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How Significant are Low-Level Flow Patterns in Tropical Cyclone Genesis over theWestern North Pacific?
RYUJI YOSHIDACIRES, University of Colorado Boulder, and NOAA/Earth System Research Laboratory, Boulder, Colorado, and RIKEN
Center for Computational Science, and Research Center for Urban Safety and Security, Kobe University, Kobe, JapanHIRONORI FUDEYASU
Yokohama National University, Yokohama, Japan(Manuscript received 28 January 2019, in final form 18 October 2019)
ABSTRACTThe low-level flow pattern characteristics of the tropical cyclone genesis (TCG) environment over thewestern North Pacific (WNP) were investigated not only for the periods during which TCG occurred, but also
for the periods during which no TCG occurred. The flow patterns investigated were the shear line (SL),confluence region (CR), and easterly wave (EW) patterns. Although these flow patterns are recognized asfavorable environmental conditions for TCG, their general characteristics, such as the climatological horizontal distribution, have not been previously investigated and there has been no comparison of the significance of the TCG cases to the climatological mean. We examined flow patterns using flow pattern indicesdefined by the modified semiobjective analysis method. SL and CR were broadly distributed over the WNP
east of the Philippines during the boreal summer season. There was a peak in the EW from summer to autumn
and it was distributed in a band running in an east–west direction at a lower latitude. Flow pattern indices of all
three flow patterns gradually intensified until the TCG date was reached. The SL and EW indices became
more intense compared to the climatological mean one day before the TCG, while that of the CR did notexceed the climatological mean. TCG occurred at the eastern edge of the area with a high genesis potential
index and relative humidity and a weak vertical shear over flow patterns. This determination of the generalcharacteristics of favorable flow pattern conditions improves our understanding of the TCG process.

1. Introduction
Tropical cyclones result in natural disasters given theirheavy rainfall and strong winds, and storm surge. Many
studies of tropical cyclone track, structure, and intensity have been conducted to improve forecast skills. For
countries with long coastlines, the grace period before atropical cyclone reaches land tends to be short. Therefore, understanding tropical cyclone genesis (TCG) anddeveloping a forecasting system for genesis is essential
for the prevention and mitigation of natural disasters
resulting from tropical cyclones.
The general environmental conditions of TCG are
relatively well understood. Gray (1968, 1998) reported a
set of suitable environmental conditions by averaging
the data of all basins where tropical cyclones are observed. It will be noted that favorable conditions andCorresponding author: Ryuji Yoshida, ryuji.yoshida@noaa.gov

indices developed from them do not identify precursor
wave and wavelike disturbances on the synoptic scale,and their favorable mesoalpha-scale subregions for
TCG. Therefore, the statistical significance of methods
based on circulation patterns alone is relatively low. The
set of suitable environmental conditions were a highsea surface temperature (SST), conditional instability ofstratification, convergence in the low-level troposphereand divergence in the upper-level troposphere, cyclonic
circulation in the low-level troposphere, and a weak deep
vertical shear of horizontal wind. Based on Gray (1968,
1998), several genesis parameter indices have been suggested to study the general characteristics of the TCG
environment (Gray 1998; Zehr 1992; and Emanuel andNolan 2004). DeMaria et al. (2001) developed a genesis
parameter to apply to TCG forecasts using vertical shearof horizontal wind, stability of stratification, and midlevel
moisture. Schumacher et al. (2009) updated the genesis
parameter by adding several variables (i.e., land coverage,

DOI: 10.1175/MWR-D-19-0023.1
Ó 2020 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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low-level circulation, and low-level divergence) for use in
short-range TCG forecasting. The performance was acceptable but the significance remained low, as in previous
studies.
In terms of the mean circulation pattern, the Asianthe monsoon trough, monsoon westerly wind and tradeeasterly wind are general characteristics of the boreal
summer over the WNP (Gray 1968, 1998; Zehr 1992;
Yanai 1961, 1968; Holland 1995). Statistical analysis
can break down the TCG environment into several
categories (Ritchie and Holland 1999; Lee et al. 2008).
Ritchie and Holland (1999) visually categorized TCG
cases into five patterns of low-level flow features: amonsoon shear line (SL) pattern, monsoon confluence
region (CR) pattern, monsoon gyre pattern, easterly wave
(EW) pattern, and Rossby energy dispersion from a preexisting tropical cyclone. They reported that the monsoon
SL, monsoon CR, and EW patterns were the most frequent flow patterns over the WNP. The environment of
the flow pattern also has an influence on the characteristics (e.g., strength and track) of the tropical cyclones
after the TCG (Fudeyasu and Yoshida 2018). Yoshida
and Ishikawa (2013) developed a semiobjective analysis
method to categorize the five flow patterns for a longterm analysis, with a stable reliable criterion. Although
the classification flow patterns were based on the categorization of Ritchie and Holland (1999), the analysis
method can automatically and reasonably categorize
and not rely on the human senses. They analyzed morethan 900 TCG cases occurring over a 30-yr period
from 1979 to 2008 and found that approximately 70%
could be explained by three flow patterns: the monsoon SL, monsoon CR, and EW patterns. This isconsistent with that found in Ritchie and Holland(1999); the semiobjective analysis method can reproduce the subjective analysis. However, forecastingof TCG is still a difficult challenge despite many efforts on understanding TCG.
One possible reason for the difficulty in forecasting isthe differences among the genesis pathways (Wang et al.
2018). The genesis pathway determines the TCG typeaccording to its energy source. The TCG probabilities
were investigated for each pathway and it was foundthat the tropical transition pathways had a lower
probability than that of the other pathways. Similardifferences in TCG probability have been found indifferent flow patterns over the western North Pacific(WNP). Another reason for the low significance of
circulation patterns and environment indices is thatthe wave and wavelike precursors to TCG are not
identified. These disturbances depend on, but do notnecessarily accompany, the circulation patterns andenvironment factors required for the disturbances.

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Similarly, the synoptic-scale wave precursors do notguarantee that favorable mesoalpha-scale conditions
exist within the disturbances. Several studies haveworked in this direction by emphasizing that TCG is
not merely about precursor circulation patterns but thewave or wavelike disturbances that arise on these patterns. Influences on TCG by planetary and synopticscale waves on TCG has been investigated (Molinari andVollaro 2000; Li 2006; Li and Fu 2006; Li et al 2006).
Schreck et al. (2011; 2012) attempted to explain a development process of the precursor emerging from various
equatorial waves. A relationship between the Madden–
Julian oscillation (MJO; Madden and Julian 1994) and
TCG has been also studied (Frank and Roundy 2006;
Camargo et al. 2009). Molinari et al. (2007) reported
a developing precursor in an equatorial Rossby wave
packet, and Dunkerton et al. (2009) suggested that theprecursor development tends to occur near a quasi-closed
pouch that is bound by the outer manifold and includes
cyclonic circulation within the closed streamline. The
pouch maintains a suitable environment along the
migration of a tropical disturbance, which is referred
to as the marsupial paradigm. Wang et al. (2012) has
shown the applicability of the marsupial paradigm.
These studies in terms of the equatorial waves canenhanced understanding of the TCG process relatedwith the dynamics of tropical atmosphere. Our study
is intended to extend and refine the description of
circulation patterns favorable to TCG, while leaving
for further study the relationships between thesepatterns and variety of disturbance types that precede
genesis.
Attempts to understand or to forecast TCG from themean circulation pattern of the precursor regardless ofbasin may have low statistical significance because theassociate precursor disturbances and their dependenceon circulation pattern have not been identified, nor
their contribution to enhanced understanding andforecast skill. The understanding in terms of low-level
flow patterns to categorize TCG cases still has usefulness because of intuitiveness. The flow pattern canbe recognized easily on weather maps. An improvement of the analysis method is necessary to acquire
more usefulness, because flow pattern characteristics
have been investigated only for periods during which
TCG occurred. The general characteristics of lowlevel flow patterns, regardless of TCG events, have
not yet been determined and there may be differences
between genesis and nongenesis cases. These differences should be studied to understand how a tropicaldisturbance can develop into a tropical cyclone. Zehr
(1992) found that a strong vertical shear of horizontal
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of a tropical disturbance. Tuleya (1991) investigated
the sensitivity of vertical shear strength through numerical simulations, with a strong vertical shear foundto be unsuitable for TCG. In numerical simulations, anondeveloped tropical disturbance develops into a
tropical cyclone with a continuous large-scale environment that contains a large amount of moisture(Yoshida et al. 2017).
In this study, low-level flow pattern climatologicalcharacteristics over the WNP were investigated to better understand the flow patterns during genesis periods.
The climatological characteristics included features ofnongenesis periods. Because there are only a few days
during which TCG is observed each year, the meanvalue of all days in 1 year can be assumed to represent
the state of nongenesis days (i.e., days in which a TCG
case was not observed). Therefore, we could determine the significance of low-level flow patterns bycomparison to climatological mean values. However,the semiobjective analysis method of low-level flow
pattern categorization suggested by Yoshida andIshikawa (2013) is dependent on cyclone track data to
determine the genesis location and time. We modified
the semiobjective analysis method to fit the aims ofthis study. In this study, we focused only on the frequent types of low-level flow patterns over the WNP
to ensure statistical significance: the SL, CR, and EW
patterns.
We explain the details of the modification of the semiobjective analysis method and the utilized data in section 2. The general characteristics of the analysis aredescribed in section 3 and details of the seasonal andinterannual variabilities are presented in section 4. We
discuss the differences in environmental conditions
between the genesis and nongenesis periods in section
5. In section 6, a summary of the study is provided.

2. Data and methodology
a. Modified semiobjective categorization method
Yoshida and Ishikawa (2013) suggested the use of
TCG contribution scores in a categorization procedure
as a means to automatically examine the contribution offlow patterns for a TCG case. This is a semiobjective
categorization method compatible with the subjective
categorization of Ritchie and Holland (1999), which
does not guarantee a perfect matching with the categorization of Ritchie and Holland (1999) on a caseby-case basis, but was tested for statistical consistencyof the categorization rate for each flow pattern. The
contribution scores for the SL, CR, and EW patterns
consist of an intensity term, distance term, and artificial
parameters. The artificial parameters were used to tune

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the score distribution in a two-dimensional space of
the intensity and distance terms. They were calculatedusing the zonal and meridional wind components at
an 850 hPa height. The categorization procedure had
three steps as follows: 1) search the flow patterns
around the TCG location using a detection algorithm,
2) calculate the contribution score of the TCG case,and 3) determine the major flow pattern of the TCG
case by comparing scores. The calculation of contribution scores was conducted for every TCG case before
moving to the third step, and the raw scores of eachflow pattern were normalized by the maximum andminimum values of the dataset. We then obtained thenormalized score of each flow pattern (i.e., from 0 to 1),
which could be compared to that of the other flow
patterns. Each step was conducted for each flow pattern
for all TCG cases in the best track data. Although thisenabled an examination of the flow pattern for a specific TCG case, it could not be applied to investigationsof nongenesis periods given the lack of track data. To
address the flow pattern climatological characteristics
during a nongenesis period, we modified the definitionof the TCG contribution scores for the SL, CR, and EW
patterns as follows: ›uexp(2Adistmin_SL ) ,(1)
scrSL 5 ave
›y
 
›u
exp(2Adistmin_CR ) ,(2)
scrCR 5 ave
›x
 
›y
exp(2Adistmin_EW ) ,(3)
scrEW 5 ave›xwhere u and y are the horizontal wind velocity at 850 hPa
height for the zonal component and meridional component, respectively; x and y are the horizontal axes inlongitudinal direction and latitudinal direction in the
input reanalysis data, respectively; A is a tuning parameter to adjust the distance range for the exponentialfunction that was empirically decided as A 5 1025 for
the SL and CR patterns and A 5 1022 for the EW pattern; the operator ave() is the average for all grids detected as a flow pattern by the detection algorithm; anddistmin is the minimum distance between a specific grid
point where the score is calculated and the grid point
detected the flow pattern. Therefore, Eqs. (1)–(3) consist of the multiplication of an intensity term [e.g.,ave(›u/›y) in Eq. (1)] and a distance term [e.g., exp(2A
distmin_SL) in Eq. (1)]. This configuration assumes that ahigher flow pattern intensity can provide a greater
contribution to the arrangement of the TCG environment and a shorter distance can have a larger impact on
the TCG environment. The essential flow structure for562

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the SL, CR, and EW patterns is a line of meridional
shear of zonal wind, a convergence of the zonal wind,
and a synoptic-scale trough, respectively. The SL pattern is identified by the meridional shear of the zonal
wind (›u/›y), CR by the convergence of the zonal wind(›u/›x), and EW by the vorticity of the meridional wind(›y/›x). The definitions of the contribution scores foreach flow pattern are not empirically derived, but are apriori defined. distmin_EW in Eq. (3) is determined considering the propagation of the EW pattern. Although
the SL and CR patterns are nearly stationary during ashort time period of less than a week, the EW patternpropagates westward even during a short time period.
Following Yoshida and Ishikawa (2013), the propagation speed was assumed to be the same as the averaged
easterly wind speed near the EW pattern and the timescale was set as 69 h.
The final form of flow pattern indices (FPIpat) is defined as follows:
FPIpat 5

scrpat 2 mean[scrpat ]stddev[scrpat ],(4)which is normalized by a climatological mean of score(indicated by mean[ ]) and a climatological standard
deviation of score (indicated by stddev[ ]). The subscript‘‘pat’’ is an abbreviation of the SL, CR, or EW patterns.
In the new definition, we used the climatological mean
and the standard deviation instead of the maximum andthe minimum values, for score normalization. The climatological values for each flow pattern were calculated
for all days in the 38-yr period from 1979 to 2016 and for
all grid points at once. Therefore, the normalized flow
pattern index (FPI) indicates how significant the flow
pattern is compared to the climatology. Furthermore,
the artificial parameters used in the original definition of
Yoshida and Ishikawa (2013) were eliminated here.
Because the FPI was normalized against the climatological values, the score distribution for the distance andstrength terms was not considered. Hereafter, we referto the flow pattern indices in association with the relevant pattern (e.g., ‘‘FPI-SL’’).
During the first step of the score calculation, the flow
patterns around the TCG location were identified basedon information obtained from the best track data inYoshida and Ishikawa (2013). In the current study, we
identified the flow patterns at every location among thegrid points of the input data inside the analysis region
instead of using the best track data. The analysis region
for flow pattern detection was from 1008E to 1708W
longitude and from 58S to 358N latitude. The analysis
region for the climatological value calculation was from1108E to 1808 longitude and from 58 to 258N latitude.

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The analysis region for detection had a sufficient margin
to freely detect the shape of the flow patterns. The flow
patterns were searched around the grid point calculating
the scores based on a flow shape of the 850 hPa flow field.
For example, the SL pattern flow shape is detected as aseries of grid points where an easterly wind is found to
the north and a westerly wind to the south. This flow
pattern search was conducted via the automatic detection algorithm for all three flow patterns. The automatic
detection algorithm used in this study was the same asthat used in Yoshida and Ishikawa (2013), except for thedetection limit settings (for details of the algorithm, seesection 3 in Yoshida and Ishikawa 2013). The detection
limit is the maximum distance between the calculation
grid point and the flow pattern, and the settings were1500 km for SL, 1500 km for CR, and approximately
2200 km (208) for the EW patterns. This detection limit
is not an area averaging radius. No area averaging wasapplied for the score at calculation of Eqs. (1)–(4). The
flow pattern detection and the calculation of the contribution scores were performed for all grid points in theanalysis region at all time steps during the analysis period. The climatological mean and standard deviation
were calculated for every flow pattern. Then, the FPI
calculation was repeated for every flow pattern for all
grid points for all time steps. Finally, we obtained the
FPI grid point values for each flow pattern at every time
step over the grid points in the analysis region.
A traditional genesis potential index (GPI) of
Emanuel and Nolan (2004) was used in this study todetermine the general condition of the TCG environment because investigating climatology of TCG is our
purpose. Although the GPI has less skill for TCG forecasting than the newly proposed genesis parameters, theGPI has been developed using a statistical approachover a long period, which has proven to be useful ininvestigating climatological and interannual variationsof TCG environment, for example relationships with the
El Niño–Southern Oscillation (Camargo et al. 2007),
modulations by the MJO (Camargo et al. 2009), and for
future climate changes (Emanuel 2013). GPI was defined as GPI 5 j105hj3/2(H/50)3(Vpot/70)3(1 1 0.1VS)22
following Emanuel and Nolan (2004), where h is theabsolute vorticity at 850 hPa height, H is the relative
humidity (RH) at 700 hPa height, and Vpot is the potential intensity calculated using a FORTRAN routine
provided by Dr. Emanuel (http://eaps4.mit.edu/faculty/
Emanuel/products).qVertical
shear of horizontalﬃ windﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

is defined as VS 5 (u200 2 u850 )2 1 (y 200 2 y 850 )2 using
horizontal wind components at 200 hPa height and
850 hPa height. We referred to these parameters indicating
the TCG environment as genesis environment parameters.

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FIG. 1. Horizontal distributions of the shear line flow pattern index over the western North Pacific based on the meanvalues from 1979 to 2016 for (a) December–February (DJF), (b) March–May (MAM), (c) June–August (JJA), and(d) September–November (SON). Each flow pattern index is the seasonal mean value of the period. The vectors are themean horizontal wind at 850 hPa height over the same averaging period. Broken lines show the location of 1408E longitude.

b. Data and analysis period
The Japanese 55-year Reanalysis Project dataset(JRA-55; Kobayashi et al. 2015; Harada et al. 2016; details are available online at http://jra.kishou.go.jp/JRA-55/
index_en.html) was used to calculate the FPI. The JRA55 was used in the TCG categorization of Yoshida andIshikawa (2013) type (Fudeyasu and Yoshida 2019);
therefore, the applicability of this reanalysis data has already been demonstrated. To increase the accuracy of theflow pattern detection by filtering out small-scale perturbations, the input data were processed using a low- or
bandpass filter. A 5-day low-pass filter was used for FPISL and FPI-CR calculation, while a 3–5-day bandpass
filter was used for FPI-EW calculation. The best trackdata produced and archived by the Joint Typhoon
Warning Center (JTWC) were also used to evaluate
the significance of the flow patterns when TCG occurred.
The analysis period was the 38 years from 1979 to 2016.
The number of addressed TCG cases during this period
was 1110 over all seasons and 761 during the boreal
summer [i.e., July–August–September–October (JASO)].
Because the focus of this study was the early TCG phase,the initial location and time of each best track record wasused as the TCG location and time following Ritchie andHolland (1999) and Yoshida and Ishikawa (2013).

3. Climatological characteristics of low-level flow
patterns
Figure 1 shows the horizontal distribution of FPI-SL overfour seasons: December–January–February (DJF; winter),March–April–May (MAM; spring), June–July–August(JJA; summer), and September–October–November(SON; autumn). The distribution of FPI-SL shows aclear seasonal variability. Large FPI-SL values arebroadly distributed from 38 to 258N latitude and from1258 to 1458E longitude over the WNP during JJA andSON. The peak area of FPI-SL moves southward fromapproximately 248N to approximately 158N with a seasonal change from JJA to SON. From winter to spring,no significant FPI-SL is found over the WNP in theequatorial and subtropical regions.
Figure 2 is the same as Fig. 1, except for FPI-CR. FPICR also shows significant seasonal variability. FPI-CR
reaches a maximum during JJA occurring over the region along 188N and a minimum during MAM. As with
the FPI-SL, the peak area moves southward to theEquator from JJA to DJF. These two flow patterns arestrongly related to the Asian monsoon westerly wind, as
explained in section 1. Because the Asian monsoon
westerly wind has a seasonal peak during the boreal
summer, these flow patterns also have a seasonal peakduring that time.
Figure 3 shows the horizontal distribution of FPI-EW.
FPI-EW also shows significant seasonal variability andreaches a maximum during JJA and a minimum during
DJF. FPI-EW is distributed in a relatively narrow region
from Luzon Island in the Philippines to 88N, 1808 andhas a bow shape. This shape and geographical location
do not largely vary with season unlike the SL and CR
patterns. The frequency of TCG with an EW pattern(hereafter, TCG-EW) is most frequent during SON(Yoshida and Ishikawa 2013). There is a seasonal gap
between the peak of TCG-EW and the peak of the EW

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FIG. 2. As in Fig. 1, but for the CR flow pattern index.

pattern. An interpretation of this seasonal gap is that themost significant flow pattern was TCG-SL or TCG-CR
during JJA rather than TCG-EW because of a strong
westerly wind induced by the Asian monsoon prevailingover the easterly trade wind during the summer season.
The westerly wind weakens over the WNP during autumn, and the EW pattern is more significant than thatof the SL or CR.
To examine the FPI compatibility with TCG, Fig. 4
shows the TCG locations by dots superposed on the FPI
horizontal distribution, in which the white dots indicate the TCG-SL, TCG-CR, and TCG-EW location in
Figs. 4a, 4b, and 4c, respectively. The FPI shows themean value for JASO from 1979 to 2016 and the dots
indicate TCG locations during the same period. Because
the TCG environment typically consists of multiple flow

patterns (Yoshida and Ishikawa 2013), a summation of
the FPIs for the three flow patterns is also shown in
Fig. 4d to show the mutual effect of multiple flow patterns. TCG locations are generally matched to the FPI
distribution, particularly to the distribution of the FPI
summation (Fig. 4d). However, the detailed fitting of the
distributions to the TCG location is not perfect. Although the most frequent latitudinal TCG location is at
approximately 128N over the WNP, the peak for FPI-SLand FPI-CR is to the north of this area where TCG
frequently occurred. The location of FPI-EW is nearer
the area where TCG frequently occurred compared tothat of FPI-SL and FPI-CR. This difference is a result ofother environmental parameters. The relationships withthe other parameters are investigated in the following
section. The FPI-EW has a distinctive distribution with aFIG. 3. As in Fig. 1, but for the EW flow pattern index.

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depression type is a wave disturbance with a period of
3–5 days propagating westward. The pressure patternassociated with the tropical depression type is inclined
toward the northwest. In addition, the tropical depression type tends to dominate over the WNP while amixed Rossby gravity wave, which has a pressure pattern parallel to the Equator, tends to dominate overthe central Pacific. According to Dunkerton (1993) and
Dunkerton and Baldwin (1995), these disturbances
capture a considerable fraction of lower-tropospheremeridional velocity variance during the boreal summer.
An additional investigation into the contribution of thetropical depression type to the environmental condition
defined as the EW pattern in the current study would be
required in a future work.

4. Variations in flow patterns
To investigate the detailed flow pattern characteristics,
we established two test areas to avoid contamination because of the differences in regional variations: the westside of the WNP (western WNP: 58–258N, 1108–1408E) andeast side of the WNP (eastern WNP: 58–258N, 1408–
1708E). The boundary between the western WNP and
the eastern WNP is indicated by the white brokenlines shown in Fig. 4.

a. Seasonal variations

FIG. 4. Horizontal distributions of flow pattern indices during all
seasons over the western North Pacific based on the mean values
from 1979 to 2016 for the (a) SL, (b) CR, and (c) EW patterns, and(d) a summation of the three indices for the SL, CR, and EW
patterns. The black dots show the locations of tropical cyclone
genesis recorded over the period from 1979 to 2016. The white dots
are the cases for the major flow patterns of SL in (a), CR in (b), and
EW in (c). The blue, orange, and green colored dots are the major
SL, CR, and EW flow patterns. The white broken lines show thelocation of 1408E longitude.

northwestward refraction at approximately 1408E(Fig. 4c). This distribution characteristic implies that
the EW pattern can be attributed to a tropical depression type (Takayabu and Nitta 1993). The tropicalThe seasonal variations in each flow pattern in the twotest areas are represented by the mean FPIs shown in
Fig. 5. The solid and broken lines are the western WNP
and eastern WNP, respectively. The correlation coefficients for the seasonal variation in TCG case numbers are listed in Table 1 and are 0.96 and 0.97 for
SL-western-WNP and EW-eastern-WNP, respectively.
The seasonal variation in the SL pattern is similar to thatof the number of TCG cases, with the seasonal variationin the SL pattern in the western WNP being particularly
close to that of the number of TCG cases. This is consistent with the fact that the SL pattern is the major
pattern of TCG in the WNP (Yoshida and Ishikawa
2013). The seasonal variability in the TCG environmentof low-level flow patterns is approximately represented
by the SL pattern. The CR pattern is significant in both
the western WNP and eastern WNP during the earlysummer season. In contrast, the EW pattern in thewestern WNP becomes significant during the late summer to autumn, which is consistent with the seasonal
peak of TCG-EW.
The FPIs were compared to the other environmental
parameters of TCG to determine their relationshipwith TCG probability. A WNP monsoon index (Wanget al. 2001) was used to evaluate the effect of monsoon

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TABLE 1. Correlation coefficients for the seasonal variations in flow
pattern indices and other environmental parameters.
Western WNP

TCG
GPI
SST
VS
RH
Monsoon index

Eastern WNP

SL

CR

EW

SL

CR

EW

0.96
0.98
0.84
20.75
0.88
0.98

0.86
0.89
0.78
20.62
0.85
0.89

0.93
0.83
0.59
20.57
0.75
0.85

0.91
0.96
0.94
20.89
0.96
0.96

0.85
0.87
0.84
20.73
0.88
0.85

0.97
0.97
0.94
20.84
0.98
0.98

the other genesis environmental parameters. A decreasing FPI-CR during August seems to correspondwith that of the monsoon index. The westerly winds
necessary to organize the CR pattern weaken over the
WNP with the decaying of the Asian summer monsoon. However, a decreasing FPI-SL is not foundduring August and slowly starts during September.
These features suggest that the CR pattern is strongly
related to the Asian monsoon and the SL pattern is
not only related to the Asian monsoon but also affectedby other large-scale phenomena. Temporary westerly
winds induced by an intraseasonal oscillation such as the
MJO is a possible condition to organize the SL pattern
combined with trade easterly winds (Yoshida et al.
2014; Zhao et al. 2015). The MJO propagates eastward with a westerly wind over the tropical WNP, andbecomes remarkable during from the boreal autumn
to winter (Kemball-Cook and Wang 2001; Kikuchi
et al. 2012).
FIG. 5. Seasonal variations of flow pattern indices associated with(a) shear line, (b) confluence region, and (c) easterly wave. Eachflow pattern index is the monthly mean value for the period from1979 to 2016. The gray bars are the accumulated number of tropical
cyclone genesis (TCG) cases based on the Joint Typhoon Warning
Center best track data, and the dark gray bars are the accumulated number of TCG cases for each flow pattern. West side of thewestern North Pacific (WWNP); east side of the western North
Pacific (EWNP).

activity on the TCG environment. Figure 6 shows theseasonal variability of the GPI, SST, and RH at
700 hPa height; vertical shear of horizontal wind; andthe monsoon index. The correlation coefficients between the FPIs and genesis environmental parameters
are shown in Table 1. As expected, the seasonal variation in GPI is near that of FPI-SL, and the SST is alsosimilar to that of FPI-SL. In addition, the GPI and SST
are positively correlated with the variation in the TCG
cases and FPI. Meanwhile, vertical shear of horizontal
wind is negatively correlated with FPIs. The correlation with RH is less significant than the correlation ofb. Interannual variation
The interannual variations in the three flow patterns
from 1979 to 2016 are shown in Fig. 7. The figure also
shows area-averaged values over the western WNP andeastern WNP. The correlation coefficients for the interannual variation in TCG are 0.34 and 0.18 for the SLwestern-WNP and EW-eastern-WNP, respectively. The
correlation coefficient for the CR pattern is less thanthat of the SL and EW patterns. Remarkable interannual variation can be seen in Fig. 7 for the three flow
patterns. The FPI amplitudes in the western WNP differ
from those in the eastern WNP, particularly for the EW
pattern, while the variation in the FPI is similar among
the two regions. Several peaks in the TCG variation
could be explained by the FPIs. For example, the significant peaks of FPI-SL in the western WNP during
1995 and 2010 correspond with the peaks of the TCG
variation during these years. A similar correspondence
is found in FPI-CR in the western WNP during 1984 and1986. Interestingly, FPI-EW shows an abrupt amplitudechange during 1998 while FPI-SL and FPI-CR do not

FEBRUARY 2020

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FIG. 6. Seasonal variations in general environmental parameters: (a) SST and GPI, (b) RH at 700 hPa height and vertical shearof horizontal wind (VS) between 200 and 850 hPa heights, and(c) western North Pacific monsoon index. The value for eachvariable is the monthly mean for the period from 1979 to 2016.

show a remarkable general trend during this period. The
FPI-EW amplitude over the western WNP decreases
after 1998. A detailed investigation of this is required in
the near future.
Figure 8 shows the interannual variations in the genesis environmental parameters and Table 2 shows thecorrelation coefficients for the relationships between the
FPIs and genesis environmental parameters. There arepositive correlations for GPI and SST with the numberof TCG cases, while vertical shear of horizontal wind isnegatively correlated with the number of TCG cases.
The same patterns are observed with regard to seasonalvariability. The GPI and RH tend to be higher in thewestern WNP than in the eastern WNP, and FPI-EW
has a similar trend to these parameters. Geographical

567

FIG. 7. Interannual variations in flow pattern indices associatedwith the (a) SL, (b) CR, and (c) EW patterns. The value for eachindex is an annual mean. The gray bars are the accumulated
number of tropical cyclone genesis (TCG) cases based on the
JTWC best track data. West side of the western North Pacific(WWNP); east side of the western North Pacific (EWNP).

relationships among these will be discussed in section 5.
The interannual variations are too complicated to determine any clear relationships among the flow patterns,
genesis environmental parameters, and number of TCG
cases. A WNP monsoon index seems to be one reasonable means to represent FPI-SL and FPI-CR. However,when FPI-SL had positive peaks during 1995 and 2010,
the WNP monsoon index had negative peaks. For the
positive peaks of FPI-CR during 1984 and 1986, the
WNP monsoon index also has large values.

5. Environmental conditions for TCG with flow
patterns
The FPIs were examined focusing on TCG events todetermine the environmental conditions that led to568

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TABLE 2. Correlation coefficients for the interannual variations inflow pattern indices and other environmental parameters.
Western WNP

TCG
GPI
SST
VS
RH
Monsoon index

FIG. 8. Interannual variations in genesis environmental parameters: (a) GPI and SST, (b) RH at 700-hPa height and vertical shearof horizontal wind (VS) between 200 and 850 hPa heights, and(c) western North Pacific monsoon index.

TCG. The relationships between the FPIs and TCG
were investigated in terms of both temporal and spatial
variations.

a. Temporal variation in the FPIs within the TCG
time scale
We calculated the temporal variation in the FPIs
based on the best track data. Although FPIs are originally 6-hourly data because of the input data time interval, the daily FPIs averaging four 6-hourly data
periods for each date were used as described in thissection. Figure 9 shows the temporal variation for each
FPI from 13 days before the TCG date. The unit of thehorizontal axis is days before TCG. Temporal variations in the FPIs were averaged around the TCG location in a horizontal space with several different
averaging radii to provide spatial representativeness.
The average radii were 400, 800, and 1200 km from theEastern WNP

SL

CR

EW

SL

CR

EW

0.34
20.58
0.05
0.08
0.06
0.47

20.16
20.28
20.12
0.30
0.21
0.36

0.08
20.50
0.23
20.15
20.15
0.16

0.23
20.25
20.14
0.16
0.35
0.16

20.10
20.07
20.05
0.28
0.27
0.15

0.18
20.42
20.10
0.22
0.26
0.06

mesoalpha scale to synoptic scale. The values were also
averaged for the 761 TCG cases during JASO from1979 to 2016. The hatched area is less than 1.0 of FPI
value. The three FPIs increase over time toward thegenesis day (i.e., 0 days as shown in these figures). FPISL and FPI-EW rapidly increase from 3 days before
TCG, with the FPI-EW increase being particularly
rapid. Because these FPI values eventually exceeded
1.0 at 1 day before TCG, the flow patterns are substantially intense for TCG events compared to the FPI
climatology. Meanwhile, FPI-CR tends to increase
from 13 to 7 days before TCG, after which it does notchange much. The FPI value only reaches 0.2, and thisis not intense comparing to the climatology. Ritchieand Holland (1999) concluded that the TCG flow pattern tends to be significant 3 days before TCG. However, it has been found in this study that the flow
patterns tend to intense just one day before the genesis.
As discussed in the introduction, statistics focusing
only on the flow patterns has less skill to predict TCG.
A large difference of temporal scale is found between
SL, EW patterns, and CR pattern. FPI-SL and FPI-EW
would represent the circulation of a tropical cyclone ortrough. In contrast, the CR pattern might not be amesoscale structure but rather a mesoscale environmental condition. The temporal variation in FPI-CR
implies that the CR pattern is controlled by a largerscale phenomenon compared to that of the SL and EW
patterns because a noticeable change commences in
the CR pattern 10 to 13 days before TCG that is more
rapid than that observed in the SL and EW patterns.
The characteristics described in this section do notconsiderably differ with the horizontal averaging scale.
Figure 10 shows a similar temporal evolution for thegenesis environmental parameters. The cross symbols
show not a large value of parameters comparing to theclimatological mean. The vertical vorticity at 850 hPa
has nearly the same temporal evolution as the FPIs. Thisis consistent with the flow patterns being recognized as avorticity source. The GPI gradually increases with time,with a rapid increase after 3 days before TCG. The SSTFEBRUARY 2020

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569

FIG. 10. Temporal evolution of genesis environmental parameters: (a) GPI and horizontal divergence at 200 hPa (DIV200) andabsolute vorticity at 850 hPa (VOR850), (b) SST and CAPE, and(c) vertical shear of horizontal wind (VS) between 200 and 850 hPa
heights and RH at 700 hPa height. Each value is the average for 761
tropical cyclone genesis cases from 1979 to 2016 during JASO.
Numbers at upper corners are 1s value for each variable, and plus
sign means less than the 1s value.
FIG. 9. Temporal evolution of the flow pattern indices associatedwith the (a) SL, (b) CR, and (c) EW patterns. Each flow pattern index
is the average value for 761 tropical cyclone genesis cases from 1979 to2016 during JASO. The hatch means less than 1.0 of FPI value.

and convective available potential energy (CAPE)
gradually decrease. These decrease trends seem to beclear after 3 days before TCG. These changes are a resultof convective activity near the TCG location. Zehr (1992)

found that a tropical disturbance developing into a tropical cyclone tended to be significant approximately 3 days
before TCG. Because the values of GPI, SST, and CAPE
are exceeded 1s value of climatological population,these characteristics are substantially intense comparing
to the climatological mean. Relative vorticity at 850 hPa,
divergence at 200 hPa and RH increases rapidly after
3 days before TCG, and vertical shear of horizontal wind

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MONTHLY WEATHER REVIEWVOLUME 148

FIG. 11. Composited horizontal FPI distributions for the (a) SL, (b) CR, and (c) EW patterns for categorized tropical cyclone genesis
cases during JASO from 1979 to 2016. The number of cases was 340 for the SL, 75 for the CR, and 247 for the EW patterns. The genesis
location is in the center of each composite. The vectors are the composited horizontal flow at 850 hPa height for each case. The hatchedarea indicates less than 1.0 of FPI value.

suddenly decreases approximately 3 to 4 days before
TCG; this might be a signal of TCG initialization.
However, all these trends are not remarkable comparing to the climatological mean. Furthermore, thetemporal evolution of GPI is gentle in this period, and aremarkable change of evolution was not found. An
understanding temporal evolution of environmental
conditions from statistics is still hard even if TCG cases
are categorized into flow patterns based on the circulation feature.

b. Horizontal variation in the FPIs in each flow
pattern
Composite images were constructed centeringon the TCG location, based on the best track data,
to obtain the typical FPI horizontal distribution(Fig. 11). The FPIs were composited for cases with amajor flow pattern during the JASO analysis period
from 1979 to 2016. The major flow pattern for the
specific TCG case was defined as the flow pattern withthe largest FPI value during the 4 days before the TCG
date. The area averaged FPI over a radius of 800 km
was used for this categorization process. The numberof cases was 340, 75, and 247 for the SL, CR, and EW
patterns, respectively, with the daily FPI value used in
the calculation. The composites for each flow patternwere constructed for each storm defined as a major
flow pattern on a single day which is 4 days before thegenesis day.
The FPI maximum value is near the center for every
flow pattern. The FPI-SL peak is slightly shifted to thewest and the FPI-EW peak is slightly shifted to thenorthwest of the TCG location. These horizontal peakareas had a horizontal scale of approximately 208 (i.e.,
2000 km). Compared to the other flow patterns, FPI-SL
is more widely distributed in the longitudinal direction.
FPI-EW has a second peak in the northwest quadrant.

This implies the existence of a previous trough of easterly
waves to the northwest. FPI-CR is more concentrated
than the other flow patterns, and a remarkable cyclonic
circulation is found near the center to the west of the
TCG location.
The genesis environment parameters affecting TCG
were superposed on these composite images as shown in
Fig. 12. Because the flow patterns can extend over abroad area, as shown in Fig. 4, the composited images
were nearly white if there was no cooperative organization of flow patterns with the genesis environmental
parameters. The flow patterns should have some systematic correlation with the genesis environmental parameters. The GPI is elongated from the western side to
the TCG location for every flow pattern. A higher GPI
value is found far from the TCG location in the TCGCR cases. Therefore, a suitable environment for TCG isavailable in a larger space in the TCG-CR cases than in
the TCG-SL or TCG-EW cases. The horizontal RH
distributions at 700 hPa height are also similar for every
flow pattern. An elongated high RH area stretches fromthe western side to the center of this domain, with aslight slant in a northwest to southeast direction. At the
TCG location, the southern part of the domain has ahigher RH. This is a favored condition for the development and maintenance of deep convection in the cyclonic circulation around the TCG location, because thesouthwestern flow can convey moisture to the TCG location. The TCG location is nearly on the eastern edgeof the area with a considerably high GPI and RH. Thereis a different horizontal distribution for vertical shear ofhorizontal wind compared to that of the GPI and RH.
The north side in this domain had a strong vertical shearthat corresponded to the midlatitudinal jet. The TCG
location is in a relatively weak vertical shear area. Areas
with a vertical shear of greater than 15 m s21 are found in
the southwestern quadrant in the TCG-SL and TCG-CRFEBRUARY 2020

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571

FIG. 12. Composited horizontal distributions of general environmental parameters: (a)–(c) GPI; (d)–(f) SST; (g)–(i) RH at 700-hPa
height, and (j)–(l) vertical shear of horizontal wind (VS) between 200 and 850 hPa heights. In each panel, flow pattern indices aresuperposed by hatched contours: (a),(d),(g),(j) SL pattern; (b),(e),(h),(k) CR pattern; and (c),(f),(i),(l) EW pattern. They were composited for categorized tropical cyclone genesis during JASO from 1979 to 2016. The number of cases was 340 for the SL, 75 for the CR,
and 247 for the EW patterns. The tropical cyclone genesis location is in the center of each composite.

cases. Although a weak vertical shear is found in thesoutheastern quadrant regardless of the flow pattern,
this area is largely not subject to TCG. This implies thatTCG occurs in the gaps among strong vertical shear.

Figure 13 shows similar composite images for CAPE,
with divergences at 200 and 850 hPa heights and verticalvorticity at 850 hPa height. The FPI distributions forevery flow pattern coincide with the CAPE distribution.

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FIG. 13. As in Fig. 12, but for (a)–(c) CAPE, (d)–(f) horizontal divergence at 200 hPa height, (g)–(i) horizontal divergence at 850 hPa
height, and (j)–(l) vertical vorticity at 850 hPa height.

Therefore, the general characteristics of conditional
instability tend to be satisfied. Divergence (convergence) is found slightly south of the composite centerat 200 hPa height (850 hPa height). This indicates thatdeep convections formed and remained in this area. The
vertical vorticity distributions match the flow patterns.
The area with a large cyclonic vorticity in TCG-SL has azonal line shape and the area in TCG-EW has a circularshape and is concentrated at the composite center. The
area of positive vertical vorticity in TCG-CR is west of
the composite center because the CR pattern is found to
the east of the monsoon low.
Geographical distributions of the differences between the mean of TCG days and mean of non-TCG

FEBRUARY 2020

YOSHIDA AND FUDEYASU

FIG. 14. Differences in the flow pattern indices between themean of the tropical cyclone genesis days and the mean of thenongenesis days; tropical cyclone genesis mean 2 nongenesis
mean. (a)–(c) SL, CR, and EW patterns, respectively. The difference was calculated as the 38-yr mean for all seasons. The
black dots show the locations of tropical cyclone genesis overthe period from 1979 to 2016. The white dots are the cases for the
major (a) SL, (b) CR, and (c) EW flow patterns. These white dots
correspond to the active period. The color shaded area is 95%
significance, and the green contour indicates a boundary of thesignificance.

days (i.e., total days during the analysis period—TCGdays) for the three flow patterns are shown in Fig. 14.
Statistical t test was conducted to determine a significance of the difference on each grid point, andcolor shaded areas indicate grid points with 95% significance from the result of the statistical test. The
FPIs indicate a higher value during TCG days thanthat during non-TCG days. The flow patterns intensify when a tropical cyclone is developing from a
tropical disturbance. The geographical distributionsof the difference are approximately coincident withthe distribution of the climatological mean of the FPIs
shown in Fig. 4, particularly for FPI-EW. The difference in the FPI-SL is extended to the east of the dateline; it seems that the westerly wind tends to prevail
toward the eastern WNP in the TCG-SL cases. Although the differences in the FPI-CR are randomly
distributed compared to the differences in the other573

flow patterns, it seems to be distributed northwestward over the WNP. This implies that the CR patternalso has a relationship with a tropical-depression-type
disturbance.
To show the correspondence between the differencesin the FPIs and the genesis environmental parameters,
geographical distributions of the difference between the
TCG cases and the climatological mean for the GPI,
RH, and vertical shear of horizontal wind are shown in
Fig. 15. As same as Fig. 14, the statistical test was conducted for Fig. 15, and color shaded areas are grid points
with 95% significance. The GPI tends to have a larger
value than the climatological mean in the TCG cases.
The RH tends to be larger at approximately 158N for allof the flow patterns. Compared to the climatologicalmean, vertical shear of horizontal wind tends to bestronger south of the composite center and weaker northof it. FPI-CR and FPI-EW are along the boundary of astrong vertical shear region. FPI-EW especially locatesover the area where the difference in vertical shear is not
significant. This implies that vertical shear condition
does not have a strong correlation with TCG with EW
over the western part of WNP. A strong vertical shear isfound when significant flow patterns are developed, but
TCG occurs away from the strong vertical shear area
and remains as close to the flow pattern as possible. For
TCG-EW, the areas with a suitable environmental
condition for TCG that have a high RH are particularly
narrow (Fig. 15h). The high GPI areas are also limited
and concentrated west of 1408E (i.e., western WNP).
This is consistent with the FPI-EW and GPI having
higher values in the western WNP than in the eastern
WNP, which was found in the interannual variability(Figs. 7 and 9). While the EW disturbances organizedalong the high FPI-EW area, the probability that the
disturbance develops into a tropical cyclone would be
low because of unfavorable environmental conditions.
This discussion might lead to the marsupial paradigm.
Interestingly, the GPI distribution pattern of the TCGCR is similar to that of the FPI-CR. This suggests that ahigher GPI value (i.e., a more suitable environment) is
required for TCG-CR than for the other TCG cases.
Furthermore, the RH for TCG-CR at a higher latitude
has an approximately 2% higher value than that for the
TCG cases of the SL and EW patterns. The vertical
shear would be strong around the confluence region of
the westerly and easterly winds. Therefore, a higher RH
in TCG-CR seems to support maintenance of convection for the disturbance prevailing over the strong vertical shear. However, a tropical cyclone organized in the
CR pattern has a possibility of developing into an intense cyclone supported by the high GPI. Actually, thefrequency of intensification of the TCG-CR is higher

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MONTHLY WEATHER REVIEWVOLUME 148

FIG. 15. Differences in the genesis environmental parameters between tropical cyclone genesis cases (active period) and the climatological mean; active period 2 climatological mean. The difference was calculated as the 38-yr mean for all seasons. In each panel, flow
pattern indices are superposed by hatched contours: (a)–(c) tropical cyclone genesis with the SL pattern; (d)–(f) tropical cyclone genesis
with the CR pattern; and (g)–(i) tropical cyclone genesis with the EW pattern. The genesis parameter in (a), (d), and (g) is the GPI; for (b),(e), and (h) it is RH; and for (c), (f), and (i) it is the vertical shear of horizontal wind between 200 and 850 hPa heights (VS). The color
shaded area is 95% significance, and the green contour indicates a boundary of the significance.

than that of the other flow patterns (Fudeyasu andYoshida 2018).

6. Conclusions
The climatological characteristics of low-level flow
patterns for the SL, CR, and EW, which are frequently
found in TCG cases over the WNP, were investigatedin this study by extending the semiobjective categorization of low-level flow patterns suggested by Yoshida
and Ishikawa (2013). Grid point values of the FPIs
were obtained using the extended analysis method andshowed the significance of the flow patterns comparedto the climatological value at the location at that time.
The analysis was conducted for the period from 1979 to2016 over the target analysis region from 1108E to 1808
longitude and from 58 to 258N latitude. Through thisanalysis, the flow pattern climatological characteristics,
regardless of TCG, were identified.
It was shown that FPI-SL and FPI-CR are distributed
around the area east of the Philippines. FPI-EW is
broadly distributed in an east–west direction at lower
latitudes. These flow patterns display seasonal variability in terms of intensity and horizontal distribution.
FPI-SL and FPI-CR have a positive peak during JJA.
FPI-EW has a peak during JJA in the eastern area and apeak during SON in the western area of the WNP. Thisseasonal variability in flow patterns is strongly correlated with genesis environmental parameters such as the

GPI, SST, RH, and vertical shear of horizontal wind.
These flow patterns also show significant interannual
variations. A part of the interannual variation in flow
patterns seems to represent the number of TCG cases.
However, the details of the relationship are not clear
because the interannual variations in the flow patterns
remain too complicated to be directly compared to thenumber of TCG cases.
Furthermore, the flow pattern temporal evolutions
were investigated. All three flow patterns tend to besignificant as the TCG date is reached. FPI-SL and FPIEW are significant 3 days before the TCG date. FPI-CR
starts to increase 13 days before TCG and then remains
stable from 7 days before TCG until the TCG date.
Compared to the climatological mean, FPI-SL and FPIEW tend to be substantially intense during the TCG
event, while FPI-CR is not as intense. The significance of
these flow patterns was determined for the first time inthis study.
The geographical relationships between the flow
patterns and genesis environmental parameters werealso investigated. For the TCG cases, three flow patterns
are recognized in a circular area near the TCG location.
The GPI and RH are also high near the flow patterns,
while vertical shear of horizontal wind tends to be weak.
From the perspective of geographical location, TCG
occurs in areas with significant low-level flow patterns
that are on the eastern edge of the high-value GPI and
RH area with a weak vertical shear of horizontal wind.

FEBRUARY 2020

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From an analysis of its temporal evolution, this favorable condition was found to gradually organize 3 days
before the TCG date. Each of the conditions consideredhere had previously been recognized as being favorable
for TCG. However, the relationships among them were
investigated for the first time in this study. These findings help clarify the overall TCG process and enable
development of a TCG forecasting system for the WNP.
Acknowledgments. The authors are grateful to theeditor and anonymous reviewers for their useful andcritical comments. The authors thank Drs. H. Ishikawa,Y. Kajikawa, Y. Miyamoto, and M. Yamaguchi for theirhelpful comments. Thanks also to the members of theComputational Climate Science Research Team, RIKEN
Center for Computational Science, for their input.
This study utilized the dataset of JRA-55 provided
by the JMA and the dataset of best track produced by
the JTWC. The monsoon index was downloaded from a
website (http://apdrc.soest.hawaii.edu/projects/monsoon/
seasonal-monidx.html). This work is supported by MEXT
KAKENHI Grants 17H02956, 17K14398, and JP19H05696.

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