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Evaluation of Long-Term Cloud-Resolving Model Simulations Using Satellite
Radiance Observations and Multifrequency Satellite Simulators
TOSHIHISA MATSUI AND XIPING ZENG
Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, and Goddard Earth Science andTechnology Center, University of Maryland, Baltimore County, Baltimore, Maryland

WEI-KUO TAO
Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland

HIROHIKO MASUNAGA
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, JapanWILLIAM S. OLSON
Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, and Joint Center for Earth Systems
Technology, University of Maryland, Baltimore County, Baltimore, Maryland

STEPHEN LANG
Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, and Science Systems andApplications, Inc., Lanham, Maryland(Manuscript received 7 May 2008, in final form 24 November 2008)
ABSTRACTThis paper proposes a methodology known as the Tropical Rainfall Measuring Mission (TRMM) TripleSensor Three-Step Evaluation Framework (T3EF) for the systematic evaluation of precipitating cloud types
and microphysics in a cloud-resolving model (CRM). T3EF utilizes multisensor satellite simulators and novel
statistics of multisensor radiance and backscattering signals observed from the TRMM satellite. Specifically,
T3EF compares CRM and satellite observations in the form of combined probability distributions of precipitation radar (PR) reflectivity, polarization-corrected microwave brightness temperature (Tb), and infrared Tb to evaluate the candidate CRM.
T3EF is used to evaluate the Goddard Cumulus Ensemble (GCE) model for cases involving the South
China Sea Monsoon Experiment (SCSMEX) and the Kwajalein Experiment (KWAJEX). This evaluation
reveals that the GCE properly captures the satellite-measured frequencies of different precipitating cloudtypes in the SCSMEX case but overestimates the frequencies of cumulus congestus in the KWAJEX case.
Moreover, the GCE tends to simulate excessively large and abundant frozen condensates in deep precipitating clouds as inferred from the overestimated GCE-simulated radar reflectivities and microwave Tb depressions. Unveiling the detailed errors in the GCE’s performance provides the better direction for model
improvements.

1. Introduction
Cloud-resolving models (CRMs) explicitly resolve
convective clouds and cloud systems on fine spatial andCorresponding author address: Toshihisa Matsui, Code 613.1,
NASA Goddard Space Flight Center, Greenbelt, MD 20771.
E-mail: toshihisa_matsui-1@nasa.gov
DOI: 10.1175/2008JTECHA1168.1
Ó 2009 American Meteorological Society

temporal scales. CRMs with a one-moment bulk microphysics scheme explicitly predict the evolution ofcloud dynamics associated with liquid and ice condensate masses and their associated latent heating andevaporative cooling in contrast to the implicit prediction
in single-column schemes (SCMs); therefore, CRM simulations agree well with the observation in comparison
with SCM simulations (Xu et al. 2002; Tao et al. 2003).

1262

JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGYWith significant improvements in computational powerover the last decade, CRM simulations can now be
conducted for longer time periods in a 3D model configuration to obtain a better understanding of cloud andprecipitation ensembles and radiative–convective equilibrium (Zeng et al. 2008; Zhou et al. 2007; Blossey et al.
2007; and many others). While they explicitly simulate
cloud dynamics and microphysics evolution, CRMs arestill subject to many uncertainties in cloud microphysical processes because of a lack of practical evaluation
frameworks that can contrast CRM simulations withroutine, extensive observations such as satellite measurements. Lang et al. (2007) recently initiated a more systematic approach to improving the microphysics in the
Goddard Cumulus Ensemble model (GCE) based mainly
on probability distributions from ground-based radarobservations and limited satellite observations, but thetwo simulations used in the study were short-term andfrom a single location.
The Tropical Rainfall Measuring Mission (TRMM)
satellite has operated continuously for over a decade,providing numerous, valuable observations of precipitating tropical cloud systems from its sensors: the Visible/
Infrared Scanner (VIRS), the TRMM Microwave Imager(TMI), and the Precipitation Radar (PR; Kummerow et al.
1998). The retrieved rainfall rates are useful datasets formodel evaluations (e.g., Zhou et al. 2007; Eitzen and Xu
2005), especially if the satellite-derived products are notbased on model simulations. However, TRMM-derived
physical products could contain their own biases due touncertainties in the particle size spectra, particularly at
the freezing level, in the retrieval algorithms (Kummerow
et al. 2006). Therefore, it is often difficult to make a detailed evaluation of CRMs using TRMM-derived physical products because of differences in their estimation
methods and microphysics assumptions. Thus, in order toevaluate CRMs more precisely against satellite observations, it is preferable to estimate satellite-consistentradiances from the model-generated microphysical distributions using radiative transfer calculations (i.e., satellite simulators) (Chaboureau et al. 2002; Chevallier andBauer 2003; Masunaga et al. 2008), since direct satellite
measurements (radiances) have much less uncertainty
than retrieved physical parameters.
This paper introduces a practical CRM-evaluation
framework using multisensor satellite simulators andfine-resolution radiance measurements from the TRMM
satellite. The evaluation framework consists of (i) a
CRM coupled with multisensor satellite simulatorsand (ii) a three-step statistical evaluation of brightness
temperatures (Tbs) and radar reflectivities from the
CRM simulations and TRMM observations. The approach is applied to long-term simulations from the GCE

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for two cases: the South China Sea Monsoon Experiment(SCSMEX) and the Kwajalein Experiment (KWAJEX).
These two cases are based on well-established field
campaigns and have already been used previously forlong-term CRM simulations to study tropical cloud andprecipitation processes (Zeng et al. 2008; Zhou et al.
2007; Blossey et al. 2007). Those studies demonstrated
that CRMs driven by the large-scale forcing could simulate the general features of the observed cloud processes but with essentially similar biases.
Zeng et al. (2008) found that the GCE tended tooverestimate surface precipitation throughout the simulation periods for both SCSMEX and KWAJEX, and,as a result, column-integrated water vapor was largely
underestimated compared to observations. They also
found more convective cores with stronger updrafts inthe 3D model configuration than in the 2D; therefore,
regardless of the chosen microphysical parameterization, simulated precipitating cloud systems can be quitesensitive to differences in the dimensionality of themodel. Zhou et al. (2007) found that GCE simulations
for the SCSMEX case tended to produce a slightly
larger convective to stratiform rain ratio than was estimated from the PR and TMI owing to less anvil (stratiform) cloud. They also found that underestimated highcloud fractions lead to an overestimation of outgoing
longwave radiation in comparison with that estimatedfrom the Clouds and the Earth’s Radiant Energy System(CERES) sensors. Although using a different CRM anddifferent observational data, Blossey et al. (2007) also
found that their CRM also tended to underestimate highcloud fraction, leading to an overestimate of the outgoing
longwave radiation and an underestimate of the top-ofthe-atmosphere (TOA) albedo during less rainy periods.
These studies evaluated the CRM physical parameters as
domain-averaged values.
In contrast to the previous studies, this paper focuses
on a satellite radiance-based systematic evaluation of
long-term CRM simulations by assessing the frequencyof occurrence of different precipitation types as wellas the microphysics of each precipitation type using
multisensor satellite simulators. The paper is organized as follows. Section 2a details the configurationand setup of the long-term CRM simulations for the
KWAJEX and SCSMEX cases. Section 2a describes
the TRMM multisensor observations and the combinations used for evaluation. Section 2c describes themultisensor satellite simulators. Section 3 introduces a
new satellite-based CRM evaluation framework. The
framework is then used to evaluate the long-term CRM
simulations in section 4. Section 5 discusses and summarizes issues related to the CRM simulations raised by
the evaluation.

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2. Numerical experiments and satellite
measurements
a. Cloud-resolving model simulations
In this study, long-term CRM simulations are performed using the GCE model (Tao 2003) for environments observed during the SCSMEX and KWAJEX
field campaigns. The simulations are driven by surface
turbulent fluxes, large-scale advective forcing for temperature and humidity, and horizontal wind tendencies
derived from objective analysis, which statistically combines a variety of field measurements (Zhang et al. 2001).
For a given high-quality long-term meteorological forcing, the GCE with imposed forcing provides a way toevaluate model configurations and physical processes(including the microphysics and cloud properties), if
the simulated fields can be validated using independent
observations. Two microphysics schemes are used inthis study. One is the default Goddard microphysics
scheme with three ice species (GM03; Tao 2003), andthe other is a newly implemented microphysics scheme(GM07; Zeng et al. 2008; Lang et al. 2007). GM07 includes ice-nuclei concentrations for the Bergeron process (Zeng et al. 2008) and lowered collection efficiencies to reduce excessive amounts of graupel (Lang et al.
2007).
The grid configurations, dynamic core, and otherphysical parameterizations are identical except for the
microphysics schemes (i.e., GM03 and GM07). The grid
domain consists of 256 3 256 3 4l grid points in a
Cartesian coordinate with a horizontal grid spacing of
1 km. The simulation domain is centered at 98N, 1678E
for the KWAJEX case and at 218N, 1168E for the
SCSMEX case. The time step is 6 s, and the simulation
periods are from 24 July to 14 September 1999 for
KWAJEX and from 6 May to 14 June 1998 for SCSMEX(Zeng et al. 2008).
Zeng et al. (2008) examined the sensitivity of simulated precipitation condensate to model dimensionality (i.e., 2D versus 3D grids) and microphysics (i.e.,
GM03 and GM07). Because precise and extensive measurements of water contents and drop size distributions(DSDs) of precipitation particles are limited even withina well-designed field campaign, in the present study
these uncertain microphysical parameters are best evaluated through their impact on simulated multisensor
radiance and backscattering signals in contrast to satellite observations (Chaboureau et al. 2002; Chevallier
and Bauer 2003; Masunaga et al. 2008). Particularly, thismethod can evaluate the geophysical parameters throughremotely sensed signals close to (or within) the deep
convective core, where aircraft in situ measurements arenearly impossible.

b. TRMM measurements
In this study, TRMM PR 13.8-GHz attenuationcorrected reflectivity from the TRMM 2A25 product,
VIRS 12-mm infrared brightness temperature (TbIR)
from TRMM 1B01, and TMI 85.5-GHz dual-polarization
microwave brightness temperature (Tb85) from TRMM
1B11 (Kummerow et al. 1998) are used to evaluate the
GCE simulations. PR reflectivity is sensitive to precipitating liquid and relatively large frozen condensates.
VIRS TbIR represents the cloud-top temperature above
optically thick clouds. TMI Tb85 depression (i.e., scattering) is correlated with the amount of relatively small
precipitation-sized ice particles (Liu and Curry 1996).
Observations from these three sensors were collected
over the KWAJEX and SCSMEX sites during the GCE
simulation periods. There are totals of 16 and 17 overpasses with enough scan coverage for the KWAJEX andSCSMEX sites, respectively. Significant PR reflectivity(above 17 dBZ) is also used to identify the radar echotop height (HET). Because the TMI sampled mixedland–ocean areas over the KWAJEX and SCSMEX
sites, a polarization-corrected brightness temperature(PCTb85) is computed (Kidd 1998) in order to compensate for the inhomogeneity of surface emissivity via
PCT b85 5 T b85V 1 a(T b85V 1 T b85H ),where Tb85V and Tb85H are the Tb from the vertical andhorizontal polarization channels at 85 GHz, respectively, and a 5 0.8, which ensures that the inhomogeneity
in surface emission is visually masked out over the twosites. VIRS TbIR and TMI PCTb85 are collocated on the
PR instantaneous field of view (IFOV). The PR andhigh-frequency TMI measurements have a fine IFOV of;5 km, close to the horizontal resolution of typical
CRMs (i.e., dx 5 dy 5 1 km in this study; thus a minimum resolvable dynamical spatial scale for the GCE
simulations should be about 5 km). VIRS TbIR measurements are convolved within the PR IFOV (i.e., 4.3 km at
the surface) via a Gaussian beam pattern because of thesmaller IFOV (i.e., 2.2 km at the surface) of the VIRS
measurements (Masunaga and Kummerow 2005).

c. Satellite simulators
The Goddard Satellite Data Simulation Unit (SDSU)
is an end-to-end multisensor satellite simulator being
built upon the original version developed at the Hydrospheric Atmospheric Research Center (HyARC),
Nagoya University, Japan (available online at http://
precip.hyarc.nagoya-u.ac.jp/sdsu/sdsu-main.html). The
Goddard SDSU simulates satellite-consistent radiances orbackscattering from vertical profiles of model-simulated

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atmospheric variables and condensates obtained fromthe Goddard Multi-Scale Modeling System with unified
physics (Tao et al. 2008). At present, there are passive
microwave, radar, passive visible–infrared, lidar, broadband shortwave and longwave, and International Satellite Cloud Climatology Project (ISCCP)-like simulators within the unified framework.
In this study, GCE-simulated atmospheric and condensate profiles are used to simulate TRMM PRconsistent reflectivity profiles via a radar simulator(Masunaga and Kummerow 2005), VIRS-consistent
TbIR through a spectrum infrared simulator [discrete
ordinate radiative transfer (R-STAR); (Nakajima andTanaka 1986; Stamnes et al. 1988)], and TMI-consistentTb85 through a passive microwave simulator [deltaEddington two-stream radiative transfer with slant pathview (Kummerow 1993; Olson and Kummerow 1996)].
All of the simulators are currently 1D and do not include 3D scattering effects. Within the simulators, theoptical properties for condensates are derived via Mietheory (spherical assumption), while the DSD parameters for precipitation particles are specified in accordancewith the GCE model (i.e., exponential size distributions
with prescribed exponent-intercept parameters and bulk
particle densities). The simulated Tbs and radar reflectivities are then convolved within the IFOV corresponding to each TRMM sensor through a Gaussian
beam pattern similar to the TRMM observations (section 2b)1 and sampled only at the actual TRMM orbiting time over the respective KWAJEX or SCSMEX
sites. It should be noted that CRM-simulated nonprecipitating cloud systems are not evaluated in thisframework, and it is beyond the scope of this study.

3. TRMM Triple-Sensor Three-Step Evaluation
Framework (T3EF)
Due to the inability of CRM simulations to accurately
predict the location of precipitating cloud systems relative to the satellite observations, ensemble statistics ofTbs and radar reflectivities from the satellite observations and GCE simulations are compared. As a result,
it is critical to identify subsets of the simulations andobservations that represent similar cloud/precipitation
systems. To this end, the TRMM Triple-Sensor ThreeStep Evaluation Framework (T3EF), which systematically examines discrepancies between the model andobservations by (i) creating joint diagrams of precipitating cloud types from collocated VIRS TbIR and PR

1
This process convolved the GCE–SDSU simulated 1-km-gridradiance into 5-km IFOV.

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HET (Masunaga et al. 2005), (ii) constructing contoured
frequency with altitude diagrams (CFADs) of PR reflectivity (Yuter and Houze 1995) for each precipitating
cloud type, and (iii) constructing cumulative probability
distributions of TMI PCTb85, is introduced. Prior toactual evaluation of the CRM simulations, an observational sketch of T3EF is introduced to address thephysical aspects of each radiance-based ensemble statistical evaluation.

a. Joint TbIR–HET diagramsLong-term simulations of the GCE model predict
precipitating cloud ensembles over the same periods as
TRMM-observed tropical precipitation systems. Different precipitating cloud systems are associated withdifferent mesoscale processes and therefore differing
amounts of latent heat release, evaporative cooling, andradiative heating (Tao et al. 2003; Olson et al. 2006).
Consequently, it is critical to subcategorize and evaluate
the frequencies of each of the different precipitating
cloud systems (Masunaga and Kummerow 2006).
In this study, collocated VIRS TbIR and PR HET areused to categorize tropical precipitation systems into
four systems: shallow, congestus, midcold, and deep
types, closely following the methods in Masunaga et al.
(2005) with minor modifications. The shallow category(TbIR . 260 K and HET , 4 km) encompasses shallow
precipitating systems. The congestus category (TbIR .
245 K and 4 km , HET , 7 km) generally represents
cumulus congestus. The midcold category (TbIR . 245 K
and 4 km , HET , 7 km) represents a combinationof cold cloud-top temperature and moderately high
HET height, inferring stratiform systems with relatively
small frozen condensate aloft. This category also encompasses cumulus congestus overlapped by the cirrus(anvil) clouds, as pointed out by Stephens and Wood
(2007). The deep category (TbIR , 260 K and 7 km ,HET) represents deep convective systems and deep
stratiform systems with relatively large frozen particles
aloft (Fig. 1). For the purpose of model evaluation, thisseparation method is advantageous in that identicalradiance-based separation methods can be applied toboth the TRMM observations and simulator-coupled
CRM simulations (Masunaga et al. 2008).
Figure 2 shows joint TbIR–HET diagrams from
TRMM observations corresponding to the SCSMEX
and KWAJEX cases. In the KWAJEX case, the TRMM
observations show two distinct peaks in probability
density (;1.2% km21 K21): in the shallow category
with TbIR near 285 K and HET near 3 km and in the deep
category with TbIR near 210 K and HET near 8 km. In
the SCSMEX case, the TRMM observations show astrong peak in the deep category centered around 200 K

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FIG. 1. Schematics of precipitating cloud types closely following the method in Masunaga
et al. (2005) but slightly modified to account for the consideration in Stephens and Wood
(2007). Gray shading represents cloud ice and liquid condensates, and contoured lines represent
precipitation radar reflectivity (dotted lines represent the minimum detectable radar echo,
while thicker solid lines represent larger echoes). Precipitation systems are categorized into1) shallow, 2) congestus, 3) midcold, and 4) deep categories based upon infrared brightness
temperature (closely related to cloud-top temperature) and precipitation radar echo-top
height.

(TbIR) and 10 km (HET) and another peak in the congestus category centered around 260 K (TbIR) and 5 km
(HET). In contrast to the KWAJEX case (17.4% of
shallow and 33.4% of deep categories), there are higher
probability densities in the deep category (41.9%) andlower in the shallow category (11.5%). These results
confirm that precipitation systems are much more organized and vigorous in the SCSMEX case than they arein the KWAJEX case (Johnson et al. 2005; Yuter et al.
2005; Zeng et al. 2008). In addition, the probability
densities for the midcold category (including stratiform
and cirrus-overlapped congestus) in the SCSMEX case(23.3%) appear to be smaller than those in the KWAJEX
case (31.8%). Johnson et al. (2005) reported that thestratiform rain fraction (26%) for convective systems
from SCSMEX is smaller than that typical (40%) in the

FIG. 2. Joint infrared brightness temperature (TbIR) and radar echo-top height (HET) diagrams based on TRMM
observations for the (a) KWAJEX and (b) SCSMEX cases. Values represent the total probability densities for eachprecipitating cloud type (1: shallow, 2: congestus, 3: midcold, 4: deep).

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tropics. Note that in the Fig. 2, the category with TbIR
less than 260 K and HET below 4 km is categorized asshallow precipitating clouds overlapped by the middle
or high clouds (Masunaga et al. 2005). Because of thenegligible population, this study does not examine thiscategory.

b. Type-classified reflectivity CFADs
CFADs are height-dependent probability density distributions of geophysical parameters (Yuter and Houze
1995). Thus, CFADs of PR reflectivities provide a usefulstatistical description that illustrates the effects of precipitation microphysics at different altitudes (Lang et al.
2007; Zhou et al. 2007; Blossey et al. 2007). Lumping thedifferent precipitating cloud categories together in theanalysis could, however, smear together the important
microphysical characteristics associated with each precipitating cloud type (Lang et al. 2007; Blossey et al.
2007). For example, in the previous section, it was
shown that SCSMEX has a higher probability of deep
precipitating clouds. As such, grouping all of the cloud
categories together would generate CFADs biased toward the characteristics of deep precipitating clouds. To
avoid this kind of bias, reflectivity CFADs should be
separately constructed for at least the convective andstratiform portions of precipitation systems (Yuter et al.
2005; Zhou et al. 2007). This study differentiates the
CFADs into separate shallow, congestus, midcold, anddeep categories as defined by the joint TbIR–HET diagrams (section 3a).
Figure 3 shows type-classified reflectivity CFADs for
the KWAJEX and SCSMEX cases. Reflectivity CFADs
were constructed by binning the reflectivities into
1-dBZ bins at each height increment (250 m). Shallow is
the weakest category in terms of reflectivity intensity.
Modal and maximum reflectivities are limited below
25 and 44 dBZ, respectively. Congestus (representing
cumulus congestus) is a more vigorous category, withlarger modal and maximum reflectivities than the shallow type. For shallow and cumulus congestus types, thereflectivity distribution broadens toward the surface,indicating the importance of coalescence and collection
processes, which widen the raindrop spectra. CFADs
for the midcold category appear to be relatively similar
to those for the congestus category, but there is a subtle
signal of bright band due to melting ice particles, especially in the SCSMEX case, at an altitude of about
5 km. However, these signals are weak at best, andgreater melting signals appear in the deep category. The
similarity of CFADs in midcold to those in congestus
suggests that large amounts of cirrus (anvil)-overlapped
cumulus congestus are categorized within the midcold
derived by the joint TbIR–HET diagrams (Stephens and

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Wood 2007). Below the 5-km altitude, the reflectivity
distributions become relatively uniform with height incontrast to the shallow and cumulus congestus types,
wherein the mode reflectivities gradually increased toward the surface.
The most remarkable CFADs are associated with the
deep category. At high altitudes (i.e., above 10 km),reflectivities are narrowly distributed, and maximum
values remain below ;30 dBZ. These low PR reflectivities can be attributed to the presence of smaller
nonspherical frozen precipitation particles. At middle
altitudes (i.e., between 5 and 10 km), maximum reflectivities increase toward lower altitudes, which suggests a broadening and increase in particle sizes due tothe aggregation and sedimentation of frozen particles.
Modal reflectivity increases dramatically below 6 km
because of the melting of frozen particles. At low altitudes (i.e., below 5 km), frozen condensates are almost
completely melted, allowing liquid raindrops to dominate the radar backscattering signals. The high dielectric
constant of liquid water results in larger reflectivities
than at high altitudes. Reflectivity distributions below
the melting layer are relatively uniform with height,
particularly for the SCSMEX case, an indication thatraindrop breakup and stochastic collection are combining to stabilize the raindrop size spectra. Compared
to KWAJEX, the SCSMEX deep category has broader
and larger reflectivities with higher echo-top height, allof which again confirm that the precipitation systems
are much more vigorous in the SCSMEX case than they
are in the KWAJEX case (Johnson et al. 2005; Yuteret al. 2005; Zeng et al. 2008).

c. Type-classified cumulative probability
distributions of PCTb85
Although a passive microwave radiometer provides
less specific information on the vertical profiles of condensates than the TRMM PR, the 85-GHz TMI channels are fairly sensitive to smaller-size frozen particles
and ice water path in the upper portions of precipitating
cloud systems (Yuter et al. 2005). At this frequency,
precipitation-sized particles scatter the upwelling microwave radiation emission and thereby depress the outgoing microwave radiances at the TOA (Liu and Curry
1996). Therefore, to augment the reflectivity CFADs, Tbs
from the TRMM satellite are assessed in terms of cumulative probability distributions of PCTb85 for the different precipitating cloud types. This evaluation is also
important for the assessment of passive microwave sensor-based rainfall/latent heating retrieval algorithms,
because the GCE simulations and simulated Tbs areused in the a priori databases of retrieval algorithms(Kummerow et al. 2006; Olson et al. 2006).

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FIG. 3. Contoured frequency with altitude diagrams (CFADs) of precipitation radar reflectivity for the shallow, congestus, midcold, anddeep categories for (a) KWAJEX and (b) SCSMEX.

Figure 4 shows cumulative probability distributions of
PCTb85 (bin size is 10 K). It is quite discernible, particularly for the SCSMEX case, that the probability
distributions trend toward lower PCTb85 values as thecloud types progress from the shallow to congestus tomidcold to deep categories. This essentially means thatthe amount of frozen precipitation particles increases
from shallow to deep precipitating clouds. It is worth
noting that the probability distributions for the midcold
category have larger PCTb85 depressions than do thosefor the congestus category, although the structures of
their reflectivity CFADs appeared to be quite similar(Fig. 3). This is a manifestation of PCTb85 (85 GHz)
depressions being highly sensitive to smaller-sized frozen precipitation particles, to which the PR (13.8 GHz)is relatively insensitive because of its longer wavelength.
In contrast to SCSMEX, the probability distributions
for the deep and midcold categories are nearly the samein KWAJEX, although the CFADs for these two types
are dissimilar (Fig. 3). The KWAJEX PCTb85 depressions are also suppressed compared to those fromSCSMEX, an indication that deep precipitating cloud ismore isolated and less vigorous in KWAJEX. These
results highlight the utility of evaluating PCTb85 in addition to PR reflectivity.

4. Evaluating the GCE simulations through T3EF
In this section, T3EF is used to evaluate the GCE
simulations for KWAJEX and SCSMEX. It should
again be noted that TRMM-consistent radiances arecomputed from the GCE simulations using multisensor
satellite simulators, and those radiances are then contrasted against observed radiances in a three-step statistical evaluation.

a. Evaluation of precipitating cloud types by joint
TbIR–HET diagrams
Joint TbIR–HET diagrams are constructed from the
GCE simulations for two different microphysics schemes(GM03 and GM07) using the visible–IR and radar
simulators (Fig. 5). In the KWAJEX case, it is clear thatboth of the GCE experiments (i.e., GM03 and GM07)
strongly overestimate the probability densities of the1268

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FIG. 4. Cumulative probability distributions of polarization-corrected TMI brightness temperature at 85 GHz(PCTb85; circles, shallow; squares, congestus; diamonds, midcold; and triangles, deep) for (a) KWAJEX and (b)SCSMEX.

shallow and congestus categories; the combined shallow
and congestus probability densities are 71.7% for GM03
and 69.3% for GM07 compared with just 33.1% fromthe TRMM observations. On the other hand, combined
probability densities of the deep and midcold categories(25.8% in GM03 and 28.8% in GM07) largely underestimate the TRMM observations (65.2%). GM07 performs slightly better in terms of the TbIR (’cloud-top
temperature) probability distributions for the deep andmidcold categories compared to GM03. It should be
noted that the probability densities for the deep category in the GCE simulations have a corrugated texture
along the HET axis. This is an artifact of the current
GCE (and almost all other CRMs) grid configurations
that use a stretched vertical coordinate, which results ina coarser layer thickness (;1 km) than the PR resolution (0.25 km) in the middle/upper troposphere.
Overall, the GCE performs better in the SCSMEX
environment than in the KWAJEX environment. The
structure of the probability densities, particularly in
GM03, is similar to that of the TRMM observations.
Although the combined deep and midcold probability
densities (71.5% for GM03 and 82.2% for GM07)
slightly overestimate the TRMM observations (65.2%),
the probability densities of the shallow category arevery close (9.01% ; 11.5%). Unlike the KWAJEX case,
GM03 performs slightly better than GM07. As noted
earlier, there is a significant difference between the
KWAJEX and SCSMEX cases that is attributable todifferences in the environmental forcing and hence thedynamics of the precipitation systems. The fact that theGCE performs better for SCSMEX is probably due to it
having more organized precipitation systems driven bystronger large-scale forcing (Johnson et al. 2005), which
are better resolved by the spatial grid spacing used inthis study. The less vigorous KWAJEX case probably
requires a finer spatial resolution to resolve the evolution of weaker, isolated, less-organized cumulus systems, as demonstrated by Lang et al. (2007). In addition,
there is the possibility of forcing errors, discussed in
section 5.

b. Evaluating precipitation microphysics by
type-classified reflectivity CFADs
Instead of showing the entire probability distributions, the mean and maximum reflectivities are highlighted and compared between the TRMM observationsand GCE simulations for the KWAJEX and SCSMEX
cases. Although not shown here, the minimum reflectivity is always 17 dBZ (the minimum PR-detectableecho) for all cases (Fig. 6).

1) SHALLOW
In both SCSMEX and KWAJEX, the mean reflectivity profiles of the TRMM observations gradually increase from 18 dBZ at the echo-top altitude to 25 dBZ
near the surface; both the GM03 and GM07 profiles
from the model agree quite well with these observations(within an accuracy of 2 dBZ). The maximum TRMMobserved reflectivities in KWAJEX are slightly smallerJULY 2009

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FIG. 5. Comparison of joint TbIR–HET diagrams and probability densities for each precipitating cloud type between the TRMM
observations and GCE simulations (GM03 and GM07) in (a) KWAJEX and (b) SCSMEX. Values as in Fig. 2.

(24–38 dBZ) than those in SCSMEX (27–42 dBZ) but
again, both are well captured by GM03 and GM07.

2) CONGESTUS
Mean reflectivity profiles from the TRMM observations for both cases range from 18 dBZ at the echo-top
altitude to 29 dBZ near the surface. While the GCE
simulations slightly overestimate the TRMM mean reflectivities by about 3 dBZ, the most discernible discrepancy between the model and observations appears
in the maximum reflectivity profiles for the KWAJEX
case. The GCE simulations overestimate the maximum
reflectivities by as much as 16 dBZ near the echo top
and by 10 dBZ near the surface. The overestimatedreflectivity in the upper layer is most likely due to thepresence of high-density large frozen precipitation
particles (see details in next section). For a given dropsize distribution and number concentration, a 16-dBZ
bias is equivalent to a mean particle diameter in the
GCE simulations that is nearly twice as large as that of
the TRMM observations in the Rayleigh approxima-tion. As parameterized in the GCE, the presence oflarge-sized particles will enhance the mean particle
terminal velocity that suppresses deeper ice particles
aloft. Thus, the presence of large ice-phase condensates
could explain why the GCE overestimates the frequency of the congestus category while underestimating
the frequency of the deep and midcold categories. But
this argument does not go beyond the speculation level,
and other potential sources of model biases are also
discussed in the section 5. On the other hand, maximum
reflectivities from the GCE simulations agree reasonably well with the TRMM observations for the SCSMEX
case. Model deviations from the observed mean andmaximum reflectivities are limited to 3 and 4 dBZ, respectively. In all cases, there is almost no difference inperformance between GM03 and GM07.

3) MIDCOLD
GM03 and GM07 generally agree well with the
TRMM observations in terms of the mean and maximum reflectivities, particularly in the KWAJEX case. In

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FIG. 6. Mean (open circles, TRMM; open squares, GM03; open triangles, GM07) and maximum (closed circles, TRMM; closed squares,
GM03; closed triangles, GM07) reflectivity profiles from PR reflectivity CFADs for the (a) KWAJEX and (b) SCSMEX cases. Different
vertical scales are used for each type of precipitating cloud.

the SCSMEX case, GM03 and GM07 tend to overestimate the maximum reflectivities by around 6 dBZ below
an altitude of 4 km. Below an altitude of 1 km, theTRMM observations show a strong reduction in the
maximum reflectivities, probably because of rain evaporation or surface clutter effect. None of the GCE simulations capture this feature. Again GM03 and GM07
do not have discernible differences in their reflectivity
CFADs.

4) DEEP
Among the four different cloud types, the largest
discrepancies between the model and observations appear in the deep category. Although GM07 does perform
somewhat better at higher altitudes in the KWAJEX
case, the GCE simulations still generally do not capture
the dramatic transitions in the reflectivity profiles observed by the TRMM satellite. At high altitudes, GM03
overestimates the mean and maximum reflectivities
by as much as 5 and 12 dBZ, respectively, which sug-gests that the GCE-simulated frozen precipitation particles are excessive both in size and amount. Near themelting layer (;5 km), both GM03 and GM07 underestimate the mean and maximum reflectivities by asmuch as 6 dBZ because of the lack of a melting signature in the simulations. Near the surface, both GM03
and GM07 agree well with the TRMM SCSMEX observations, but they tend to overestimate reflectivities
for the KWAJEX case. As discussed in other previous
modeling studies (Zhou et al. 2007; Blossey et al. 2007),the classified reflectivity CFADs highlight the uncertainties in the microphysics of simulated mixed-phase
clouds.

c. Evaluating ice water paths by type-classified
cumulative probability distributions of PCTb85
Cumulative probability distributions of PCTb85 wereconstructed from Tb85 values calculated from the GCE
simulations for the KWAJEX and SCSMEX cases(Fig. 7). To better understand this statistical evaluation,

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MATSUI ET AL.

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FIG. 7. Cumulative probability distributions of polarization-corrected TMI brightness temperature at 85 GHz (PCTb85; closed circles,
TRMM; open squares, GM03; open triangles, GM07) for the shallow, congestus, midcold, and deep categories for (a) KWAJEX and(b) SCSMEX.

condensates from the GCE simulations are verticallyintegrated over the same sampling periods for the shallow, congestus, midcold, and deep categories (Table 1).

1) SHALLOW
Because of the absence of appreciable amounts of ice
particles in this category (Table 1), cumulative probability distributions of shallow clouds can be characterized by the following parameters: background Tb, radiance emission from clouds, scattering of microwave
radiance due to raindrops, and scattering of ice water
path from overlapped neighboring pixels. Errors in the
background PCTb85 are very small, because the GCE
simulations are forced by an observation-assimilated
variational analysis (Zhang et al. 2001). The emission of
microwave radiance from clouds is also a small contribution (Liu and Curry 1996). The amount of rain is also
very small (Table 1). Thus, discrepancies between themodel and observations in the probability distributions
most likely represent noise from neighboring pixels.
Because the conical-tracking view of precipitation systems from the TMI channels (52.88 viewing angle) is
collocated with the cross-tracking view from the VIRS
and PR sensors (2178 ; 1178), so is the combinationof GCE–SDSU simulated radiances. In particular, theGCE simulations for the SCSMEX case tend to haveunrealistically large depressions of PCTb85 for the shal-

low type. Precipitation systems in SCSMEX are so organized that shallow precipitation types are frequently
accompanied by deep precipitating clouds. In addition,
because of the cyclic boundary conditions used in the
GCE simulations, precipitation systems with fast propagation speeds tend to be densely populated within thesimulation domain. These factors increase the likelihoodof deep clouds overlapping shallow ones along the slantview of the TMI sensor and PCTb85.

2) CONGESTUS
The GCE simulations tend to overestimate PCTb85
depressions for both KWAJEX and SCSMEX. These
depressions can also be attributed to noise from neighboring pixels, but for midcold, the GCE simulates appreciable amounts of snow and graupel that can increase PCTb85 depressions (Table 1). This is unlikely tobe observed in real cumulus congestus as they commonly do not glaciate (Johnson et al. 1999). The GM07
simulations produce less graupel than the GM03 while
increasing the combined amount of liquid cloud, rain,
and snow (Table 1). Because graupel is defined to be ahigher-density frozen particle in the GCE model andalso in the passive microwave simulator, it has a greater
single-scattering albedo for a given ice water content.
As a result, the PCTb85 depressions in GM03 are slightly
reduced in GM07. The GCE simulations for KWAJEX

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TABLE 1. Mean vertically integrated condensates (kg m22) for the shallow, congestus, midcold, and deep categories.
Shallow

Congestus

Midcold

Deep

GM03

GM07

GM03

GM07

GM03

GM07

GM03

GM07

KWAJEX
Cloud
Rain
Cloud ice
Snow
Graupel

0.16
0.12
0.00
0.01
0.01

0.16
0.12
0.00
0.01
0.00

0.35
0.70
0.00
0.05
0.31

0.37
0.78
0.00
0.10
0.23

0.03
0.18
0.06
0.09
0.24

0.27
0.46
0.19
0.17
0.19

0.24
1.39
0.37
0.15
2.32

0.31
1.33
0.47
0.43
1.42

SCSMEX
Cloud
Rain
Cloud ice
Snow
Graupel

0.27
0.15
0.00
0.01
0.01

0.30
0.15
0.00
0.01
0.00

0.39
0.64
0.00
0.04
0.18

0.42
0.78
0.00
0.08
0.13

0.18
0.41
0.07
0.12
0.29

0.23
0.47
0.28
0.25
0.14

0.25
1.03
0.65
0.22
2.70

0.24
0.95
0.88
0.82
1.67

generate nearly twice as much graupel than those for
SCSMEX. This could explain the overestimation of
maximum radar reflectivity near echo top in the cumulus congestus for the KWAJEX simulations (Fig. 6).

3) MIDCOLDThe GCE simulations slightly overestimate but, ingeneral, reasonably capture the observed probability
distributions in the SCSMEX case. Probably because of
the relatively low amount of graupel (0.14 kg m22;
Table 1), the PCTb85 depressions in GM07 are suppressed in comparison with GM03. In conjunction withthe reflectivity CFAD analysis, it appears that the GCESCSMEX simulations fairly well predict the precipitationsized ice water path in this particular environment, which
would lend more confidence to the passive-microwave
retrieval algorithms in this type of precipitation system(Kummerow et al. 2006; Olson et al. 2006). Similarly,
GM07 performs better than GM03 for the KWAJEX
case; however, both simulations overpredict PCTb85
depressions, implying that the GCE simulations containtoo much precipitation-sized ice (graupel and snow).

4) DEEP
Similar to the midcold type, the GCE overpredicts
the PCTb85 depressions, probably owing to excessive
amounts of frozen precipitation particles. For both the
KWAJEX and SCSMEX cases, GM07 performs better
than GM03, because GM07 tends to reduce the amountof high-density frozen condensate (graupel) in the deep
category by as much as 40% (Table 1). These results
suggest that simulated Tb85 from the GCE is biased
toward lower values for the deep category. Together
with the reflectivity CFAD analysis (section 4b), it appears that the GCE generates too much frozen precipitation in the deep category for both the KWAJEX andSCSMEX cases. Note that large frozen particle amounts
translate into larger particle sizes in the exponential
drop-size distributions with their fixed intercept. Therefore, excessive amounts of simulated frozen particles
could exacerbate the model biases in the reflectivity
CFADs and PCTb85 probability distributions. In comparison with the SCSMEX case, observed depressionsof PCTb85 are rather small in the KWAJEX case.
Nevertheless, GCE simulations cannot capture the observed regional difference in PCTb85 distributions.

5. Summary and discussion
Long-term simulations of convective cloud systems
observed during SCSMEX and KWAJEX using the
GCE model are evaluated through comparison between
TRMM observations and simulated radiances and reflectivities using multisensor simulators. A proposed
methodology for evaluating the simulated radiances andreflectivites using three of TRMM’s sensors, known as
T3EF, was used to systematically evaluate the performance of the GCE. While the GCE simulations are in
reasonable agreement with the TRMM measurements
in some aspects, major simulation biases found in thisstudy include the following:a tendency for the GCE to overestimate the frequency of congestus and underestimate the occurrence of the deep and midcold categories for the
KWAJEX case, andd a tendency for the GCE to produce excessive amounts
and therefore sizes of frozen condensate in the congestus and deep categories for both the KWAJEX
and SCSMEX cases.
d

These biases appear to be common features in longterm 3D CRM simulations with one-moment bulk microphysics (Zeng et al. 2008; Zhou et al. 2007; Blossey

JULY 2009

MATSUI ET AL.

et al. 2007) and could be related to the following threeissues:(i) Large-scale forcing. The long-term GCE simulations were driven by meteorological forcing obtained from variational analysis (Zhang et al. 2001).
This analysis blended all possible observations
to obtain the best estimates of area-averaged variables for the analysis domain over the KWAJEX andSCSMEX sites. Unlike KWAJEX, the precipitation
systems in SCSMEX are dominated by mesoscaleconvective systems (MCSs) driven by stronger largescale environmental forcing (Johnson et al. 2005).
Thus, it may be more realistic to drive the GCE witharea-averaged (approximately hundreds of kilometers) forcing from SCSMEX than from KWAJEX.
On the other hand, the vertical resolution of meteorological forcing should be even finer for simulating
relatively weak precipitation systems, such as in the
KWAJEX case.
(ii) Grid configurations. In addition to the scale of themeteorological forcing, a finer spatial resolution in
the GCE may be needed to better simulate the lessorganized precipitation systems in the KWAJEX
case. For example, Lang et al. (2007) found that
GCE simulations using 1-km grid spacing tended
to form deep convection abruptly, while 250-m grid
spacing realistically simulated the gradual transition from shallow to deep convection that was observed during the TRMM Large-Scale Biosphere–
Atmosphere Experiment in Amazonia (TRMM
LBA). However, at present, it is impractical toconduct long-term CRM simulations with 250-m
grid spacing. This would require a lot more (;50times) computing time.
(iii) Ice microphysics. Besides the issues related to largescale forcing and grid configurations, the GCE
simulates overly large radar reflectivities in the upper troposphere and overly strong microwave Tb
depressions. All of these results suggest that the
one-moment bulk microphysics tends to simulate
excessively large amounts and sizes of precipitation
ice. The new Goddard microphysics scheme(GM07) results in some improvement in terms of
microwave Tbs by reducing the amount of graupel;
however, it worsened the already poor estimation ofshallow and cumulus congestus frequencies in the
SCSMEX case. The ice microphysics issues havebeen discussed in previous modeling studies (e.g.,Lang et al. 2007; Zeng et al. 2008; Zhou et al. 2007;
Blossey et al. 2007). The overly large-sized precipitation ice simulated by the model could enhance the terminal velocity and thus the precipi-

1273

tation efficiency, suppressing deeper convection in
the KWAJEX case. This could be the reason that the
GCE simulations generate too many cumulus congestus and too few midcold and deep precipitatingcloud types in the KWAJEX case. Although thisidea does not go beyond the speculation level,
Blossey et al. (2007) proposed a similar hypothesis
from their long-term simulations for KWAJEX.
Technically, these three modeling deficiencies arecommonly discussed to different degrees in previous
work (e.g., Lang et al. 2007; Zeng et al. 2008; Zhou et al.
2007; Blossey et al. 2007; Eitzen and Xu 2005), andcomplete resolution of these deficiencies is not attempted in this manuscript. Nevertheless, the intentionof this paper is to introduce a new practical CRM evaluation framework, the so-called TRMM Triple-Sensor
Three-Step Evaluation Framework (T3EF).
Instead of using the in situ (or aircraft)-observed
geophysical parameters, the new framework (T3EF)
uses multisensor satellite simulators and direct radianceobservation from the TRMM satellite. T3EF revealed
detailed errors in the CRM’s performance that could
not be assessed by surface precipitation analysis only.
Of course, although it is limited in time and space, thein situ (aircraft or ground-based sampling) observation
during field campaigns is also a valuable framework forevaluating/improving CRM performances (Xu et al.
2002; Luo et al. 2008). In addition to traditional field
campaigns, satellite-based evaluation techniques will bemost valuable for evaluating and improving model performance over the satellite-overpassing area and periods.
The multisensor simulator-based evaluation approach
can reduce the uncertainties and discrepancies in assumption between CRMs and satellite retrieval algorithms (Chaboureau et al. 2002; Chevallier and Bauer
2003; Masunaga et al. 2008).
T3EF is a TRMM stand-alone framework that canevaluate CRMs over most of the tropics, including overland and ocean through day and night. Therefore, withaccurate global reanalysis, a satellite-radiance-based
CRM intercomparison study over different tropical environments can be proposed in the near future following
the method in Eitzen and Xu (2005), in order to test/
improve 3D CRM simulations with a more complex
microphysics scheme at different model grid spacings.
Acknowledgments. This research was supported by
the NASA Headquarters Atmospheric Dynamics andThermodynamics Program and the NASA TropicalRainfall Measuring Mission (TRMM). The authors are
grateful to Dr. R. Kakar at NASA HQ for his support ofthis research. This program was also supported by the

1274

JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY

NASA MAP program. The authors are grateful to
Dr. D. Anderson at NASA HQ. The authors also
acknowledge NASA GSFC and Ames Research Centerfor computer time used in this research, and thankWesley Berg and two anonymous reviewers and an
editor for their constructive comments and suggestions.
REFERENCES
Blossey, P. N., C. S. Bretherton, J. Cetrone, and M. Kharoutdinov,
2007: Cloud-resolving model simulations of KWAJEX: Modelsensitivities and comparisons with satellite and radar observations. J. Atmos. Sci., 64, 1488–1508.
Chaboureau, J.-P., J.-P. Cammas, P. J. Mascart, J.-P. Pinty, and
J.-P. Lafore, 2002: Mesoscale model cloud scheme assessment
using satellite observations. J. Geophys. Res., 107, 4301,
doi:10.1029/2001JD000714.
Chevallier, F., and P. Bauer, 2003: Model rain and clouds overoceans: Comparison with SSM/I observations. Mon. Wea.
Rev., 131, 1240–1255.
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.
Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski,
and W. H. Schubert, 1999: Trimodal characteristics of tropicalconvection. J. Climate, 12, 2397–2418.
——, S. L. Aves, P. E. Ciesielski, and T. D. Keenan, 2005: Organization of oceanic convection during the onset of the 1998
East Asian Summer Monsoon. Mon. Wea. Rev., 133, 131–148.
Kidd, C., 1998: On rainfall retrieval using polarization-corrected
temperatures. Int. J. Remote Sens., 19, 981–996.
Kummerow, C., 1993: On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies.
J. Geophys. Res., 98, 2757–2765.
——, W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The
Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817.
——, W. Berg, J. Thomas-Stahle, and H. Masunaga, 2006: Quantifying global uncertainties in a simple microwave rainfall algorithm. J. Atmos. Oceanic Technol., 23, 23–37.
Lang, S., W. K. Tao, J. Simpson, R. Cifelli, S. Rutledge, W. Olson,
and J. Halverson, 2007: Improving simulations of convective
systems from TRMM LBA: Easterly and westerly regimes.
J. Atmos. Sci., 64, 1141–1164.
Liu, G., and A. Curry, 1996: Large-scale cloud features during
January 1993 in the North Atlantic Ocean as determinedfrom SSM/I and SSM/T2 observations. J. Geophys. Res., 101,
7019–7032.
Luo, Y., K. M. Xu, H. Morrison, and G. McFarquhar, 2008: Arctic
mixed-phase clouds simulated by a cloud-resolving model:
Comparison with ARM observations and sensitivity to microphysics parameterizations. J. Atmos. Sci., 65, 1285–1303.
Masunaga, H., and C. D. Kummerow, 2005: Combined radarand radiometer analysis of precipitation profiles for a parametric
retrieval algorithm. J. Atmos. Oceanic Technol., 22, 909–929.
——, and ——, 2006: Observations of tropical precipitating clouds
ranging from shallow to deep convective systems. Geophys.
Res. Lett., 33, L16805, doi:10.1029/2006GL026547.

VOLUME 26——, T. S. L’Ecuyer, and C. D. Kummerow, 2005: Variability inthe characteristics of precipitation systems in the tropical
Pacific. Part I: Spatial structure. J. Climate, 18, 823–840.
——, 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.
Nakajima, T., and M. Tanaka, 1986: Matrix formulations for thetransfer of solar radiation in a plane-parallel scattering atmosphere. J. Quant. Spectrosc. Radiat. Transfer, 35, 13–21.
Olson, W. S., and C. D. Kummerow, 1996: Simulated retrieval ofprecipitation profiles from TRMM Microwave Imager andprecipitation radar data. Preprints, Eighth Conf. on Satellite
Meteorology and Oceanography, Atlanta, GA, Amer. Meteor.
Soc., 248–251.
——, and Coauthors, 2006: Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I:
Improved method and uncertainties. J. Appl. Meteor. Climatol., 45, 702–720.
Stamnes, K. H., S.-C. Tsay, W. Wisecombe, and K. Jayaweera,
1988: Numerically stable algorithm for discrete-ordinatemethod radiative transfer in multiple scattering and emitting
layered media. Appl. Opt., 27, 2502–2509.
Stephens, G. L., and N. B. Wood, 2007: Properties of tropicalconvection observed by millimeter-wave radar systems. Mon.
Wea. Rev., 135, 821–842.
Tao, W. K., 2003: Goddard Cumulus Ensemble (GCE) Model:
Application for understanding precipitation processes. Cloud
Systems, Hurricanes and the Tropical Rainfall Measuring
Mission (TRMM): A Tribute to Dr. Joanne Simpson, Meteor.
Monogr., No. 51, Amer. Meteor. Soc., 103–138.
——, D. Starr, A. Hou, P. Newman, and Y. Sud, 2003: A cumulus
parameterization workshop. Bull. Amer. Meteor. Soc., 84,1055–1062.
——, and Coauthors, 2008: Goddard multi-scale modeling systems
with unified physics. GEWEX Newsletter, Vol. 18, No. 1, International GEWEX Project Office, Silver Spring, MD, 6–9.
Xu, K.-M., and Coauthors, 2002: An intercomparison of cloudresolving models with the atmospheric radiation measurement summer 1997 intensive observation period data. Quart.
J. Roy. Meteor. Soc., 128, 593–624.
Yuter, S. E., and R. A. Houze Jr., 1995: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus.
Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123,
1941–1963.
——, ——, E. A. Smith, T. T. Wilheit, and E. Zipser, 2005: Physicalcharacterization of tropical oceanic convection observed in
KWAJEX. J. Appl. Meteor., 44, 385–415.
Zeng, X., W. K. Tao, S. Lang, A. Y. Hou, M. Zhang, and
J. Simpson, 2008: On the sensitivity of atmospheric ensemble
states to cloud microphysics in long-term cloud-resolving
model simulations. J. Meteor. Soc. Japan, 86A, 45–65.
Zhang, M. H., J. L. Lin, R. T. Cederwall, J. J. Yio, and S. C. Xie,
2001: Objective analysis of ARM IOP data: Method andsensitivity. Mon. Wea. Rev., 129, 295–311.
Zhou, Y. P., and Coauthors, 2007: Use of high-resolution satellite
observations to evaluate cloud and precipitation statistics
from cloud-resolving model simulations. Part I: South China
Sea Monsoon experiment. J. Atmos. Sci., 64, 4309–4329.