November
Journalof the2008
Meteorological Society of Japan, Vol.T.86A,
INOUE
pp. 1–15,
et al.2008

Characteristics of Cloud Size of Deep Convection Simulated
by a Global Cloud Resolving Model over the Western Tropical PacificToshiro INOUE
Center for Climate System Research, The University of Tokyo, Kashiwa, Japan

Masaki SATOH
Center for Climate System Research, The University of Tokyo, Kashiwa, Japan
Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology,
Yokohama, Japan

Hiroaki MIURA
Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology,
Yokohama, Japan

and
Brian MAPES
Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida, USA(Manuscript received 4 October 2007, in final form 28 January 2008 )Abstract
Cloud horizontal size distributions of cloud clusters were analyzed for global cloud resolving simulations with the global nonhydrostatic model NICAM whose mesh interval is about 3.5 km and 7 km. The 3.5
km-mesh simulation was performed for 7 days starting at 00 UTC 25 Dec 2006 by giving an initial condition of reanalysis data, while the 7 km-mesh simulation was run for 32 days from 00 UTC 15 Dec 2006.
We used outgoing long-wave radiation (OLR) simulated by NICAM to calculate size distributions of deep
convection, and compared them with those analyzed using equivalent blackbody temperature (TBB) of theinfrared channel of 11 µm of the Japanese geostationary meteorological satellite (MTSAT-1R). We selected two threshold temperatures, 208 K and 253 K, to identify deep convective areas including anvil cloud.
Specifically, we call clouds defined by the 208 K-threshold “deeper” convective clouds. Over the tropicalregion covering the maritime continent and the western tropical Pacific (10S−10N, 90E−160W), we examined the size of cloud areas defined by the two TBB threshold values and corresponding threshold valuesof OLR of 90 W m−2 and 210 W m−2, which were chosen by comparing cumulative histograms of TBB and
OLR in this region.
Resolution dependency by NICAM shows that the overall cloud size distribution of the 3.5 km-meshsimulation is much closer to that of the MTSAT-1R observation than that of the 7 km-mesh simulation.
Size distributions of deep convection in both simulations indicate nearly lognormal as is seen in the
MTSAT-1R observations. The 3.5 km-mesh simulation shows slightly less frequency than the MTSAT1R observation for smaller size of deeper convection, and it does not reproduce very large clouds. When
comparing cloud characteristics over land and ocean, simulated cloud size statistics are slightly closer tothe MT-SAT-1R observation in the maritime continent region (westward of 160E) than in the open ocean
region (eastward of 160E). A comparison of temporal variation of cloud area shows that the 3.5 km-meshJournal of the Meteorological Society of Japan

Vol. 86A

simulation captures clear signals of diurnal cycles over the maritime continent, together with amplification
associated with the Madden-Julian Oscillation (MJO) event. Morning and afternoon difference of
convective activity over a large island within the maritime continent is also simulated by 3.5 km-meshsimulation.
When one uses a global cloud resolving model for climate studies, the analysis of cloud size
distributions gives another dimension to improve the cloud properties of simulations. It is not only
relevant to realistic representations of deep convection, but is also useful for improving the radiationbudget of global cloud resolving simulations.

1. Introduction
Recently, simulations of a global cloud resolving model are becoming available using enormous
computer power (Tomita et al. 2005; Satoh et al.
2005). The Nonhydrostatic ICosahedral Atmospheric Model (NICAM) has been developed(Tomita and Satoh 2004; Satoh et al. 2007a) andis used for global cloud resolving simulations. A
global cloud resolving model (GCRM), which isdefined here as a global nonhydrostatic model witha mesh size of a few kilometers and uses an explicit
cloud process without cumulus parameterizations,
simultaneously calculate meso-scale circulations
associated with deep convection and large-scale
circulations driven by organized convection. As
an organized structure of meso-scale circulations,cloud clusters are naturally resolved in these simulations with NICAM (Nasuno et al. 2007), while
they are not simulated well by traditional generalcirculation models (GCMs) with cumulus parameterizations. Simulations with GCRM enable us todiscuss realisms of cloud characteristics, particularly those of cloud clusters whose scale is a few
hundreds of kilometers. At this new stage of highresolution global atmospheric modeling, we cananalyze numerically simulated data with GCRM
using the methods used for geostationary meteorological satellite (GMS) observations, whose resolution is comparable to the mesh size of GCRM.
Analysis of cloud clusters using the GMS observations has a long history (e.g., Nakazawa 1988;
Mapes and House 1993; Sui et al. 1997). To identify deep clouds, appropriate values of equivalent
blackbody temperature (TBB) of infrared data areused. Convective clouds over the tropics have a
Corresponding author: Toshiro Inoue, Center for
Climate System Research, The University of Tokyo,
5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568,
JapanE-mail: toshiro@ccsr.u-tokyo.ac.jp©2008, Meteorological Society of Japan

spectrum in size ranging from small isolated cumulus to large convective clusters. These convective
clouds affect the vertical distribution of heatingin the troposphere and radiation budget as well.
Size distributions and temporal evolutions of deep
convection have been studied using satellite observation data (e.g., Williams and Houze 1987; Mapes
and Houze 1993; Machado et al. 1993). Methods
to analyze spatial and temporal variations of deep
convection defined by infrared data can be used
for simulated data with GCRM, because both data
are provided by a few kilometer-interval. Similar
analysis methods could be used for data producedby traditional GCMs, but direct comparison cannot
be made because of the gap of the resolutions between GCMs and observations.
Using GCRM data, we obtain statistical properties of cloud and precipitation systems similar to
those retrieved from satellite observations. Satohet al. (2007b) showed the statistics of the precipitation height for the aqua planet experiment with
NICAM (Tomita et al. 2005; Satoh et al. 2005) andcompared them with those of the Precipitation Radar (PR) on board the Tropical Rainfall Measurement Mission (TRMM) (Takayabu 2002). It was
found that the frequency of the precipitation top
height of convective rains with GCRM has profiles
similar to satellite observations at least qualitatively. However, since the simulation was an idealized
one under the aqua planet condition, the comparison was limited to statistics over a wide domain of
ocean in general and it was difficult to identify thecauses of discrepancies.
Recently, simulations with GCRM under more
realistic conditions with land and sea distributions
have been performed (Miura et al. 2007a, 2007b).
Giving initial fields interpolated using reanalysis
data, they integrated NICAM for one month withthe mesh interval of about 7 km and for one weekwith the mesh interval of about 3.5 km at highest resolution. Miura et al. (2007b) captured therealistic behaviors of a Madden-Julian Oscillation

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event seen in the observation; slowly eastward
propagation of cloud systems was reproduced andfine scale structure within the MJO was resolved.
In this simulation, not only large-scale organized
cloud systems associated with the MJO, but cloudclusters whose size was hundreds of kilometers
were also simulated. Figure 1 compares the cloud
distribution of the Japanese geostationary meteorological satellite of the Multi-functional Transport
Satellite (MTSAT-1R) observation and that of the
NICAM simulation at 00 UTC 26 Dec 2006 over
the western tropical Pacific covering the maritime
continent: TBB colder than 253 K is shown for the
infrared image of MTSAT-1R, while the outgoing
longwave radiation OLR lower than 210 W m−2 isshown for the NICAM simulation of about 3.5 kmmesh size (DX3.5km). This shows that sizes andlocations of cloud clusters are realistically simulated, particularly over the maritime continent andthe ITCZ region over the ocean between 160E−
160W.
In the present study, we analyzed deep convective clouds over the western tropical Pacific using
the simulated data by NICAM shown above (Miuraet al. 2007b). Cloud size distribution by NICAM
is compared with that analyzed by using TBB of
MTSAT-1R. From this comparison, we will argue
quantitatively how cloud clusters of the model
simulation are comparable to those of the observations. We will investigate the dependency on themodel resolution, and contrast between the maritime continent and the open ocean regions, together with temporal variations. Since OLR is directly
related to the energy transfer, this analysis is useful to investigate the radiation budget of the model.
In Section 2, we will describe the experimental
setting, and the analysis method is described in
Section 3. Results are shown in Section 4, andsummary and discussion are followed in the final
section.

2. Experimental design
We used the NICAM for global cloud resolving
simulations (Tomita and Satoh 2004; Satoh et al.
2007a). The horizontal mesh size are about 7 km
and 3.5 km, which correspond to the recursive
grid-division level 10 and 11, respectively (Tomita
et al. 2001, 2002). The 3.5 km-mesh simulation wasstarted at 00 UTC 25 Dec 2006 and integrated for 7days, while the 7 km-mesh simulation was started
at 00 UTC 15 Dec 2006 and integrated for 32 days
to study the whole cycle of the MJO event (Miura

Fig. 1. Distributions of TBB by MTSAT1R (top) and OLR by DX3.5km of the
NICAM simulation (bottom) at 00 UTC 26
Dec 2006. Only the areas with TBB colder
than 253 K and OLR lower than 210 W
m−2 are shown as deep convection.

et al. 2007b). These simulations will be denoted by
DX3.5km and DX7km. The computation was performed using the Earth Simulator. All the physicalschemes are the same regardless of the spatial
resolutions and time steps. No cumulus parameterization was used, and the explicit cloud physics
scheme of Grabowski (1998) is used. The boundary layer scheme based on Mellor and Yamadalevel 2 with moist process is implemented (Yamada
and Mellor 1979; Nakanishi and Niino 2004). The
radiation scheme MSTRNX (Nakajima et al. 2000;
Sekiguchi 2004) is called every 5 minutes. We use
40 levels in the vertical with thinner depths near
the surface using the terrain following coordinate.

Journal of the Meteorological Society of JapanThe time step interval is 20 sec for DX7km, and 15
sec for DX3.5km.
The output data were stored at 90 min intervals.
The original icosahedral grid data were converted
to the longitude-latitude grid by bi-linear interpolation. The numbers of grid points along the equatorare 10,240 for DX3.5km and 5,120 for DX7km, sothat the typical grid intervals are 3.9 km and 7.8
km. Note that the resolution of the infrared dataof MTSAT-1R is provided by 0.05 deg interval,
which is almost comparable to the grid interval of
DX3.5km.

Vol. 86A

percentages of TBB corresponding to the 208 K and
253 K thresholds are 3% and 32% in Fig. 2a. The
equivalent OLR to the TBB threshold is determinedfrom the cumulative distributions of OLR over thesame area and period. The corresponding OLR values to 208 K and 253 K in DX3.5km are 90 W m−2

3. Data and analysis
In this study, we focus on evaluations of deep
convection simulated by NICAM over the western
tropical Pacific. Simulated upwelling longwave
radiation (OLR) by NICAM is compared with TBB
observed by MTSAT-1R. The target area of ourstudy is confined over the maritime continent region of 10S−10N and 90E−160E and the open ocean
region of 10S−10N and 160E−160W. The brightness temperatures of 208 K and 253 K are used todefine deep convective areas associated with anvil
cloud. We specifically call clouds defined by the
208 K threshold “deeper” convective clouds. The
208 K TBB threshold value is selected considering
the study by Mapes and Houze (1993). Machado
et al. (1993) used the 253 K threshold as the largest TBB to define deep convective clouds. The temperature 253 K corresponds to about 7.9 km height(400 hPa) in the US tropical standard atmosphere.
Although OLR and TBB are different physical parameters, we can see the similarity of distribution
patterns associated with deep convection. Inoueand Ackerman (2002) studied the OLR in termsof cloud types. Inoue et al. (2006) showed that theconventional OLR threshold to define deep convection corresponds to the optically thicker cirrus
type cloud rather than cumulonimbus type cloud.
Cumulative distributions of TBB and OLR are used
to bridge the two parameters. The corresponding
OLR values for the TBB threshold values are determined by comparing the cumulative distributionsof TBB and OLR.
Figure 2 shows cumulative distributions of TBB
observed by MTSAT-1R (a) and OLR simulated by
NICAM of DX3.5km and DX7km (b) over the areaof 10S−10N and 90E−160W during 26−31 Dec 2006.
The arrows show the cumulative percentage corresponding to 208 K and 253 K TBB in Fig. 2a and corresponding OLR values in Fig. 2b. The cumulative

Fig. 2. Cumulative distributions of TBB ob­
served by MTSAT-1R (a) and OLR simulated by NICAM (b) over the area of 10S−10N and 90E−160W between 26−29 Dec
2006.

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T. INOUE et al.

and 210 W m−2, respectively. For DX7km, we used
the same OLR threshold values 90 W m−2 and 210
W m−2 despite the cumulative distribution beingslightly different from that of DX3.5km. Using theabove thresholds, we studied the characteristics ofdeep and deeper convective clouds simulated by
NICAM and compared them with the MTSAT-1R
observations over the tropical western Pacific of
10S−10N and 90E−160W.

4. Resultsa. Cloud size of deep convection
Cloud size of deep convection is defined ascontiguous regions of TBB colder than the threshold values for infrared image of the MTSAT-1R
observations and those of OLR smaller than thecorresponding threshold values for the NICAM
simulations. Figure 3 is a symbolic representationof the horizontal size of deep convection shown by
a circle for 00 UTC 26 Dec 2006. Deep convective
areas with TBB colder than 253 K by the MTSAT-1R
observations and those with OLR less than 210 W
m−2 by the NICAM DX3.5km and DX7km simulations are shown in (a), (b), and (c), respectively.
The size of a circle represents the horizontal sizeof a contiguous region of clouds and the center ofFig. 3. Deep convection regions defined by 253 K (210 W m−2) are represented by a circle for the
MTSAT-1R observation (top), DX3.5km of the NICAM simulation (middle) and DX7km of the NICAM
simulation (bottom) at 00 UTC 26 Dec 2006.



Journal of the Meteorological Society of Japanthe circle is located at the areal center of the contiguous cloud region. Note that circles are depicted
only for deep convective areas whose centers arelocated within the analysis domain (10S−10N, 90E−
160W). The same scale is used for these three figures. The area coverage over 10S−10N and 90E−
160W in this case is 5.8 × 106 , 9.1 × 106, and 8.4 ×
106 km2 in total and 2.8 × 103, 5.1 × 103, and 8.7 × 103
km2 as average, respectively. The maximum sizes
are 5.8 × 105, 4.2 × 105 and 3.2 × 105 km2 and thenumber of deep convective clouds are 2107, 1790
and 973, respectively. As seen from Fig. 3, the largest size of cloud clusters appeared in the MTSAT1R infrared images, while there are more numbersof cloud that have a relatively large area appearing
in both NICAM DX3.5km and DX7km simulations
at this time. The mean size of deep convection islarger in DX7km than in DX3.5km, while numbers of deeper and deep convection are smaller in
DX7km than in DX3.5km.
Similar analysis for deeper convective areas de-

Fig. 4. Histograms of cloud size of TBB and
OLR for deeper convection defined by the
208 K and 90 W m−2 thresholds (top) anddeep convection by the 253 K and 210
W m−2 thresholds (bottom). The size inabscissa is defined as square root of area.

Vol. 86A

fined by the 208 K threshold (not shown) indicatedthat the area coverage over 10S−10N and 90E−
160W is 6.3 × 105, 7.3 × 105 and 3.7 × 105 km2 in total
and 1.8 × 103, 1.5 × 103, and 4.5 × 103 km2 as averagefor MTSAT-1R, DX3.5km and DX7km. The maximum size is 8.4 × 104, 4.1 × 104 and 6.0 × 104 km2
and the number of cloud clusters are 357, 485, and85, respectively. Again, the largest size of deep convection is observed in the MTSAT-1R analysis. The
mean size of deeper convection is larger in DX7km
than in DX3.5km, while numbers of deeper convection are smaller in DX7km than in DX3.5km, as in
the case with deep convection shown in Fig. 3.
Figure 4 shows histograms of the size dimension(square root of area) in km for deeper and deep
convection defined by TBB = 208 K and OLR = 90
W m−2 (top) and by TBB = 253 K and OLR = 210 W
m−2 (bottom). This shows that the DX7km simulation represents a relatively larger size of deep anddeeper convection compared with the MTSAT-1R
observation and DX3.5km simulation. We also notice that the frequency of the smaller size of deep
and deeper convection is larger in the MTSAT-1R
observation compared with the DX3.5km simulation.
Figure 5 depicts the cumulative distribution

Fig. 5. Size distributions of TBB and OLR
for deeper convection defined by 208 K
and 90 W m−2. Statistics are analyzed fordata between 26 and 29 Dec 2006.

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functions with log scale for cloud area of deeper
convection. Each set of data appears as a straightline in Fig. 5; therefore, cloud size is log-normally
distributed for both simulations and the MTSAT1R observation. The size of deeper convection of
DX3.5km is much closer to that of MTSAT-1R thanthat of DX7km. This figure also indicates that thesize of deeper convection of DX7km is larger thanthat of DX3.5km and MTSAT-1R. The size of deeper convection of MTSAT-1R becomes less frequent
for the size larger than 5,000 km2 compared withthat of DX3.5km. On the other hand, smaller sizeof deeper convection is more frequently observedby MTSAT-1R than DX3.5km. This implies that thesmaller clouds marginally resolved by MTSAT-1R
cannot be simulated well by DX3.5km as seen in
relative size distribution.
The contrasting feature of cloud clusters between the maritime continent and the open ocean
was examined using the size distributions of deep
and deeper convection. Figure 6 compares size
distributions over the maritime continent and over
the open ocean for the 208 K threshold (top) and
253 K threshold (bottom). Although difference issmall, the MTSAT-1R observation indicates thatthe smaller clouds (cloud size smaller than 104
km2 for deeper convection and 5 × 104 km2 for deep
convection) appeared over the open ocean region
more frequently than the maritime continent region. The larger clouds (cloud size between 2 ×
104 km2 and 105 km2) frequently appeared overthe maritime continent region for deeper convection. While clouds of about 105 km2 appeared more
frequently over the open ocean region, considering
the slope between the two. This tendency is notclear for the 253 K threshold.
Figure 7 shows the contrast of size distributions
over the maritime continent and the open ocean
regions for the 90 W m−2 threshold (top) and the
210 W m−2 threshold (bottom) in the NICAM DX
3.5km simulation. The cloud size is larger overthe maritime continent region than over the open
ocean region. This tendency is consistent with the
MTSAT-1R observation, although the difference isslightly enhanced in DX3.5km. However, contrary
to the MTSAT-1R observation, the larger clouds
are simulated more over the maritime continent
region for the 90 W m−2 threshold.
Comparisons between the NICAM simulationand the MTSAT-1R observation over both regions
are shown in Figs. 8 and 9, which can be reproduced from Figs. 6 and 7. The deeper clouds (de-

fined by the 208 K and 90 W m−2 thresholds) arecompared in Fig. 8 over the maritime continent(top) and over the open ocean (bottom). The size
distribution is generally similar for both regions.
The number of clouds smaller than 6,000 km2 isless frequent in DX3.5km over both the maritime
continent and the open ocean regions. The difference for larger clouds is more clearly seen over
the open ocean region, although the number ofsuch large clouds is small. This suggests that the
NICAM DX3.5km simulation has deficiency indepicting larger clouds of deeper convection overboth the maritime continent and the open ocean regions. The deeper convection is related to cores ofdeep cumulonimbus. It is difficult to underpin thereasons for this discrepancy only from the present
analysis alone. We speculate that the detrainment
process near the tropopause might not be represented well by the simulation due to insufficientvertical resolution near the tropopause transition
layer. We note that cloud amount in the core ofdeep convection is also related to cloud microphysics schemes through precipitation efficiency (Suiet al. 2005).
Figure 9 is the comparison of deep convection
defined by the 253 K and 210 W m−2 thresholds.
The cloud size of DX3.5km is larger than that of
MTSAT-1R over both regions. This suggests thatthe cloud areas defined by OLR = 210 W m−2 (TBB
= 253 K) is overestimated by DX3.5km. Figure 10
shows the cloud type map constructed from thesplit window method (Inoue 1987) at 00 UTC 26
Dec 2006. The brightness temperature difference
between the split window, BTD = TBB (11 µm) − TBB(12 µm), indicates optical thickness. BTD is larger
for optically thinner clouds, while BTD becomeszero for optically thick clouds that can be assumed
to be black-body clouds. In Fig. 10, we select the
0.5 K BTD threshold to classify optically thick or
thinner clouds. Clouds shown in the figure are allcolder than 253 K. Clouds colored in purple, blue,and green correspond to clouds colder than 208 K,
optically thick clouds, and optically thinner clouds.
As seen in the figure, larger clouds whose TBB
is between 208 K and 253 K are mostly opticallythinner clouds (green in the figure), while smaller
clouds are mostly optically thick clouds (blue inthe figure). Further, most of these optically thinnerclouds are surrounding the clouds colder than 208
K (purple in Fig. 10) over the 10S−10N area. There­
fore, this larger size of deep convective cloud areas
corresponds to optically thinner clouds. The sizeJournal of the Meteorological Society of Japan

Vol. 86A

Fig. 6. Size distributions of TBB for deeper
convection defined by 208 K (top) and
253 K (bottom) for the MTSAT-1R observation over the maritime continent andthe open ocean regions.

Fig. 7. Size distributions of OLR for deep
and deeper convection defined by 90 W
m−2 (top) and 210 W m−2 (bottom) for the
NICAM DX3.5km simulation.

of clouds defined by the 253 K threshold is generally smaller than those of the DX3.5km simulation (Figs. 3 and 9); this implies that the NICAM
DX3.5km simulation overestimates optically thinner clouds such as cirrus anvil or underestimates
optically thick clouds that have a smaller scale. The

representation of cirrus anvil is sensitive to detailsof cloud microphysics (Iga et al. 2007).
b. Temporal variations of cloud size
Figure 11 shows temporal variations of the total
area of deeper convection defined by the 208 K,

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

Fig. 8. Size distributions of TBB and OLR
for deeper convection defined by 208 K
and 90 W m−2 over the maritime continent (top) and the open ocean (bottom).

Fig. 9. Same as Fig. 8 except for TBB and
OLR for thresholds of 253 K and 210 W
m−2.

90 W m−2 thresholds over the maritime continent
region (a) and the open ocean region (b) during
00 UTC 26 Dec 2006 and 00 UTC 1 Jan 2007. In
this figure, cloud areas whose centers are locatedwithin the respective regions are all counted.
The total sizes of deep convection simulated by

DX3.5km and DX7km are generally smaller thanthe MTSAT-1R observations. However, the temporal variation observed by satellite is reasonably
depicted by DX3.5km except for the first few daysof the simulation. The DX7km simulation cannot
capture the characteristics of this temporal varia-

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Fig. 10. Cloud type map constructed fromthe split window method at 00 UTC 26
Dec 2006. Clouds colored in purple, blue,and green correspond to clouds colder
than 208 K, optically thick clouds, andoptically thin clouds colder than 253 K,
respectively.

tion and the amplitude is generally smaller thanthat of DX3.5km.
The simulated temporal variation by DX3.5km
becomes more evident when the thresholds arechanged to 253 K and 210 W m−2 as depicted inFig. 12. The correlation coefficients increase from
0.62 to 0.81 for the maritime continent region
and from 0.13 to 0.54 for the open ocean region.
While the DX7km simulation has systematic time
lag in phase over the maritime continent region, it
captures some signal of the temporal variation. Insummary, the temporal variation of deep convective cloud area is reasonably represented by theDX3.5km simulation over both the maritime continent and the open ocean regions, but the DX7km
simulation has biases in amplitude and phase of thetemporal variation. However, further studies will
be required to explain the reason for the remaining
differences between MTSAT-1R and NICAM, considering the initial conditions, spatial resolution,and so on.
Diurnal variation of deep convection has beenstudied over this area using the infrared data of
GMS and the PR data of TRMM (e.g., Murakami
1983; Takayabu 2002; Hirose and Nakamura 2005).
It has been shown that the maximum activity of
deep convection appears late afternoon over theland area, and early morning over the ocean area.
In the present study, we define the two areas ofthe maritime continent region and the open ocean

Fig. 11. Temporal variations of the total
area of deeper convection defined by
the 208 K and 90 W m−2 thresholds overthe maritime continent (top) and theopen ocean (bottom) during 00 UTC 26
and 21 UTC 31 Dec 2006.

region with relatively larger longitudinal area,
so that there is some time lag in identifying local
time. Further, the MJO was able to modulate thediurnal variation of convective activity (Tian et al.
2006; Ichikawa and Yasunari 2007). However, as isNovember 2008

T. INOUE et al.

Fig. 12. Same as Fig. 11 except for the 253
K and 210 W m−2 thresholds.

seen in Fig. 11 (top), the MTSAT-1R observationsuggests the early morning maximum (21 UTC; 5LT) in convective activity over the maritime continent region. This phase is not consistent with previous studies. However, we can see clear diurnal signal during this specific period and relatively larger
cloud area in the MTSAT-1R observation (circle inFig. 11 (top)). The NICAM DX3.5km simulation
depicts this diurnal variation, although the peak11

of convective activity appears about 3 hours later.
The correlation coefficient between DX3.5km and
MTSAT-1R observation is 0.62 for Fig. 11 (top),
although 0.13 for Fig. 11 (bottom). However, theDX7km simulation cannot represent the temporalvariation appearing in the MTSAT-1R observation.
As is seen in Fig. 12, the diurnal variation ofdeep convection defined by 210 W m−2 over the
maritime continent region corresponds well to thatof the MTSAT-1R observation, considering thetime lag. The peak time of cloud area appears at 9
UTC (17 LT) over the maritime continent region,
while that over the open ocean region appears at
about 6 UTC (17 LT). The correlation coefficient
between DX3.5km and the MTSAT-1R observa­
tion is 0.82 for Fig. 12 (top) and 0.54 for Fig. 12(bottom). The time lag between the peak of deeper
convective cloud area colder than 208 K and that ofcloud area colder than 253 K is about 12 hours overboth the maritime continent and the open ocean
regions. A similar time lag is reported in Machado
et al. (1993). They studied 15 large cloud clusters
and found that the time lag between the peak ofcloud cluster size defined by 207 K threshold and
253 K threshold was 12 hours. They conclude that
this time lag is a reflection of the developmentof cloud cluster and clusters merging into larger
clusters.
Mapes and Houze (1993) discussed the morningand afternoon difference of deep convection using GMS data. They reported a clear difference of
convective activity between morning and afternoon
over large islands in the maritime continent forcloud clusters of 1,000−3,000 km2 in size although
they reported no difference for cloud clusters
larger than 30,000 km2. Figure 13 shows the geographical distribution of deeper convection definedby the 208 K threshold at 00 UTC and 09 UTC in
a composite of 6 days during 26 Dec and 31 Dec
2006. Over the large islands of the maritime continent (New-Guinea, Borneo, and Sumatra), there
are almost no deep clouds at 00 UTC (about 8 LT),while deep convection becomes active at 09 UTC(about 17 LT). Over the open ocean region, on theother hand, deep convection is more active at 00
UTC (about 11 LT) than at 09 UTC (about 20 LT).
This feature is consistent with the previous studies.
Figure 14 depicts the geographical distribution ofdeeper convection defined by the 90 W m−2 threshold in the NICAM DX3.5km simulation for 00 UTC
and 09 UTC. The DX3.5km simulation represents
similar characteristics of deeper convective activ-

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Fig. 13. Geographical distribution of deeper convection defined by 208 K-threshold and size shown incolor for MTSAT-1R observation at 00 UTC and 09 UTC during 26−31 Dec 2006.

Fig. 14.

Same as Fig. 13 except for the deeper convection defined by 90 W m−2 thresholds.

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ity. The deeper convection over the large islandsof the maritime continent is less active at 00 UTC(about 8 LT) and enhanced at 09 UTC (17 LT). The
deeper convection over the open ocean region ismore active at 00 UTC (about 11 LT) than 09 UTC(about 20 LT).

5. Summar y and discussion
We have compared the size distributions of deep
convection between the global cloud resolving simulations by NICAM and the satellite observations
by MTSAT-1R. The numerically simulated data by
NICAM with the horizontal mesh-size of 3.5 km(DX3.5km) and 7 km (DX7km) from 00 UTC 26
Dec 2006 to 00 UTC 1 Jan 2007 were used for the
analysis. To define deep convection, we first determine threshold values of the outgoing longwave
radiation (OLR) corresponding to the equivalent
blackbody temperature TBB of the infrared channelof the MTSAT-1R observations using the cumulative distribution functions of TBB and OLR. The contiguous cloud areas are calculated by using thesethresholds both for the NICAM simulations andthe MTSAT-1R observations.
The size distribution of deep convective clouds
simulated by both DX3.5km and DX7km indicates
log-normal distribution as is seen in the MTSAT1R observation. In general, the size distributionof DX3.5km is much closer to that of the MTSAT1R observation than that of DX7km. In detail,DX3.5km has less frequency in clouds smaller
than 5,000 km2 than the MTSAT-1R observation.
This might be due to the fact that DX3.5km cannot
resolve smaller convective clouds. From the split
window analysis based on Inoue (1987), it was
found that these smaller clouds are mostly optically thick and could be categorized as cumulus congestus clouds. Size distributions over the maritime
continent and the open ocean are basically similar,
but the larger clouds in the maritime continent are
represented better than in the open ocean. This isbecause the convection in the maritime continentis stronger than the open ocean due to the diurnal
cycle caused by the land surface heating.
To examine the diurnal cycle of deep convection, the temporal variations of the total cloud area
were analyzed over the maritime continent and theopen ocean regions. DX3.5km captured the diurnal
variations of the total cloud area very well, particularly over the maritime continent region. Althougha slight time delay exists in the simulation, the lag
becomes smaller as the model resolution becomes13

finer. The peak times of the cloud size are different between the cases when the 208 K and 253 K
thresholds are used. The time lag is about 12 hours
both for MTSAT-1R and DX3.5km; this suggeststhat the spread of anvil cloud from the core of deep
convection or merging of cloud clusters is well
represented by DX3.5km.
The size distribution analysis of cloud clusters
presented in this study has the following importance. First, we can evaluate quantitatively the difference and realism of clouds by the global cloud
resolving simulations. We compared the size andtemporal variation of deep convective clouds to understand the behaviors of the model simulations.
From these results, we have obtained guidance inimproving model simulations. For example, thepresent analysis showed that NICAM has less frequency in smaller scale clouds. These clouds are
categorized as cumulus congestus as detected by
the split window analysis. This result suggests thatthe temporal variation of smaller scale convection
is not well represented by the model. Therefore, itwill be a future task to examine how the evolutionof each cumulus affects simulations of successive
cloud clusters. We examined cloud characteristics
over the western tropical Pacific covering the maritime continent. Recently, it has been realized thata more realistic representation of convection overthe maritime continent is one of the major challenges to global atmospheric modeling becauseof its complex system of islands and oceans (e.g.,
Neale and Slingo 2003). Here, we examine overallbehaviors of deep convection simulated by a global
cloud resolving model in a statistical sense using
cloud size distribution analysis. We will further
proceed to detailed comparisons of individual convective events.
Second, the energy budget of global cloud resolving simulations are directly affected by thesize of deep convection, i.e., the coverage of anvil
clouds. It is also very sensitive to the cloud microphysical processes used in the model. We can evaluate the cloud microphysical processes in termsof the radiation budget through the size of upper
clouds. Figure 15 shows the latitudinal distributions of OLR for the NICAM simulations between
90E−160W and compares them with the observed
OLR by the interpolated OLR data of the National
Oceanic and Atmospheric Administration (NOAA).
For the NICAM simulations, we compared threedifferent resolutions: DX3.5km, DX7km, andDX14km, which is the same simulation as DX7km

14

Journal of the Meteorological Society of Japan

Vol. 86A

Tetsuo Nakazawa and Kotaro Bessho at the Meteorological Research Institute (MRI/JMA).

References

Fig. 15. Latitudinal distributions of OLR
between 90E-160W for one-day average of 26 Dec 2006. Red curve is forDX3.5km, blue curve is for DX7km,
green curve if for DX14km, and black
curve is for the NOAA-interpolated OLR.

but for the grid interval of 14 km. Here, we concentrate on the equatorial region between 10S−10N.
This shows that the three simulated values of OLR
do not have a systematic bias over the equatorial
region, and the three profiles are around the curveof the NOAA interpolated OLR. It should be notedthat the NOAA interpolated OLR data is only provided as a daily value, and cannot be regarded as atruth but only as a reference (Inoue and Ackerman2002). The important point is that, for the equatorial cloud region, it is hard to evaluate the simulated results only from the averaged OLR as in Fig.
15. By further analyzing the size of deep convective clouds as shown in the present study, we haveclarified the resolution dependency. If we change
parameters in physical processes such as cloud
microphysics and boundary schemes, the zonal
mean OLR changes (Iga et al. 2007). The size ofclouds will provide another dimension to improve
the performance of global high-resolution models
through the radiation budget process.

Acknowledgment
Numerical experiments of NICAM were carried out using the Earth Simulator. This research
was supported by Core Research for Evolutional
Science and Technology, Japan Science and Technology Agency (CREST/JST). We also thank members of the NICAM team: Hirofumi Tomita, Tomoe
Nasuno, Shin-ichi Iga, Akira Noda, Wataru Yanase,
and Kazuyoshi Oouchi for their valuable work.
MTSAT-1R was processed and provided by

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