slower development versus the othertypes of TCs at the time of TSF. It is interesting to notethat the rate of occurrence of RI was the highest for the
CR-TCs (19%), that is, more than 2 times that of the
GY-TCs.
At the mature time, there was a significant differencein the average intensity (Table 1): the PTC-TCs tended
to have lower central pressure and higher maximum
winds. To determine the tendency of an intense TC todevelop, this study also examined the occurrence ofspecific classes of TC intensity categorized using theSaffir–Simpson hurricane wind scale (Simpson 1974).
Over the 35-yr analysis period between June andNovember, one category 5 (more than 70 m s21 or 135 kt)
TC, 14 category 4 (59–69 m s21 or 114–134 kt) TCs, and 86
category 3 (50–58 m s21 or 96–113 kt) TCs were classified as
major TCs, that is, having a maximum wind speed of
50 m s21 (96 kt) or more. Among the major TCs, the
SL-TCs were the most frequent (44), followed by the
CR-TCs (18), the EW-TCs (14), and the PTC-TCs (11).
The rate of occurrence divided by the number of occurrences for each environmental factor that influenced TCG is shown in Fig. 3. The rate of occurrenceof major TCs for the SL-TCs (15%) was the same as
the average (15%). Interestingly, the rate of occurrence

FIG. 1. The locations of TS formation of (a) SL, (b) CR, (c) GY, (d) EW, (e) PTC, and (f) UCF over the 35-yr analysis period between Juneand November. Shading indicates areas of numbers greater than one.

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MONTHLY WEATHER REVIEW

FIG. 2. Rate of occurrence of RI divided by the number of occurrences of each environmental factor that influences TCG overthe 35-yr analysis period between June and November.

for the PTC-TCs was 22%, which was about 50% higher
than the average. By contrast, among ‘‘weak TCs,’’ that
is, TSs, the rate of occurrence of the PTC-TCs was 24%,
which was about half that of the climatology (42%). Thismeans that the PTC-TCs tended to develop into intenseTCs but rarely remained at the intensity of weak TCs.

d. Storm size
The storm size of TCs in the horizontal direction was
defined as the radius of the 30-kt (1 kt 5 0.5144 m s21)
sustained wind provided by the Regional Specialized
Meteorological Center (RSMC) Tokyo Typhoon Center of the IBTrACS. There was a significant difference atthe time of TSF (Table 1). The average 30-kt wind radiusof the GY-TCs was about 20% larger than that of theother types of TCs, whereas the EW- and PTC-TCs were
smaller. In addition, this study examined the radius ofmaximum wind (RMW) provided by the JTWC of the
IBTrACS. The RMW of the GY-TCs was large at theVOLUME 146

time of TSF, and this difference from the other types of
TCs was significant at the 5% level.
Figure 4 shows composites of the TBB distribution
made with respect to TC centers during the period between the time of TSF and 12 h before the time of TSF.
The region of low TBB (,270 K) near the center corresponds to the cloud cluster of TC, and the size of thecloud cluster of the EW- and PTC-TCs is small. By
contrast, the low TBB of the GY-TCs has an asymmetricstructure extending from the northeast to the southwest.
Interestingly, the storm size of TCs at the mature time
did not differ significantly from the average TCG, except
for the PTC-TCs (Table 1). In other words, the storm
size of the EW- and GY-TCs did not affect the futurestorm size at the mature time, although there were significant differences at the time of TSF. The RMW of
PTC-TCs was small at the mature time, whereas theradius of 30-kt sustained winds increased with the increases in the intensity of PTC-TCs.

e. Life span and track
Table 1 shows the averages of two measures of TC
duration; one is the duration of the developmental
stage, that is, from the time of TSF to mature time, andthe other is the life span from the time of TSF to the
decaying time. The decaying time was defined as thepoint at which the system had TS intensity for the final
time. The duration of the developmental stage differed with a 5% level of significance. The mean duration of the PTC-TCs was 3.9 days, which wasapproximately 20% longer than the average of
3.2 days. The longer mean duration of the PTC-TCs
was likely due in part to the farther-southeast TSF
location. The longer duration of the developmental
stage contributed to the life span of the PTC-TCs
being longer than those of the other TCG types.

FIG. 3. Rates of occurrence of different classes of TC intensity divided by the number ofoccurrences of each environmental factor that influences TCG over the 35-yr analysis period
between June and November. The classes are defined as maximum 1-min sustained wind speed;
TS and the Saffir–Simpson hurricane wind scale. Note: category 5 is 5; category 4, 4; category 3,
3; category 2, 2; category 1, 1; and TS, S.

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FUDEYASU AND YOSHIDA

441

FIG. 4. Composites of TBB distributions of (a) SL, (b) CR, (c) GY, (d) EW, (e) PTC, and (f) UCF over the 35-yr analysis period between
June and November. The composite was made with respect to the centers of TCs from the time of TSF to 12 h before the time of TSF. The
contour interval is 10 K. The bold contour indicates 230, 250, and 270 K.

The movements of TCs according to their TCG factors were investigated, and Fig. 5 shows the tracks ofTCs. Also, the direction of movement at the time of TSF,
as shown in Table 1, was defined as the distance moved by
the east–west and the north–south components from thetime of TSF to 1 day later. The EW- and PTC-TCs tended
to move westward at the time of TSF, and this difference
from the other types of TCs was significant at the 5% level.
Additionally, the distance of northward movement of the
PTC-TCs was less than that of other types of TCs. By
contrast, the westward distance moved by the CR-TCs wasless than that moved by other types of TCs; the CR-TCs
tended to move northward. Because the CR-TCs mainly
form at the eastern extremity of westerly monsoon winds,
the CR-TCs rarely moved westward.
Along the destination of the track, the number oflandfalls in each country (Japan, China, Vietnam, and
the Philippines) was statistically investigated. This study
adopted the definition of a TC landfall as the intersection of the TC track with a coastline, as describedby Fudeyasu et al. (2014). Countries bordering the WNP
experience the highest number of TC landfalls in theworld (Fudeyasu et al. 2014). In all, 89 TCs (13%) madelandfall in Japan in the 35-yr analysis period between Juneand November. Among these, the SL-TCs were the mostfrequent (47), followed by the CR-TCs (22). The rate ofoccurrence divided by the number of occurrences for eachenvironmental factor that influenced TCG (Table 1) isshown in Fig. 6 and Table 2. The rate of occurrence for the
CR-TCs (17%) was almost the same as that for the SL-TCs(16%). As the rate of occurrence for the EW-TCs (5%)
was very low, the EW-TCs moved toward Japan more
rarely than the other types of TCs. The low rate of occurrence of the EW-TCs that made landfall in Japan was
likely influenced by the westward trend of TC movement
at the time of TSF (Table 1).
The rates of occurrence of the TCs that made landfallin the other countries varied, as shown in Fig. 6 andTable 2. The rate of occurrence of TCs that madelandfall in China was lowest for the PTC-TCs, among
the other TC factors, while the rate in the Philippines
was highest for PTC-TCs. The results obtained from thisstatistical analysis provide important information fordisaster prevention.

f. Environmental physical parameters
Table 3 shows the averages of the atmosphericenvironmental physical parameters—the lower- and442

MONTHLY WEATHER REVIEWVOLUME 146

FIG. 5. TC tracks (lines) and first locations (crosses) of (a) SL, (b) CR, (c) GY, (d) EW, (e) PTC, and (f) UCF. The tracks represent
10 recent TSs for each environmental factor.

midtropospheric humidity, zonal and meridional vertical shear, magnitude of the vertical shear, CAPE, MPI
maximum wind, and TC central pressure—averaged towithin 58 of the TC center at both the time of TSF andmature time. Additionally, Table 4 shows the following
ocean environmental physical parameters beneath the
TC center: SST, TCHP, depth of the 268C isotherm in
the ocean, and sea surface 100-m depth-averaged ocean
temperature. Statistical analyses for the atmosphericparameters were used to associate TCs with each factor
over the 35-yr analysis period between June andNovember, while analysis periods for ocean parameters were from 1982 to 2013, the same as the available period of FORA-WNP30.
The lower- and midtropospheric humidity differedamong factors, with a 5% level of significance (Table 3).
Relatively wet conditions in the SL and CR environments were apparent at both TSF and mature times.
By contrast, the lower- and midtropospheric humidity
for EW were lower than those for the other types ofTCs. The relatively drier conditions around the EWTCs were apparent throughout the troposphere (notshown). The drier conditions were linked to the average storm size of the EW-TCs at the time of TSF,
smaller than the other types of TCs (Table 1 andFig. 4).
It is interesting that there were no significant differences in average SST at both the TSF and mature times,
except for those of UCF (Table 4 and Fig. 7). However,at the time of TSF, the TCHP and sea surface 100-m
depth-averaged temperature of the PTC were higher,
and the depth of the 268C isotherm was deeper, compared to the other types of TCs. This ocean condition of
PTC was likely due in part to the TSF location being
farther southeast.
At both the TSF and mature times, the maximum
wind and central pressure of the MPI for the PTC-TCs
were significantly intensified, most likely as a result of
the higher CAPE (Table 3). With a longer duration ofdevelopment under the weak vertical shear and higher
TCHP, the large-scale environments associated with
PTC were favorable for TC development.
Although relatively wet conditions in the CR environments were apparent at the mature time, the centralpressure of the MPI for the CR was significantly higher.
The exact reasons why the environments associated with
the CR at the mature time were less favorable remain
unclear.

4. Discussion
The storm sizes of the EW-, GY-, and PTC-TCs
generated by different environmental factors differedsignificantly at the time of TSF. According to the resultsof an idealization experiment conducted by Xu andWang (2010), whose experimental design followed
Wang (2009), the simulated size of a TC defined as
the radius of azimuthal mean damaging-force wind

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FUDEYASU AND YOSHIDA

FIG. 6. The rate of occurrence of TCs that made landfall in Japan,
China, Vietnam, and the Philippines, by country, divided by the
number of occurrences of each environmental factor that influenced
TCG over the 35-yr analysis period between June and November.
Note: monsoon shear line is S; monsoon confluence region, C; monsoon gyre, G; easterly wave, E; Rossby wave energy dispersion, P;
unclassified flow, U; and average of all TCs (i.e., climatology), A.

(25.7 m s21) was largely determined by the horizontal
size of the large-scale initial circulation field. In theirstudy, strong winds in an initial large-scale circulation
led to the development of active spiral rainbands. The
latent heat released in spiral rainbands played a key rolein increasing the low-level radial inflow and acceleratingtangential winds outside, resulting in the outward expansion of tangential wind fields and, thus, increasing
the size of the TC.
It was found that the storm size of a GY-TC at thetime of TSF was larger than other types of TCs, but theEW- and PTC-TCs were smaller. In the large-scale environment associated with the GY, a TC could form
within the monsoon trough, where the initial large-scale
circulation field was large. By contrast, the EW-TCs
were associated with a trough of easterly trade winds,
and the PTC-TCs formed in the low pressure areas of the

wave train as the Rossby wave energy dispersed from apreexisting TC, where large-scale circulation was relatively small. This is consistent with the results of Xu andWang (2010).
While the TCG detection method used for classification accounted for most of the TCG mechanisms,
there were 41 TCs with an unclassified flow. The features of UCF-TCs were a more northerly location of
TSF and a larger average size and slower development
at the time of TSF, as well as weaker intensity at themature time, compared with the other types of TCs.
Given the features of the UCF-TCs, it is worth considering whether there could be another genesis
mechanism. Davis and Bosart (2003, 2004, 2006) foundthe mechanism of tropical transition: the system begins
as an extratropical cyclone, which occludes and drifts
within subtropical latitudes; convection builds, andthen there is full transition to a TC structure. However,this study did not find any significant effect from an
extratropical cyclone on the TCG of UCF-TCs inweather charts. The mechanism of tropical transition
has been linked primarily to TCG in the Atlantic basin
but not TCG in the WNP. Because the purpose of thisstudy was to investigate the characteristics of TCs
stratified by TCG environment, the clarification of
unclassified flow modifying the TCG detection method
is reserved for future studies.

5. Conclusions
This study used the TCG detection method tocategorize intense TCs according to the environmental factors that influenced TCG over the WNP
for a 35-yr period. Examining seasonal changes in TCG
cases for each environmental factor that influenced TCG led
to the conclusion that the SL- and EW-TCs occurred annually, whereas CR-, GY-, and PTC-TCs mainly occurred in
the summer and autumn. The characteristics of TCs associated with the different factors were studied using 677 TCG
cases that occurred between June and November, when
all of the environmental factors could occur. Our results canbe summarized, as synthesized schematically in Fig. 8, as
follows:

TABLE 2. Statistical summary of the number and rate of occurrence of TCs making landfall in Japan, China, Vietnam, and the Philippines, divided
by the number of occurrences of each environmental factor that influenced TCG over the 35-yr analysis period between June and November.

Japan
China
VietnamPhilippines

SL

CR

GY

EW

PTC

UCF

ALL

47 (16%)
93 (31%)47 (16%)45 (15%)22 (17%)37 (28%)16 (12%)13 (10%)7 (11%)
21 (34%)8 (13%)
11 (18%)5 (5%)
31 (34%)13 (14%)15 (16%)6 (9%)
10 (15%)10 (15%)17 (26%)2 (7%)
15 (54%)3 (11%)
3 (11%)

89 (13%)207 (31%)
97 (15%)104 (14%)

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MONTHLY WEATHER REVIEWVOLUME 146

TABLE 3. As in Table 1, but for the atmospheric environmental physical parameters averaged to within 58 of the TC center over the 35-yr
analysis period between June and November. Areas where a t test indicates significant differences with a 5% level of significance arepresented in boldface font.
SL

CR

GY

EW

PTC

UCF

ALL

21

0.928 310.927 910.928 58
0.926 110.927 72
0.927 87
0.927 89
Time of 850-hPa mixing ratio (kg kg )0.769 92
0.771 47
0.772 01
0.746 05
0.764 05
0.767 98
0.766 510
TSF 500-hPa mixing ratio (kg kg21)
Zonal vertical shear (m s21)
21.5
21.9
21.8
0.2
22.1
22.4
21.4
Meridional vertical
21.9
22.7
21.8
21.0
21.5
21.8
21.9
shear (m s21)
Total vertical shear (m s21)
6.2
6.6
6.5
5.8
5.5
5.8
6.1
CAPE (J kg21)
13 074.4
12 978.8
12 837.9
13 247.1
13 651.5
12 09613 073.9
115.7
115.8
114.7
116.5
118.4
110.9
115.7
MPI maximum wind (m s21)
MPI central pressure (hPa)
858.1
859.2
860.4
858.0
853.8
868.5
858.5
Mature 850-hPa mixing ratio
0.928 75
0.929 04
0.928 61
0.925 60
0.927 56
0.927 710.928 21time 500-hPa mixing ratio
0.773 53
0.776 410.776 73
0.756 190.760 140.785 71
0.771 11
Zonal vertical shear
20.7
21.0
20.8
0.4
20.0
20.2
20.5
Meridional vertical
20.7
22.0
20.2
0.0
0.4
21.0
20.7
shear
Total vertical shear
8.0
7.6
7.5
7.7
6.6
6.2
7.6
CAPE
10 331.2
10 007.0
10 031.2
10 526.9
10 468.0
9402.310 249.8
MPI maximum wind
99.6
94.4
98.5
100.1
102.7
87.8
98.4
MPI central pressure
881.7
889.8
884.7
881.5
878.0
898.8
883.8

The average location of TSF of the EW-TCs (PTCTCs) was farther east and north (south), while thatof the SL-TCs was farther west than those of theother types of TCs.
d The average size of the GY-TCs at the time of TSF
was larger than that of the other types of TCs, while
the EW- and PTC-TCs were smaller.
d The average size of the RMW of the GY-TCs at thetime of TSF was larger than the average of the othertypes of TCs, while the RMW of the PTC-TCs at themature time was smaller.
d The GY-TCs tended to develop slowly at the time of TSF.
d Rates of RI occurrence for the CR-TCs were higher
than for the other types of TCs, whereas the GY-TCs
rarely experienced RI.
d The EW- and PTC-TCs tended to move westward at thetime of TSF. Conversely, the CR-TCs tended to movenorthward.
d

The occurrence rate of the PTC-TCs that made landfallin the Philippines was higher, and those of the EW-TCs(PTC-TCs) that made landfall in Japan (China) werelower, than the average for all TCs.
d The duration of the developmental stage and the TC
life span of the PTC-TCs were longer than for theother types of TCs.
d There were no significant differences in the averagestorm size of TCs at the mature time, except for the PTCTCs, which were larger than the other types of TCs.
d The PTC-TCs tended to develop as intense TCs in theenvironments favorable for TC development.
d According to MPI theory, the large-scale environments associated with the PTCs were more favorable
for TC development.
d There were no significant differences in SST aroundthe TCs at the both of TSF and mature times, except
for UCF-TCs.
d

TABLE 4. As in Table 1, but for the ocean environmental physical parameters beneath the TC center over the 32-yr analysis period
between June and November. Areas where a t test indicates significant differences with a 5% level of significance are presented in boldface
font.

Time of TSF

Mature time

SST (8C)
TCHP (kJ cm22)
Depth of the 268C isotherm (m)0–100-m average temperature (8C)SST
TCHP
Depth of the 268C isotherm
0–100-m average temperature

SL

CR

GY

EW

PTC

UCF

ALL

29.0
81.1
80.1
27.1
27.8
40.2
51.2
25.0

29.0
78.5
77.4
27.1
27.9
40.5
49.6
24.9

28.9
76.2
76.9
27.0
27.9
34.6
44.5
24.7

29.0
80.9
78.6
27.0
27.8
40.3
50.7
24.9

29.0
92.9
90.3
27.9
27.8
41.0
55.5
25.3

28.7
66.2
67.6
26.5
27.6
42.0
48.6
24.3

29.0
80.7
79.6
27.1
27.8
40.3
50.8
24.9

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FUDEYASU AND YOSHIDA

445

FIG. 7. Boxplots of (a),(b) SST and (c),(d) sea surface–100-m depth-averaged ocean temperature of each environmental factor that influenced TCG at (a),(c) the time of TSF and (b),(d) the mature time over the 32-yr analysis
period between June and November. The vertical bar shows the full range of the distributions, and the box shows
the interquartile range of 25%–75% of all cases. The bold horizontal line within each box indicates the median.

The TCHP and the sea surface100-m depth-averaged
temperature of the PTC-TCs at the time of TSF werehigher, with deepening of the depth of the 268C
isotherm.
d Thelower- and midtropospheric humidity values
were higher for the SL- and CR-TCs, but lower for
the EW-TCs, than for the other types of TCs.
d

The exact reasons why CR-TCs tended to experience
RI remain unclear. Furthermore, the UCF-TCs thatwere not categorized by this TCG detection method are
not yet well understood. However, the relationship between large-scale environmental factors that influence
TCG and the characteristics of TCs can be used in cyclogenesis studies from a climate perspective, and thetrends for landfall in each country will be important fordisaster prevention.
Acknowledgments. The authors thank Drs. K. Ito,
A. Wada, M. Yamaguchi, C. Muroi, K. Bessho, andN. Koide for their helpful comments and support of data
analysis. The authors are also grateful for the assistance oflaboratory members from the Yokohama National University. This study utilized the dataset of JRA-25, thecooperative research project carried out by the Japan
Meteorological Agency (JMA) and the Central ResearchInstitute of Electric Power Industry (CRIEPI). Also, thisstudy utilized the dataset of FORA-WNP30, which was
produced by the Japan Agency for Marine-Earth Scienceand Technology (JAMSTEC) and the Meteorological Research Institute of the JMA (JMA/MRI). This work is
supported by MEXT KAKENHI Grants 17H02956 and
17K14398, Joint Usage/Collaborative Research Center for
Multidisciplinary Disaster Prevention Study of the Disaster
Prevention Research Institute (DPRI) at Kyoto University

FIG. 8. Schematic image of the five flow patterns contributing to
TCG based on RH99. The westerly and easterly winds are indicated by arrows, and the TCG location (cyclone symbol) of eachpattern is shown.

446

MONTHLY WEATHER REVIEW(29G-05), Collaborative Research Project on Computer
Science with High-Performance Computing at Nagoya
University (2017), and the Fujitsu PRIMEHPC FX10
System (Oakleaf-FX) in the Information Technology Centerof the University of Tokyo (2015, 2016).
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