Journal of the Meteorological Society of Japan, Vol. 87A, pp. 381–391, 2009
DOI:10.2151/jmsj.87A.381

Life Cycle of Deep Convective Systems over the Eastern Tropical Pacific Observedby TRMM and GOES-W
Toshiro INOUE
Center for Climate System Research, The University of Tokyo, Kashiwa, Japan
Meteorological Research Institute/JMA, Tsukuba, Japan

Daniel VILA
Cooperative Institute for Climate Studies (CICS/ESSIC), University of Maryland at College Park,
College Park, Maryland, USA

Kavirajan RAJENDRANCSIR-Center for Mathematical Modeling and Computer Simulation, National Aerospace Laboratories,
Bangalore, India

Atsushi HAMADA
Graduate School of Science, Kyoto University, Kyoto, Japan

Xiangqian WU
Center for Satellite Applications and Research/NOAA, Camp Springs, Maryland, USA
andLuiz A. T. MACHADO
Instituto Nacional de Pesquisas Espacias (INPE/CPTEC), Cachoeira Paulista, Brazil(Manuscript received 13 June 2008, in final form 11 January 2009)

Abstract
The life cycle of deep convective systems over the eastern tropical Pacific (30°N to 30°S, 180 to90°W) was studied in terms of cloud types, as classified by a split window (11 mm and 12 mm). Hourly
split window image data of Geostationary Operational Environmental Satellite (GOES-W) fromJanuary 2001 to December 2002 was used in this study. Deep convective systems consist mostly of
optically thick cumulus type clouds in the earlier stage and a cirrus type cloud area that increases withtime in the later stage. During this analysis period and over the analysis area, the life stage of deep
convective system, to a large extent, can be identified by computing the percentage of cirrus typeclouds within the deep convective system from a single snap shot of the split window image.
Coincident Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations wereused to study the relationship between the percentage of cirrus type clouds within a deep convective

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system (i.e., its life stage) and the rainfall rate. It was found that the rainfall rate tends to be larger in theearlier stage of the life cycle when a smaller percentage of cirrus type cloud is present within the deep
convection.

1. Introduction
Deep convective systems (DC) are significant
meteorological phenomena, since severe thunder­storms are often associated with them. Advancing ourknowledge of how DC originate, mature and decay is
a fundamental issue in atmospheric science. A
pioneering study by Byers and Braham (1949) demon­
strated the life cycle of a DC using aircraft and radarobservations. They summarized the life cycle of a DC
as comprising a developing stage, a mature stage anda dissipating stage. The dominant cloud types at thethree stages are cumulus, cumulonimbus and anvil
clouds. They also suggested that the rainfall type
differed depending on the life stage. Thus, to identify
the life stage of a DC from a single satellite image
provides us with important information on the DC.
Satellite observations from a geostationary orbit
are useful in studying the evolution of DCs, con­sidering the high number of temporal observations
and wide range of coverage. Many studies have been
conducted to study DCs using infrared and/or visible
radiance images from geostationary weather satellites(e.g., Murakami 1983; Williams and Houze 1987;
Nakazawa 1988; Duvel 1989; Takayabu 1994). Thesestudies mostly used single infrared data to delineate
DCs by determining the threshold of brightness
temperature in the infrared channel (TBB).
Mapes and Houze (1993) studied cloud clusters
over a warm pool in the western Pacific using an
improved the objective cloud-cluster tracking
technique developed by Williams and Houze (1987).
They used a TBB threshold of -65°C to identify acloud cluster and showed the early morning maximumof the cluster. Chen and Houze (1997) suggested thatthe diurnal cycle of a cloud cluster reflects the life
cycle of a larger DC area over the western tropical
Pacific. They used three TBB thresholds of 208 K,
235 K and 260 K for cloud cluster identification.
Machado et al. (1998) investigated the life cycle ofCorresponding author: Toshiro Inoue, Center for
Climate System Research, The University of Tokyo,
5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8568, Japan
E-mail: toshiro@ccsr.u-tokyo.ac.jp©2009, Meteorological Society of Japan

DCs over America and the eastern Pacific using
International Satellite Cloud Climatology Project(ISCCP) B3 data for 1987 and 1988. In their study,
they developed an automated method to track
convective systems using TBB thresholds of 218 K
and 245 K. Their results indicated that convective
systems with shorter lifetimes and smaller areas ex­­hibited lower initial expansion rates, lower cloud tops,
and lower anvil cloud reflectance than con­vective
systems with longer lifetimes and larger areas.
Mathon and Laurent (2001) studied the life cycle ofconvective cloud systems over the Sahelian region
using the TBB thresholds of 213 K, 233 K and 253
K. They found that the DC radius was related to its
lifetime, as shown by Machado et al. (1998). Further,
they found that DC is likely to propagate faster
during the mature stage when convection is stronger.
In these studies, the relatively colder TBB thresh­olds of 235 K to 208 K have been used for delineating
the core of a DC and for inferring the anvil cloud by
also using the second and third warmer TBB
thresholds. However, there is no physical rationale fordelineating an anvil by using the second and third
warmer TBB threshold. Further, if we use a colder
threshold, we might discard both the beginning (lowlevel cumulus) and ending (thin anvil cloud) stages of
the DC in the life cycle, since the colder threshold
can only delineate the deeper parts of the DC.
Inoue (1985) demonstrated the feasibility of cirrus
type cloud detection using split window (11 mm and
12 mm) data. Furthermore, a simple objective method
for classifying several cloud types was developed
based on a threshold technique applying the twodimensional diagrams of the TBB and brightness
temperature difference (BTD) between the split
window (Inoue 1987, 1989). The 11 mm channel ismore transparent than the 12 mm channel for ice.
Thus, the TBB at 11 mm is higher than that at 12 mm
for an ice cloud. Therefore, the BTD indicates alarger value for a cirrus type cloud consisting of ice,while an optically thick cumulus type cloud exhibitsa smaller BTD due to its black-body characteristics.
Utilizing these characteristics of the split window, we
can classify optically thick cumulus type clouds andoptically thinner cirrus type clouds on a more
physical basis using a single infrared threshold.

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The advent of the Tropical Rainfall Measuring
Mission (TRMM) satellite enables us to bridge cloud
characteristics and rainfall characteristics over a wide
area of the tropics, since a Precipitation Radar (PR),a microwave radiometer (TRMM Microwave Imager;
TMI) and a Visible Infrared Scanner (VIRS) areonboard the TRMM satellite (Kummerow et al.
2000). The PR is the first satellite-borne Precipitation
Radar to observe three-dimensional rainfall structures
over a 215 km swath. We can obtain the rainfall rate,
sea surface tem­perature (SST), total precipitablewater (TPW) and so on from TMI observation. VIRS
has one visi­ble, one near-infrared, and three infrared
channels, identical to the visible/infrared sensors on
current polar-orbital and geostationary satellites.
However, temporal sampling is a drawback of the
TRMM data in studying the evolution of DCs.
Combining the TRMM and infrared observations
from the Japanese Geostationary Meteorological Sat­ellite (GMS), Kondo et al. (2006) investigated therelationship between the evolutionary stage of DCs
and rain information. They tracked cloud systems
defined by a TBB threshold of 235 K using GMS
data for June, July, and August 2000 over the western
tropical Pacific. They found that during the lifecycleof the DC the maximum rainfall rate appeared first,
then the minimum TBB appeared followed by the
maximum area size.
Inoue and Aonashi (2000) examined the relation­
ship between cloud information from the split windowof VIRS and the precipitation area from PR. They
found that the clouds colder than 260 K in TBB withsmaller BTD corresponded well to the rain area
observed by PR, based on a dataset of rain events
during June 1998 over the frontal zone in East Asia.
However, they noted some drawbacks of the VIRS
calibration. The BTD for a very cold cloud andcumulonimbus type cloud, which can be assumed tobe black-body, is larger than that observed by the
split window of current polar-orbital satellite andgeostationary satellites.
In this study, we aim to investigate the charac­
teristics of DCs and rainfall over the tropical eastern
Pacific using TRMM and Geostationary Operational
Environmental Satellite (GOES-W) data. The area
was selected because of the wide range of SST andthe split window on GOES-W was reasonably cali­brated. Applying cloud type classification by the split
window, we can identify the anvil (cirrus type) clouds
within the DC with more physical rationale than
through the use of single infrared channel data. We

can also monitor a long lasting DC, since cloud typeclassification by the split window is effective for bothday and night. First, we study the life cycle of the DC
in terms of cloud type classified by the split window,
then we study the characteristics of DC and rainfall
using the coincident observations by TRMM and
GOES-W.

2. DataHourly split window data acquired by GOES-W
from January 2001 to December 2002 (excluding
September 2001) was used to study the life cycle ofDCs. The split window data were gridded to a
0.1-degree latitude/longitude grid over 30°N to 30°S
and 180 to 90°W. Clouds were classified at each grid
point using the split window. Figure 1 presents thecloud type classification diagram used in this study.
We used TBB thresholds of 213 K, 235 K, 253 K,
and 273 K to classify the optically thick clouds into
high, middle, and low levels. Thresholds of 213 K
and 235 K are often used in studies of DCs (e.g.,
Machado et al. 1998; Mathon and Laurent 2001) anda 253 K threshold corresponding to the temperature at
400 hPa in the US tropical standard atmosphere is usedas the warmer end of the high-level cloud category.

Fig. 1. Cloud type classification diagram
using the split window data. Cumulo­
nimbus type clouds (colder TBB withsmall BTD) are indicated by numbers 9,
10, 13, and 14 (blue ellipse) as well as 17(purple ellipse). Dense cirrus type clouds(colder TBB with larger BTD) are indi­cated by numbers 11, 12, 15, and 16 (green
ellipse). Cirrus type clouds (warmer TBB
with larger BTD) are indicated bynumbers 4 and 8 (light green ellipse).
Low-level cumulus type cloud (warmer
TBB with small BTD) are indicated bynumbers 1, 2, 5, and 6. Numbers of 3 and
7 are named as N-type in Inoue (1987).

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Journal of the Meteorological Society of JapanFor BTD thresholds, we used 0.5 K and 1 K toclassify optically thick cloud, and BTD over clear sky
to classify the optically thinner cirrus type clouds.
The temperature resolution of infrared data at lower
TBBs is generally worse than at higher TBBs. Thus
the BTD (BTD=TBB11mm – TBB12mm) becomesnoisy at lower TBBs. Therefore, we used the 1 K
thresholds to classify the optically thick cloud in thisstudy. Over clear sky areas of the tropics, the BTD
indicates about 2 K due to differ­ential absorption bywater vapor in the atmosphere. Therefore, we usedthe 2 K thresholds to classify the optically thin cirrus
type clouds. Here we classified 17 cloud types basedon the TBB and BTD as seen in Fig. 1, althoughsome cloud types are combined; these appear incolored ellipses in this study.
We define a DC as a cloud area colder than 253 K(cloud types numbers 9 to 17 in Fig. 1). Compared tothe previous study, the threshold of 253 K is on thewarmer end for defining a DC. Using a BTD thresh­
old of 1 K, we can identify the anvil cloud (an opti­
cally thinner area: cloud types 11, 12, 15, and 16 in
Fig. 1) within the DC on a more physical basis thanwith conventional single infrared channel usage. We
also used a cloud lidar (Cloud-Aerosol LIdar with
Orthogonal Polarization (CALIOP)) to provide anVol. 87A

example of height of cloud type classified by the split
window.
The TRMM is in a non-sun-synchronous orbit withan inclination of 35°. The TRMM PR observes threedimensional rain structures. 3G68 data was used inthis study as rainfall data. Rainfall rate, TPW, andSST are retrieved from TMI.. The rainfall information
observed by TRMM PR and TMI was assembled on
a 0.5-degree latitude/longitude grid.
We compiled information on DCs, such as its sizeand percentage of cirrus type clouds, extracted using
the tracking technique applied to hourly data for the
23 months from January 2001 to December 2002 (nodata for September 2001). No-merge and no-split DCs
were selected from the tracked DC information forstudying the life cycle of a DC. We also selected a
DC that coincided with TRMM observations within
15 minutes in order to study the relationship between
life stage and rainfall rate.

3. Results
3.1 Cloud type classified by the split window
The classification of cloud types by the split win­
dow has been validated in terms of radiative effect by
a comparison between coincident and collocatedobservations from the Earth Radiation Budget

Fig. 2a. Cloud type map (cumulonimbus type clouds in blue color and cirrus type clouds in green) as
classified by the split window over 15°S to 25°S and 165°W to 145°W at 0000 UTC on 30 December 2006
with CALIPSO orbit (yellow line). Clouds labeled A and B along the CALIPSO orbit correspond to A and
B in Fig. 2b.

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385

Fig. 2b. Vertical profile of backscatter coefficient observed by CALIOP over 15°S to 25°S along the
CALIPSO orbit in Fig. 2a Corresponding cloud types are shown on top with the same colors as in Fig. 2a.

Experiment (ERBE) and the cloud types determinedby split window (Inoue and Ackerman 2002). As
expected, cirrus type clouds and low-level strato­
cumulus type clouds classified by the split window
exhibit small differences in the values of longwave
fluxes, while cirrus type clouds exhibit much smaller
values than low-level stratocumulus type clouds inshortwave fluxes. Some of the thin cirrus type cloudclassified by the split window indicated positive cloudradiative forcing at the top of the atmosphere. Cloud
types other than thin cirrus type clouds classified by
the split window indicated significant negative cloudradiative forcing.
Here, we demonstrate an example of the compar­
ison between the cirrus and cumulonimbus typeclouds classified by the split window and CALIOP
observation. Figure 2 depicts a cloud type classi­
fication map constructed from the split window of theGOES-W with Cloud-Aerosol Lidar and Infrared

Pathfinder Satellite Observation (CALIPSO) orbit(Fig. 2a) over the area of 15°S to 25°S and 165°W to
155°W; the data were acquired by GOES-W at 0000
UTC on 30 December, 2006. The vertical profile ofbackscatter co­­efficients observed by CALIOP and thecloud types encountered along the orbit (top) are
given in Fig. 2b. We can see that the cumulonimbus
type clouds (cloud types 9, 10, 13, and 14) surroundedby cirrus type clouds (cloud types 4, 8, 12, and 13)
over 19°S (Fig. 2a). This cloud area corresponds to acomplicated vertical cloud structure as seen in Fig.
2b. The cumu­lonimbus type cloud labeled B
corresponds to larger values of the backscatter
coefficient. Other cirrus type clouds correspond to
relatively smaller values of the backscatter coefficientwith the heights greater than 8 km seen in Fig. 2b.
Tiny cirrus type clouds labeled as A, were seen over
15°S. This cloud also corresponds to cloud signals
higher than 8 km in the CALIOP observations (Fig.

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Journal of the Meteorological Society of Japan2b). This example indi­cates the reasonability of cloudtype classification by split window for cumulonimbus
and cirrus type clouds.
3.2

Evolution of spatial distribution of cloud typeassociated with DC
Figure 3 shows the two-hourly cloud type maps forthe area of 4°N to 12°N and 157°W to 140°W from1200 UTC, 09 May to 0200 UTC, 10 May 2001
(except for 2100 UTC). Time increases from left toright and from top to bottom. The purple area
corresponds to clouds colder than 213 K (cloud type17). The blue area corresponds to the cumulonimbus
type clouds (cloud types 9, 10, 13, and 14). This cloudis colder than 253 K with BTD smaller than 1 K,
signifying an optically thicker and colder cloud. The
green area corresponds to cirrus type clouds (cloudtypes 11, 12, 15, and 16) that are colder than 253 K
but optically thinner than a cumu­lonimbus typecloud.

Vol. 87A

The DC labeled A in the figure (highlighted byblack ellipse) developed until 1600 UTC (top right)
with the expansion of a cumulonimbus type cloudand 213 K cloud. An anvil cloud area (green) colder
than 235 K and with BTD larger than 1 K then
expands with time. The cumulonimbus type cloud
had almost disappeared by 2100 UTC on 9 May(middle right). The single infrared channel techniquecannot identify differences in optical features within
a cloud colder than 253 K.
DC labeled B (highlighted by red ellipse) devel­
oped very rapidly with a larger area of 213 K cloud
until 0000 UTC on 10 May (bottom-middle). There­
after, the 213 K cloud decreased and the anvil cloud
expanded at 0200 UTC, 10 May (bottom-right). The
DC disappeared at 0700 UTC on 10 May with nocumulonimbus type clouds remaining, just cirrus typeclouds (not shown).
We have discussed the life cycle of a DC in termsof cloud type classified by the split window. We see a

Fig. 3. Two hourly cloud type maps for the case of deep convective systems from 09 May to 10 May, 2001.
Time increases from left to right and from top to bottom.

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similar evolution of DCs in many cases (not shown).
In the developing stage, cumulonimbus type clouds
are dominant, then cirrus type clouds increase withtime. The percentage of the cirrus type clouds be­­comes very large in the decaying stage.
3.3

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Temporal variation of cloud amount for eachcloud type associated with DC
Figure 4 plots the temporal variations in cloudamount for each cloud type against time from thegenesis of the DC labeled A in Fig. 3. The cloudamount peaks with a sequence of clouds colder than
213 K, cumulonimbus type cloud, then dense cirrus
type clouds colder than 253 K with BTD larger than 1
K appear. The cloud amount (percentage within theDC) is normalized to the maximum value for eachcloud type during the life cycle. Cumulonimbus typeclouds or very cold clouds appear in the early stage andcirrus type clouds appear in the later stage of the life
cycle. This delineates the DC life cycle charac­teristics.
We have provided an example of the lifetime of a
DC in terms of cloud type. Such DCs can be tracked
using the automated technique developed by Vila etal. (2008). The technique was applied to hourly
images captured between January 2001 andDecember 2002. We selected the DCs that did not
split or merge during the lifetime of the DC. The
areal center of the DC was assigned as the position of
the DC. The number of grids within the DC and thepercentage of anvil cloud were archived during theanalysis period.
Figure 5a illustrates the temporal variation in theFig. 5a. Evolution of DC size (number of
0.1-degree latitude/longitude grids) sortedby duration of lifetime.

Fig. 4. Temporal variations in cloud amountwithin DC for each cloud type during thelife cycle for the DC called A in Fig. 3.
The cloud amount is normalized to the
maximum value for each cloud type. CB,
DCI and 213 K denote the respective
cumulonimbus type cloud (cloud types 9,10, 13, and 14 in Fig. 1), dense cirrus typeclouds (cloud types 11, 12, 15, and 16 in
Fig. 1) and clouds colder than 213 K(cloud type 17 in Fig. 1), respectively.

Fig. 5b. Evolution of the percentage of cirrus
type clouds within a DC, sorted byduration of lifetime.

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Vol. 87A

non-splitting DC. In other words, we can, to a large
extent, identify the life stage of a DC from a single
snapshot satellite image. The percentage of cirrus
type clouds in the developing stage is less than 20%,and at the decaying stage it is more than 27%. One
might think that this value for the decaying stage
seems slightly smaller than expected. However, wenote that the end of a DC may not be captured in thehourly data. The actual end of a DC might be athinner cirrus type cloud that cannot be detected by
the 253 K threshold.

Fig. 5c. Evolution of the percentage of cirrus
type clouds, normalized to the lifecycle.
The middle line indicates the mean andother lines indicate the offset of thestandard deviation from the mean value.

number of grids within the DC (DC size) sorted by
the duration of the DC. From the two years, we have3178 samples for 3 hours duration, 2633 samples for
5 hours duration, 786 samples for 7 hours duration,
262 samples for 9 hours duration, and 132 samples
for 11 hours duration. We can see that the DCs withlonger lifetime exhibit a larger area (number of grids)
at their maximum size. This finding is similar to
those of Machado et al. (1998). Machado et al. (1998)
suggested that the initial expansion of the DC is agood indicator for the duration of the DC. When we
observe the first two time steps in this figure, we cansee indications that the initial expansion of the DC islarger for a long lasting DC.
Figure 5b plots the temporal variation in thepercentage of cirrus type clouds (anvil) within the
DC. Regardless of lifetime, the percentage of cirrus
type clouds increases with time. The evolution of
cirrus type clouds can be seen in the example in Figs.
3 and 4. Figure 5c plots the percentage of cirrus typeclouds within a DC, using linear interpolation overten intervals between the initiation time andtermination time regardless of the duration of the DC.
Although the variance increases toward the end of
the lifetime, the percentage of cirrus type clouds
increases with time from the initiation of DC.
Thus, the percentage of cirrus type clouds within a
DC is a good indicator for determining the life stage(developing, mature, or decaying) of a non-merge or

3.4 DC characteristics and TRMM PR rainfall data
In the previous section, we discussed the DC sizeand cirrus type percentage during the DC lifecycle
for a non-merging or non-splitting DC. We have ageneral idea that a larger rainfall rate from a DC isassociated with the developing or mature stage of theDC lifecycle. Here, we study the relationship between
DC characteristics and rainfall information using
TRMM PR data. Using the coincident GOES-W andTRMM observation, the closest grid of 3G68 wasused for comparisons between DC characteristics andrainfall information. The areal center of the DC was
assigned as the position of the DC. Considering the

Fig. 6. Rainfall rate (mm day-1) from TRMM
PR and the percentage of cirrus typeclouds within the DC. The bold bars andbox indicate the respective median value
in each category and the first and third
quartile.

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PR observation swath, we selected a DC smaller than
400 grids (about 48,400 km2 ).
Figure 6 indicates the rainfall rate (mm day-1) from
PR for each percentage category of cirrus type cloudwithin the DC. There was a total of 4434 samples.
The rainfall rate is larger for smaller percentages of
cirrus type clouds within the DC, which indicates an
earlier stage in its lifetime (Fig. 5). The mean rainfall
rate becomes very small with a larger percentage of
cirrus type clouds within the DC. This suggests that
we can qualitatively infer the rainfall rate from asnapshot image by computing the percentage of cirrus
type clouds within the DC.
Kondo et al. (2006) demonstrated that the max­
imum rainfall rate occurs before the maximum sizeof the DC during its lifetime is reached. The rainfall
rate is generally higher in the earlier stage or themature stage in the DC lifecycle when the DC issmaller. We often see very large cluster where therainfall rate is low. Thus we believed that there wassome relationship between DC size and rainfall rate.
Figure 7 indicates the rainfall rate for different
numbers of grids (DC size). We cannot see any
relationship between DC size and PR rainfall rate(the correlation coefficient is 0.08), although therainfall rate is slightly higher for 200 to 250 grid
category. The smaller DC does not necessarily
indicate an earlier stage of its lifetime since the DC
is also smaller in the decaying stage, as seen in Fig.
5a.
Therefore, we construct the mean rainfall rate as afunction of cirrus type cloud percentage and DC size,

Fig. 8. Mean rainfall rate indicated withboth shade and contour as a function of
the cirrus type cloud percentage and DC
size.

using the DCs smaller than 400 grids (about 48,400
km2). Figure 8 indicates the rainfall rate with shade
and contour. The rainfall rate is higher for larger DCs
when cirrus type cloud percentage is less than 20%.
In contrast, the rainfall rate is generally smaller forboth larger DCs and greater percentages of cirrus
type clouds within the DC. However, there seems to
be a local maximum around a DC size of 200–300
for a cirrus type cloud percentage of 20 to 60%.
Because of the narrow swath of TRMM PR, it isdifficult to obtain an accurate rainfall rate for a DC
larger than 400 grids. If the mature stage of a DC isdefined as the maximum rainfall rate during its life
cycle, further study is required to determine whether
a local maximum of rainfall rate exists in terms of
DC size.

4. Concluding remarks

Fig. 7. Mean rainfall rate (mm day -1) from
TRMM PR, and DC size (number of
0.1-degree latitude/longitude grids). Bars
indicate standard errors.

Generally, DCs (deep convective systems) exhibits
complex features in terms of cloud type. It is rare tosee a simple evolution from initiation to decay. As
seen in Fig. 3, a new deep convection merges withthe older convection cell, with deep convection cells
appearing one after the other. Some DCs may split
into several areas. Through the use of cloud typeclassification by a split window, we can delineate theanvil cloud (the optically thinner part within the DC)
and the core (the optically thicker part) of the DC ona physical basis. In contrast to previous studies that

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Journal of the Meteorological Society of Japanuse the single TBB threshold to identify DC, ourcloud type classification can trace the DC life cyclefrom the early stages to the later stages of the DC interms of cloud type.
The lifecycle of a DC in terms of cloud type asclassified by the split window was studied over theeastern tropical Pacific (30°N to 30°S, 180 to 90°W).
Hourly split window image data from GOES-W for
the two years from January 2001 to December 2002
was used in this study. A DC consists mostly of
optically thick cumulonimbus type cloud in an earlier
stage and cirrus type cloud area increases with timein a later stage. The life stage of DCs can beclassified by computing the percentage of cirrus typeclouds within the DC. Coincident TRMM PR
observation was used to study the relationshipbetween the rainfall rate and the percentage of cirrus
type clouds defined by the split window within the
DC. As expected, we found that the rainfall rate
tends to be higher with a smaller percentage of cirrus
type clouds within the DC (during the earlier stagesof the life cycle).
Here, we used the hourly data observed from thesplit window onboard GOES-W. Generally, deep
convection is a vigorous phenomenon, with the cloudfeatures changing significantly over time. To capture
the features of deep convection, we should use morehigh frequency observations. Currently Meteosat-8
and 9 observe every 15 minutes, and geostationary
satellites planned for the future are also expected toobserve with higher frequency. Therefore, further
study using these high frequency observations will
provide us with another perspective on DCs.

Acknowledgments
This study was supported in part by the TRMM
program of the Japan Aerospace Exploration Agency(JAXA) and by the program of Core Research for
Evolutional Science and Technology (CREST) under
the Japan Science and Technology Agency (JST).
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