Click
Here

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D00H14, doi:10.1029/2009JD012247, 2010

for

Full
ArticleEvaluations of cloud properties of global and local cloud system
resolving models using CALIPSO and CloudSat simulators
Masaki Satoh,1,2 Toshiro Inoue,1 and Hiroaki Miura2,3Received 14 April 2009; revised 18 October 2009; accepted 23 October 2009; published 27 February 2010.

[1] This study proposes a method of using a local-area cloud system resolving model(LCRM) to evaluate and improve cloud properties simulated by a global cloud system
resolving model (GCRM). We study the sensitivity to cloud microphysics schemes bycomparing the simulated data of LCRM with CloudSat and Cloud-Aerosol Lidar andInfrared Pathfinder Satellite Observation (CALIPSO) using satellite simulators. In
particular, the impacts of the improved cloud microphysics scheme, which is morecomprehensive than the scheme used for the GCRM experiment, with six water categories
are studied. We focus on the active convective phase over the maritime continent. During
the 4 day integration period of LCRM, 11 tracks of A-Train satellite observations areavailable. Cloud properties along the cross sections of these tracks are first examined tofind the bias of the simulated data compared to the CloudSat and CALIPSO observations.
The improved cloud microphysics scheme used in the LCRM reproduces the overallcharacteristics of the observed contoured frequency by altitude diagrams (CFADs) well,although biases are still present in the upper layers. We find that the simulated
domain-averaged cloud properties can be evaluated using the observed track data. The
comparison of the CFADs between LCRM and GCRM clarifies the bias in GCRM,
and the new cloud microphysics scheme improves this bias. Using LCRM, sensitivities
to cloud microphysics parameters are examined, and the CFADs are further improved if
the ice sedimentation speed is increased. These results indicate that introduction of thegraupel category or faster sedimentation of ice clouds reduces the total amount ofhydrometeors and leads to more efficiency of precipitation.
Citation: Satoh, M., T. Inoue, and H. Miura (2010), Evaluations of cloud properties of global and local cloud system resolving
models using CALIPSO and CloudSat simulators, J. Geophys. Res., 115, D00H14, doi:10.1029/2009JD012247.

1. Introduction[2] Numerical simulations by a global cloud system
resolving model (GCRM) with a mesh size of a few kilometers are now available, and they reproduce realistic multiscale structures of cloud systems over the globe [Miura et al.,2007; Satoh et al., 2008a]. The advantage of a GCRM
compared to general circulation models (GCMs) is that deep
convective systems are explicitly simulated without using
cumulus parameterizations, which cause major ambiguities
in GCMs used for climate simulations [Randall et al., 2007].
However, it is expected that clouds simulated by a GCRM
depend on the details of cloud microphysics schemes introduced instead of cumulus parameterizations. To reproduce
climatological states more realistically with a GCRM, we
1Center for Climate System Research, University of Tokyo, Chiba,
Japan.
2
Research Institute for Global Change, Japan Agency for Marine-Earth
Science and Technology, Kanagawa, Japan.
3
Department of Atmospheric Science, Colorado State University, Fort
Collins, Colorado, USA.
Copyright 2010 by the American Geophysical Union.
0148-0227/10/2009JD012247$09.00

need to understand how cloud microphysics schemes affectsimulated cloud properties through comparison between
simulated cloud properties and observations.
[3] For evaluations of cloud properties of GCRM, satellite
remote sensing such as geostationary satellites, the TropicalRainfall Measuring Mission (TRMM), and CloudSat/CloudAerosol Lidar and Infrared Pathfinder Satellite Observation(CALIPSO) is useful in terms of spatial uniformity of global
coverage and horizontal resolution. Miura et al. [2007]
conducted a 3.5 km mesh GCRM simulation using theNonhydrostatic Icosahedral Atmospheric Model (NICAM)
and reproduced the multiscale cloud structures of the
Madden-Julian Oscillation (MJO) [see also Nasuno et al.,2009]. Shown in Figure 1 are comparisons of cloud distributions around the maritime continent produced by the
global simulation by NICAM and observed by the geostationary satellite MTSAT-1R by Inoue et al. [2008], who
compared statistical properties of high-cloud areas by using
the blackbody temperature (TBB) observed by the 11 mm
infrared channel of the geostationary satellite. Such analysis mainly focuses on the areal coverage of clouds toevaluate energy fluxes at the top of the atmosphere. Cloud
depths and vertical profiles of clouds also need to beevaluated. To this end, the new satellite observations by

D00H14

1 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

Figure 1. Comparison of the upper clouds between (a) thegeostationary observation and (b) the global cloud system
resolving simulation at 0000 UT on 26 December 2006.
Figure 1a shows the blackbody temperature (K). Figure 1b
shows the outgoing longwave radiation (W m2) [after
Inoue et al., 2008].
CloudSat/CALIPSO have advantages. Masunaga et al.
[2008] analyzed differences between the wet and dryphases of MJO simulated by NICAM. They used theobserved data of the TRMM-PR (Precipitation Radar) andCloudSat and evaluated the model results by using asatellite simulator, or the forward radiative-transfer calculation. Satoh et al. [2008b] analyzed another GCRM data
set for an aqua planet experiment by Tomita et al. [2005],
obtained spectral distributions of precipitation, and compared them with those from the TRMM-PR analysis. These
results show general agreement between the simulation andobserved data, but some discrepancies such as bias in snow
amount are apparent [Masunaga et al., 2008]. In order toevaluate and improve cloud properties of GCRM, we needto further understand the sensitivities of the models to their
physics, especially cloud microphysics schemes, and evaluate the results using the cloud profile data from CloudSat/
CALIPSO.
[4] In contrast to GCRM, regional experiments using a
local-area cloud system resolving model (LCRM) are widelyused, particularly for operational numerical weather forecasts[Saito et al., 2007; Tao, 2007; Kain et al., 2008]. It is known

D00H14that the cloud microphysics scheme affects the precipitation
forecast by a LCRM [e.g., Saito et al., 2006], and dependency
on cloud microphysics schemes is an active area of LCRM
studies [e.g., Krueger et al., 1995; Redelsperger et al., 2000;
Xu et al., 2002; Tao et al., 2003; Hong et al., 2004; Eitzen andXu, 2005; Eito and Aonashi, 2009; Lopez et al., 2009].
Although climatological properties are also sometimes discussed [e.g., Luo et al., 2007; Eitzen and Xu, 2005, 2008],
short-term forecasting of precipitation has been a main issue
with the operational models with LCRM until recently.
Recent understandings of model behaviors in which longterm simulations by climate models and short-term simulations by numerical weather forecast models give similarbiases, even for different integration time scales, motivate
us to unify two different purpose models [Palmer et al.,2008]. NICAM can be used as a LCRM by transforming thegrid structure to focus on a specific region [Tomita, 2008a], sothat the two models (GCRM and LCRM) of NICAM possess
the same dynamical core and physical packages. Thus thesensitivity of GCRM to cloud microphysics schemes can betested by LCRM. The supplementary use of LCRM isessential for improving biases of GCRM since GCRMs arecomputationally demanding in terms of both numerical timeand output data and hence limit the amount of sensitivity
experiments that can be performed. Of course, the twomodels each have their own advantages: LCRM only gives
the characteristics of specific regions of interest, while
GCRM give ample samples of different regions at once.
[5] In the present study we propose a method of using
LCRM for evaluation of cloud properties of GCRM. We
perform LCRM simulations for the maritime continent
region in the tropics (90°– 130°E, 20°S – 20°N) and compare the simulated data with observed data from CloudSat
and CALIPSO. In particular, we use satellite simulators to
directly compare the model results with observations fromCloudSat/CALIPSO [Haynes et al., 2007; Chepfer et al.,2008; Bodas-Salcedo et al., 2008]. The use of the satellite
simulator is a recent innovative method since it is free fromambiguities concerning the algorithms for obtaining cloud
quantities, although there are still ambiguities with regard tosome assumptions, such as the size distributions of cloudparticles. The target area of this study is included in the regionof the tropical warm pool analyzed by Bodas-Salcedo et al.
[2008]. Our approach is a combination of their statisticaland case study approaches. The target of our case study isthe cloud systems of the tropics, and is different to that ofBodas-Salcedo et al. [2008], who selected the cyclone
system in midlatitudes. Another difference lies in our useof the satellite simulator in cloud-resolving mode rather
than the subgrid ensemble mode. As a companion to thispaper, Inoue et al. [2010] describe comparisons of the
GCRM simulation with the observations using the casestudy approach.
[6] In section 2 the experimental designs of GCRM andLCRM are described, and the observation data and theanalysis tool are described in section 3. The general characteristics of the radar and lidar signals by CloudSat/CALIPSO
are shown and compared with the GCRM simulation insection 4. In section 5 we evaluate cloud properties of the
LCRM simulation by comparing the simulated data with theobserved data. Resolution dependency is also examined in
section 5. In section 6 we discuss biases of cloud properties of

2 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

Figure 2. Topography of the local-area cloud system
resolving model, stretched-NICAM, shown by color (m).
The grid division level is 8, and the stretch factor is 100 for
the control experiment gl8r100. Dotted contours are meshsizes of 3, 5, 10, and 15 km from the inner to outer contours.
The abscissa is longitude from 90°E to 130°E, and theordinate is latitude from 20°S to 20°N.
LCRM and examine impacts of differences in cloud microphysics schemes using LCRM. We conclude with a summary
in section 7.

2. Experimental Design[7] The Nonhydrostatic Icosahedral Atmospheric Model(NICAM) [Satoh et al., 2008a; Tomita and Satoh, 2004] isused to perform numerical experiments with GCRM andLCRM configurations. The GCRM experiments by NICAM
are described by Miura et al. [2007]. For the finest-resolution
experiment with a mesh size of about 3.5 km, GCRM is
integrated for seven days starting at 0000 UT on 25 December
2006. Inoue et al. [2008] analyzed this data to compare statistics of high-cloud areas with those of geostationary satellite
observations (Figure 1). Inoue et al. [2010] further analyzed
properties of ice clouds using the split window analysis of thegeostationary satellite and the CloudSat/CALIPSO data.
They found good agreement between the observations andthe simulation. However, since the output data of the 3.5 km
mesh experiment are available only once a day owing to thelimitation of computational resources, there are limitations on
the potential evaluation of GCRM simulation using CloudSat/CALIPSO. Even though the horizontal distribution ofcloud systems produced by the model are similar to observations (Figure 1), we cannot attribute the difference between
them to the cloud microphysics scheme since the bias mightcome from the difference in location and timing of the cloudsystems.
[8] To make more statistical comparisons, we use LCRM
for evaluation of cloud properties by NICAM. Our experiments are designed so that the analysis is linked to theevaluation of the GCRM experiment by Miura et al. [2007].
We analyze cloud properties of the convectively active region
around the maritime continent using LCRM and compare theresults from LCRM with those of GCRM. Since our purpose
is to improve GCRM, we use the same model, NICAM, for
the LCRM and GCRM simulations. NICAM can be used as aregional model by transforming grid locations to focus on a
specific region [Tomita, 2008a]. Using the grid divisionlevel 8, which is the eighth times recursive division of the

D00H14

triangles of the original icosahedrons, the icosahedral grid istransformed so that the ratio of the maximum/minimum meshsizes (stretching factor) becomes 100. The minimum meshsize is about 2.5 km, and the maximum is about 250 km. The
central point is at 110°E on the equator. We analyze the area
between 20°S – 20°N and 90°E–110°E. This is the standard
grid, hereinafter referred to as gl8r100. In section 4 weinvestigate resolution dependency in which finer and coarser
grid configurations are used. The former is the same grid
division level 8 but with a stretching factor of 200 (hereinafterreferred to as gl8r200), so that the minimum mesh size isabout 1.8 km. The latter is the grid division level 7 with astretching factor of 100; the minimum mesh size is about
5.0 km (hereinafter referred to as gl7r100). Topography andmesh size of the two types of grid used for the standard gridare presented in Figure 2. The mesh sizes of 3, 5, 10, and
15 km are shown by dashed contours. The contour of themesh size, 5 km of the standard grid, is almost the same, withthe contour of the mesh size 5 km for gl8r200 and 10 km for
gl7r200; we will mainly use the domain within this contour
for the analysis. In the vertical direction the model top is at
about 40 km, and 40 layers are adopted with increasing
intervals from the bottom of the atmosphere to the top(Table 1). The initial conditions are at 0000 UT on 1 January
2007 given by the grid point values of the Japan Meteorological Agency (GPV-JMA). The LCRM is integrated for
4 days. We chose this date because we want to focus on themature phase of the MJO [Masunaga et al., 2008]. We
analyze output data with an interval of 1 h.
[9] The same physics schemes are used all over the globe
despite variation in the mesh size. In particular, cloudparameterizations are not used, and all clouds are represented
by cloud microphysics schemes, even in the coarser meshdomain outside of the target region. The cloud microphysics
scheme is the six water category single-moment bulk scheme
including cloud water, cloud ice, rain, snow, and graupel forhydrometeors (NICAM single-moment six water categories
scheme (NSW6); see Tomita [2008b]). NSW6 is based on thescheme by Lin et al. [1983], and the statistics of high clouds
produced by NSW6 have been extensively studied in an
idealized experiment by Satoh and Matsuda [2009]. In thisstudy the sedimentation of cloud ice is set to zero. This
assumption is different from the control experiment of Satohand Matsuda [2009]. We examine the impact of sedimentation of cloud ice and snow in section 6.2. The planetary
boundary layer scheme is the level 2 of Nakanishi and Niino[2004], and the radiation scheme is based on the two-stream k
distribution method called MSTRNX [Nakajima et al., 2000;
Sekiguchi and Nakajima, 2008]. In contrast, the cloud
microphysics scheme used in the GCRM of Miura et al.
[2007] is from Grabowski [1998] (hereinafter referred to asG98), which has two prognostic variables of hydrometerswith temperature-dependent liquid/ice phases partitioning.
[10] As stated earlier, we use the same physical processes
over the domain of both the finer mesh region and thecoarser mesh regions outside of the targeted region. One of
the reasons for this is that we are interested in the numericalresults in the finer mesh region, and thus cloud properties in
the coarser regions are not important. Another reason is thatside effects may result from using different physical schemes
between the finer and coarser mesh regions. If we use
cumulus parameterization in the coarse mesh region, artificial

3 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

Table 1. Heights of the Model Levelsa
Level Number

Height (m)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

162
336
524
726
945
1180
1433
1707
2001
2319
2661
3030
3428
3856
4318
4816
5353
5932
6556
7228
7952
8733
9575
10,483
11,461
12,515
13,652
14,877
16,197
17,62019,15420,80722,59024,511
26,581
28,81331,21933,81236,60839,620a

These levels are interfaces of control volumes of the model. Hydrometeors are defined at the middle levels of the control volumes.

noise might occur. Thus we think that it is better to use thesame physics over the whole domain.

3. Data[11] We use the infrared-equivalent blackbody temperature (TBB) of geostationary satellites, Global-IR and the
CloudSat/CALIPSO data. Global-IR is globally merged,
full-resolution (about 4 km) infrared (IR) data formed
from the 11 mm IR channels aboard the geostationary
satellites, compiled by the National Centers for Environmental Prediction/Climate Prediction Center (NCEP/CPC),
and is available every 1/2 h (see http://www.cpc.noaa.
gov/products/global_precip/html/wpage.full_res.html andJanowiak et al. [2001]). We use the 0.05 degree interval
data of CloudSat/CALIPSO [Stephens et al., 2002, 2008;
Winker et al., 2007] which are processed by the same
method described in the work of Inoue et al. [2010].
[12] To compare the data by NICAM against the observed
data by CloudSat/CALIPSO, we use the satellite simulator
CFMIP Observational Simulator Package (COSP; see http://
cfmip.metoffice.com/COSP.html) [Haynes et al., 2007;
Chepfer et al., 2008; Bodas-Salcedo et al., 2008] version 1.1
in cloud-resolving mode; that is, no fractional clouds are

D00H14assumed to lie at the grid points of the model. The simulationof radar reflectivities requires the size distributions of hydrometeors. We use the same size distributions for rain, snow,
and graupel in the cloud microphysics scheme NSW6[Tomita, 2008b] and the radar simulator in COSP; that is,
the Marshall-Palmer distribution is assumed, and the intercept parameters are specified. For calculation of the lidarattenuated backscattering coefficient, we assume 10 and
40 mm for the effective radii of cloud water and cloud ice,
respectively. We will discuss dependency on the effective
radius in section 6.1.
[13] In sections 4 and 6.2 we compare results from thedifferent cloud microphysics schemes of NSW6 and G98. In
the latter case we use the size distribution assumed by G98
for the COSP simulation. It has been reported that simulations using this scheme have too much snow and insufficient
cloud ice [Tomita, 2008b; Masunaga et al., 2008]. If we use
only cloud ice for the simulation of the lidar signals, very
weak signals result. Therefore we use the sum of cloud ice
and snow for the simulation of lidar signals in this case.

4. Comparison Between the Observations
and the GCRM Simulation[14] First, we show the general characteristics of the radarand lidar signals of the observations and compare these withthose of the GCRM simulation. Contoured frequency byaltitude diagrams (CFADs) [Yuter and Houze, 1995] of theradar/lidar signals observed by CloudSat and CALIPSO are
shown in Figure 3. To make CFADs, probability distributionfunctions (PDFs) of radar signals are defined at each altitudewith 10 bins between 25 and 25 dBZ, and PDFs of the lidar
signals are defined at each altitude with 10 bins between 0
and 0.05 km1 str1. This produces the statistics of all thedata presented in section 5, data from the 11 tracks of the
A-train crossing the maritime continent region (Figure 3) inthe simulated period. Note that the radar signals below thealtitude of 1 km are omitted because the data are contaminated by surface clutter.
[15] The observed radar signals are strongest in the
middle troposphere, and they become weaker as altitudes
become lower or higher, forming a boomerang-type structure. This structure is similar to that analyzed by BodasSalcedo et al. [2008]. The lidar signals have a strong signal
layer near 13– 15 km. The black curve in Figure 3 is themean vertical profile of the lidar signals. It shows threepeaks of the signals occurring in the midtroposphere around
6– 7 km and the lower levels around 2 – 3 km, in addition to
the high altitudes around 13– 15 km. This profile is probably
related to the trimodal structure of convection mentioned by
Johnson et al. [1999].
[16] We compare the simulated radar/lidar signals of the
GCRM experiment by Miura et al. [2007] with thoseobserved as shown in Figure 3. We use the snapshot dataof the 3.5 km mesh GCRM at 0000 UT on 1 January 2007
over the same domain of the maritime continent, shown by
Figure 2. In this GCRM simulation, Miura et al. [2007]
used the cloud microphysics scheme of G98. CFADs of thesimulated radar/lidar signals of GCRM are shown in Figure 4,
which can be compared to Figure 3. Although the grossstructures of the CFADs are captured by the simulation, thesimulated radar signals have biases especially in the middle

4 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 3. Contoured frequency by altitude diagrams (CFADs) of radar and lidar signals for all 11 tracks
of data from CloudSat/CALIPSO. The bin size is 10 in all CFADs. Black curves in the lidar plots aremean lidar signals. Units of the radar and lidar signals are dBZ and km1 str1, respectively.
troposphere. The more frequent signals around the altitudesof 3 –7 km are due to rain. The lidar signal shows that theupper signals are stronger and deeper in the altitudes of 10–
15 km. There are also the lidar signals down to the altitude of1 – 2 km in the simulations. There is no peak in the middle
troposphere. One of the reasons for the difference of the depthof the upper signals is the vertical resolution between theobservations and the simulation. The cloud microphysics
scheme also affects these characteristics. In section 6.2 wereturn to a comparison in cloud properties between G98 and
NSW6 using LCRM.

5. Results From LCRM
[17] The simulated outgoing longwave radiation (OLR)
and TBB observed by geostationary satellites are comparedin Figures 5a, 5b, and 5c. Also shown in Figures 5a, 5b, and5c is the track of the A-Train, which crosses the domain
within 1/2 h of the designated time. The total number oftracks crossing this region was 11 for the 4 day integration
period, except for the first half day during the spin-up time.
The overall impression from Figures 5a, 5b, and 5c is thatthe simulation generally captures the observed cloud pattern.
In the maritime continent region, cloud systems are modulated by diurnal variation of deep convection associated withthe land-sea contrast [Sato et al., 2009]. The present simulation realistically reproduces these cloud systems. This period
is the active phase of MJO and is relatively wet [Masunaga etal., 2008, Figure 1]. At 1700 UT on 2 January 2007, tropical
cyclone (TC) Isobel was seen near the northwest coast ofAustralia in both the simulation and the observation. TC
Isobel is also simulated well by GCRM [Miura et al., 2007]
and was analyzed by Fudeyasu et al. [2008].
[18] Cloud profiles of the cross sections along one trackof the A-Train at 0500 UT on 2 January 2007 are shown in
Figure 6. Distributions of mass concentration of cloud water,
cloud ice, rain, snow, graupel, and vertical velocity are shownin the latitude-altitude cross section. Strong upward motions
are seen near 7°N, 8°S, and 14°S and active clouds areassociated with the upward motion regions. As seen in
Figures 5a, 5b, and 5c, the cloud system near 8° – 14°S is
associated with TC Isobel. The cloud system near 7°N is
associated with the cloud band that covers the northeast partof Kalimantan Island. A relatively broader cloud system isseen between 6° and 16°S near TC Isobel.
[19] Figure 7 (top) shows the corresponding observationalsignals from CloudSat and CALIPSO. The CloudSat data
show strong echoes near 6°– 18°S and 3° – 9°N. These
correspond to the outer cloud systems of TC Isobel andthe cloud band near the northeast part of Kalimantan Island.
In order to directly compare the NICAM data with theobserved data of CloudSat/CALIPSO, we use the satellite
simulator COSP to calculate radar and lidar signals from theprofiles of hydrometers simulated by NICAM. Figure 7(bottom) shows the simulated signals corresponding to thecloud profiles shown in Figure 6. The rain, snow, andFigure 4. The same as Figure 3 but for CFADs of radar and lidar signals for the GCRM simulation forthe domain of the maritime continent at 0000 UT on 1 January 2007.
5 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 5a. Comparison of the upper clouds between (left) the geostationary observation and (right) the
LCRM simulation. The left plots show TBB (K), and the right plots show OLR (W m2). Black curves are
the tracks of the A-Train. The domain is the same as in Figure 2. Time and day are shown at the top ofeach plot: 0500, 0700, and 1700 UT on 2 January 2007 and 0600 UT on 3 January 2007.

graupel profiles mainly determine the radar signals, while
the cloud water and cloud ice profiles are used to obtain thelidar signals. The magnitude of the simulated radar signals issimilar to that of the observed CloudSat signals. The
locations of cloud systems are slightly different between
the simulation and the observations, probably because of thetiming and model deficiencies in the representation of cloud
tops. We also note the difference in the vertical extent; thesimulation generally shows taller radar signals. NICAM hasstronger and more frequent signals in the upper layers closer
to the cloud top than the observation. The simulated lidar
signals are deeper and weaker than the observed CALIPSO

6 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 5b. Same as Figure 5a, but for 1700 and 1800 UT on 3 January 2007 and 0500 and 0700 UT on
4 January 2007.
signals near the cloud top at 10 –15 km, probably because of
the difference in vertical resolution. The lidar signals generally correspond to the top of cloud ice and the top of cloudwater.
[20] The difference between observations and the simulation described above can be seen clearly in the CFADs in
Figure 8. Figure 8 (top) shows the CFADs of the single
track at around 0500 UT on 2 January 2007. The CFADs of

all 11 tracks crossing the target region during the simulated
period have already been presented in Figure 3 (see theblack lines in Figures 5a, 5b, and 5c). Although the overall
structure is similar, the difference comes from the different
cloud fields between the single-track data (Figure 8) and thestatistics of 11 tracks of data (Figure 3). As for the comparison between the simulation and the observations, thesimulated radar signals are stronger and more frequent than

7 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 5c. Same as Figure 5a, but for 1700 and 1900 UT on 4 January 2007 and 0600 UT on 5 January
2007.
the observations in the upper layer around 10– 15 km. The
simulated signals also show taller vertical extent, as shownin Figure 7. Near the altitude of 3 – 5 km, the frequency ishigher, although the signal intensities are similar at around
5 – 10 dBZ. For the lidar signal the simulated signal isdeeper than the observed in the upper troposphere because
of insufficient vertical resolution, as in the GCRM simulation shown in Figure 4. However, in this case, the midtroposphere signals at 5 – 6 km are commonly seen in boththe observations and the simulation. The lidar signals at thisaltitude correspond to the top of liquid clouds. The lower
clouds near the altitudes of 1 – 2 km are also captured by thesimulation.
[21] From Figures 7 and 8 we obtain some characteristicsof the simulated cloud properties and their differences fromobservations even though the comparison is made for asingle track of data. However, the difference between thesignals might come from the difference in the location and/or
timing of cloud evolutions between the simulation and observations, as opposed to only from the bias of cloud properties.
For example, if the simulated clouds are in a mature phase

while the real clouds are still growing, the simulated clouds
would be higher than the observations. Further statisticalcomparisons are required to understand the nature of thedifferences in cloud properties.
[22] Figure 9 (top) shows CFADs of the simulated radar/lidar signals for the 11 tracks, which are represented byblack lines in Figures 5a, 5b, and 5c. We compare these
CFADs to the observations in Figure 3. The difference inhigh clouds has the same bias as shown in Figure 8; thatis, the simulated radar signal is stronger than observed,
and the lidar signals are also stronger in the simulation.
For the lower layers less than 5 km the agreement between
the simulation and observations is much better than for thecomparison of a single track (Figure 8). The solid curve in
Figure 9, which is the horizontal mean of the attenuatedbackscatter coefficient, has a maximum around an altitudeof 7 km in the observations (Figure 3), while the simulated
signal fails to capture this maximum. This is different
from Figure 8, where a small maximum is reproduced at analtitude around 5 – 7 km as in the observations. It seems that

8 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 6. Latitude-altitude cross sections of hydrometeors and vertical velocity (m s1) along the trackof the A-Train at 0500 UT on 2 January 2007. Cloud water (qc), cloud ice (qi), rain (qr), snow (qs), andgraupel (qg) are in g kg1. The abscissa is latitude from 20°S to 20°N, and the ordinate is altitude from0 to 20 km.
the simulation does not clearly reproduce congestus-type
clouds whose cloud tops are around 5 – 7 km.
[23] To examine how the statistics shown in Figure 9
represent the general cloud properties of the NICAM
simulation, CFADs for the focal domain whose mesh size
is less than 5 km (see Figure 2) are shown in Figure 10(bottom). These are made from the snapshot data at 1200 UT
on 3 January 2007. We found that time dependency of the
CFADs is not appreciable (figures not shown), so that one
snapshot of data can represent the cloud properties of themodel simulation. The overall structure of the CFADs
along the 11 tracks is also confirmed by a snapshot data.
The upper radar signals are enhanced, and the small
maximum of the lidar signals at an altitude of about
5 km is emphasized by the CFAD of the snapshot dataof the focal domain. It seems that this maximum corresponds
to the observed maximum near an altitude around 5 – 7 km.

[24] In order to examine sensitivities of the signals to themodel resolution, the results from the different resolution
experiment using the stretched NICAM are shown in
Figure 10: the minimum mesh size is about 1.8 km (gl8r200;
Figure 10, top) and 5.0 km (gl7r100; Figure 10, bottom). Notethat the standard grid of the control experiment has a finest
mesh size of about 2.5 km (gl8r100). Although the distributions of the CFADs are similar to those of the standard grid, theradar/lidar signals in the upper layers become more frequent ashorizontal resolution increases. In the lower layer the coarse
model produces more signals. The maximum lidar signal
around an altitude of 5 km also becomes stronger as theresolution becomes coarser. In the upper layers, cloud ice
and snow slightly increase as resolution increases, while in the
middle and lower layers, cloud water, rain, and graupel
increase as resolution becomes coarser (figures not shown).
It is interesting to find that the middle-level lidar signals

9 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

Figure 7. Same as Figure 6 but for radar and lidar signals. (top) Observed signals from CloudSat and
CALIPSO. (bottom) Simulated signals by NICAM using the satellite simulator COSP. Units of the radarand lidar signals are dBZ and km1 str1, respectively.

Figure 8. CFADs of radar and lidar signals at 0500 UT on 2 January 2007. (top) Observed signals fromCloudSat and CALIPSO. (bottom) Simulated signals by NICAM. The bin size is 10 in all CFADs. Black
curves in the lidar plots are mean lidar signals.
10 of 18

D00H14

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

Figure 9. Same as Figure 8 but for (top) CFADs of radar and lidar signals for all 11 tracks of data of
the NICAM simulation and (bottom) those for the snapshot data of the NICAM control simulation at
1200 UT on 3 January 2007 for the domain whose mesh size is less than 5 km.

Figure 10. CFADs of radar and lidar signals showing resolution dependency. (top) Finer meshsimulation (gl8r200) with a finest mesh size of about 1.8 km and for the domain whose mesh size is lessthan 5 km. (bottom) Coarser simulation (gl7r100) with a finest mesh size of about 5.0 km for the domain
whose mesh size is less than 10 km.
11 of 18

D00H14

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 11. CFADs of hydrometeors of the NICAM control simulation at 1200 UT on 3 January 2007
for the domain whose mesh size is less than 5 km. Cloud water (qc), cloud ice (qi), rain (qr), snow (qs),and graupel (qg) are in g kg1. The bin size is 100, and the contours are shown as percentages. The black
curves are the domain mean.
become stronger as the resolution becomes coarser. We
speculate that, as the resolution becomes coarser, water vapor
is more accumulated in the boundary layer before convection
is triggered, and it results in more hydrometeors in the middle
layer by grid-scale convection.
[25] The above results indicate that the comparison between the CFADs of the simulated domain data and theobserved 11-track data can show the model bias in the cloudproperties. Although the integration time is only 4 days, we
can evaluate the cloud properties of the model by comparison
with the observations, at least over this maritime continental
region in this period, characterized as the wet phase of the
MJO. In section 5 we found that the new cloud microphysics
scheme NSW6 produces CFADs closer to the observations
than the original G98 by GCRM (Figure 4): the frequencyof the radar signals is reduced over almost all the altitudes.
However, the new scheme also has a bias, particularly inthe upper troposphere. This is the subject of section 6, in

which sensitivities to cloud microphysics parameters arestudied.

6. Discussion
6.1. Characteristics of Simulated Cloud Properties[26] There may be many reasons for the difference
between the CFADs of the observations and the simulation
shown in Figures 3 and 9. The specific implementation of
the cloud microphysics scheme directly affects the cloudproperties. The horizontal resolution of the model is also
thought to have an impact. However, Figure 10 shows that thepattern of bias does not change as the horizontal resolution
varies although the amplitude of the bias changes. A spurious
bias might appear if the location and timing of cloud systems
are incorrectly simulated. As we have seen, however, thestatistics of the 11-track data are similar to those of the total
domain. Other factors such as the vertical resolution and the

12 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 12. Same as Figure 11, but for CFADs of effective radius (mm) of snow (reffs), graupel (reffg),
and rain (reffr).
dynamical cores (advection schemes) might also cause biases
in the cloud properties. It is also recognized that individualclouds are not resolved until the model resolution is increased
to less than 1 km and that subgrid convective schemes are
required to realistically simulate precipitation in operational
regional models [Saito et al., 2007]. However, sensitivities tothese factors are beyond of the scope of the present study. In
the following discussion we will examine how cloud microphysics schemes affect the CFADs of the radar/lidar signals.
[27] The CFADs of the hydrometeors simulated by the
NICAM control experiment are shown in Figure 11. These
CFADs are made from PDFs at each altitude with 100 bins
between 0 and 0.1 g kg1. The bias in the lidar signals near
15 km altitude suggests that in reality thin cloud ice prevails
near the cloud top layer and that the simulated cloud ice hasa thicker depth. In fact, the CFAD of cloud ice in Figure 11
has a profile similar to that of the lidar signals. To obtain amore realistic profile of lidar signals, or cloud ice, it issuggested that the vertical resolution should be increased toas much as a few hundred kilometers.
[28] The maximum of the lidar signal near around 5 to
10 km altitude is not well represented by the simulation. It is
speculated that the liquid water might be simulated lessfrequently in this layer because congestus-type clouds, whose
top reaches 5 – 10 km, are underestimated by the model. The
bias in the middle-level clouds is an interesting issue and acommon problem seen in other climate models [Zhang et al.,2005; Noda et al., 2010]. The difference might also comefrom ambiguities in specific implementations of satellite
simulators as discussed below.
[29] The simulated radar signals are stronger than theobservation in the upper layers at 10 to 15 km altitude (cf.

Figures 3 and 9). This suggests that the model produces too
many ice clouds in the upper layers. It is interesting to seethat the coarse model produces fewer signals in the upper
layers and more signals in the lower layers (Figure 10). As
the model horizontal resolution becomes coarser, cloud ice
and snow slightly decreases in the upper layers, and rain andgraupel increases in the layers lower than 7 km (not shown).
As the resolution becomes finer, maximum vertical velocity
within deep convective core generally increases, resulting in
more ice clouds in the upper layers. In contrast, as resolution
becomes coarser, water vapor accumulates in the bottom
layers and, if convective clouds are triggered, more rain andgraupel are produced in the middle and lower layers.
[30] The CFADs of the effective radius of rain, snow, andgraupel, together with the domain mean profiles of theeffective radius Reff, are shown in Figure 12. The CFADs
are made of PDFs with 100 bins between 0 and 100 mm. We
use the same size distribution profiles for these hydrometeors
between the cloud microphysics scheme NSW6 and the radar
simulator in COSP. The effective radius is diagnosed by:

reff ;j 

3rqj
4prj Nj

!13

!14
 13
3
rqj
¼
;
4
prj N0jð1Þ

where rain, snow, and graupel are designated by j = r, s, andg, respectively; qj is the specific ratio; rj is the density ofeach species; r is the air density; Nj is the total number ofdensity of each species; and N0j is the intercept parameter.
As indicated in Figure 12, the effective radius of rain has
three peaks around the altitudes of 10.0, 5.0, and 1.5 km. It
seems that these three modes correspond to simulated deep

13 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 13. CFADs of the lidar signals of the control simulation showing dependency on the effective
radius of (left) 20 mm and (right) 60 mm at 1200 UT on 3 January 2007 for the domain whose mesh size isless than 5 km. The bin size is 10, and black curves are the mean of the lidar signals.
clouds, congestus clouds, and shallow clouds [Johnson etal., 1999]. For snow and graupel, there are two modes
above an altitude of 10 km: one is the mode with Reff lessthan 30 mm, and the other has much larger particles whichreach altitudes above 20 km. We have not yet identified thecause of the mode or established how realistic this mode is.
The graupel also shows another peak around an altitude of
5 – 10 km.
[31] In addition, the effective radii of cloud water andcloud ice are assumed to be 10 and 40 mm for simulation of
the lidar signals, respectively. Although these values arewithin the range of the values observed by satellite remotesensing [e.g., Han et al., 1994; Nakajima and Nakajima,
1995], we need to show how the resultant lidar signals
depend on the assumed values of the effective radius. The
sensitivity of the lidar signals to the effective radius of cloud
ice for the cases Reff = 20 and 60 mm are shown in Figure 13.
As Reff increases, the upper signals decrease and the middleand low-level signals slightly increase. This is because thecross section of cloud particles is inversely proportional to theeffective radius for a specified amount of cloud particles.
According to Figure 13, the magnitudes of the upper signals
vary by a factor of two within the range of these plausible
values of the effective radius of cloud ice. Similar dependencies on the particle size of ice cloud regarding the radarreflectivities are discussed by Masunaga et al. [2008] andBodas-Salcedo et al. [2008]. The shape of cloud ice also
affects the signals and such effects should be evaluated infuture cloud microphysics schemes and simulators.
6.2. Sensitivities to Cloud Microphysics Scheme[32] To examine the model sensitivity to the cloud microphysics scheme, we compare the simulated radar/lidar
signals using LCRM by varying the cloud microphysics
scheme or parameters. Satoh and Matsuda [2009] studied
dependencies of properties of upper clouds on parametersof NSW6 and found that the sedimentation of ice clouds
affects upper clouds. On the basis of these parameter studies
by Satoh and Matsuda [2009], here we perform two additional experiments. The first experiment introduces the sedimentation of cloud ice using the formula of Heymsfield andDonner [1990] and is referred to as VTI. This experiment has
the same parameter set as the control experiment of Satoh andMatsuda [2009]. The second experiment has a faster snow
fall speed, introduced by doubling the factor of snow fall

speed cs, and is referred to as CS2. In addition, we test thecloud microphysics scheme by G98, which was used for the
GCRM simulation (see section 4). This third experimentexamines whether different grid configurations between
LCRM and GCRM with the same cloud microphysics
scheme produce the same cloud properties.
[33] The CFADs of the simulated radar/lidar signals forthese three experiments are shown in Figure 14, and thedomain mean vertical profiles of the hydrometeors are shownin Figure 15. A snapshot of data at 1200 UT on 3 January
2007 is used, and the grid points whose mesh size is less than
5 km are analyzed. This can be compared to Figure 9, whichis the result of using the cloud microphysics scheme NSW6
with LCRM. For the experiments VTI and CS2, the frequency of upper clouds near 10– 15 km decreases comparedto the control experiment (Figure 9). The lidar signals in theupper troposphere also are reduced for VTI, while there is no
significant change for CS2. The bias in the midtroposphere isnot changed much.
[34] These results show that the sedimentation of cloud
ice and snow affects upper clouds and that faster fall speedof ice clouds reduces the bias of the radar signals in theupper troposphere. In the case of VTI, both cloud ice andsnow amounts are reduced compared to the control experiment, while in the case of CS2, only snow amounts are
reduced, and cloud ice amounts remain almost the same
(Figure 15). Since cloud ice covers the layers above thereduced snow, the lidar signals change very little for CS2.
[35] We confirm that CFADs of the G98 experiment arevery similar to those by the GCRM simulation (Figure 4).
This result indicates the usefulness of the LCRM; that is,
LCRM captures the cloud properties of the GCRM. The
difference can be seen where the radar signals are stronger
than the GCRM signals, probably because the LCRM has afiner mesh size (minimum is about 2.5 km) than the GCRM
simulation (about 3.5 km).
[36] In general, cloud microphysics schemes that containa category of graupel have more efficiency of precipitation
because the fall speed of graupel is much faster than snow
[Tompkins and Craig, 1999; Satoh and Matsuda, 2009].
Since G98 is the cloud microphysics scheme without graupel,G98 produces more snow than NSW6 [Tomita, 2008b;
Masunaga et al., 2008]. We found that the snow amount of
G98 is much larger than that of NSW6 (Figure 15), but
the upper level radar signals are similar. As examined by

14 of 18

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

D00H14

Figure 14. Same as Figure 9, but for CFADs of radar and lidar signals, showing dependency on cloud
microphysics for the NICAM simulation at 1200 UT on 3 January 2007 for the domain whose mesh size
less than 5 km. (top) VTI, (middle) CS2, and (bottom) G98.
Masunaga et al. [2008], the radar signals of G98 are almost
saturated; thus the difference in snow amount is not clearlyshown by the radar signals.

7. Summary[37] The present study proposes a method of using a
local-area cloud system resolving model (LCRM) for the
purpose of improving cloud properties of the global cloudsystem resolving model (GCRM). The unique aspect of the
proposed method is that the two models, LCRM and
GCRM, have a common dynamical core and physics packages except for the grid configuration. The sensitivity of thesimulated radar/lidar signals to the cloud microphysics
scheme is the main focus of the present research. We particularly focus on the region of the maritime continent for theperiod of the wet phase of the Madden-Julian Oscillation(MJO) from 1 to 4 January 2007 [Masunaga et al., 2008].
This MJO event was simulated by the 3.5 km mesh GCRM

using the Nonhydrostaic Icosahedral Atmospheric Model(NICAM) by Miura et al. [2007]. Using the stretch NICAM
as a LCRM, we reproduced cloud systems in the region of themaritime continent and compared these with the observed
data from CloudSat and CALIPSO. The advantage of using
LCRM is that we can store more frequent data for comparisonand can perform sensitivity studies more efficiently.
[38] The 4 day simulation by LCRM reproduces cloud
patterns similar to those observed by the geostationary
satellites at an almost corresponding location and timing.
We can correlate simulated cloud systems with thoseobserved in a single track of data from the A-Train, andsome characteristics of cloud properties can be captured by
the model. To make a quantitative comparison, we use thesatellite simulator (COSP) and make contoured frequency
by altitude diagrams (CFADs) of the radar and lidar signals.
We found that the CFADs of the 11-track data are similar to
those of the total domain of a snapshot of data. This result
suggests that we can evaluate cloud properties of the

15 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

D00H14

Figure 15. Same as Figure 14 but for domain mean vertical profiles of mass concentration ofhydrometeors (g kg1): cloud water (qc), rain (qr), cloud ice (qi), snow (qs), and graupel (qg). Black
curve, the control experiment; red curve, VTI; blue curve, CS2; and green curve, G98. Graupel is not
calculated for G98.
simulation using the observed track data from CloudSat/
CALIPSO even for the short duration integration of 4 days.
Note that the analysis is only for the specific region of themaritime continent and over a specific period. Since thesimulated period is the wet phase of the MJO characterized
by active deep clouds, cloud systems of the target region ofthis period play a representative role of the tropical convective cloud systems. We use the single-moment bulk cloud
microphysics scheme with six categories of water species
for LCRM (NSW6; see Tomita [2008b]), while the GCRM
simulation uses a relatively simpler cloud microphysics
scheme of Grabowski [1998]. The results isolate differencesin the CFADs of the simulated radar/lidar signals between
the simulations with the two different schemes. In particular,
we found bias in the G98 result: excessive rain in addition
to excessive snow and insufficient cloud ice.
[39] The improved simulation with NSW6 by LCRM
shows better general agreement with the observations.

Although the biases are improved by NSW6, the following
characteristics are found: the simulated radar signals arestronger in the upper layer, and the simulated lidar signals
are deeper and stronger than the observations in the upper
layer around altitudes of 10– 15 km. The maximum of thelidar-attenuated backscattering around altitudes of 5 – 7 km
is not well reproduced by the model, although a small
maximum exists near 5 km. The differences in lidar signalsof the upper layer might be due to insufficient verticalresolution, and that of the middle layer might be due toinsufficient congestus-type clouds in the simulation. Resolution dependency using LCRM shows that upper levelsignals become stronger as the mean mesh size becomes
finer and that the lower-level signals become stronger as themean mesh size becomes coarser.
[40] We have shown that the use of the LCRM is effective
to improve cloud properties of the GCRM. Numerical simulations with the LCRM are computationally more efficient.

16 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

More statistical comparisons and sensitivity studies are
possible using LCRM to find suitable parameter set for cloud
microphysics schemes. However, LCRM only simulates a
specific region of Earth, and we cannot draw general conclusions for the whole globe. Many more simulations need tobe performed if only the LCRM is used. The advantage of the
GCRM is that it gives many samples for different regions
from a single experiment. Thus the use of LCRM and GCRM
are complementary and necessary to improve cloud properties of the numerical models efficiently.
[41] The cloud microphysics scheme used in this study,
NSW6, is based on the work of Lin et al. [1983] and sharescommon processes with other single-moment bulk schemes
used in CRMs [e.g., Rutledge and Hobbs, 1984; Chen andSun, 2002; Hong et al., 2004]. Thus we suppose that thefindings from this study will be applicable to other CRM
experiments with a similar set of six category single-moment
schemes. Our results show that the introduction of thecategory of graupel as in NSW6 reduces the amount ofhydrometeors in the atmosphere. This reduction is related
to the increase in precipitation efficiency owing to the fasterfalling speed of graupel, as discussed by Satoh and Matsuda[2009]. Fall speeds of cloud ice and snow also have the same
effect on precipitation efficiency. As sedimentation of cloud
ice or snow becomes faster, the amount of upper clouds
decreases. The present results are consistent with Satoh andMatsuda [2009], although we use additional information ofcloud profiles using CloudSat/CALIPSO. For future directions we will need more synthetic evaluations of performanceof cloud schemes using radiative energy flux and precipitation in addition to cloud profiles.
[42] Acknowledgments. We thank H. Okamoto for the interpretationof the observation data. CALIPSO data were obtained from the NASA
Langley Research Center Atmospheric Science Data Center. CloudSat data
were provided by Colorado State University. We thank Mark Webb at the
UK Met Office for the use of COSP. We also thank three anonymous
reviewers for constructive comments. This research is supported by the
program of Core Research for Evolutional Science and Technology under
the Japan Science and Technology Agency and Innovative Program of
Climate Change Projection for the 21st Century project funded by the
Ministry of Education, Culture, Sports, Science and Technology, Japan. The
NICAM GCRM simulations were carried out using the Earth Simulator at
JAMSTEC, and the LCRM simulations were carried out using SX8R at the
National Institute for Environmental Studies.

References
Bodas-Salcedo, A., M. J. Webb, M. E. Brooks, M. A. Ringer, K. D.
Williams, S. F. Milton, and D. R. Wilson (2008), Evaluating cloudsystems in the Met Office global forecast model using simulated
CloudSat radar reflectivities, J. Geophys. Res., 113, D00A13,
doi:10.1029/2007JD009620, [printed 114(D8), 2009].
Chen, S.-H., and W.-Y. Sun (2002), A one-dimensional time dependent
cloud model, J. Meteorol. Soc. Jpn., 80, 99 – 118, doi:10.2151/jmsj.80.99.
Chepfer, H., S. Bony, D. Winker, M. Chiriaco, J.-L. Dufresne, and G. Seze(2008), Use of CALIPSO lidar observations to evaluate the cloudinesssimulated by a climate model, Geophys. Res. Lett., 35, L15704,
doi:10.1029/2008GL034207.
Eito, H., and K. Aonashi (2009), Verification of hydrometeor properties
simulated by a cloud-resolving model using a passive microwave satellite
and ground-based radar observations for a rainfall system associated with
the Baiu front, J. Meteorol. Soc. Jpn., 87A, 425 – 446, doi:10.2151/
jmsj.87A.425.
Eitzen, Z. A., and K.-M. Xu (2005), A statistical comparison of deep
convective cloud objects observed by an Earth Observing System satellite
and simulated by a cloud-resolving model, J. Geophys. Res., 110,
D15S14, doi:10.1029/2004JD005086.
Eitzen, Z. A., and K.-M. Xu (2008), Sensitivity of a large ensembleof tropical convective systems to changes in the thermodynamic and

D00H14

dynamic forcings, J. Atmos. Sci., 65, 1773 – 1794, doi:10.1175/
2007JAS2446.1.
Fudeyasu, H., Y. Wang, M. Satoh, T. Nasuno, H. Miura, and W. Yanase (2008),
Global cloud system-resolving model NICAM successfully simulated thelifecycles of two real tropical cyclones, Geophys. Res. Lett., 35, L22808,
doi:10.1029/2008GL036003.
Grabowski, W. W. (1998), Toward cloud resolving modeling of large-scale
tropical circulation: A simple cloud microphysics parameterization,
J. Atmos. Sci., 55, 3283 – 3298, doi:10.1175/1520-0469(1998)055<
3283:TCRMOL>2.0.CO;2.
Han, Q.-Y., W. B. Rossow, and A. A. Lacis (1994), Near-global survey ofeffective cloud droplet radii in liquid water clouds using ISCCP data,
J. Clim., 7, 465 – 487, doi:10.1175/1520-0442(1994)007<0465:
NGSOED>2.0.CO;2.
Haynes, J. M., R. T. Marchand, Z. Luo, A. Bodas-Salcedo, and G. L.
Stephens (2007), A multi-purpose radar simulation package: QuickBeam,
Bull. Am. Meteorol. Soc., 88, 1723 – 1727, doi:10.1175/BAMS-88-111723.
Heymsfield, A. J., and L. J. Donner (1990), A scheme for parameterizing
ice-cloud water content in general circulation models, J. Atmos. Sci., 47,
1865 – 1877, doi:10.1175/1520-0469(1990)047<1865:ASFPIC>2.0.
CO;2.
Hong, S.-Y., J. Dudhia, and S.-H. Chen (2004), A revised approach to ice
microphysical processes for the bulk parameterization of clouds and precipitation, Mon. Weather Rev., 132, 103 – 120, doi:10.1175/15200493(2004)132<0103:ARATIM>2.0.CO;2.
Inoue, T., M. Satoh, H. Miura, and B. Mapes (2008), Characteristics ofcloud size of deep convection simulated by a global cloud resolving
model, J. Meteorol. Soc. Jpn., 86A, 1 – 15, doi:10.2151/jmsj.86A.1.
Inoue, T., M. Satoh, Y. Hagihara, H. Miura, and J. Schmetz (2010), Comparison of high-level clouds represented in a global cloud system resolving model with CALIPSO/CloudSat and geostationary satellite
observations, J. Geophys. Res., doi:10.1029/2009JD012371, in press.
Janowiak, J. E., R. J. Joyce, and Y. Yarosh (2001), A real-time global halfhourly pixel-resolution infrared dataset and its applications, Bull. Am.
Meteorol. Soc., 82, 205 – 217, doi:10.1175/1520-0477(2001)082<0205:
ARTGHH>2.3.CO;2.
Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, andW. H. Schubert (1999), Trimodal characteristics of tropical convection,
J. Clim., 12, 2397 – 2418, doi:10.1175/1520-0442(1999)012<2397:
TCOTC>2.0.CO;2.
Kain, J. S., et al. (2008), Some practical considerations regarding horizontal
resolution in the first generation of operational convection-allowing
NWP, Weather Forecast., 23, 931 – 952, doi:10.1175/WAF2007106.1.
Krueger, S. K., Q. Fu, K. N. Liou, and H. N. S. Chin (1995), Improvementsof an ice-phase microphysics parameterization for use in numerical simulations of tropical convection, J. Appl. Meteorol., 34, 281 – 287.
Lin, Y.-L., R. D. Farley, and H. D. Orville (1983), Bulk parameterization of
the snow field in a cloud model, J. Appl. Meteorol., 22, 1065 – 1092,
doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
Lopez, M. A., D. L. Hartmann, P. N. Blossey, R. Wood, C. S. Bretherton,and T. L. Kubar (2009), A test of the simulation of tropical convective
cloudiness by a cloud-resolving model, J. Clim., 22, 2834 – 2849,
doi:10.1175/2008JCLI2272.1.
Luo, Y., K.-M. Xu, B. A. Wielicki, Z. A. Eitzen, and T. Wong (2007),
Statistical analyses of satellite cloud object data from CERES. part III:
Comparison with cloud-resolving model simulations of tropical convective clouds, J. Atmos. Sci., 64, 762 – 785, doi:10.1175/JAS3871.1.
Masunaga, H., M. Satoh, and H. Miura (2008), A joint satellite and global
cloud-resolving model analysis of a Madden-Julian Oscillation event:Model diagnosis, J. Geophys. Res., 113, D17210, doi:10.1029/
2008JD009986.
Miura, H., M. Satoh, T. Nasuno, A. T. Noda, and K. Oouchi (2007), A
Madden-Julian oscillation event simulated by a global cloud-resolving
model, Science, 318, 1763 – 1765, doi:10.1126/science.1148443.
Nakajima, T. Y., and T. Nakajima (1995), Wide-area determination of cloud
microphysical properties from NOAA AVHRR measurements for FIRE
and ASTEX regions, J. Atmos. Sci., 52, 4043 – 4059, doi:10.1175/15200469(1995)052<4043:WADOCM>2.0.CO;2.
Nakajima, T., M. Tsukamoto, Y. Tsushima, A. Numaguti, and T. Kimura(2000), Modeling of the radiative process in an atmospheric generalcirculation model, Appl. Opt., 39, 4869 – 4878, doi:10.1364/
AO.39.004869.
Nakanishi, M., and H. Niino (2004), An improved Mellor-Yamada level-3
model with condensation physics: Its design and verification, Boundary
Layer Meteorol., 112, 1 – 31, doi:10.1023/B:BOUN.0000020164.04146.98.
Nasuno, T., H. Miura, M. Satoh, A. T. Noda, and K. Oouchi (2009), Multiscale organization of convection in a global numerical simulation of the
December 2006 MJO event using explicit moist processes, J. Meteorol.
Soc. Jpn., 87, 335 – 345, doi:10.2151/jmsj.87.335.

17 of 18

D00H14

SATOH ET AL.: EVALUATIONS OF CLOUD PROPERTIES USING CALIPSO

Noda, A. T., K. Oouchi, M. Satoh, H. Tomita, S.-I. Iga, and Y. Tsushima(2010), Importance of the subgrid-scale turbulent moist process: Cloud
distribution in global cloud-resolving simulations, Atmos. Res.,
doi:10.1016/j.atmosres.2009.05.007, in press.
Palmer, T. N., F. J. Doblas-Reyes, A. Weisheimer, and M. J. Rodwell(2008), Toward seamless prediction: Calibration of climate change projections using seasonal forecasts, Bull. Am. Meteorol. Soc., 89, 459 – 470,
doi:10.1175/BAMS-89-4-459.
Randall, D. A., et al. (2007), Climate models and their evaluation, in
Climate Change 2007: The Physical Science Basis. Contribution ofWorking Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., pp. 589 –
662, Cambridge Univ. Press, New York.
Redelsperger, J.-L., et al. (2000), A GCSS model intercomparison for atropical squall line observed during TOGA-COARE I: Cloud-resolving
model, Q. J. R. Meteorol. Soc., 126, 823 – 863, doi:10.1256/smsqj.56403.
Rutledge, S. A., and P. V. Hobbs (1984), The mesoscale and microscale
structure and organization of clouds and precipitation in midlatitude
cyclones, XII: A diagnostic modeling study of precipitation development in narrow cloud-frontal rainbands, J. Atmos. Sci., 41, 2949 – 2972,
doi:10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2.
Saito, K., et al. (2006), The operational JMA nonhydrostatic mesoscalemodel, Mon. Weather Rev., 134, 1266 – 1298, doi:10.1175/MWR3120.1.
Saito, K., J. Ishida, K. Aranami, T. Hara, T. Segawa, M. Narita, andY. Honda (2007), Nonhydrostatic atmospheric models and operational
development at JMA, J. Meteorol. Soc. Jpn., 85B, 271 – 304,
doi:10.2151/jmsj.85B.271.
Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Wang (2009), Diurnal
cycle of precipitation in the tropics simulated in a global cloud-resolving
model, J. Clim., 22, 4809 – 4826, doi:10.1175/2009JCLI2890.1.
Satoh, M., and Y. Matsuda (2009), Statistics on high-cloud areas and their
sensitivities to cloud microphysics using single-cloud experiments,
J. Atmos. Sci., 66, 2659 – 2677, doi:10.1175/2009JAS2948.1.
Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga (2008a),
Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for global
cloud resolving simulations, J. Comput. Phys., 227, 3486 – 3514,
doi:10.1016/j.jcp.2007.02.006.
Satoh, M., T. Nasuno, H. Miura, H. Tomita, S. Iga, and Y. Takayabu (2008b),
Precipitation statistics comparison between global cloud resolving simulation with NICAM and TRMM PR data, in High Resolution Numerical
Modelling of the Atmosphere and Ocean, edited by W. Ohfuchi and
K. Hamilton, chap. 6, pp. 99 – 112, Springer, New York.
Sekiguchi, M., and T. Nakajima (2008), A k-distribution-based radiation
code and its computational optimization for an atmospheric general circulation model, J. Quant. Spectrosc. Radiat. Transfer, 109, 2779 – 2793,
doi:10.1016/j.jqsrt.2008.07.013.
Stephens, G. L., et al. (2002), The CLOUDSAT mission and the A-TRAIN,
Bull. Am. Meteorol. Soc., 83, 1771 – 1790, doi:10.1175/BAMS-83-121771.

D00H14

Stephens, G. L., et al. (2008), CloudSat mission: Performance and earlyscience after the first year of operation, J. Geophys. Res., 113, D00A18,
doi:10.1029/2008JD009982, [printed 114(D8), 2009].
Tao, W.-K. (2007), Cloud resolving modeling, J. Meteorol. Soc. Jpn., 85B,
305 – 330, doi:10.2151/jmsj.85B.305.
Tao, W.-K., et al. (2003), Microphysics, radiation and surface processes in
the Goddard Cumulus Ensemble (GCE) model, Meteorol. Atmos. Phys.,
82, 97 – 137, doi:10.1007/s00703-001-0594-7.
Tomita, H. (2008a), A stretched grid on a sphere by new grid transformation, J. Meteorol. Soc. Jpn., 86A, 107 – 119, doi:10.2151/jmsj.86A.107.
Tomita, H. (2008b), New microphysics with five and six categories withdiagnostic generation of cloud ice, J. Meteorol. Soc. Jpn., 86A, 121 – 142,
doi:10.2151/jmsj.86A.121.
Tomita, H., and M. Satoh (2004), A new dynamical framework of nonhydrostatic global model using the icosahedral grid, Fluid Dyn. Res., 34,357 – 400, doi:10.1016/j.fluiddyn.2004.03.003.
Tomita, H., H. Miura, S. Iga, T. Nasuno, and M. Satoh (2005), A global
cloud-resolving simulation: Preliminary results from an aqua planet
experiment, Geophys. Res. Lett., 32, L08805, doi:10.1029/2005GL022459.
Tompkins, A. M., and G. C. Craig (1999), Sensitivity of tropical convection
to sea surface temperature in the absence of large-scale flow, J. Clim., 12,462 – 476, doi:10.1175/1520-0442(1999)012<0462:SOTCTS>2.0.CO;2.
Winker, D. M., W. H. Hunt, and M. J. McGill (2007), Initial performanceassessment of CALIOP, Geophys. Res. Lett., 34, L19803, doi:10.1029/
2007GL030135.
Xu, K.-M., et al. (2002), An intercomparison of cloud-resolving models
with the atmospheric radiation measurement summer 1997 intensive observation period data, Q. J. R. Meteorol. Soc., 128, 593 – 624, doi:10.1256/
003590002321042117.
Yuter, S. E., and R. A. Houze (1995), Three-dimensional kinematic andmicrophysical evolution of Florida cumulonimbus. part II: Frequency
distributions of vertical velocity, reflectivity, and differential reflectivity,
M o n . We a t h e r R e v. , 1 2 3 , 1 9 4 1 – 1 9 6 3 , d o i : 1 0 . 11 7 5 / 1 5 2 0 0493(1995)123<1941:TDKAME>2.0.CO;2.
Zhang, M. H., et al. (2005), Comparing clouds and their seasonal variationsin 10 atmospheric general circulation models with satellite measurements,
J. Geophys. Res., 110, D15S02, doi:10.1029/2004JD005021.


T. Inoue and M. Satoh, Center for Climate System Research, Universityof Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan.
(satoh@ccsr.u-tokyo.ac.jp)
H. Miura, Research Institute for Global Change, Japan Agency for
Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku,
Yokohama-shi, Kanagawa 236-0001, Japan. (miurah@jamstec.go.jp)

18 of 18