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Cloud Clusters and Tropical Cyclogenesis: Developing and Nondeveloping Systemsand Their Large-Scale Environment
BRANDON W. KERNS AND SHUYI S. CHEN
Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida
(Manuscript received 19 September 2011, in final form 13 July 2012)
ABSTRACT
Tropical cyclone (TC) genesis occurs only when there is persistent, organized convection. The question of
why some cloud clusters develop into a TC and others do not remains unresolved. This question cannot be
addressed adequately without studying nondeveloping systems in a consistent manner together with de-
veloping systems. This study presents a systematic approach in classifying developing and nondeveloping
cloud clusters based on their large-scale environments.
Eight years of hourly satellite IR data and global model analysis over the western North Pacific are used. A
cloud cluster is defined as an area of#208-K cloud-top temperature, generally mesoscale in size. Based on the
overlapping area between successive hourly images, they are then tracked in time as time clusters. The initial
formations of nearly all TCs during July–October 2003–10 were associated with time clusters lasting at least
8 h (8-h clusters). The occurrence of an 8-h cluster is considered to indicate theminimumdegree of convective
organization needed for TC genesis. A nondeveloping system is defined as an 8-h cluster that is considered to
be a viable candidate for TC genesis, but was not associated with the TC genesis.
The large-scale environmental conditions of cyclonic low-level vorticity, low vertical wind shear, low-level
convergence, and elevated tropospheric water vapor are statistically more favorable for developing systems.
Generally, the environment became more (less) favorable with time for the developing (nondeveloping)
systems. Nevertheless, many developing (nondeveloping) systems formed (dissipated) in seemingly un-
favorable (favorable) environments within a lead time of ,24 h.
1. Introduction
The dominant mode of organization of convection in
the tropics is mesoscale convective systems (MCSs) with
convective cores and stratiform precipitation (Houze
1977; Zipser 1977). Tropical cyclogenesis [(TC) genesis]—
the initial development of a self-sustaining, warm-core
vortex—does not occur without persistent convection and
low-level convergence (Gray 1968). Much of our knowl-
edge on TC genesis is based on developing systems that
often occur in environments with enhanced low-level
convergence, vorticity, and atmosphericmoisture that are
favorable for TC genesis. However, these favorable en-
vironmental conditions occur much more often than
TC genesis events. What are the sufficient conditions
for TC genesis, and what is the role of mesoscale
convective systems leading up to TC genesis? To answer
these questions, it is necessary to study both developing
and nondeveloping systems in an objective, consistent
manner.
a. Definition of TC genesis
Part of the ambiguity in studying developing and
nondeveloping systems is the definition of TC genesis.
Frank (1987) defined TC genesis as the transition from
a tropical disturbance to a tropical depression (TD),
with a closed surface wind circulation. This is similar to
the definition used by the National Hurricane Center
(NHC) and the Joint Typhoon Warning Center (JTWC).
The subsequent transformations of the TD to a tropical
storm (TS) and the TS to a hurricane are referred as
‘‘development’’ and ‘‘intensification,’’ respectively. Others
have proposed a two-stage genesis process: 1) large-scale
development to describe the development of the envi-
ronment favorable for TC genesis and 2) core formation
(e.g., McBride 1995). Note that convective cloud systems
can modulate their environment through moistening and
Corresponding author address: Dr. Brandon Kerns, Rosenstiel
School of Marine and Atmospheric Science, University of Miami,
4600 Rickenbacker Causeway, Miami, FL 33149.
E-mail: [email protected]
192 MONTHLY WEATHER REV IEW VOLUME 141
DOI: 10.1175/MWR-D-11-00239.1
� 2013 American Meteorological Society
heating. The interaction between deep convection and its
environment is complex. The two stages of TCgenesismay
be impossible to separate in some cases.
Because of the ambiguous nature of defining TC gen-
esis and the general lack of detailed, high-resolution in
situ data over tropical oceans, it is impossible to de-
termine the exact time of TC genesis for most storms,
even at the 6-h temporal resolution of best-track datasets.
Indeed, different operational centers frequently have
different TD initiation times in their best tracks for the
same storm. In this study, the initial appearance of a TD
or TS in the JTWC best track (‘‘best-track genesis’’) is
used as the reference point for TC genesis.
b. Large-scale environment and TC genesis
The large-scale environment, including area-averaged
vorticity, vertical wind shear, low-level convergence, and
moisture content, strongly influences TC genesis (Gray
1968; DeMaria et al. 2001; Schumacher et al. 2009).
However, the actual genesis process depends critically on
convective systems of various scales and their interaction
with the large-scale disturbance (Simpson et al. 1997,
1998; Houze 2010). Tory and Frank (2010) provide a de-
tailed summary on various hypotheses on TC genesis
from both large-scale and mesoscale perspectives.
Tropical cyclone genesis is most favored in regions
with elevated low-level relative vorticity and low verti-
cal wind shear (Gray 1968; McBride and Zehr 1981;
DeMaria et al. 2001). The Saharan air layer with dry air
and high dust concentration may play a significant role
in modulating TC formation in the Atlantic (Dunion
and Velden 2004), though this is still a topic of active
research (Braun 2010). Camargo et al. (2007) suggested
that moisture is most likely to be a limiting factor in the
Atlantic during El Nino years.
While there are statistical differences between the
environment of the developing and nondeveloping dis-
turbances, they cannot be entirely distinguished based
on the large-scale conditions, using the current genera-
tion of atmospheric analysis products (Perrone and
Lowe 1986; Hennon and Hobgood 2003; Hennon et al.
2005; Kerns and Zipser 2009; Fu et al. 2012; Peng et al.
2012). The within-sample variance is comparable to or
larger than the difference between the developing and
nondeveloping sample means. Note that this appears to
be the case regardless of the tracking method. These
results can be interpreted to imply that the large-scale
environment determines the likelihood of TC genesis,
but other factors at smaller scales determine whether
and when genesis occurs. That is, TC genesis is proba-
bilistic to some extent (Simpson et al. 1998). It is also
possible that a more favorable environment may simply
indicate that the large-scale development component of
TC genesis (McBride 1995) is under way, and therefore
the inner core is more likely to form.
Recently several studies have shown that a deep
(up to ;700–600 hPa) meso-a-scale (200–2000 km)
Lagrangian closed circulation in the wave-relative flow
is favorable for TC genesis, especially in easterly waves
(Dunkerton et al. 2009; Wang et al. 2009, 2012). The so-
called Kelvin cat’s eye configuration (referred to as
a ‘‘wave pouch’’) is favorable because it affords a degree
of protection from the harsh environment and provides
a focus point for sustained deep convection and vorticity
accumulation.
c. Convective processes during TC genesis
Several mechanisms have been identified for organi-
zation of convective systems into the core of a TC. Chen
and Frank (1993) showed that long-lived MCSs can
create inertially stable areas in saturated, stratiform rain
regions. This is favorable for the development of the
initial warm core and hydrostatic surface pressure falls
because a greater fraction of the latent heat released
contributes to columnwarming (Schubert andHack 1982;
Rogers and Fritsch 2001). Houze et al. (2009) found that
large areas of saturated, mesoscale ascent led to the
genesis of Hurricane Ophelia (2005). On the other hand,
Bister and Emanuel (1997) proposed that a long-lived
mesoscale rain area (‘‘showerhead’’) and associated
evaporative cooling would lead to a saturated, cold-cored
midlevel vortex. However, it is unclear how the cold
core evolves into a warm-core system for TC genesis to
occur. Ritchie and Holland (1993, 1997) emphasized
the merger of multiple mesoscale convective vortices.
Vortex merger results in increasingly deeper, stronger
vortices, eventually leading to the development of a
TD or TS.
Simpson et al. (1997) present a case in which the in-
teraction of multiple MCSs leads to development of
a ‘‘nascent eye’’ (e.g., the initial warm-core vortex) in
the subsidence regions between the convective systems.
Dolling and Barnes (2012) also related the nascent eye
of a TS tomesoscale subsidence. Harr et al. (1996) found
that the initial round of MCSs modifies the subsynoptic
environment such that the newMCSs organize in bands,
leading to the development of some western North Pa-
cific TCs. Hendricks et al. (2004) and Montgomery et al.
(2006) emphasized the role of ‘‘vortical hot towers’’ as-
sociatedwith strong updrafts in convective cores, which is
important for concentrating vorticity near the surface.
In all the above studies as well as operational practice,
persistent and organized convection is vital for TC gen-
esis.However, themeaning of ‘‘persistent and organized’’
can be difficult to define and is qualitative in many of
these studies.
JANUARY 2013 KERNS AND CHEN 193
d. Tracking developing and nondeveloping systems
Two types of tracking methods have been used in
previous studies: ones based primarily on dynamic fields
and others based on infrared satellite data. Thorncroft
and Hodges (2001) used an automated tracking method
using filtered 850-hPa relative vorticity to develop a 20-
yr climatology of Atlantic African easterly wave (AEW)
activity. Hopsch et al. (2007, 2010) have also used this
vorticity tracking technique. Kerns et al. (2008), Fu et al.
(2012), and Peng et al. (2012) use variations of tracking
filtered relative vorticity. Dunkerton et al. (2009) focus
on the intersection of the wave trough and the critical
layer, which is viewed as the focal point of TC genesis.
The use of clouds as tracers for developing and non-
developing disturbances in the tropics goes back to the
first meteorological satellite images. Gray (1968) con-
sidered tropical disturbances to be ‘‘distinctly orga-
nized cloud and wind patterns in the width range of
100–600 km, which possess a conservatism in time of at
least a day or more.’’ This concept was apparently moti-
vated by observations that midlatitude cloud patterns
generally correspond with circulation features of similar
scale (Hayden 1970). This viewpoint crystallized into the
concept of the synoptic disturbance ‘‘cloud cluster’’
(Gray 1973; Ruprecht and Gray 1976; McBride 1981).
Ruprecht and Gray (1976) described the cloud cluster
quite qualitatively: it is ‘‘a bright, solid white blob’’ in
satellite imagery. McBride (1981) described the synoptic
disturbance cloud cluster as ‘‘a loosely organized collec-
tion of deep convective clouds and covered . . . by a thick
cirrus shield.’’ Note that they used the same dataset as
Ruprecht and Gray (1976), which had considerable lim-
itations in terms of temporal resolution. Modern satellite
data allows for a much more detailed analysis of con-
vective systems through their life cycles.
More recent studies using synoptic cloud cluster con-
cept as a means to identify synoptic-scale disturbances
include Perrone and Lowe (1986), Hennon andHobgood
(2003), Hennon et al. 2005, and Hennon et al. (2011).
Hennon et al. (2011) described an objective method for
tracking these synoptic-scale cloud systems globally.
Cloud clusters may ‘‘disappear’’ for up to 12 h and still be
tracked as the same system, as long as redevelopment
occurs within a reasonable radius. Note that redevelop-
ment of convection does not always imply coherent,
continuous propagation of a synoptic weather system. In
fact, large areas of cloudiness can occur without an easily
identifiable synoptic-scale wind disturbance (Simpson
et al. 1967, 1968).
An alternative approach to objectively define and track
cloud clusters is to focus on mesoscale organization.
Williams and Houze (1987) defined cloud clusters using
an infrared brightness temperature threshold of 208 K,
tracking them in time as time clusters to study winter
monsoon convection in the vicinity of Borneo. This def-
inition of cloud clusters/time clusters emphasized co-
herent mesoscale convective systems, in contrast to the
synoptic-scale cloud clusters discussed above. In this
framework, the multiple convective ‘‘burst’’ events lead-
ing up to TC genesis observed by Zehr (1992) are related
to the development and decay of individual mesoscale
cloud clusters. The cloud cluster/time cluster tracking
framework allows for quantifying multiscale variability
from the mesoscale up to intraseasonal and interannual
time scales (e.g.,Mapes andHouze 1993; Chen et al. 1996;
Chen and Houze 1997a,b). The cloud cluster identifica-
tion and tracking used in these studies are completely
automated and objective, in contrast to previous studies
which relied on manual disturbance identification and
tracking (McBride and Zehr 1981; Perrone and Lowe
1986; Hennon and Hobgood 2003; Kerns et al. 2008;
Dunkerton et al. 2009; Fu et al. 2012; Peng et al. 2012).
Defining developing and nondeveloping systems based
on the cloud cluster tracking method using hourly satel-
lite data is less ambiguous and easy to reproduce in both
operational and research environments.
The organization of this manuscript is as follows. The
data used are described in section 2. The tracking of cloud
clusters/time clusters and vorticity maxima are described
in sections 3 and 4. Classification between developing and
nondeveloping clusters is described in section 5. Section 6
presents an illustrative example of a developing and
nondeveloping case. The results of the large-scale envi-
ronment of developing and nondeveloping systems as
well as their evolution in time are presented in sections 7
and 8. Finally, conclusions are given in section 9.
2. Data
a. TC tracks
The JTWCbest track contains the 6-hourly coordinates
and intensity of named TCs and unnamed depressions
and subtropical cyclones. The best-track data are based
on postseason reanalysis of the available data for each
storm. Because of the need to infer storm initiation using
satellite estimates (e.g., Dvorak 1975), the initial ap-
pearance of a TD or TS in the best-track data is consid-
ered to be a reference point for defining a developing
system in this study, which may not necessarily be the
precise time of TC genesis.
The storms considered in this study occurred in 08–358N, 1008–1608E during July–October, 2003–10 (Fig. 1).
This includes: 84 typhoons, 39 tropical storms, and 17
tropical depressions. Of these, 4 typhoons and 1 trop-
ical storm entered the study region from the east, and 3
194 MONTHLY WEATHER REV IEW VOLUME 141
typhoons formed in late June then tracked through the
study region in July. Hurricane Ioke (2006) also entered
the study region from the east. For these storms the
precursors to TC genesis are not identified in this study.
b. Satellite data
The geostationaryMultifunctional Transport Satellites
(MTSAT) infrared (IR) channel (10.5–11.5 mm) data
from July–October 2003–10 are used for identification of
cloud clusters. The data are at 0.058 (approximately 5 km
near the equator) resolution. Hourly satellite data are
used to track the evolution of cloud clusters through their
life cycle.
The Tropical Rainfall Measuring Mission (TRMM)
Microwave Imager (TMI) retrieved column-integrated
water vapor [or total precipitable water (TPW)] and
optimum interpolated sea surface temperature (SST)
data are used for examining cloud cluster environmental
moisture and SST. The retrieved data are provided by
Remote Sensing Systems (RSS). For SST, daily fields are
used. The daily fields are optimum interpolation com-
posites of TRMM TMI data and AMSRE data at 0.258spatial resolution (Gentemann et al. 2004). They are
corrected for the diurnal cycle to;0800 local time using
an empirical function of solar insolation, wind speed,
and local time of observation (Gentemann et al. 2003).
SST data are averaged over an area with a radius of 18,and valid observations must have at least 50% coverage.
This criterion is effective as a land mask to eliminate
cases near or over land.
For the water vapor fields, the version 4 average of the
past 3 days of TMI data (bmaps_v04 d3d data) is used.
Because the tropical ocean is relatively moist in the
boundary layer, most of the variance in TPW is due
to variations in the midlevel water vapor content. The
disadvantage of the polar-orbiting satellite TMI and
Advanced Microwave Scanning Radiometer for Earth
Observing System (EOS) (AMSR-E) data is the limited
temporal sampling and gaps between swaths, as well as
rain contamination. This is alleviated by using 3-day-
averaged data, at the expense of some temporal vari-
ability. Because of the 48 averaging radius used and the
relatively small TPW gradients within the west Pacific
(WPAC) warm pool, the loss of information from the
temporal averaging does not significantly affect the
results. For TPW, cases are included only if there are
TPW data for at least half of the 48 radius. Cases withoutsufficient coverage (,50%), usually due to land cover-
age, are not considered in the statistical analysis of de-
veloping and nondeveloping cases.
c. Global analysis data
The National Centers for Environmental Protection
(NCEP) final analysis (FNL) is used to identify and
track TC-related vorticity maxima as well as vorticity
maxima that did not develop into TCs. It is also used to
determine the large-scale environment of the clusters
such as the vertical wind shear and near-surface con-
vergence. The FNL analysis incorporates additional
observations that were not available for inclusion in the
real-time NCEP Global Forecast System (GFS) analy-
sis. However, because of the sparse observational net-
work over most of the WPAC, the NCEP FNL relies
heavily on the GFS model first guess. In particular, it is
sensitive to the model convective parameterization. An
upgrade to the GFS model physics, including convective
parameterization, was implemented on 28 July 2010,
which could potentially affect the results for the last 3
months of the 8-yr study. Nevertheless, the upgrade is not
expected to affect the main conclusions of this study. The
data are 6-hourly and at 18 resolution. Area-averaged
fields are calculated from the analysis by averaging all
grid points within a 68 radius from the center. The most
recent available analysis data are used for each case (e.g.,
a case at 0500 UTC uses data from 0000 UTC).
3. Tracking cloud clusters
a. Cloud clusters
Following Williams and Houze (1987), cloud clusters
are identified as contiguous areas of IR brightness tem-
peratures below a threshold value encompassing a con-
tiguous area of at least 5000 km2 (equivalent diameter of
80 km), but in some cases over 100 000 km2 (350 km),
including meso-b- and small meso-a systems (Orlanski
1975). Theminimumarea threshold is arbitrary; however,
the subset of long-duration convective systems we focus
on attained areas of well above this threshold. The IR
FIG. 1. The number of TC genesis events within a 2.58 radius ofeach point (contours) and genesis locations (plus signs) for July–
October 2003–10. Contours are for every 1 event, starting from 3,
and bold for 5 and above.
JANUARY 2013 KERNS AND CHEN 195
threshold is 208 K based on previous studies over the
western Pacific during the Tropical Ocean and Global
Atmosphere Coupled Ocean–Atmosphere Response
Experiment (TOGA COARE; Mapes and Houze 1993;
Chen et al. 1996). There is generally a close correspon-
dence between 208-K cloud area and contiguous meso-
scale areas of rainfall observed by radar. The choice of
208 K filters out many nonprecipitating anvil clouds.
While the correspondence between cold clouds and pre-
cipitation is not one-to-one (Arkin and Ardanuy 1989),
most, if not all, of the 208-K cloud clusters represent
convective systems that at one point in time had a signif-
icant precipitating area.
Note that inmany previous studies synoptic-scale cloud
clusters were tracked, using warmer brightness tem-
perature thresholds and/or subjective manual tracking
methods. This study focuses on objectively tracked me-
soscale convective systems defined by 208-K cold cloud
tops roughly corresponding to rain areas. After the cloud
clusters are identified, the center of the cloud cluster is
defined as the geometric center of the,208-K area. The
size of the cloud cluster is defined as the diameter that
the cloud cluster would have if it were a circle with the
same area.
Figure 2 shows a map of objectively identified cloud
clusters (dark blue shading) at 0000 UTC 8 September
2008. The largest cloud clusterwith an area of 90 400 km2
(size of 340 km) near 168N, 1268E is associated with Ty-
phoon Sinlaku. The rainbands surrounding Sinlaku also
appear as cloud clusters. Several other relatively large
cloud clusters, the largest of which is;75 900 km2 (size
; 310 km), are not associated with TCs, but with a
monsoon disturbance in the South China Sea. Another
area with several large cloud clusters is the intertrop-
ical convergence zone (ITCZ; roughly 58–108N) east of
1458E.
b. Time clusters
Time clusters are cloud clusters that can be tracked in
time with various lifetimes and sizes. Cloud clusters that
overlap by at least 50% or 10 000 km2 between con-
secutive hourly satellite images are considered to be part
of the same time cluster. Time clusters may consist of
cloud clusters that merge and/or split between consec-
utive frames. Note that the term time cluster refers only
to convective systems with continuity in time between at
least two consecutive images.1
FIG. 2. EnhancedMTSAT-1R infrared satellite image at 0000 UTC 8 Sep 2008. Blue regions
are where cloud-top temperature is lower than 208 K, representing the extent of the cloud
clusters. Cyan circles indicate the geometric centers of cloud clusters with the circle area
proportional to the cluster area.
1 Time cluster tracking resets at the beginning of each month.
This results in some long-lasting time clusters occurring at the end
of the month not being continuously tracked into the next month.
Because of the limited number of 8-h clusters lasting from one
month into the next month, this does not affect significantly affect
the results of this study.
196 MONTHLY WEATHER REV IEW VOLUME 141
At any given time, a time cluster may consist of more
than one cloud clusters that merged at a future time or
split at a previous time, and therefore belong to the same
time cluster. The center and area of the time cluster at
a given time are the geographical center and the com-
bined area of all the constituent cloud clusters, re-
spectively. The maximum area (size) associated with the
lifetime of the time cluster is the maximum area (size)
among the time cluster snapshots. As inTOGACOARE,
time clusters with longer duration generally attained
larger sizes (Fig. 4a and Chen and Houze 1997a,b).
The results presented in rest of this study will use time
clusters exclusively to represent convective cloud sys-
tems. The terms ‘‘cluster,’’ and ‘‘time cluster’’ are used
interchangeably from here on.
Similar to the near-equatorial region during TOGA
COARE (Chen et al. 1996), most time clusters move
westward for a few hours and remain relatively small
(Fig. 3) Only a small minority of time clusters are as-
sociated with TCs. The development and life cycles of
Supertyphoons Sinlaku, Hagupit, and Jangmi and Trop-
ical Storms Mekkhala and Higos in September 2008 are
clearly associated with long-lived time clusters (time
clusters lasting at least 24 consecutive images are high-
lighted). For the case of Sinlaku, the highlighted cluster
lasted 159 h and could be tracked starting 9 h prior to
best-track genesis. Hagupit and Higos also have west-
ward-moving groups of time clusters that are easily
identified as precursors to the storm formation (e.g., de-
veloping clusters). In addition to the developing systems,
there are long-lived time clusters that move westward,
often in distinct envelopes.
c. Initial tropical cyclone time clusters
As a first step to determine the properties of time
clusters that develop into TCs, the initial TC time cluster
for each TC that formed within the study area in July–
October 2003–10 (e.g., Fig. 1) is identified objectively
as follows. It is the longest-lasting time cluster among
either 1) the largest time cluster present at the time of
initial TD appearance in best track or 2) the first time
cluster that formed within 24 h after the initial TD ap-
peared in the JTWC best-track data. For a few weak,
disorganized storms (e.g., unnamed TDs and minimal
tropical storms with intermittent convection), a domi-
nant time cluster could not be tracked until up to 24 h
after best-track genesis.
For all TDs and TCs considered in this study from
July–October 2003–10, the initial TC time clusters lasted
at least 8 h, while the time clusters at later stages of the
TChad a range of sizes and durations similar to the set of
all time clusters (Fig. 4). A time cluster that persists for
at least 8 h (e.g., nine consecutive images) is referred to
as an 8-h cluster. There are 48 609 time clusters tracked
in the study region during July–October 2003–10. A total
of 4379 of them (e.g., 9%) lasted at least 8 h. Physically,
8-h clusters are convective systems with long durations,
which are likely to have large stratiform rain areas at
some time in their lifetime and are likely to persist be-
yond the time of dominant diurnal surface forcing (e.g.,
Chen and Houze 1997a).
4. Tracking vorticity maxima
To help identify the 8-h clusters leading up to a TC
genesis (i.e., the precursors to TC genesis), an objective
vorticity maximum tracking algorithm is used to track
the system in time prior to best-track genesis. AGaussian
smoothing function was applied to the NCEP FNL rela-
tive vorticity at 850 hPa. The weighting function is a
Gaussian bell curve with standard deviation of three grid
points (38, ;330 km) extending out nine grid points
(;990 km) in each direction. This smoothing filters out
small-scale features that are not fully resolved by the
analysis. Kerns et al. (2008) showed that coherent vor-
ticity maxima could be tracked back several days prior to
initial formation of some TCs.
Vorticity maxima are first identified and then tracked
in time by matching them with the closest one within 38radius in the next time frame using the 6-hourly data.
Tracks end when a vorticity maximum can no longer be
found within 38 at the next analysis time. The minimum
threshold value of the vorticity tracking is 23 1026 s21.
It is a low threshold chosen to be as inclusive as possible.
Vorticity maxima can be seen moving generally
westward, often together with coherent envelopes of
cloud clusters (Fig. 5). Some are evidently precursors
leading to TC formations and others are not associated
with TC genesis. For example, Typhoons Hagupit and
Higos were associated with well-defined vorticity fea-
tures and pre-TC vorticity centers that could be tracked
from east of 1608E (outside of the cluster tracking do-
main). The vorticity maxima associated with each TC
are found by searching within 38 of the best-track data.
Most vorticity maxima that are associated with best-
track TDs are within 18 distance of the best track (not
shown). The vorticity maxima tracks extend up to sev-
eral days prior to TC formation and can cross a signifi-
cant part of the basin during that time (Fig. 6a).
5. Classification of developing and nondevelopingsystems
Because the initial developments of all TCs during
July–October 2003–10 are associated with an 8-h cluster,
the occurrence of an 8-h cluster is considered to indicate
JANUARY 2013 KERNS AND CHEN 197
aminimumduration and level ofmesoscale organization
that is necessary, but not sufficient, for TC genesis to
occur. Therefore, only 8-h clusters over the ocean, which
are not already TCs, are included as candidates for
further identification of developing and nondeveloping
systems. The 8-h minimum duration threshold greatly
reduces the number of time clusters to only those that
have a potential to develop into a TC and, therefore, are
considered a viable candidate to TC genesis.
a. Developing systems
A developing system is defined as a time cluster or a
group of time clusters that last at least 8 h and are as-
sociated with a tractable vorticity maximum that pro-
vides a way to identify the precursors leading up to TC
formation. Developing systems include pre-TC clusters
that are backtracked in time using the vorticity maxima
tracks. For many storms the initial TC time cluster that
FIG. 3. Time–longitude diagram of cloud clusters and time clusters within the domain shown
in Fig. 2, for September 2008. Black circles represent cloud cluster locations. The size of the
circles is proportional to the size of the cloud clusters. Time clusters are connected with red
lines. Time clusters lasting at least 24 h are highlighted with yellow circles and dark blue lines.
TC best tracks (green lines) are labeled by storm names (unnamed tropical systems are labeled
by storm number). The initial appearance of each storm as a TDor TS in the JTWCbest track is
indicated with a thick, black circle.
198 MONTHLY WEATHER REV IEW VOLUME 141
existed at the time of initial appearance of a TD in the
best track (e.g., initial TC clusters) could be tracked
several hours back before genesis as a time cluster and in
some cases more than a day (Fig. 6b). Additionally, any
8-h cluster passing within 58 of a pre-TC vorticity track is
also considered to be a developing system (Fig. 7). The
developing systems have a similar range of lead times as
the pre-TC vorticity maxima (Figs. 6a,b).
b. Nondeveloping systems
A nondeveloping system is defined as an 8-h clus-
ter that is not a TC cluster and is not previously iden-
tified as a developing cluster (Fig. 7). As illustrated by
Fig. 5, many nondeveloping systems are associated with
westward-moving vorticity features and trackable vor-
ticity centers.
Among the 31 289 time clusters tracked over the
ocean, 3144 were 8-h clusters (Fig. 7). A total of 398 of
the 3144 8-h clusters were associated with active TCs and
are excluded as candidates for TC genesis. Among the
other 2746 8-h clusters, 435 (15.8%) were developing
systems and 2311 (84.2%) were nondeveloping systems.
6. Examples of developing and nondevelopingsystems
a. Precursors to Typhoon Hagupit (2008)
As shown in Figs. 3 and 5, Typhoon Hagupit was a
case with a well-defined sequence of precursor devel-
oping clusters going back several days prior to TC for-
mation. Further details on the evolution of vorticity and
time clusters leading up to the development of Hagupit
are shown in Fig. 8. An envelope of latitude-averaged
vorticity with an embedded vorticity center can be traced
back to east of 1608E at least as far back as 12 September,
the 8-h clusters associated with the vorticity maximum
began on 14 September, and a 4-day series of 8-h clusters
occurred during 15–18 September, leading up to the ini-
tial appearance of the TD in best track at 1800 UTC 18
September.
The 8-h clusters occurred preferentially to the south of
the center of vorticity, and remained skewed to the
south even after the initial TD had formed. This is
probably due to the effect of large-scale vertical shear on
the system (Black et al. 2002; Corbosiero and Molinari
2002; Lonfat et al. 2004). Also note that the vorticity
maximum weakened somewhat on 16 September, but
then it intensified after ;1 day of sustained 8-h cluster
activity. As shown in the relative vorticity swath map
(e.g., the maximum vorticity along the track of the
vorticity center), the relative vorticity gradually increased
as the system moved westward on 17–18 September. It
is likely that the persistent, organized convection on
16–18 September played a strong role in modifying the
mesoscale environment. That is, the time clusters can
be considered distinct precursors to the TC genesis. The
convection also likely contributed to the preconditioning
of the large-scale vorticity and moisture leading up to the
formation of Hagupit.
b. Nondeveloping system
A sequence of nondeveloping systems during 24–30
September 2010 was associated with a westward-moving
envelope of vorticity maxima, which is shown as an
FIG. 4. Time cluster duration and maximum size (e.g., equivalent diameter) for (a) all time clusters and (b) time
clusters passing within 38 of JTWC best track (e.g., TC time clusters). In (b), dominant clusters associated with the
initial formation of the TC are indicated as solid black dots, and other TC clusters at a later stage in the storm
evolution are drawn as open circles. The vertical dashed line indicates the 8-h duration threshold, and the arrow
indicates time clusters lasting at least 8 h (8-h clusters).
JANUARY 2013 KERNS AND CHEN 199
example in Fig. 9. In this case, a vorticity center could
be tracked over 3 days moving from ;1408E to the
Philippines, along ;128N. The envelope of enhanced
vorticity can be traced back farther to the east, but not the
vorticity center. Several 8-h clusters occurred along and
to the south of the vorticity center track. Based on the
vorticity swath map, the vorticity center attained an am-
plitude similar to pre-Hagupit on 15 September 2008, but
FIG. 5. Time–longitude diagram of 8-h clusters and NCEP FNL relative vorticity for Sep-
tember 2008. The background color shading is 850-hPa relative vorticity averaged from 58–258N along each longitude grid. The 8-h cluster tracks are denoted as magenta circles with the
circle area proportional to the time cluster area. Vorticity maxima lasting at least 24 h are
drawn as triangles. JTWC TC best tracks are drawn as thick, black lines.
200 MONTHLY WEATHER REV IEW VOLUME 141
despite of the collocated 8-h clusters and the vorticity
maxima over a period of 3 days before moving into the
vicinity of the Philippine islands, the system did not in-
tensify further into a TC. This nondeveloping system
eventually moved over the Philippines. Note that TC
genesis does occur in close proximity to the Philippines
(Fig. 1), which means the islands were not necessarily a
factor in this case, especially since it failed to develop
over a 3-day period prior to approaching the islands.
Clearly, the collocation of relative vorticity and 8-h
clusters is not sufficient to determine whether TC genesis
will occur, and it is necessary to consider other factors.
7. Large-scale environment of developing andnondeveloping systems
The developing and nondeveloping systems are as-
sociated with a wide range of environment conditions,
quantified by low-level vorticity, low-level convergence,
vertical wind shear, moisture content, and SST. In the
subsequent analysis, the large-scale environmental con-
ditions are computed at each hourly snapshot throughout
the 8-h clusters’ lifetimes. In addition to developing and
nondeveloping systems, the environment predictors are
also calculated for the first 24 h of the TCs for com-
parison purpose. There is a significant overlap in these
parameters between developing and nondeveloping
systems (Fig. 10). Nevertheless, there are significant sta-
tistical differences (above 95% confidence in the Stu-
dent’s t test) in all the parameters, except SST. This
separation in the environments of the developing versus
nondeveloping systems can be used to obtain a skillful
discrimination between the two subsets of 8-h clusters.
Additionally, the information provided by the time se-
quence of cloud clusters allows the time history of the
developing and nondeveloping systems to be quantified.
a. Environmental conditions
The strongest statistical predictor of TC genesis is
low-level relative vorticity (Fig. 10a). Consistent with
McBride and Zehr (1981), the median relative vorticity
for the developing cases (18 3 1026 s21) is nearly twice
FIG. 6. Scatter diagram of lead time vs distance from the initial tropical depression (TD) formation based on JTWC
best track: (a) pre-TC vorticity maxima and (b) developing 8-h clusters. In (b), the triangle markers are for the initial
TC time clusters, which are trackable directly to the TC.
FIG. 7. Schematic of the classification of 8-h clusters into de-
veloping and nondeveloping systems. The number of time clusters
represented is in parentheses for each category.
JANUARY 2013 KERNS AND CHEN 201
that of the nondevelopers2 (9 3 1026 s21). This is much
greater discrimination than Fu et al. (2012) who found
only a 20% difference using filtered vorticity fields. For
reference, the relative vorticity distribution of the first
24 h of the TCs is also shown. As expected, the TCs have
somewhat higher relative vorticity than the developing
systems. This suggests that most of the large-scale rela-
tive vorticity was generated prior to TC genesis. Note
that the system vorticity and the large-scale environ-
ment vorticity cannot be entirely distinguished.
In addition to low-level vorticity, themedian low-level
convergence is ;30% greater for the developing sys-
tems versus nondeveloping systems (Fig. 10b). As with
relative vorticity, the difference between developing
systems and the first 24 h of TCs is less significant.
Vertical wind shear is significantly lower on average
for the developing systems (Fig. 10c). The median ver-
tical wind shear of the developing systems is ;9 m s21
(17 kt). This median value of shear is similar to the
threshold below which rapid intensification of TCs gen-
erally occurs (Kaplan and DeMaria 2003). Half of the
developing systems occurred in an environment with
shear .9 m s21. Also, about 30% of the nondeveloping
cases were associated with shear values ,9 m s21.
Satellite-derived TPW was somewhat higher in de-
veloping cases compared with nondeveloping cases,
but it is not a strong discriminating factor (Fig. 10d).
Hennon and Hobgood (2003) also found TPW to be
a poor predictor in the WPAC. This is probably due to
a generally moist environment compared with the At-
lantic. A similar calculation was done using only the
subset of cases with individual TMI swaths in close
proximity in space and time, and similar results were
obtained (not shown).
Satellite-derived SST did not suggest a significant dif-
ference between developing and nondeveloping systems
FIG. 8. Evolution of vorticity maxima and cloud clusters associated with the genesis of Typhoon Hagupit (2008).
The best track is shown as the thick black line. The vorticity maxima associated with the particular storm is drawn as
triangles. The 8-h cloud clusters associated with the particular storm (e.g., TC clusters and developing clusters) are
drawn as magenta circles, proportional to the cloud cluster size. (left) Time–longitude Hovmoller plot. The back-
ground color shading is relative vorticity at 850 hPa averaged from 58–258N at each longitude. (right) Map of the
tracks of the features shown in (left). The color shading is the maximum relative vorticity within 108 of the vorticity
maximum center.
2 A sensitivity test was performed including only the non-
developing systems tracking within 58 of a vorticity center lasting
over 24 h. The median value of relative vorticity obtained was
;2 3 1026 s21 higher.
202 MONTHLY WEATHER REV IEW VOLUME 141
(Fig. 10e). This is probably because of the generally warm
SSTs in the WPAC during July–October.
b. Genesis productivity
The preferred areas of TC genesis (Fig. 1) generally
correspond with the preferred areas of 8-h clusters
(Fig. 11a). Nevertheless, the maximum of 8-h cluster
occurrence is in the South China Sea just west of the
Philippine islands, whereas theTCgenesis ismost frequent
east of the Philippines near 1308E. The South China Sea
has a relatively larger number of nondeveloping clusters
than the warm pool region east of the Philippines. The
relative frequency of developing and nondeveloping
clusters is summarized using a genesis productivity simi-
lar to Kerns et al. (2008) and Hennon et al. (2013). The
genesis productivity of 8-h clusters was calculated as the
number of developing time clusters divided by the total
number of candidate time clusters (developing and non-
developing clusters, excluding TCs).
The genesis productivity is greatest over the west
Pacific warm pool near 158–208N, 1308E, which is col-
located with the relatively high occurrence of 8-h clus-
ters (Fig. 12). In contrast, the South China Sea has
relatively low genesis productivity. The peak in genesis
productivity east of the Philippines is farther north than
the maximum track density of both vorticity maxima
and 8-h clusters (Figs. 11a,b). Similar to the Atlantic,
the maximum genesis productivity is located to the
north of the maximum track density of disturbance
centers (Kerns et al. 2008). Note that the latitude of
maximum genesis productivity corresponds to the
maximum latitude—and highest planetary vorticity—
within the geographical region where 8-h cluster occur-
rences are relatively frequent. Motivated by the maxi-
mum of genesis productivity at ;178N, the following is
considered to be an additional predictor of TC genesis in
the WPAC:
ALAT175 jlatitude2 17j ,
where the vertical bars indicate the absolute value.
c. Linear discriminant analysis
The developing systems statistically form in more fa-
vorable environmental conditions (e.g., lower shear,
higher humidity, stronger low-level vorticity, and con-
vergence) than the nondevelopers. Linear discriminant
analysis (LDA) was performed using the five environ-
mental parameters shown in Figs. 10a–e as well as
ALAT17 (section 7b). LDA can determine the overall
FIG. 9. As in Fig. 8, but for the nondeveloping cases associated with a moderately strong vorticity maximum from
27–30 Sep 2010.
JANUARY 2013 KERNS AND CHEN 203
discrimination between developing and nondeveloping
systems using an optimal linear combination of multi-
ple predictors (Perrone and Lowe 1986; Hennon and
Hobgood 2003; Kerns and Zipser 2009). The probability
of development from LDA (or posterior probability) is
considered to be an indication of how favorable the large-
scale environment is for TC genesis. A climatological
probability of development is defined as the fraction of
the developing systems in all 8-h cluster instances, which
is ;20%. When LDA probability is significantly greater
FIG. 10. Cumulative distributions of the large-scale environment conditions associated with 8-h cluster snapshots:
(a) 850-hPa relative vorticity, (b) 925-hPa divergence, (c) 850–200 hPa vertical wind shear, (d) TMI 3-day mean
TPW, and (e) TMI/AMSRE SST. Nondeveloping clusters are indicated by the dashed curves. Developing clusters
are indicated by the solid curves. The first 24 h of TCs are indicated by the light, dotted curves. The dynamic fields
from the FNL analysis are based on 08–68 averages in (a)–(c), TPW is based on 08–48 averages in (d), and SST is based
on 18 averages in (e).
204 MONTHLY WEATHER REV IEW VOLUME 141
(less) than the climatological background probability of
development, the environment can be considered to be
favorable (unfavorable) for TC genesis. An environment
with LDA probability to develop close the climatology is
considered to be marginally favorable for TC genesis.
To determine how effective the LDA is for distin-
guishing between developing and nondeveloping sys-
tems, the leave-one-out (‘‘jackknife’’) cross validation
is used. A single sample is left out, and the LDA is re-
calculated using the remaining samples. This process is
repeated for each hourly instance of the 8-h cluster, to
obtain the probability of that case developing. The prob-
ability of detection is the fraction of developing systems
that were correctly predicted to be developing systems.
The false-alarm rate is the fraction of nondeveloping sys-
tems that were erroneously predicted to be developing
systems. The probability of detection and false-alarm rate
depend on the threshold posterior probability used to
decide when to predict development, which is also re-
ferred to as the decision threshold.
The Heidke skill score (HSS) is used to determine
the predictive skill relative to making random guesses
(Marzban 1998). HSS ranges from below 0 to 1. HSS.0.3 is considered to indicate a benchmark for a useful
level of skill. Skill scores above the benchmark are seen
for posterior probabilities between 0.15 and 0.50 (Fig. 13).
Using the climatological likelihood (20% LDA proba-
bility to develop), a 70% detection rate with a 28% false-
alarm rate is obtained (Fig. 13). At the threshold with
FIG. 11. The number of tracks passing through each 58 3 58 during July–October 2003–10: (a) time clusters lasting at
least 8 h (8-h clusters) and (b) vorticity maxima lasting at least 24 h.
FIG. 12. The TC genesis productivity of 8-h clusters. Genesis
productivity is defined as the percentage of candidate (e.g., de-
veloping plus nondeveloping) 8-h clusters passing through each 58box that are identified as developing systems.
FIG. 13. The probability of detection (POD, dashed), false-alarm
rate (FAR, dash–dot), and Heidke skill score (HSS, solid) as
a function of the threshold LDA probability of development. The
climatological likelihood of development, which is the fraction of
candidate time cluster snapshots that are developing systems, is
indicated by the vertical gray line (;0.2). The POD, FAR, andHSS
are determined using leave-one-out (‘‘jackknife’’) cross validation.
See text for details.
JANUARY 2013 KERNS AND CHEN 205
peak skill as indicated by the HSS (;30% LDA proba-
bility to develop), there is roughly a 50% probability of
detection and a 12% false-alarm rate. Cloud cluster area,
growth rate, and aspect ratio were considered as potential
predictors, but they did not improve the skill, probably
because individual convective systems each go through
their individual life cycles with significant changes to their
morphology over periods of a few hours.
The HSS obtained in this study compares favorably
with previous studies using various tracking methods
and predictors. Hennon and Hobgood (2003) obtained
an HSS of ;0.4 for the 12- and 24-h lead-time periods.
Note that the lead time in this study is significantly
longer (median of 39 h). Hennon et al. (2005) expanded
the range of posterior probability above the benchmark
of HSS 5 0.3 using a neural network classifier, but did
not significantly increase the peak skill score. The range
of posterior probability with HSS . 0.3 in this study is
somewhat greater than their LDA classification, but less
than their neural network classifier. The skill in this
study is comparable to Kerns and Zipser (2009) for the
Atlantic, but significantly lower than their results in the
eastern North Pacific. Peng et al. (2012) and Fu et al.
(2012) did not present skill scores, but their filtered
relative vorticity appears to provide somewhat less
discrimination than the spatial averaged vorticity used
in this study (their Fig. 9). Because relative vorticity is
the strongest predictor, it is likely that the skill of their
studies would be somewhat lower than the current study.
Note that these skill score comparisons are limited be-
cause of the unique mesoscale classification method
used here compared with the synoptic-scale classifica-
tion used in previous studies. Nevertheless, the skill
scores obtained are not fundamentally different than
previous studies using synoptic classification.
Despite the statistically significant differences be-
tween the developing and nondeveloping systems, there
are many nondeveloping systems occur in environments
that are apparently favorable for TC formation (e.g.,
LDA probability to develop .20%); 20% of the non-
developing systems have at least the same chance to
develop as the median of the developing systems. Recall
that the sample size of nondeveloping systems is 2311,
while there are only 436 developing (Fig. 7). For cases
with the LDA probability to develop greater than the
climatological likelihood of development, the fraction
of developing systems is larger than the fraction of
nondeveloping systems (Fig. 14a); however, the num-
ber of nondeveloping cases with a probability to de-
velop larger than the climatological background is
considerably larger than the number of developing sys-
tems with the same probability, up to posterior proba-
bility of 0.4 (Fig. 14b).
8. Evolution of developing and nondevelopingsystems
To better understand and further distinguish the char-
acteristics of the developing and nondeveloping systems,
evolutions of both groups are examined in context of
their large-scale environmental conditions in terms of the
LDA probability to develop. The developing systems are
backtracked in time so that their evolution leading up to
TC genesis can be examined in an evolving large-scale
environment (Fig. 15a). A small group of the developing
systems can be tracked back as long as 8 days prior to TC
FIG. 14. (a) The probability density function (PDF) of posterior probabilities from linear discriminant analysis. (b)
The number of instances (e.g., hourly snapshots) of nondeveloping and developing systems in each bin (0.05) of LDA
posterior probability. The gray vertical line represents the climatological likelihood of an 8-h cluster being a de-
veloping cluster (;0.2).
206 MONTHLY WEATHER REV IEW VOLUME 141
genesis. Not surprisingly, the environment is not partic-
ularly conducive to TC genesis at that early stage. As the
genesis time approaches, cloud clusters are statistically
more likely to occur in increasingly favorable environ-
ments.On the other hand,many other developing systems
occurred inmoderate to less favorable environments even
with short lead times ,48 h. In fact, the peak of the dis-
tribution at,48-h pregenesis (e.g., contours in Fig. 15a) is
in a marginally favorable environment near the climato-
logical likelihood of TC genesis (;20%).
The evolution of nondeveloping systems is examined
from the formation of a 8-h cluster to its dissipation
(Fig. 15b). In contrast to the developing systems, the
nondeveloping systems statistically occurred in less
favorable environments as time progressed. Themajority
of ‘‘false alarms’’ occurred with clusters lasting ,36 h,
which represents the vast majority of nondeveloping
systems. A minority of very long-lasting (.24 h) clusters
remained in a favorable environment for .24 h prior to
their dissipation. However, 806 (274) of the 2311 non-
developing systems had LDA probability to develop
above 0.2 (0.3) within the first 24 h after cluster initiation.
The distinct evolution of the relatively lasting de-
veloping and nondeveloping systems may indicate that
their large-scale environmental conditions are better re-
solved by the global FNL analysis fields, whereas the
majority of developing and nondeveloping systems
formed in the environments that are less distinguish-
able and/or or unresolved by the global analysis. The
latter presents a major challenge for TC genesis fore-
casting using the large-scale environmental conditions,
which can lead to many false alarms.
The interaction of long-lasting cloud clusters with
their environment may modulate the conditions leading
up to TC genesis, which is unresolved in the current
observational data and global analysis fields. This could
FIG. 15. The time history of LDA probability to develop for (a) developing systems and (b)
nondeveloping systems. Each of the light gray lines represents the entire time history of an
individual 8-h cluster. In (a), time is with respect to the time of TD/TS formation in the JTWC
best track (gray lines). The initiation points of the time clusters are indicated with black dots. In
(b), time is with respect to the initial formation of the 8-h cluster, and the dissipation points are
drawn as triangles. Contours are drawn for the number of snapshots (e.g., hourly points along
the gray lines) in each bin: (a) 24-h and (b) 6-h bins. Both panels use 0.1 LDA probability bins.
Contours are for 10, 20, 50, 100, 200, 300, 400, 500, 1000, and 2000 snapshots for both (a) and (b).
The climatological probability to develop (0.2) is indicated by the black horizontal line in both
(a) and (b).
JANUARY 2013 KERNS AND CHEN 207
be due to a single dominant cloud cluster lasting ex-
tremely long (.24 h) or the repeated occurrence of
shorter duration clusters. In developing systems, the
persistent convection can feed back into the large-scale
environment, making it increasingly more favorable.
This interaction is more likely to occur in an environ-
ment with higher relative vorticity, which is more likely
to be inertially stable with a smaller Rossby radius of
deformation in those cases (Schubert and Hack1982). In
nondeveloping systems this positive interaction does not
take place, and instead the environment of the clusters
instead becomes more hostile with time. Determining
the mechanism that allows feedback to the environment
is an ongoing research topic. It is hypothesized that the
mesoscale environment in which convection occurs is
a critical factor in determining to what extent the con-
vection can feed back to the environment (e.g., Chen
and Frank 1993).
Another possible interpretation of the increasingly
favorable large-scale environment of developing sys-
tems with time is that the large-scale environment is
partly a symptom of the outer-core component of TC
genesis. This process may occur concurrently with inner-
core formation and may be inseparable from it.
9. Conclusions
Tropical convective cloud systems (cloud clusters) are
a key ingredient for TC genesis. The characteristics of
the cloud clusters and their environmental conditions
are examined in terms of developing and nondeveloping
systems. An objective cloud cluster tracking method is
used to track and classify developing and nondeveloping
systems. The key findings are summarized as follows:
d A total of 31 289 time clusters were tracked from July–
October 2003–10 over thewesternNorthPacificOcean.
Only 2311 8-h clusters were considered as viable
candidates for TC genesis, among which 435 (15.8%)
were developing systems and 2311 (84.2%) were non-
developing systems (Fig. 7).d The developing systems generally have greater low-
level vorticity, greater low-level convergence, lower
vertical wind shear, and higher water vapor content
than the nondeveloping systems (Fig. 10). There is no
significant difference in SST.d Linear discriminant analysis (LDA) can skillfully
distinguish between developing and nondeveloping
systems. The Heidke skill score obtained is ;0.4
(Fig. 13), among the highest skill scores presented in the
literature. A probability of detection of 70% and false-
alarm rate of 27% is obtained.d Generally, the environment became more favorable
with time for developing systems with long (1–7 days)
lead time to TC genesis (Fig. 15a), whereas nondevel-
oping systems tend to evolve into unfavorable environ-
ments with time (Fig. 15b).d Many developing (nondeveloping) systems formed
(dissipated) in seemingly unfavorable (favorable)
environments within a lead time (duration) of ,24 h,
which presents a major challenge for TC genesis
forecasting.
Incorporating the 8-h cluster duration can improve
statistical prediction of TC genesis by eliminating many
disturbances with weaker, short-lived convective sys-
tems and providing a more stringent criterion to de-
termine systems that have the potential to develop. In an
operational environment, the cloud cluster tracking
method and LDA classification could be used to provide
an objective, statistical guidance on the potential for
operational invest systems to develop into TCs.
The lack of a more robust separation of the environ-
mental conditions for developing and nondeveloping
systemsmay be in part due to the relative low-resolution
of FNL analysis fields. It is possible that the skill could
be improved by using higher-quality analyses. Al-
though observations from targeted field programs can
provide insights about TC genesis on mesoscale and
subsynoptic scales, the lack of continuous temporal
and spatial coverage makes it difficult to address the
convective–environmental interactions. A combination of
in situ observations and high-resolution, cloud-resolving
modeling over a large region allowing both convective
systems and their environment to interact freely will be
a key to better understand and predict the interaction of
the convection (e.g., long-lasting cloud clusters) with the
environment during TC genesis.
Acknowledgments. The authors wish to thank Mr. Ed
Ryan for his assistance on processing the satellite data
and cloud cluster tracking analysis. The comments and
suggestions from the two anonymous reviewers helped
to improve the manuscript significantly. This work was
supported by the Office of Naval Research (ONR) under
the Tropical Cyclone Structure 2008 (TCS-08) Research
Grant N00014-08-1-0258 and the Impact of Typhoon on
Ocean in the Pacific (ITOP) Research Grant N00014-08-
1-0576. The IR satellite data were processed by Kochi
University, Japan. Microwave OI SST, and TRMM TMI
water vapor retrievals are produced by Remote Sensing
Systems (http://www.remss.com).
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