Journal
Februaryof2020
the Meteorological Society of Japan, 98(1),H. FUDEYASU
61−72, 2020.et al.
doi:10.2151/jmsj.2020-004

Development Conditions for Tropical Storms
over the Western North Pacific Stratified by Large-Scale Flow Patterns
Hironori FUDEYASU
Yokohama National University, Yokohama, Japan

Ryuji 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

Munehiko YAMAGUCHI
Meteorological Research Institute, Tsukuba, Japan

Hisaki EITO, Chiashi MUROI, Shuji NISHIMURA, Kotaro BESSHO,
Yoshinori OIKAWA, and Naohisa KOIDE
Japan Meteorological Agency, Tokyo, Japan(Manuscript received 18 July 2019, in final form 8 October 2019)

Abstract
This study investigated the characteristics and environmental conditions of tropical cyclones (TCs) over thewestern North Pacific from 2009 to 2017 that dissipated before reaching tropical storm strength (TDs) underunfavorable environmental conditions; we compared TDs with TCs that reached tropical storm strength (TSs)in terms of modulations of relevant large-scale flow patterns. The flow patterns were categorized based on fivefactors: shear line, confluence region, monsoon gyre, easterly waves, and Rossby wave energy dispersion froma preexisting cyclone. Among 476 cases, 263 TDs were detected using best-track data and early stage Dvorak
analysis. The TCs in the environments associated with the confluence region or Rossby wave energy dispersion(easterly waves) tended to reach tropical storm strength (remain weak) compared with the other factors. The average locations of TDs at the time of cyclogenesis in the confluence region, monsoon gyre, and easterly waves (Rossby
wave energy dispersion) in the summer and autumn were farther to the west (east and north) than those of TSs
that exhibited the same factors. The environments around TDs were less favorable for development than thosearound TSs, as there were significant differences in atmospheric (oceanic) environmental parameters between
TDs and TSs in the factors of confluence region, easterly waves, and Rossby wave energy dispersion (shear line,
monsoon gyre, and Rossby wave energy dispersion). The environmental conditions for reaching tropical storm
strength over their developing stage, using five factors, can be summarized as follows: higher tropical cyclone
heat potential in the shear line and monsoon gyre, weak vertical shear in the confluence region, wet conditionsin the easterly waves, and higher sea surface temperatures and an intense preexisting cyclone in Rossby wave
Corresponding author: Hironori Fudeyasu, Faculty of Edu­
cation, Yokohama National University, 79-2 Tokiwadai,
Hodogaya-ku, Yokohama, 240-8501, Japan
E-mail: fude@ynu.ac.jp
J-stage Advance Published Date: 20 October 2019
©The Author(s) 2020. 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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energy dispersion from a preexisting cyclone.
Keywords tropical cyclogenesis; early stage Dvorak analysis; large-scale flow patterns; unfavorable environmental conditions
Citation Fudeyasu, H., R. Yoshida, M. Yamaguchi, H. Eito, C. Muroi, S. Nishimura, K. Bessho, Y. Oikawa, andN. Koide, 2020: Development conditions for tropical storms over the western North Pacific stratified by large-scale
flow patterns. J. Meteor. Soc. Japan, 98, 61–72, doi:10.2151/jmsj.2020-004.

1.

Introduction

Tropical cyclones (TCs) form under the large-scale
environmental conditions favorable for cyclogenesis(TCG) during their TCG stage. According to Gray
(1968, 1998), the environmental conditions favorable
for TCG include sea temperatures to a depth of 60 mthat exceed 26°C and large-scale flow patterns. Ritchieand Holland (1999) grouped the lower-troposphericlarge-scale atmospheric flow patterns contributing to
TCG over the western North Pacific (WNP) into fivefactors: shear line (SL), confluence region (CR), monsoon gyre (GY), easterly waves (EW), and Rossby
wave energy dispersion from a preexisting cyclone(PTC).
Some TCs during their developing stage may
continue to strengthen, eventually reaching tropicalstorm strength, with maximum sustained wind speedsof 34 knots. However, some weaken and eventuallydissipate before reaching tropical storm strength underunfavorable environmental conditions; in this study,
the former are referred to as TSs and the latter as TDs.
Although some previous studies (e.g., Zehr 1992;
Peng et al. 2012; Fu et al. 2012; Zhang et al. 2015)
have attempted to characterize differences in developing and non-developing tropical disturbances, none
has addressed the favorable/unfavorable environmental conditions for TSs stratified by relevant large-scale
flow patterns due to a lack of reliable TD data.
The Japan Meteorological Agency (JMA) developed a unique TC analysis technique, early stage
Dvorak analysis (EDA), which has been in operation
since 2001 (Tsuchiya et al. 2001; Kishimoto et al.
2007; Kishimoto 2008; Yamaguchi and Koide 2017).
In a conventional Dvorak technique, a T number is assigned to a TC based on its strength; T numbers range
from 1.0 to 8.0 at intervals of 0.5. In general, whenTCs reach tropical storm strength, they are categorized
as a T number of 2.5 (Koba et al. 1990; Dvorak 1984).
In the EDA, the T numbers 0.0 and 0.5 are added tothe conventional Dvorak technique to make it possible

to analyze TDs in their developing stage. Therefore,
the operational EDA data list tracks for not only TSs
but also TDs.
Although some weather prediction models arecapable of detecting TS cases, there are still a lotof false alarm cases (Halperin et al. 2013, 2017;
Yamaguchi et al. 2015). This study used operational
EDA and reanalysis data to statistically investigate
the unfavorable environmental conditions that resultin maintaining TD status compared with those thatlead to TS development, including both atmosphericand oceanic physical parameters. Large-scale flow
patterns which contribute to TCG are expected during
the development of a TD into a TS. Thus, the purposeof this study was to determine how different environmental factors contributing to TCG classified by
Ritchie and Holland (1999) modulate the unfavorable
environmental conditions resulting in TDs. The ability
to identify these conditions would greatly facilitate
forecasting operations (Blake and Kotal 2018; Yamaguchi et al. 2018).
This paper is organized as follows. Section 2
describes the data set and environmental physicalparameters. Section 3 presents the statistical results
depicting the characteristics and environmental
conditions of TDs and TSs, as well as those stratified
by five relevant environmental factors classified by
Ritchie and Holland (1999). The favorable environmental conditions for TS development are discussed
in Section 4. A summary and conclusions are presented in Section 5.
2.

Data and definitions

This study examined 476 TCs that occurred over
the WNP during the 9 years from 2009 to 2017 fromoperational EDA data and best-track archive dataof the Regional Specialized Meteorological Centers(RSMC) Tokyo–Typhoon Center (hereafter, best-trackdata). The EDA date added T numbers of 0.0 and 0.5
based on the following conditions: 1) a convective
cloud system has persisted for 12 h or more; 2) theFebruary 2020

H. FUDEYASU et al.

cloud system, a cyclonic circulation, has a cloudsystem center defined within a diameter of 2.5° latitude or less; 3) the cloud system persisted for 6 h ormore; 4) the cloud system has an area that is dense
and cold (−31°C or colder) and that appears less than
2° latitude from the center; and 5) the above overcast
is more than 1.5° latitude in diameter. A T number of
0.0 is assigned when three out of the five conditions
are met, and a T number of 0.5 is assigned when fourout of the five conditions are met. Details of the EDA
algorithm were described in a recent study of Yamaguchi and Koide (2017).
TCs were categorized into TDs and TSs. Here, aTD refers to a TC listed in EDA data only; 213 TDs
were revealed in the EDA data. Specifically, a TD is a
TC detected by operational EDA that did not achieve
tropical storm strength. A TS refers to a TC listed inboth the EDA and best-track data; 263 TSs were identified from 476 TCs.
This study investigated the environmental physicalparameters associated with TDs compared with those
associated with TSs. The atmospheric parameters were
calculated from the Japanese 55-year Reanalysis Project datasets (JRA55; Kobayashi et al. 2015) (details
are available online at http://jra.kishou.go.jp/JRA-55/
index_en.html), and the oceanic parameters were
derived from the three-dimensional variational-based
parent domain output from the Four-dimensional
Variational Ocean Reanalysis for the WNP (FORAWNP30) (Usui et al. 2017). The JRA55 reanalysis
contains data collected at 6-h intervals with the horizontal resolution of 1.25° from 1958 to the present.
FORA-WNP30 contains data at 6-h intervals with thehorizontal resolution of 0.5° from 1982 to 2016, with
54 vertical levels from 0.5 to 6000 m in depth.
We evaluated the following atmospheric and oceanic physical parameters: upper-tropospheric divergenceat 200 hPa (DIV200); lower-tropospheric relative
vorticity at 850 hPa (VOR850); vertical shear definedas the difference in horizontal winds between 200
and 850 hPa (SHRD) and between 500 and 850 hPa(SHRS); lower- and middle-tropospheric relative
humidity calculated over 850 – 700 hPa (RHLO) and
500 – 300 hPa (RHHI); maximum potential intensity(MPI) (Emanuel 1986, Bister and Emanuel 1998);
convective available potential energy (CAPE) derived
from the MPI equation; sea surface temperature (SST);
tropical cyclone heat potential (TCHP); and depth of
the 26°C oceanic isotherm. The TCHP was calculated
by summing the ocean heat content from the sea surface to the depth of the 26°C isotherm (Leipper andVolgenau 1972; Wada 2015). The SST, TCHP, 26°C

63

oceanic isotherm, CAPE, and MPI were evaluatedfrom 2009 to 2016, the period that coincided with
FORA-WNP30. Two area averages were used in thisstudy, similar to the approaches taken in several previous studies (e.g., Knaff et al. 2005): 1) the average forthe circular area with an 800-km radius from the location of TCG and 2) an annulus of 200 – 800 km fromthe location of cyclone center. The former was used
to evaluate oceanic physical parameters, and the latter
was applied to the atmospheric physical parameters.
This study enhanced the statistical analysis of thecharacteristics and environmental conditions of TDs
by examining modulation by five relevant environmental factors which contributed to TCG, namely, SL,
CR, GY, EW, and PTC (Ritchie and Holland 1999).
To categorize the five environmental factors, we used
the TCG score detection method (TGS) developedby Yoshida and Ishikawa (2013) based on EDA andJRA55 data. The location and time of the first entryof EDA data were used as the TCG location and time.
When the major environmental factors could not befound by the TGS, the flow pattern was categorized as
unclassified flow (UCF). Details of the TGS algorithm
are presented in Appendix A.
3.

Results

3.1 Characteristics and environmental conditions of
TDs and TSs
Among the 476 TCs over the analysis period from2009 to 2017, 263 TDs and 213 TSs were revealed.
The occurrence rate of TDs, the number of TD divided by the total number of TCs, was 55 %, which isa higher rate than that of TSs. Figure 1a presents theaverage number of TDs and TSs for each month. TSs
tended to occur in the summer and autumn, whereas
TDs occurred throughout the year. Because this seasonal variation was due to seasonal variation in all TC
occurrences, we divided the number of TDs and TSs
by the monthly total of TCs, as presented in Fig. 1b.
The TD occurrence rate was higher than that of TSs
in the winter and spring, whereas the trend was completely opposite for the period from May to October.
Table 1 presents the average locations of TDs andTSs between June and October, typically a period
during which many TDs and TSs may occur at almost
the same monthly occurrence rate. Differences in theparameters between TDs and TSs were assessed using
t-test. The average location of TDs at the time of TCG
was farther to the west and north than that of TSs with
a 5 % level of significance. Tracks of TDs and TSs,
presented in Fig. 2, start at the TCG location, i.e.,
the locations of the first entry of EDA for each case.

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Fig. 1. Seasonal variation in tropical depression (TD) and tropical storm (TS) occurrence over the 9-year analysis
period: (a) monthly average numbers of TDs and TSs and (b) monthly occurrence rates normalized by the monthly
total. Gray bars indicate TDs, and white bars indicate TSs.

Table 1. Statistical summary of the tropical depressions (TDs) and tropical storms (TSs) associated with the environmental factors over a 9-year analysis period between June and October. The speed of TC movements isdefined from the change of latitude and longitude, but it is different from the actual speed. Areas for which a t-test
indicated differences at a 5 % level of significance are presented in bold and italic fonts, except for the number
and duration. Note: average of all TCs, ALL; shear line, SL; confluence region, CR; monsoon gyre, GY; easterly
waves, EW; Rossby wave energy dispersion, PTC; and unclassified flow, UCF.
ALL
NumberAve. lon. (°E)
Ave. lat. (°N)
Speed (°day-1)
Duration (day)

SL

GY

TD

TS

TD

TS

TD

TS

TD

TS

158
138.61
15.36
3.16
1.39

173
142.31
14.40
2.85
1.21

41
134.86
15.23
3.07
1.49

46
138.63
15.01
2.77
1.09

40
134.37
14.60
3.44
1.24

46
139.15
14.52
2.47
1.24

23
131.91
14.29
2.58
1.62

23
140.99
14.36
2.98
1.15

TD

TS

TD

TS

TD

TS

29
141.79
16.30
3.31
1.11

24
149.43
15.01
2.91
1.66

14
162.52
13.44
3.42
1.93

23
153.67
11.19
3.48
1.07

11
143.24
20.79
3.04
1.14

11
134.36
16.78
2.67
1.00

EW
NumberAve. lon. (°E)
Ave. lat. (°N)
Speed (°day-1)
Duration (day)

CR

PTC

The TCG locations of TSs were concentrated along
10 – 20°N longitude.
The statistical characteristics of TC movements
were also investigated in this study. The speed of TC
movements at the time of TCG was defined from thechange in the location from the time of TCG to 1 daylater. Note that TDs which had a lifetime of less than 1
day were excluded for the average of TC movements.
The average speed of the movement of TDs and TSs
at the time of TCG differed significantly, with TDs
tending to move faster than TSs (Table 1).
Figure 3 presents the distribution of TD durations
between the first and the final EDA entry for each TD

UCF

between June and October over the analysis period.
A TD duration of 6 – 12 h was the most common
duration (30 %) observed, followed by the 18 – 24-h
duration (25 %). Thus, over half of all TDs had aduration of less than 24 h, and the occurrence rates of
TDs decreased as the duration increased. The average
TD duration was 1.39 days (Table 1).
Tables 2 and 3 present the average atmospheric andoceanic environmental parameters for TDs and TSs at
the time of TCG. There were significant differencesin the average values of all parameters except RHLO,
with a 5 % level of significance. The VOR850,
DIV200, RHHI, CAPE, MPI, and all oceanic param-February 2020

H. FUDEYASU et al.

65

Fig. 2. TC tracks (lines), the first locations (closed circles), and the final locations (crosses) of (a) TSs and (b) TDs
between June and October over a 9-year analysis period. The first locations of both TSs and TDs correspond to
the first entry of EDA data for each TC. The end location of a TS is the location at which point TS strength wasreached. The end location of a TD is the location of the last EDA record for each TC, i.e., when a TD dissipated.

Fig. 3. TD occurrence rate classified into 12-hclasses of duration between the first and final
records of EDA data for each TD over the 9-year
analysis period.

eters of TDs were lower, and the SHRS and SHRD
of TDs were higher than those of TSs. Therefore, thelarge-scale environment around TDs was less favorable for TC development than that around TSs.
3.2 Characteristics of TDs and TSs stratified byenvironmental factors
Figure 4 presents the TD and TS occurrence distribution stratified by the five environmental factors and
UCF. Among the 476 cases, SL was the most frequent
factor (155, 33 % of the total), followed by CR (101,21 %) and EW (80, 17 %). GY (70 TCs) and PTC (44TCs) were 14 % and 10 %, respectively, and UCF (26TCs) accounted for 5 %. These percentages were similar to those reported by Yoshida and Ishikawa (2013)using the Joint Typhoon Warning Center best-trackdata.
The occurrence rates of TDs and TSs stratified
by the five environmental factors and UCF are alsopresented in Fig. 4. The occurrence rate of TDs thatformed in environments associated with the CR or
PTC was 48 %, which was the lowest among the otherfactors. Thus, TDs that formed in an environment
associated with CR or PTC tended to reach tropicalstorm strength in their developing stage, resulting in
an increase in the number of CR-TSs or PTC-TSs(hereafter, CR-TD, CR-TS, PTC-TD, and PTC-TS;
similar terms indicate a TD or a TS that formed in alarge-scale flow pattern mainly associated with a CR,
PTC, or a similar factor). In contrast, the occurrence
rate of EW-TD was 65 %, the highest among the factors. The TDs that formed in an environment associated with EW tended to remain weak in intensity during
their developing stage and were therefore less likely
to develop into TSs.
In some cases, multiple environmental factors may
contribute to TCG. Given that the TGS considered
five environmental factors, it is possible to examine
not only the main factor but also the factor of the next
largest contribution (hereafter secondary factor) (seeAppendix A for the definition of the main and secondary factors). Table 4 presents the occurrence numbers
and rates of these secondary factors, stratified by themain factors. Secondary-factor occurrence rates aredefined as the number of TD or TS secondary factors
divided by the number of TD or TS main factors.
Interestingly, there were larger differences in the EW
secondary-factor occurrence rates between TDs andTSs, specifically differences in the occurrence rates

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Table 2. As in Table 1, except the atmospheric environmental physical parameters during the 9-year analysis period
between June and October. The CAPE and MPI were computed over 8 years from 2009 to 2016, coinciding with
FORA-WNP30.
ALL
−5

−1

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

−1

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

GY

TD

TS

TD

TS

TD

TS

TD

TS

6.72
6.23
72
48
4.4
9.8
12694.0
113.9

8.74
7.39
72
51
3.7
8.5
13085.4
115.7

7.95
6.07
73
50
4.6
10.0
12680.0
113.9

9.58
7.19
72
51
3.9
8.8
12839.8
114.5

6.16
7.16
72
51
5.3
11.0
12753.7
114.2

8.64
7.83
73
52
4.0
8.8
13115.0
115.9

8.96
6.27
73
48
3.5
10.4
12675.3
113.8

9.59
7.59
72
49
3.5
7.9
12786.6
114.4

TD

TS

TD

TS

TD

TS

4.33
5.45
71
43
2.8
6.8
13054.8
115.6

5.81
5.97
71
48
2.6
7.2
13411.3
117.3

4.68
5.13
71
45
4.1
8.4
12873.0
114.9

9.06
8.26
72
51
3.8
8.1
13591.0
118.1

8.43
6.81
71
49
6.4
13.2
11404.5
107.6

9.53
7.3
72
52
4.4
11.4
12839.9
114.7

EW
−5

CR

PTC

UCF

Table 3. As in Table 1, except the oceanic physical parameters during the 8-year analysis period from2009 to 2016 between June and October.
ALL
SST (ºC)
TCHP (kJ cm−2)26-°C-depth (m)

SL

GY

TS

TD

TS

TD

TS

TD

TS

29.2
98.9
103.1

29.4
116.7
117.3

29.3
95.1
103.0

29.3
113.8
114.4

29.2
101.2
101.8

29.4
113.7
117.2

29.3
95.0
93.0

29.3
115.8
117.9

EW
SST (ºC)
TCHP (kJ cm−2)26-°C-depth (m)

CR

TD

PTC

UCF

TD

TS

TD

TS

TD

TS

29.4
110.8
113.8

29.5
122.7
120.1

29.2
109.1
115.0

29.5
126.2
121.6

28.4
67.7
83.1

29.5
109.7
112.7

between SL-EW-TD (hereafter main factor–secondary
factor) and SL-EW-TS (0.60 vs. 0.45), between CREW-TD and CR-EW-TS (0.54 vs. 0.32), and between
PTC-EW-TD and PTC-EW-TS (0.67 vs. 0.48). Thus,
environments associated with the secondary factor of
EW were less favorable for TS development, resulting
in an increase (reduction) in the occurrence rates of
TDs (TSs). In addition, there were larger differencesin the PTC secondary-factor occurrence rates between
TDs and TSs, i.e., the occurrence rates between SLPTC-TD and SL-PTC-TS (0.09 vs. 0.20), between

CR-PTC-TD and CR-PTC-TS (0.13 vs. 0.23), andbetween GY-PTC-TD and GY-PTC-TS (0.03 vs. 0.16).
Therefore, the large-scale environments associatedwith the secondary factor of PTC were more favorable
for TS development, resulting in a reduction (increase)in the occurrence rate of TDs (TSs).
Table 1 also presents the statistical characteristics of
TDs and TSs stratified by five environmental factors
and UCF; tracks of TDs and TSs are presented in Fig.
5. There were differences in the average location of
TCG between TDs and TSs. The CR-TDs, GY-TDs,

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H. FUDEYASU et al.

of all TDs (1.39 days). The average duration of all
TSs between the first entry of EDA and the time when
tropical storm strength was reached was 1.21 days.
The average duration of EW-TSs was 1.66 days,
which is approximately 40 % longer than that of all
TSs.

Fig. 4. Distribution of TD and TS occurrence undereach environmental factor over the 9-year analysis period. SL: shear line; CR: confluence region;
GY: monsoon gyre; EW: easterly waves; PTC:
Rossby wave energy dispersion; and UCF: unclassified flow. Gray bars indicate TDs, and whitebars indicate TSs. The numbers refer to the occurrence rates of TDs stratified by factors.

and EW-TDs occurred on average farther to the west
than did CR-TSs, GY-TSs, and EW-TSs, respectively.
In contrast, the average TCG location of PTC-TDs
was more biased to the east and north than that of
PTC-TSs. As presented in Table 1, there were no differences in the average velocity except for the CR-TD
and CR-TS. The average speed of CR-TDs was higher
than that of CR-TSs with a 5 % level of significance.
This study compared the average duration of TDs(Table 1) between the first and last entries of EDA
data. The average duration of PTC-TDs was 1.93
days, which is approximately 40 % longer than that

3.3 Development conditions of TDs and TSs
stratified by environmental factors
Tables 2 and 3 present environmental parameters
for TDs and TSs at the time of TCG, stratified by five
relevant environmental factors and UCF. There weredifferences in atmospheric environmental parameters
between TD and TS, specifically in the factors of CR,
EW, PTC, and UCF. The VOR850 of CR-TDs and
PTC-TDs were lower than those of CR-TSs and PTCTSs, respectively. In addition, the DIV200, RHHI,
CAPE, and MPI of PTC-TDs were lower than thoseof PTC-TSs. The RHHI of EW-TDs was lower thanthat of EW-TSs. There were no significant differencesin SHRS and SHRD, except for the CR; the vertical
shear of CR-TDs was significantly higher than thatof CR-TSs. It is interesting to note that atmosphericparameters did not differ in SL and GY between TDs
and TSs with a 5 % level of significance.
There were differences in the oceanic parameters
between TDs and TSs in the factors of SL, GY, PTC,
and UCF. The TCHP and the depth of the 26°C
isotherm of SL-TDs and GY-TDs were lower thanthose of SL-TSs and GY-TSs, respectively. The SSTof PTC-TDs was lower than that of PTC-TSs. The
oceanic parameters did not differ between TDs and
TSs for the factors CR and EW with a 5 % level ofTable 4. Occurrence numbers and rates of secondary factors. Secondary-factor occurrence rates were defined as thesecondary-factor numbers for TDs or TSs divided by the main-factor numbers for TDs or TSs, respectively.
Secondary factor
Main factor

SL
TD

CR
TS

GY

EW

PTC

TD

TS

TD

TS

TD

TS

TD

TS

13
0.15

16
0.24

37
0.42

29
0.44

53
0.60

30
0.45

8
0.09

13
0.20

12
0.25

18
0.34

26
0.54

17
0.32

6
0.13

12
0.23

24
0.63

23
0.72

1
0.03

5
0.16

0
0

2
0.07

SL

NumberRate

CR

Number
Rate

20
0.42

20
0.38

GY

Number
Rate

17
0.45

13
0.41

5
0.13

5
0.16

EW

Number
Rate

0
0

0
0

0
0

0
0

1
0.02

2
0.07

PTC

Number
Rate

7
0.33

6
0.26

3
0.14

5
0.22

8
0.38

5
0.22

14
0.67

11
0.48

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Fig. 5. Same as Fig. 2, except for the tracks of TDs and TSs of (a) SL, (b) CR, (c) GY, (d) EW, (e) PTC, and (f)UCF between June and October over the 9-year analysis period. Blue indicates TD; red indicates TS.

Fig. 6. Locations of preexisting TCs for PTC-TDs(open triangles) and PTC-TSs (closed circles)
relative to the TCG location for each TCG case at
the time of TCG over the 9-year analysis period
between June and October.

Fig. 7. Boxplots of maximum sustained windspeeds of preexisting TCs for PTC-TDs and PTCTSs at the time of TCG over the 9-year analysis
period between June and October. The vertical bar
indicates the full range of the distribution, and thebox indicates the interquartile range of 25 – 75 %of all cases. The bold horizontal line within eachbox indicates the median.

significance.
A PTC classified by Ritchie and Holland (1999) isan inherent environmental factor, in that a TC northwest of the TCG location contributes to the TCG of
another TC. Figure 6 presents the geographic locationsof preexisting TCs, categorized as PTC-TDs and PTCTSs, with respect to the location of each PTC case at
the time of TCG. The average distances between pre-existing TCs and TCG locations did not differ significantly, with both averages about 2,800 km. However,the northward average distance of PTC-TDs was significantly shorter than that of PTC-TSs. Interestingly,
there was a significant difference between PTC-TDs
and PTC-TSs in the intensity of a preexisting TC (Fig.
7); the maximum sustained wind speeds of preexisting
TCs were higher for a PTC-TS than for a PTC-TD.

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H. FUDEYASU et al.

The intense preexisting TC formed the more favorable
environment for a development to reach tropical storm
strength.
4.

Discussion

The environmental factors in which the oceanic environmental parameters of TCHP, including the depthof the 26°C isotherm, significantly differed between
TDs and TSs were the SL and GY. The importance ofthe ocean thermal structure in TC development wasrecognized by Leipper and Volgenau (1972) and Shayet al. (2000); specifically, SST plays a key role in
TCG, but the TCHP plays a more important role in TC
intensity changes. Our results therefore suggest that
TS developments require higher TCHP values in thelower-tropospheric flow patterns mainly associatedwith SL or GY. Thus, the development conditions ofSL and GY found to be important was higher TCHP.
In the other words, an atmosphere–ocean coupledmodel is needed to improve the TCG prediction associated with SL or GY.
The relevant environmental factors whose atmospheric parameters differed significantly between TDs
and TSs were CR, EW, and PTC. The magnitudes ofthe vertical shear in the mid-lower and upper-lower
troposphere were significantly larger in the CR-TD
than in the CR-TS. Fudeyasu and Yoshida (2018)
focused on TCs with TS intensity or greater in thesummer and autumn over the WNP, stratified by thefive factors (Ritchie and Holland 1999). Comparedwith the other factors, the magnitude of vertical shear
in the CR was larger at the point in time when tropicalstorm strength was reached. Despite the differencesin the analysis periods used by Fudeyasu and Yoshida(2018), our results clearly showed that CR is associated with intense vertical shear, resulting in an unfavorable environment. Regarding the conditions just prior
to reaching TS strength, TSs required a weak vertical
shear during their developing stage in environments
associated with CR.
The middle-tropospheric relative humidity of EWTD was significantly lower than that of EW-TS.
Dunkerton et al. (2009) showed the center of a closed
cyclone circulation (recirculating Kelvin cat’s eye)from the TCG cases associated with EW over the
North Atlantic and northeastern Pacific Ocean. The
Kelvin cat’s eye within the critical layer represents asweet spot for TCG because it provides a containmentarea for moisture entrained by the developing circulation and/or lofted by the deep convection therein.
Numerical simulation studies (e.g., Yoshida et al.
2017) revealed that a TD develops into a TS, with a69

continuous large-scale environment that contains alarge amount of moisture. Our results also revealed
that development into a TS associated with EW requires higher-humidity environments.
Both atmospheric and oceanic parameters exhibiteda significant difference between TDs and TSs in the
PTC and UCF. In addition, an intense preexisting TC
formed the more favorable environment for TS development in the PTC (Fig. 7), enhancing the lowertropospheric relative vorticity (VOR850) and uppertropospheric divergence (DIV200) (Table 2). Previous
studies (e.g., O’Brien and Reid 1967; Leipper 1967;
O’Brien 1967; Price 1981) revealed that the passage
of a TC induces sea surface cooling, decreasing thesea surface heat flux. Therefore, a preexisting TC
in the PTC leads not only to the positive effect of
atmospheric environments on TS development but
also to the negative effect of the oceanic environment
due to sea surface cooling. The developing conditionsof PTC-TSs were recognized as higher SST and an
intense preexisting TC.
5.

Conclusion

This study statistically investigated the characteristics and environmental conditions of TDs over the
WNP during the 9 years from 2009 to 2017 that disappeared before reaching tropical storm strength underunfavorable environmental conditions during theirdeveloping stage. Using best-track and operational
EDA data developed by the JMA, 263 TDs (occurrence
rate of 55 %) of 476 cases were detected, based on our
definition. The occurrence rate of TD was higher thanthat of TSs in the winter and spring, whereas the trend
was completely the opposite for the period from May
to October.
This study also statistically investigated the TD
occurrence rates stratified by the environmental factors classified by Ritchie and Holland (1999) using
the TGS. The TDs that formed in an environment
associated with CR or PTC tended to reach tropicalstorm strength in their developing stage, resulting in
an increase in the number of CR-TSs or PTC-TSs. In
contrast, the TDs that formed in an environment associated with EW tended to remain weak in intensity
and were therefore less likely to develop into EW-TSs.
The atmospheric and oceanic parameters over the
analysis period between June and October differed
between TDs and TSs, demonstrating that the largescale environments around TDs were less favorable
for a development than those around TSs. Our results,
stratified by five factors and UCF, revealed significant
differences in atmospheric (oceanic) parameters be-

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Vol. 98, No. 1

Fig. 8. Schematic image of the five flow patterns and conditions prior to a TC’s reaching TS strength. The westerly
and easterly winds are indicated by arrows, and the TCG location (cyclone symbol) of each factor is shown.

tween TDs and TSs in CR, EW, PTC, and UCF (SL,GY, PTC, and UCF). Therefore, the environmental
conditions for reaching tropical storm strength during
their developing stage, synthesized schematically in
Fig. 8, can be summarized as follows: higher TCHP
in the SL and GY, weak vertical shear in the CR, wetconditions in the EW, and higher SST and an intense
preexisting TC in the PTC.
Differences in the environmental parameter
stratified by the lower-tropospheric large-scale flow
patterns which contribute to TCG could be considered
key determinants of whether a TC develops to tropicalstorm strength. Thus, the development conditions
stratified by environmental factors can be used for
operational forecasting. Our results also indicate thatan atmosphere–ocean coupled model is needed to
improve the TCG prediction skills as well as to the TC
intensity prediction skills.
Acknowledgments
The authors would like to thank Drs. A. Wada, and
K. Ito for their helpful supports of data analysis. This
study utilized the dataset of JRA55 provided by theJapan Meteorological Agency (JMA), and utilized thedataset of FORA-WNP30 produced by Japan Agency
for Marine-Science and Technology (JAMSTEC)
and Meteorological Research Institute of JMA (JMA/
MRI). This work is supported by the Ministry of
Education, Culture, Sports, Science and Technology(MEXT) KAKENHI Grant 17H02956, 17K14398,
19H00705, and 19H05696. This work also is support-

ed by Collaborative Research Project on Computer
Science with High-Performance Computing at Nagoya
University (2018) and Integrated Research Programfor Advancing Climate Models (TOUGOU program)from the MEXT, Japan.
Appendix A
The TGS developed by Yoshida and Ishikawa (2013)
was used in this study. To describe the contributions
from the five flow patterns to each TCG case, we firstcompute five scores, SCRSL , SCRCR , SCRGY , SCREW ,and SCRPTC , using the following equations:SCRSL = A( ∂∂uy ) ⋅ exp(B ⋅ dist),( ∂∂ux ) ⋅ exp(B ⋅ dist ),∂v
SCR = A( ) ⋅ exp( B ⋅ dist ),∂x
SCRCR = A
EW

(1)

ave

ave

ave

(2)
(3)

SCRGY = exp(−M ) × (ζ − ζ std ),(4)

2
SCRPTC = Cwv
1,(5)

where u and v indicate the zonal and meridional winds
at the 850 hPa level, dist indicates the distance fromthe location of TCG, and ζ indicates the relative vorticity which is expressed as the excess above the standard value ζ std = -1.0 ´ 10-5 s-1. A and B are
arbitrary constants with A = -1.0 (2.0 ´ 10-1) and B= -1.0 ´ 10-5 (−1.0 ´ 10-2) for SCRSL and SCRCR

February 2020

H. FUDEYASU et al.
ninj(SCREW). For SCRGY , M =∑ i=0∑ j=0 (C(′i , j ) −data(′i , j ) )2is computed where C ¢ indicates the sea-level pressure
field to a composite of typical GY cases reported byLander (1994), data¢ indicates the sea-level pressure
field of a TCG case, i and j indicate the horizontal
grid, and ni and nj indicate the width and length, respectively. For SCRPTC , we compute the amplitude of
the first Fourier-component C 2wv1 of sea-level pressure
along the line connecting the preexisting TC and TCG
locations. An upper limit of 1,500 km is set for the
dist, and a lower limit of 1.01 is set for C 2wv1. Aftereach score is normalized by its maximum and minimum values, the flow pattern with the highest score isdetermined to be the main factor. The flow pattern ofthe next largest score is determined to be the secondary factor. Details of the TGS are illustrated by
Yoshida and Ishikawa (2013) and Fudeyasu andYoshida (2018, 2019).
References
Bister, M., and K. A. Emanuel, 1998: Dissipative heating andhurricane intensity. Meteor. Atmos. Phys., 65, 233–
240.
Blake, E., and S. D. Kotal, 2018: Cyclogenesis: Operational
forecasting perspectives. Sub-topic report at the 9th
WMO International Workshop on Tropical Cyclones,
28 pp.
Dunkerton, T. J., M. T. Montgomery, and Z. Wang, 2009:
Tropical cyclogenesis in a tropical wave critical layer:easterly waves. Atmos. Chem. Phys., 9, 5587–5646.
Dvorak, V. F., 1984: Tropical Cyclone Intensity Analysis
Using Satellite Data. NOAA Tech. Rep. NESDIS 11,
47 pp.
Emanuel, K. A., 1986: An air-sea interaction theory fortropical cyclones. Part I: Steady-state maintenance. J.
Atmos. Sci., 43, 585−604.
Fu, B., M. S. Peng, T. Li, and D. E. Stevens, 2012: Developing versus nondeveloping disturbances for tropical
cyclone formation. Part II: Western North Pacific.
Mon. Wea. Rev., 140, 1067–1080.
Fudeyasu, H., and R. Yoshida, 2018: Western North Pacifictropical cyclone characteristics stratified by genesis
environment. Mon. Wea. Rev., 146, 435–446.
Fudeyasu, H., and R. Yoshida, 2019: Statistical analysis of
the relationship between upper tropospheric cold lows
and tropical cyclone genesis over the western North
Pacific. J. Meteor. Soc. Japan, 97, 439–451.
Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669–700.
Gray, W. M., 1998: The formation of tropical cyclones.
Meteor. Atmos. Phys., 67, 37–69.
Halperin, D. J., H. E. Fuelberg, R. E. Hart, J. H. Cossuth, P.
Sura, and R. J. Pasch, 2013: An evaluation of tropical

71

cyclone genesis forecasts from global numericalmodels. Wea. Forecasting, 28, 1423–1445.
Halperin, D. J., R. E. Hart, and H. E. Fuelberg, 2017: The
development and evaluation of a statistical–dynamical
tropical cyclone genesis guidance tool. Wea. Forecast­ing, 32, 27–46.
Kishimoto, K., 2008: Revision of JMA’s early stage Dvorak
analysis and its use to analyze tropical cyclones inthe early developing stage. RSMC Tokyo—Typhoon
Center Technical Review, 10, 1–12. [Available at
http://www.jma.go.jp/jma/jma-eng/jma-center/rsmchp-pub-eg/techrev/text10-1.pdf.]
Kishimoto, K., T. Nishigaki, S. Nishimura, and Y. Terasaka,
2007: Comparative study on organized convective
cloud systems detected through early stage Dvorak
analysis and tropical cyclones in early developing
stage in the western North Pacific and the South China
Sea. RSMC Tokyo—Typhoon Center Tech. Review,
9, 14 pp. [Available at http://www.jma.go.jp/jma/jmaeng/jma-center/rsmc-hp-pub-eg/techrev/text9-2.pdf.]
Knaff, J. A., C. R. Sampson, and M. DeMaria, 2005: An
operational statistical typhoon intensity prediction
scheme for the western North Pacific. Wea. Forecasting, 20, 688–699.
Koba, H., T. Hagiwara, S. Asano, and S. Akashi, 1990:
Relationships between CI number from Dvorak’s
technique and minimum sea-level pressure or maximum wind speed of tropical cyclone. J. Meteor. Res.,42, 59–67 (in Japanese).
Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H.
Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H.
Endo, K. Miyaoka, and K. Takahashi, 2015: The JRA55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5–48.
Lander, M. A., 1994: Description of a monsoon gyre and its
effects on the tropical cyclones in the western North
Pacific during August 1991. Wea. Forecasting, 9, 640–
654.
Leipper, D. F., 1967: Observed ocean conditions and Hurricane Hilda, 1964. J. Atmos. Sci., 24, 182–196.
Leipper, D. F., and D. Volgenau D, 1972: Hurricane heatpotential of the Gulf of Mexico. J. Phys. Oceanogr., 2,
218–224.
O’Brien, J. J., 1967: The non-linear response of a two-layer,
baroclinic ocean to a stationary, axially-symmetrichurricane: Part II. Upwelling and mixing induced bymomentum transfer. J. Atmos. Sci., 24, 208–215.
O’Brien, J. J., and R. O. Reid, 1967: The non-linear responseof a two-layer, baroclinic ocean to a stationary, axiallysymmetric hurricane: Part I. Upwelling induced bymomentum transfer. J. Atmos. Sci., 24, 197–207.
Peng, M. S., B. Fu, T. Li, and D. E. Stevens, 2012: Developing versus nondeveloping disturbances for tropical
cyclone formation. Part I: North Atlantic. Mon. Wea.
Rev., 140, 1047–1066.
Price, J. F., 1981: Upper ocean response to a hurricane. J.

72

Journal of the Meteorological Society of Japan

Phys. Oceanogr., 11, 153–175.
Ritchie, E. A., and G. J. Holland, 1999: Large-scale patterns
associated with tropical cyclogenesis in the western
Pacific. Mon. Wea. Rev., 127, 2027–2043.
Shay, L. K., G. J. Goni, and P. G. Black, 2000: Effects of awarm oceanic feature on Hurricane Opal. Mon. Wea.
Rev., 128, 1366–1383.
Tsuchiya, A., T. Mikawa, and A. Kikuchi, 2001: Methodof distinguishing between early stage cloud systems
that develop into tropical storms and ones that do not.
Geophys. Mag., 4, 49–59.
Usui, N., T. Wakamatsu, Y. Tanaka, N. Hirose, T. Toyoda, S.
Nishikawa, Y. Fujii, Y. Takatsuki, H. Igarashi, H. Nishi­
kawa, Y. Ishikawa, T. Kuragano, and M. Kamachi,
2017: Four-dimensional variational ocean reanalysis:
A 30-year high-resolution dataset in the western North
Pacific (FORA-WNP30). J. Oceanogr., 73, 205–233.
Wada, A., 2015: Verification of tropical cyclone heat potential for tropical cyclone intensity forecasting in thewestern North Pacific. J. Oceanogr., 71, 373–387.
Yamaguchi, M., and N. Koide, 2017: Tropical cyclone genesis guidance using the early stage Dvorak analysis andglobal ensembles. Wea. Forecasting, 32, 2133–2141.

Vol. 98, No. 1

Yamaguchi, M., F. Vitart, S. T. K. Lang, L. Magnusson, R.
L. Elsberry, G. Elliott, M. Kyouda, and T. Nakazawa,
2015: Global distribution of the skill of tropical
cyclone activity forecasts on short- to medium-range
time scales. Wea. Forecasting, 30, 1695–1709.
Yamaguchi, M., H. Titley, and L. Magnusson, 2018: Current
and potential use of ensemble forecasts in operational
TC forecasting. Sub-topic report at the 9th WMO
International Workshop on Tropical Cyclones, 28 pp.
Yoshida, R., and H. Ishikawa, 2013: Environmental factors
contributing to tropical cyclone genesis over the western North Pacific. Mon. Wea. Rev., 141, 451–467.
Yoshida, R., Y. Miyamoto, H. Tomita, and Y. Kajikawa,
2017: The effect of water vapor on tropical cyclone
genesis: A numerical experiment of a non-developing
disturbance observed in PALAU2010. J. Meteor. Soc.
Japan, 95, 35–47.
Zhang, W., B. Fu, M. S. Peng, and T. Li, 2015: Discriminating developing versus nondeveloping tropical disturbances in the western North Pacific through decision
tree analysis. Wea. Forecasting, 30, 446–454.
Zehr, R. M., 1992: Tropical cyclogenesis in the western
North Pacific. NOAA Tech. Rep. NESDIS 61, 181 pp.