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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D00H22, doi:10.1029/2009JD012371, 2010

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ArticleComparison of high‐level clouds represented in a global cloudsystem–resolving model with CALIPSO/CloudSat
and geostationary satellite observations
Toshiro Inoue,1 Masaki Satoh,1,2 Yuichiro Hagihara,3 Hiroaki Miura,1,2,4
and Johannes Schmetz5
Received 30 April 2009; revised 20 October 2009; accepted 28 December 2009; published 20 May 2010.

[1] Vertical and horizontal distributions of high‐level clouds (ice and snow) simulated

in high‐resolution global cloud system–resolving simulations by the Nonhydrostatic
Icosahedral Atmospheric Model (NICAM) are compared with satellite observations. Ice andsnow data in a 1 week experiment by the NICAM 3.5 km grid mesh global simulation
initiated at 0000 UTC 25 December 2006 are used in this study. The vertical structure of ice
and snow represented by NICAM was compared with Cloud‐Aerosol Lidar and Infrared
Pathfinder Satellite Observation (CALIPSO) and CloudSat observations. High‐level clouds(cumulonimbus and cirrus type clouds) classified by the split window (11 and 12 mm) dataon board geostationary meteorological satellites (GMSs) were used for comparison of thehorizontal distributions of ice and snow in NICAM. The vertical distributions of ice andsnow simulated by NICAM qualitatively agree well with those of cloud signals observed by
CALIPSO and CloudSat. We computed corresponding cloud lidar backscatter coefficientsand cloud radar reflectivity signals from ice and snow data of NICAM using Cloud
Feedback Model Intercomparison Project (CFMIP) observational simulator packages. The
contoured frequency by altitude diagram for the cloud lidar backscatter coefficients shows
lower frequency at higher altitude of 8–14 km by NICAM than CALIOP observations.
This suggests that the amount of ice is not well represented in NICAM. The simulated cloudradar reflectivity signals by NICAM indicated higher frequency at 8–10 km altitude than
CloudSat observations, although there were some differences between over oceans andcontinents. This implies that the amount of snow is larger in NICAM simulations. The
horizontal pattern of ice clouds (column‐integrated ice and snow of greater than 0.01 kg/m2)
in NICAM shows good agreement with that of high‐level clouds identified by the split
window analysis. During this 1 week simulation, 48–59% of ice clouds in NICAM matcheswith observed high‐level clouds. The cross correlation between the spatial distributions ofsimulated ice clouds and satellite‐observed high‐level clouds is 0.40–0.51, and the equitablethreat score is 0.31–0.45. Furthermore, temporal variations of column‐integrated ice clouds
in NICAM are compared with high‐level clouds classified by the split window at the
decaying stage of deep convection over the tropics. The results indicate that the mean decaying
speed of ice clouds of NICAM and high‐level clouds by satellite observations agrees well forthis analysis area and period, although the variances are larger in NICAM. This implies
that the fall speed of snow in this NICAM experiment is appropriate to depict the decay ofanvil clouds by compensating for the excess of snow in NICAM simulations, when we assume
that the decay of anvil clouds is largely controlled by the evaporation of ice and snow.
Citation: 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., 115,
D00H22, doi:10.1029/2009JD012371.

1
Center for Climate System Research, University of Tokyo, Kashiwa,
Japan.
2
Research Institute for Global Change, Japan Agency for Marine‐Earth
Science and Technology, Yokohama, Japan.

Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2009JD012371

3
Center for Atmospheric and Oceanic Studies, Tohoku University,
Sendai, Japan.
4
Department of Atmospheric Science, Colorado State University, Fort
Collins, Colorado, USA.
5
EUMETSAT, Darmstadt, Germany.

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1. Introduction[2] In the Earth‐atmosphere system, clouds are understoodas modulating dynamics, hydrology, and radiation field. The
importance of clouds and their associated processes inatmospheric models has been emphasized since themidseventies [e.g., Arakawa, 1975]. Recently, optically thincirrus and low‐level boundary layer clouds have beenrecognized as important clouds that modulate the climate
system [Stephens, 2005]. High‐level clouds generally have a
cooling effect on the radiation budget. However, cirrus clouds
are known to play a different role in radiative forcing thanother high clouds in some cases. Cirrus clouds, which exist at
higher altitude and have thinner optical thickness, warm theatmosphere more effectively [Liou, 1986; Stephens, 2005]. In
the tropics, cirrus clouds are generally associated with deep
convection as anvil clouds and have large impacts on theenergy budget. Therefore, proper representation of cirrus
clouds is one of the key issues for improving climate models.
In order to ensure the reliable performance of models, better
representation on various cloud properties, such as cloud
fraction, cloud top height, and amounts of liquid and icehydrometeors in the global atmospheric models are now
essentially required.
[3] In general, cirrus clouds are not easily identified bysatellite observations because of their semitransparent characteristics. Many methods have been proposed to detect cirrus
clouds using the visible and infrared data of meteorologicalsatellites [e.g., Inoue, 1985; Prabhakara et al., 1988; Rossow
and Schiffer, 1991; Minnis et al., 1998; Heidinger andPavolonis, 2009]. Among them, the use of split window iseffective to detect long‐lasting cirrus clouds from geostationary satellite data, since no visible data are required. Lidar
observation is also considered a powerful tool to detect cirrus
clouds and to retrieve the optical properties of cirrus clouds[e.g., Platt, 1979; Sassen et al., 1990; Mace et al., 1998a;
Okamoto et al., 2003]. With the launch of the Cloud‐Aerosol
Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat, we now have a significant amount of
new‐generation observations for the vertical structure ofcloud and aerosol from space. Therefore, we can use the
CALIPSO and CloudSat observations to evaluate the verticalstructures of ice and snow represented in global atmosphericmodels. We also use split window analysis to identify cirrus
clouds by geostationary meteorological satellites and compare horizontal distributions and temporal variation of ice
clouds appearing in global atmospheric models.
[4] Global atmospheric models are roughly categorized
into climate models and numerical weather prediction(NWP) models. For evaluations of clouds simulated by climate models, long‐term statistical data have been primarily
used so far. One popular method is the comparison of themeridional section of zonal mean cloud properties to see
whether climate models have adequate amounts of clouds[Chepfer et al., 2008]. However, another approach withshort‐term simulations is now becoming popular, and that istargeting particular events of cloud systems for evaluations ofclimate models. This reflects a recent trend of the unified
framework of climate and NWP models [Davies et al., 2005;
Palmer et al., 2008]; short‐term forecast skill should be better
for more reliable climate simulations, and climatologicalfields should be better for more accurate numerical weather

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forecasts. Clouds with midlatitude synoptic cyclones havebeen examined by this approach [Illingworth et al., 2007].
However, comparison of clouds in low latitudes has notbeen straightforward, since cloud clusters are not well
represented by global atmospheric models with cumulus
parameterization.
[5] A new type of high‐resolution atmospheric global
model, global cloud‐resolving models (GCRMs), whosemesh size is a few kilometers globally, is becoming available.
As an existing GCRM, Satoh et al. [2008] describe theNonhydrostatic Icosahedral Atmospheric Model (NICAM)
and argue that GCRMs will open a new era of research withatmospheric global models. Using the NICAM 7 km grid meshsimulation, Miura et al. [2007] succeeded in reproducing therealistic structure of the Madden‐Julian Oscillation (MJO)
event that occurred during December 2006. They also integrated NICAM for a week with 3.5 km grid mesh globally.
Since this was a short‐term integration, cloud systems in
the tropics (cloud clusters) could be directly evaluated byobservations. Inoue et al. [2008] statistically analyzed deep
convective cloud areas in this NICAM 3.5 km grid meshsimulation over the western tropical Pacific and comparedthem with those of the Japanese geostationary meteorologicalsatellite (MTSAT‐1R) data. The results show that cloud coverage and the diurnal cycle of deep convection over the tropics
are in good agreement between the NICAM simulation andthe observation.
[6] The aim of this paper is to examine how GCRMs
reproduce realistic high‐level clouds, especially in the tropics. Since the simulation by Miura et al. [2007] was the firstrealistic global simulation by high resolution GCRM performed in the Earth Simulator, it is vital to know the reproducibility of clouds by this type of simulation. We compare
the vertical and horizontal distributions of ice clouds between
the 3.5 km grid mesh NICAM simulation and the satellite
observations in sections 3.2 and 3.3. In section 3.4, we tried toargue a new approach to evaluate the behavior of simulated
clouds by comparing time evolutions of high‐level clouds atthe decaying stage of deep convection. In section 2, the data
used in this study is described and a summary of this paper isgiven in section 4.

2. Data
2.1. Global Cloud‐Resolving Model[7] NICAM was configured to run with an explicit cloud
microphysics scheme using a mesh size of a few kilometers
without cumulus parameterization [Tomita and Satoh, 2004;
Satoh et al., 2008]. Using the 7 km grid mesh NICAM, Miuraet al. [2007] reproduced the realistic structure of an MJO
event that occurred during December 2006. They also integrated a 3.5 km grid mesh NICAM globally for a week withan initial condition from the National Centers for Environmental Prediction (NCEP) Global Tropospheric Analyses at
0000 UTC on 25 December 2006 [Nasuno et al., 2009]. Some
aspects of deep convective clouds in this NICAM experiment
have already been analyzed by Inoue et al. [2008] andMasunaga et al. [2008].
[8] In this study, we use an output data of the 3.5 km gridmesh simulation by Miura et al. [2007]. Although this is a
short‐term integration, cloud systems can be directly compared with those of the real world. Here, we focus on high‐2 of 15

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level ice clouds including deep convective clouds and optically thin cirrus clouds. In this NICAM simulation, the cloud
microphysics scheme by Grabowski [1998] is used. It is a
simple three category scheme in which airborne and precipitating hydrometeors are prognostic variables. Cloud ice andsnow are diagnosed by the temperature‐dependent ratio
between respective liquid and ice phases from airborneand precipitating hydrometeors. These two categories are
regarded as ice clouds in this simulation. Two‐dimensional
fields of column‐integrated and time‐mean cloud ice andsnow were saved every 1.5 h, while three‐dimensional
fields of cloud ice and snow were saved only once a day
at 0000 UTC for this 3.5 km mesh experiment. We used thethree‐dimensional snapshot data for the comparison with
the CALIPSO and CloudSat data described below.
2.2. CALIPSO and CloudSat Data
[9] The CALIPSO combines an active lidar instrumentwith passive infrared and visible images to probe verticalstructures and properties of thin clouds and aerosols. The
Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP)
is a two‐wavelength polarization‐sensitive lidar that provides
high‐resolution vertical profiles of aerosol and clouds. The
CALIOP measures backscatter intensity from aerosol andclouds at 532 nm and 1064 nm with 70 m horizontal resolution sampled every 333 m and 30–60 m vertical resolution[Winker et al., 2007].
[10] The CloudSat is an experimental satellite that provides
much needed measurements of the vertical structure of clouds
and precipitation from space [Stephens et al., 2002, 2008].
The Cloud Profiling Radar (CPR) on board CloudSat is a
94 GHz nadir‐looking radar that measures the power backscattered by clouds. Spatial resolution of the CPR data issampled at 1.7 km along track and 1.3 km across track andvertical resolution is 480 m oversampled at 240 m. Calibration accuracy of CPR is 1.5 dB.
[11] CALIPSO and CloudSat fly in formation with threeother satellites as the A‐train constellation [Stephens et al.,2002]. The observation time lag is about 15 s between
CALIPSO and CloudSat. The level 1‐b data of CALIPSO andCloudSat were used in this study. Both CALIPSO andCloudSat data were gridded onto a 0.05 degrees latitude/
longitude grid map by selecting the nearest neighbor pixel
with original vertical resolution.
2.3. Split Window (11 and 12 mm) Data
[ 12 ] Using the split window data, Inoue [1987, 1989]
developed a simple cloud type classification method. Inoue[1985] found that brightness temperature difference between
the 11 and 12 mm (BTD) become larger for cirrus cloud due todifferential absorption by ice between the two wavelength.
The BTD becomes larger for cirrus clouds, although the
BTD depends on optical thickness and effective radius ofice particle. The method can basically classify optically thincirrus clouds that consist of ice and optically thick clouds
utilizing the differential absorption by ice between 11 and
12 mm. Inoue [1997] compared the cloud type classified by
the split window and optical thickness by International Satellite Cloud Climatology Project (ISCCP). He showed thatmean optical thickness for thin cirrus, thick cirrus, low‐level
cumulus/stratocumulus and cumulonimbus type cloud classified by the split window are 2.2, 7.4, 15.3 and 33.7. Inoue

D00H22and Ackerman [2002] studied the radiative effect of eachcloud type classified by the split window using the coincident
and collocated Earth Radiation Budget Experiment (ERBE)
data. They showed that low‐level cumulus/stratocumulus
type cloud and cirrus type cloud indicated similar values inlongwave fluxes, however, low‐level cumulus/stratocumulus
type clouds indicated very large shortwave fluxes, whilecirrus type clouds indicated very low values in shortwave
fluxes. These characteristics are reasonable for each cloud
type. Further they showed that some of the cirrus type clouds
classified by the split window indicate positive net cloudradiative forcing (warming) at the top of the atmosphere.
[13] In this study, cloud type classification was performed
based on the diagram used by Inoue et al. [2009]. We arbitrarily classify cloud types depending on the brightness
temperature (TBB: at 11 mm) and BTD. The TBB is a good
indicator for cloud top temperature for optically thick clouds,
while the BTD is a good indicator to classify optically thincirrus type clouds that consists of ice [Inoue, 1987]. We selectthree TBB thresholds of 213K, 253K and clear TBB to assign
very high/high/middle and low clouds. Here, the clear TBB
was determined as the maximum TBB within 1° latitude/
longitude area between 0000 UTC 26 December 2006 and0000 UTC 1 January 2007. We also select three BTD
thresholds as 1K, and clear BTD to assign optically thicker
clouds and ice clouds. Here, the clear BTD was determined as
the BTD corresponding to clear TBB. We define high‐levelclouds as clouds colder than 253K of TBB, which corresponds to 8 km in the U.S. tropical standard atmosphere,
and cirrus type clouds that are warmer than 253K with larger
BTD.
[14] Calibration accuracy is essential in cloud type classification, since our cloud type classification uses the brightness temperature difference between 11 and 12 mm. Under the
Global Space‐based Inter‐Calibration System (GSICS) program organized by World Meteorological Organization[2006], each Agency is processing intercalibration between
geostationary satellite and low‐orbit satellite. The split window data from the U.S. Geostationary Operational Environmental Satellite (GOES‐W) and the European geostationary
satellite (METEOSAT‐8) were used to identify high‐levelclouds in this study, since the calibration accuracy is reasonable for these satellites. The split window data were also
gridded onto a 0.05 degrees latitude/longitude grid map.

3. Results
3.1. Comparison Between Split Window Analysis
and CALIPSO Data
[15] Here, we first studied the effectiveness of the split
window analysis. Clouds warmer than 253K are used to beclassified as middle‐ or low‐level cloud in a single infraredanalysis. However, optically thin semitransparent cirrus
cloud TBB is often warmer than 253K, which is warmer than
the temperature where cirrus clouds exist. The split window
analysis utilizes the BTD to detect cirrus type clouds. Figure 1
shows the frequency in percentage in the TBB‐BTD diagram
for the clouds higher than 8 km by CALIOP observation. The
percentage in the diagram is shown for each category of TBB(<253K, 253K–273K, >273K). The diagram is constructedfrom only the clouds that are higher than 8 km in CALIOP
observations from 30 orbits over the GOES‐W observation

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Figure 1. Frequency in percentage in TBB‐BTD diagram,for clouds that have CALIOP signals higher than 8 km. Frequency is computed for categorized TBB (<253K, 253K–
273K, >273K) shown by ellipsoids. The numbers in each column add up to 100, i.e., 100%. The diagram was constructedfrom 30 orbits that match within 30 min observation time between CALIOP and GOES‐W during 26–31 December 2006.
area. Sample number of each TBB category is 4541, 1436 and2018. Beyond the TBB warmer than 253K, the frequency
becomes higher over larger BTD in this diagram. This diagram shows 75% (82%) of clouds warmer than 273K (253K–
273K) indicate larger BTD. This region in the diagram corresponds to the cirrus type clouds in split window analysis.
Generally, clouds warmer than 253K are middle‐ or low‐level cloud in the single infrared cloud type classification.
Using the split window, we can detect cirrus clouds among
these clouds even when clouds are warmer than 273K.
[16] Clouds in the region of colder than 253K TBB in the
diagram correspond to high‐level clouds of cumulonimbus
type clouds or thick cirrus clouds. Depending on cloud typethe BTD in this region also shows some variety, although
the percentage of smaller BTD (optically thicker) clouds
indicates higher values of 58%.
[17] Thus, Figure 1 demonstrates that clouds higher than
8 km observed by CALIOP are mostly classified as cumulonimbus type or cirrus type by the split window. This isconsistent with the research by Hamada et al. [2008], which
studied upper tropospheric clouds observed by ship‐borne
cloud radar and by the split window of GMS‐5. Some
examples of correspondence between high‐level clouds
by split window and CALIOP observations are shown insections 3.2 and 3.3.
3.2. Case 1, 0000 UTC 26 December 2006
3.2.1. Spatial Distributions of Ice Clouds in NICAM
and Split Window Analysis[18] Figure 2a illustrates an example of high‐level clouds
classified by the split window over the area covered by
GOES‐W at 0000 UTC 26 December 2006. The backscatter
coefficients by CALIOP observations and cloud radarreflectivity by CloudSat observations along this orbit (anorange line in Figure 2a) are shown in Figures 3a and 3b. The
lower limit of CALIOP backscatter coefficients in Figure 3a
was set to 0.0075 km−1 sr−1 to detect thinner cirrus basedon the findings by Liu et al. [2004], although it is slightly
noisy because of day time observation. High‐level clouds
classified by the split window are seen along the orbit over

D00H2250°N (area A), 30°N (area B), 5°N (area C), 10°S–20°S (areaD), 30°S–40°S (area E), and 57°S (area F) in Figure 2a. The
cloud tops of these clouds (areas C, D, E, F) correspond wellto the cloud signals higher than 8 km by CALIOP and cloudradar observations (Figures 3a and 3b), although the clouds(areas A and B) over higher latitude than 30°N are slightly
lower than 8 km.
[19] In Figure 2b, we now show the spatial distributionof column‐integrated cloud ice and snow represented in the
NICAM 3.5 km grid mesh simulation [Miura et al., 2007] at
0000 UTC 26 December 2006 to compare with high‐levelclouds by the split window analysis of GOES‐W (Figure 2a).
Wylie et al. [1995] suggested that optical thickness of 0.3 was
the lower limitation of cirrus retrieval from satellite measurements. Ackerman et al. [2008] indicated that the Moderate Resolution Imaging Spectroradiometer (MODIS) cloudmask algorithm was sensitive to clouds with an optical thickness greater than 0.4. Thus, we plot the area containing column‐
integrated ice and snow contents greater than 0.01 kg m−2 thatroughly corresponds to 0.4 in optical thickness.
[20] We can see good agreement of high‐level cloud areain Figure 2a and column‐integrated ice and snow area in
Figure 2b. For example, deep convective clouds over theIntertropical Convergence Zone (ITCZ) and the South PacificConvergence Zone (SPCZ), clouds over North America,
and clouds over 40°S–50°S are captured in both results. In
Figure 2a, the purple, blue, green and yellow colors illustrate
respective clouds colder than 213K, cumulonimbus typeclouds, thicker cirrus colder than 253K and optically thinner
ice clouds. On the other hand, in Figure 2b, the color corresponds to the amount of vertically integrated ice and snow
simulated in NICAM; where purple color indicates a larger
amount, while green and yellow color indicates a smaller
amount of column‐integrated ice and snow. The larger amountof column‐integrated ice and snow area in NICAM generally
corresponds to cumulonimbus type clouds classified by the
split window, while a smaller amount of ice and snow area in
NICAM generally corresponds to optically thin cirrus clouds
classified by the split window. There are of course somediscrepancies between the simulation and the observation.
[21] We determine the ability of NICAM for reproducing high‐level clouds using the whole area of Figure 2 byassuming the satellite observations as truth. A contingency
table was constructed, showing the number of times cloud
occurred in both NICAM and satellite (area A), the number oftimes cloud occurred in neither NICAM nor satellite (area D),
and the number of times that cloud occurred in either NICAM
or satellite but not both (areas B and C). The simple scores,
probability of detection (POD) = A/(A + C) and false alarm
ratio (FAR) = B/(A + B), were computed [e.g., Mace et al.,1998b]. The POD was 59% and FAR was 27% for thiscase. The cross correlation between the two was 0.51 and theequitable threat score (ETS) = (A − E)/(A + B + C − E), whereE = (A + B)(A + C)/(A + B + C + D) [Illingworth et al., 2007],
was 0.45. The scores for 6 days over this area are shown in
Figure 4. As is expected, these scores become worse overtime, in particular the temporal change of FAR becomesworse than POD.
3.2.2. Vertical Distributions of Ice and Snow
in NICAM and CALIPSO/CloudSat Observations[22] Since vertical distributions of cloud ice and snow
were saved only once a day in this experiment, the number

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Figure 2. Horizontal distributions of high‐level cloud classified by the split window of (a) GOES‐W andof (b) column‐integrated ice and snow (kg m−2) simulated by NICAM. Time is 0000 UTC 26 December
2006. In Figure 2a, clouds colder than 213K, cumulonimbus‐type clouds, thick cirrus‐type clouds, and thincirrus‐type clouds are shown as <213 (purple), Cb (blue), thick cirrus (green), and thin cirrus (yellow),respectively. The CALIPSO orbit is also shown by an orange curve in Figures 2a and 2b. Typical high‐levelcloud areas are labeled as A to F in Figure 2a.
of coincident comparisons between the observations of
CALIPSO and CloudSat with NICAM are limited. Figures 5aand 5b show vertical profiles of ice and snow (mixing ratio;
kg/kg) represented in NICAM along the orbit in Figure 2a.
Comparing Figures 5a and 5b and Figures 3a and 3b, we cansee good correspondence between the vertical and latitudinal
profiles of ice and snow in NICAM and those of observed
cloud signals by cloud lidar and cloud radar. In this experi-ment, we note that ice amount is much smaller than snow
amount, since the color bar indicates a difference of 10 times.
[23] To quantify the comparison, we can compute thecorresponding lidar and radar signals using the 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. We use
the vertical profiles of cloud properties together with ther-

Figure 3. (a) Backscatter coefficients (km−1sr−1) at 532 nm observed by CALIOP and (b) reflectivity(dBZ) observed by cloud radar on board CloudSat along the orbit in Figure 2a. Cloud signals labeled A
to F in Figure 3a correspond to high‐level cloud areas in Figure 2a.
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Figure 4. Temporal variations of POD, FAR, and ETS computed over the area of Figure 2a from 0000 UTC 26 December
2006 to 0000 UTC 1 January 2007 over GOES‐W area.
modynamic variables produced by NICAM, assuming 10mm
for liquid clouds and 40 mm for ice clouds. However, wefound that the simulated lidar signals are sensitive to the sizeof cloud ice assumed in COSP. The results are shown by
Figures 6a and 6b, which correspond to the respective crosssections shown by Figures 3a and 3b. In computation of lidar
signals, we use both ice and snow data in NICAM. We can see
a clearer similarity of vertical and latitudinal profile between
simulated and observed. The CALIOP observations cangenerally detect signals near the cloud top for deep convective clouds over 5°N (cloud area C in Figure 3a). This featureis also simulated.
3.2.3. Vertical Distributions of Ice and Snow in
NICAM and CALIPSO/CloudSat Observations
Over GOES‐W Coverage[24] For statistical comparison, we constructed contoured
frequency of altitude diagrams (CFAD) using the data higher
than 4 km in altitude over 30°S–30°N because we focused onhigh‐level cloud in this study. The CFAD that show the two‐
dimensional frequency of occurrence in altitude and signal
strength are generally used to diagnose the simulated cloud incomparison with satellite observations [Masunaga et al.,2008]. The vertical distributions of ice and snow simulated
by NICAM were saved once a day at 0000 UTC. However,day orbit of CALIPSO and CloudSat passed over the Pacific
Ocean around 0000 UTC (less than 1 h difference) during
these days. Thus, we construct mean CFAD using six orbits
data to study the structure of high‐level cloud over ocean.
Masunaga et al. [2008] used CFAD of cloud radar and precipitation radar to study NICAM performance. Here we used
the CFAD of cloud lidar and cloud radar.
[25] Figure 7 shows the mean CFAD for simulated lidar
signals (Figure 7a), CALIOP observations (Figure 7b), difference (NICAM – CALIOP) (Figure 7c), simulated radar
signals (Figure 7d), CloudSat observations (Figure 7e), anddifference (NICAM – CloudSat) (Figure 7f). Simulated lidar
signals (Figure 7a) show double peaks in frequency centeredat 10 km altitude and backscatter coefficients of 0.01 km−1 sr−1
and 0.005 km−1 sr−1. CALIOP observations (Figure 7b) show
similar distribution patterns with semidouble peaks in CFAD,

D00H22although the peak at the weaker backscatter coefficient is
not significant. The simulated lidar signal is slightly lower
than CALIOP observations at higher backscatter coefficient.
Figure 7c shows less frequency at higher altitude and astronger backscatter coefficient region in CFAD by NICAM
simulation. As seen in section 3.2.2, this also suggests that theamount of ice is not well represented in NICAM at higher
altitude.
[26] The simulated radar signals (Figure 7d) show a robust
higher frequency at 8–10 km altitude and slightly higher
frequency at lower altitude and stronger radar reflectivity.
While corresponding CloudSat observations showed an arc‐
like structure of higher frequency in CFAD as given by
Masunaga et al. [2008]. The distribution pattern is similar
for both simulated and observed, however, the difference
between the two (Figure 7f) shows clear higher frequency at
8–10 km altitude by NICAM and lower frequency at 5–8 km
altitude with stronger radar reflectivity region in CFAD. Thissuggests an excess of snow with larger particle size at higher
altitude, which is discussed by Masunaga et al. [2008].
3.3. Case 2, 0000 UTC 29 December 2006
3.3.1. Spatial Distributions of Ice Clouds in NICAM
and Split Window Analysis[27] From the limited available three‐dimensional data, wechose another case at 0000 UTC 29 December 2006. At thistime, the orbit of CALIPSO overlaps with the METEOSAT‐8
observational domain. A comparison of the cloud properties
of NICAM and METEOSAT‐8 is shown in Figure 8. The
color code is the same as in Figure 2. Even though this is the
fourth day of the integration from the initial condition, it
shows that size and location of cloud clusters in the tropics arein good agreement between the simulation and the observation. In particular, cloud areas classified by the split window
over the southern part of Africa, southwestern part of northwest of Africa and the southeastern part of east of Brazil
indicate remarkable correspondence with the higher amountof column‐integrated ice and snow simulated by NICAM.
[28] Again in this case, we compute the reproducibility of
ice clouds by NICAM assuming that the satellite observationis truth. The POD by NICAM computed from the whole areaof Figure 8 was 53% and the FAR was 49%. The cross correlation between the two was 0.41 and the equitable threatscore was 0.39. The scores were worse than those of Case 1
since the integration time was longer in Case 2 (the fourthday) than in Case 1 (the first day) and large land exists in thisarea.
3.3.2. Vertical Distributions of Ice and Snow
in NICAM and CALIPSO/CloudSat Observations[29] Figure 9 shows the cross sections of the backscatter
coefficients at 532 nm of CALIOP (Figure 9a) and those of
the cloud radar reflectivity of CloudSat (Figure 9b) along theorbit (orange curve) shown in Figure 8. The time difference
between the NICAM data and the satellite observations is lessthan 15 min. The cloud signals of CALIOP and CloudSat are
seen at higher altitude over the latitudes around 50°N (area G),
5°N–25°S (area H), and 50°S (area I). At these latitudes,
NICAM reasonably represented larger amounts of ice andsnow as seen in Figures 10a and 10b. Here, we again note thatthe color scale is 10 times larger for snow.
[30] To make the comparison clearer, we show the crosssections of lidar and radar signals simulated by COSP in

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Figure 5. Vertical profile of mixing ratio (kg/kg) of (a) ice and (b) snow simulated by NICAM along theorbit in Figure 2a.
Figure 11. The vertical and latitudinal profile of simulated
cloud lidar backscatter coefficient and cloud radar reflectivity
correspond well to those of the cloud signals by CALIOP and
CloudSat, especially over the latitudes near 50°N, 5°N–25°S,
and 50°S. We found that the simulated lidar signals are sensitive to the size of cloud ice, however, the simulated featurecorresponds well to the observations with the default mode.
The simulated radar signals also show reasonable agreementwith the CloudSat observations.
3.3.3. Vertical Distributions of Ice and Snow
in NICAM and CALIPSO/CloudSat Observations
Over Meteosat‐8 Coverage[31] The CFAD for lidar backscatter coefficients and radarreflectivity over 30°S–30°N is constructed using 6 night
orbits over Meteosat‐8 coverage during the experimentperiod and shown in Figure 12. The distribution pattern oflidar is similar between the simulated (Figure 12a) and theobserved (Figure 12b) as seen in Figure 7 with double peaks
in CFAD. In the Meteosat‐8 coverage area, the weakerbackscatter coefficient is more dominant than the GOES‐W
coverage. The peak of frequency is slightly higher altitude(13 km) in CALIOP observations over the Meteosat‐8 coverage area than the GOES‐W coverage area. Although thehigher‐frequency altitude is similar to the GOES‐W coverage,
the NICAM simulated higher‐altitude signals over thisMeteosat‐8 coverage, where it seems to be affected by theAfrican continent. Again, frequency at higher altitude andstronger backscatter coefficient in NICAM is smaller. Thissuggests that ice production in NICAM is less at higher altitude.
[32] The arc‐like feature of higher frequency is seen in
CloudSat observations (Figure 12e) as in Figure 7, however,the simulated signals (Figure 12d) are rather flat in altitude

Figure 6. (a) Simulated cloud lidar backscatter coefficients and (b) simulated cloud radar reflectivity using
NICAM cloud properties along the orbit in Figure 2a.
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Figure 7. The contoured frequency of altitude diagrams (CFAD; %) of backscatter coefficient height for(a) NICAM simulation, (b) CALIOP observation, and (c) difference (NICAM – CALIOP) and those of reflectivity for (d) NICAM simulation, (e) CloudSat observation, and (f) difference (NICAM – CloudSat) over 30°N–
30°S GOES‐W coverage constructed from six day orbits around 0000 UTC during 26–31 December 2006.

at 8–10 km, which is similar to that over GOES‐W area.
The frequency of simulated radar reflectivity is lower than
CloudSat observations over higher altitude and the weakerreflectivity region in the CFAD (Figure 12f) is also seen. The
simulated signals show higher frequency at 5–10 km altitudewith 0–10 dBZ, although not so clear as in Figure 7. Thissuggests that larger amount of snow is produced by NICAM
simulation.
3.4. Temporal Variation of High‐Level Cloudat the Decaying Stage of a Deep Convective System[33] Although the NICAM simulation is not intended to
predict the precise location of each individual deep convective cloud, we have seen that the location and shape of cloudclusters defined by outgoing longwave radiation (OLR) arevery similar to those of the satellite observations [e.g., Inoueet al., 2008]. Here, we also see the similarity between thespatial distributions of ice clouds in this NICAM experimentand that of high‐level clouds by split window analysis.
To trace the temporal variations of high‐level clouds, weshow 6‐hourly spatial distributions of high‐level clouds over

40°N–40°S in GOES‐W coverage in Figure 13, and those of
the ice clouds by NICAM over the same area in Figure 14.
The time varies from Figures 13a to 13d and from Figures 14a
to 14d. There are several similarities in shape such as thecloud areas labeled as A and B in Figures 13a and 14a. Thus,
we can trace these cloud areas in the sequential imagesof Figures 13b and 14b, Figures 13c and 14c and Figures 13d
and 14d as labeled A′ and B′. We also notice that the decayof cloud size in terms of time is almost the same for both
NICAM and satellite observations.
[34] Temporal variations of the size of deep convection
over the tropics have been studied using satellite data [Machado
et al., 1998; Luo and Rossow, 2004; Kondo and Nakamura,
2006; Inoue et al., 2009]. They all showed that the size ofdeep convection shrank at the decaying stage. Here, we tried
to study the decaying rate of the size of deep convection
including anvil clouds using sequential images with 3‐hourlyintervals. The ratio of cloud size to the maximum cloud size
during the life cycle was computed for isolated deep convections. We compute the number of grid points (0.05 degree
lat/lon) enclosed by the threshold value of 0.01 kg m−2 for

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Figure 8. Same as Figure 2 but for (a) METEOSAT and (b) NICAM. Time is 0000 UTC 29 December
2006. Typical high‐level cloud areas are labeled G to I in Figure 8a.
NICAM, and high‐level cloud area classified by the split
window for each sequential image.
[35] Figure 15 shows the temporal variation of high‐levelcloud area after the maximum size appeared during the life
cycle of deep convection. The cloud area is represented as theratio of area size to the maximum area size. Squares and solidline bars indicate mean and standard deviations computedfrom 17 cases selected from NICAM, while dots and dottedbars indicate mean and standard deviation for 11 cases
selected from satellite observations during the analysis period.
Selected deep convections are independent in the NICAM
simulation and the satellite observation. Both the NICAM
simulation and the satellite observation show a quite similar
temporal tendency in shrinking the size at the decaying stage,
although NICAM indicates a larger variance with time. It isinteresting to note that the variance is smaller in satellite
observations. The decay of anvil clouds in real world might
be complicated depending on the meteorological parameters
around the cloud area [e.g., Yuter and Houze, 2003; Luo andRossow, 2004; Horvath and Soden, 2008]. However, thisresult suggests that the fall speed of snow in this NICAM
experiment is appropriate to compensate the excess snow
produced by NICAM, when we select the purely decaying
anvil cloud that is largely controlled by the evaporation of ice
and snow. It should be noted, however, that we need more
careful analysis to identify the precise timing of the cloud area
evolution by a statistical approach.
[36] We only show a small number of cases because of thelimited data of this experiment, and it is therefore perilous tocome to a general conclusion. Here, we simply show one

Figure 9. Same as Figure 3 but for the orbit in Figure 8a. Cloud signals G to I correspond to high‐levelcloud areas in Figure 8a.
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Figure 10. Same as Figure 5 but for the orbit in Figure 8a.
possible approach for the improvement of cloud microphysics schemes. The combination of the comparisons of
CFAD and of the time evolutions of cloud areas somewhat
isolate the cause of the discrepancy, and give insight into
cloud microphysics schemes. We therefore require a larger
amount of samples to discuss more statistical results.

4. Concluding Remarks[37] Miura et al. [2007] conducted a global cloud system–
resolving simulation for a week using NICAM with 3.5 km
grid mesh globally using Earth Simulator. Although thissimulation was a short‐term integration, the GCRM has many
grid points that can give data for a statistical comparison andcloud systems in the tropics (cloud clusters) could be directlycompared with those of satellite observations. Three‐dimensional distributions of cloud configuration defined by
ice and snow in NICAM were compared with high‐levelclouds classified by the split window from geostationary

satellites and with the CALIPSO/CloudSat observations.
Both categories of ice and snow, in the cloud microphysics
scheme of NICAM are regarded as ice clouds (high‐levelclouds) in this study.
[38] The vertical profiles of ice and snow by NICAM
compared with the CALIPSO and CloudSat observations
along the orbit. The latitudinal position and height of ice
and snow by NICAM correspond well to the respective lidar/
radar signals by CALIOP and CloudSat. The lidar and radar
simulations of COSP are used to make a clear comparison
between the NICAM cloud properties and the CALIOP and
CloudSat observations. Cloud lidar simulations with default
particle size show good agreement with CALIOP observations, although we found the dependency on cloud particle
size for satellite simulators. Cloud radar simulations suggestthat excessive large particle size snow production in NICAM
as discussed by Masunaga et al. [2008].
[39] The spatial distributions of ice clouds (column‐
integrated ice and snow of greater than 0.01 kg/m2) by

Figure 11. Same as Figure 6 but for the orbit in Figure 8a.
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Figure 12. Same as Figure 7 but for six night orbits over Meteosat‐8 coverage.

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Figure 13. Six‐hourly images of high‐level cloud classified by the split window over 40°N–40°S GOES‐W
coverage starting at 0000 UTC 26 December 2006 (time goes from left to right and top to bottom).

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Figure 14. Same as Figure 13 but for ice clouds in NICAM.
NICAM agreed well with those of high‐level clouds classified by the split window. If the satellite observations are
assumed to be truth, the NICAM simulation indicates a 48–
59% probability of detection and a 27–49% false alarm ratio
during the 6 days from 26 December 2006 over the GOES‐W
observation area. The cross correlation between the spatial
distributions of simulated ice and snow areas and satellite
observed high‐level clouds is 0.40–0.51, and the equitablethreat score is 0.31–0.45.
[40] The time variations of areas of cirrus clouds at the
decaying stage of deep convection are studied by comparing
the cloud size change in time in both the simulation and thesplit window analysis. We show that the areas of ice clouds
in NICAM decay almost the same as satellite observations.
This suggests that the fall speed of snow in this NICAM
experiment is appropriate to depict the decay of anvil clouds
by compensating for the excess of snow in NICAM simulations, when we assume that the decay of anvil clouds islargely controlled by the evaporation of ice and snow. Thisimplication comes only from the results of a small numberof case studies using the limited data of the simulation, and is
far from conclusive. However, the significance of this study
is that the combination of the comparisons of CFAD and of
the time evolutions of cloud areas will lead to improvements
in cloud microphysics schemes. Therefore, we will further

Figure 15. Temporal variation of high‐level cloud area after
the maximum size appeared during the life cycle of deep convection. The cloud area is represented as the ratio of area
size to the maximum area size. Blue squares and bars indicate
mean and standard deviations computed from 17 cases fromNICAM, while red dots and bars indicate the same as bluefor 11 cases from satellite observations.

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explore systematic analysis using future GCRM experimental
data.
[ 41 ] Acknowledgments. CALIPSO data were obtained from theNASA Langley Research Center Atmospheric Science Data Center. CloudSat data were provided by Colorado State University. We thank Mark Webbat the UK Met Office for the use of COSP. This research is supported by the
program Core Research for Evolutional Science and Technology (CREST)
under the Japan Science and Technology Agency (JST). The NICAM simulation was carried out using the Earth Simulator. This research is also supported by projects of JAXA/EarthCARE, MEXT/VL for Climate System
Diagnostics, MOE/Global Environment Research Fund B‐083, and NIES/GOSAT.

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