Satellite Data Simulator Unit

A Multisensor, Multispectral Satellite Simulator Package
by Hirohiko Masunaga , Toshihisa Matsui, Wei- kuo Tao, Arthur Y. Hou, Christian D. Kummerow,

Teruyuki Nakajima, Peter Bauer, William S. Olson, Miho Sekiguchi, and Takashi Y. NakajimaS

ince the earliest meteorological satellites were sent
into orbit in the 1960s, satellite re­mote sensing
has been the vital means to monitor clouds andprecipitation uniformly across the Earth. Present-day
spaceborne remote sensors have great variety in termsof spec­tral range (visible, infrared, and microwave)
and measuring principle (active and passive), each ofwhich has its own strengths and limitations. Satellite
imagers equipped with visible and infrared channels
are an optimal instrument for deriving cloud-top
height and optical thickness, while microwave radiometry is sensitive to the whole cloud column, providing more of a physical link to the underlying rainfall
structure. Microwave radiometers, however, typicallyhave a spatial resolution as low as 50 km at the lowest microwave frequencies (e.g., 6 and 10 GHz) anddo not resolve the vertical structure of atmosphericconstituents. Two spaceborne radars—the TRMM PR
and CloudSat CPR (expansions of all acronyms arelisted at the end of the article)—launched within thelast decade literally added a new dimension to cloudAffiliations : Masunaga—Hydrospheric AtmosphericResearch Center, Nagoya University, Nagoya, Japan; Matsui,Tao, Hou, and Olson —NASA Goddard Space Flight Center,Greenbelt, Maryland; Kummerow —Department of Atmospheric
Science, Colorado State University, Fort Collins, Colorado;
Te. Nakajima—Atmosphere and Ocean Research Institute,
University of Tokyo, Chiba, Japan; Bauer—European Centre for
Medium-Range Weather Forecasts, Reading, United Kingdom;
Sekiguchi —Faculty of Marine Technology, Tokyo University ofMarine Science and Technology, Tokyo, Japan; Ta . Y. Nakajima—
Research and Information Center, Tokai University, Tokyo, Japan
Corresponding Author : Hirohiko Masunaga,
Hydrospheric Atmospheric Research Center, Nagoya University,F3-1(200) Furocho Chikusa-ku, Nagoya 464-8601, Japan
E-mail: masunaga@hyarc.nagoya-u.ac.jp

DOI:10.1175/2010BAMS2809.1
In final form 30 April 2010©2010 American Meteorological Society

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and precipitation measurements from space. The
increasing variety of satellite sensors has greatly expanded the applicability of satellite data, particularly
when different sensors are combined to exploit the
information content beyond the capability of an individual sensor. Multisensor data analyses vastly enrich
the quality (and quantity) of data to be processed,
requiring sophisticated analysis software that helps
us interpret the observa­tions. Potentially useful forthis purpose is a multisensor satellite simulator, or acomputer program to derive synthetic measurementsfor various satellite instruments computed with given
meteorological parameters virtually representing theatmospheric and ground state.
Several multisensor simulator packages are being
developed by different research groups across theworld. Such simulator packages [e.g., COSP (http://
cfmip.metoffice.com/COSP.html), CRTM (www.star.
nesdis.noaa.gov/smcd/spb/CRTM), ECSIM (Voorset al. 2007), RTTOV (Matricardi et al. 2004; Baueret al. 2006), ISSARS (under development, Tanelli2009), and SDSU (this article), among others] shareoverall aims, although some are targeted more on
particular satellite programs or specific applications(for research purposes or for operational use) thanothers. The SDSU or Satellite Data Simulator Unit
is a general-purpose simulator composed of Fortran
90 codes and applicable to spaceborne microwave
radiometer, radar, and visible/infrared imagers
including, but not limited to, the sensors listed inTable 1. Table 1 shows satellite programs particularly
suitable for multisensor data analysis: some are single
satellite missions carrying two or more instruments,
while others are constellations of satellites flying information. The TRMM and A-Train are ongoingsatellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued
or augmented within the decade to come by future
multisensor missions such as the GPM and EarthCARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds
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Fig. 1. SDSU flowchart.

parameters (e.g., snapshots from a CRM simulation
containing temperature, humidity, and hydrometeor
profiles) are then ingested and passed on to individual
simulator components. As such, the I/O routines andeach simulator are designed to be strictly modular sothat the user can flexibly cus­tomize the SDSU (e.g.,modify the I/O interface in a way compatible with theuser-provided input data format and/or add a new
simulator component to expand the SDSU capability).
Technical details about each simulator component are
documented in the papers cited in Table 1 and in the
SDSU User’s Guide. Antenna pattern convolution isapplied to the simulator outputs—that is, simulatedsynthetic measurements are made “out of focus” so
the spatial resolution matches the field of view (FOV)of satellite sensors.
The PSD library is a feature unique to the SDSU
that allows the user to set arbitrary PSD models forindividual hydrometeor species, facilitating the flexible implementation of various bulk microphysicalschemes in radiative transfer calculations. As discussed later, remote-sensing measurements are often

and precipitation but are to better understand theirinteractions with atmospheric dynamics/chemistry
and feedback to climate. The SDSU’s applicability is
not technically limited to hydrometeor measurements
either, but may be extended to air temperature andhumidity observations by tuning the
SDSU to sounding channels. As such, Table 1. SDSU components and applicable multisensor satellite
the SDSU and other multisensor simu- missions.
lators would potentially contribute to a
Simulator
Microwave
Radar
Visible/IR
broad area of climate and atmosphericradiometer
imagersciences.
Numerical
Kummerow
Masunaga andNakajima et al.
The SDSU is not optimized toscheme(1993)
Kummerow (2005)(2003)
any particular orbital geometry ofMissions/sensors applicablesatellites. The SDSU is applicable not
TRMM
TMI
PR
VIRS
only to low-Earth orbiting platforms
A-TrainAqua AMSR-E
Cloudsat CPR
Aqua MODIS
as listed in Table 1, but also to geo­
stationary meteorological satellites.
GPM
GMI
DPR
Although no geosynchronous satelEarthCARE
CPR
MSI
lite carries microwave instruments atpresent or in the near future, the SDSU
would be useful for future geosta­t ionary satellites sensitive to the microphysical properties of clouds
with a microwave radiometer and/or a radar aboard, and precipitation such as, for example, hydrometeor
which could become more feasible as engineering particle size. The SDSU PSD library offers templates
challenges are met.
of typical PSD functions such as a single- and doubleIn this short article, the SDSU algorithm architec- moment exponential and gamma distributions forture and potential applications are reviewed in brief.
users’ convenience. The users can either choose fromthese template PSDs or create their own PSDs andSDSU Structure. Figure 1 outlines the SDSU add them to the library. Given the PSD specified byalgorithm flow. General simulator settings including the user, the radiative properties of hydrometeors are
sensor specifications such as channel frequencies/ computed assuming that all particles are spherical.
wavelengths are defined first by the user, followed by In a future version of the SDSU, a more sophistithe microphysical model setting where the particle cated radiative transfer model will be implemented
size distribution (PSD) models are cus­tomized using in which the nonsphericity of hydrometeors is taken
the PSD library (see below). Input meteorological into account.
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Fig. 2. Synthetic observations computed with the SDSU, applied to a snapshot of a GCE-simulated tropicalsquall line. (a) Plan view of 19-GHz microwave brightness temperature [K] in horizontal polarization. Dashedline indicates x = 160 km where the radar cross-section shown in (c) and (d) is sampled. (b) Same as (a) but
for 85 GHz. (c) Vertical cross section of 14-GHz radar reflectivity (dBZ) sliced along the y axis at x = 160 km.
(d) Same as (c) but for 94 GHz. (e) Plan view of visible (0.62 µm) radiance (W m -2 µm -1 str-1). (f) Plan view ofthermal infrared (12 µm) brightness temperature (K).

A lookup table (LUT) generator creates precomputed tables of the hydrometeor radia­tive properties,
hereafter called the Mie LUTs. The simulator works
several times (or even an order of magnitude) faster
when the radiative properties are calculated offlinein advance and stored in LUTs so that the simulator
quickly refers to the LUTs without repeating timeconsuming computations. Since the hydrometeor
radiative properties depend on mi­crophysics, the Mie
LUT generator is designed to be directly linked with
the PSD library when it is compiled for execution.
These features make the SDSU unique compared
to conventional satellite simulators used by spacecraft
designers and manufacturers. While these simulators
are well specialized for testing engineering details
of instruments, the SDSU is targeted more on meteorological applications and is flexibly tunable to
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realistic atmospheric and surface models. Examplesof synthetic satellite measurements simulated with
the SDSU are presented in the next section.
Synthetic Satellite Observations.
The SDSU is applied to a CRM-generated mesoscaleconvective system to illustrate how they would be observed if the TRMM satellite flew over the scene. The
example shown in Fig. 2 is a mature tropical squall line
simulated by the GCE model and virtually observedby the three primary TRMM sensors: TMI, PR, and
VIRS (see Table 1). Synthetic microwave brightness
temperature at a low frequency of 19 GHz (Fig. 2a)shows thermal emission from liquid clouds and rainfall that form the main body of the convective system.
Deep convective cores are identified as brightness
temperature depressions in the map of high-frequency
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Fig. 3. (left column) A-Train multisensor observations and (right col­umn) synthetic measurements computedwith the Goddard SDSU applied to a WRF simulation. The time stamp is approximately 0750 UTC, 20 Jun 2006.
(Top) AMSR-E 36.5-GHz brightness temperature (K); (middle) MODIS 11-µm bright­ness temperature (K);and (bottom) CloudSat radar reflectivity (dBZ). Red, dashed line crossing the top and middle panels indicates
the CloudSat overpass.

(85 GHz) microwave brightness temperature, resulting
from microwave scattering by frozen precipitating
particles (Fig. 2b). The spatial distribution of brightness
tempera­tures is smoother at 19 GHz than at 85 GHz, as
the antenna pattern convolution works more aggres1628 |December 2010

sively at lower frequencies (note that the radiometer
FOV size is inversely proportional to the channel frequency when observed with the same antenna).
Shown in Fig. 2c is the vertical cross section of simulated TRMM PR (14 GHz) reflectivity in a verticalplane at x = 160 km. Some of the deep convective cores
identified by 85-GHz scattering signals are clearly
captured as areas with distinctly large PR echoes. The
SDSU, with a simple melting-particle model installed,
is able to reproduce the radar bright band, observed
around the height of 4 km. For comparison, CloudSat(94 GHz) radar reflectivity is simulated with the same
snapshot (Fig. 2d). This frequency is sufficiently highto thoroughly map nonprecipitating cloud decks
extending into the upper troposphere, whereas lower
portions of deep convective cores are left undetected
as a result of the severe attenuation of radar echo by
the thick layer of condensate above.
While microwave radiometry is a rough proxy
for the total liquid and ice water constituted ofmillimeter-size precipitation particles, visible imagery is sensitive to smaller cloud droplets that may or
may not accompany precipitation. When observedin the visible (Fig. 2e), the convective system in thisparticular case exhibits a complicated texture, including a line of shallow clouds newly spawned along a
bow-shaped convective leading edge. Infrared brightness temperatures (Fig. 2f) delineate the cloud-top
temperature, nearly as cold as 200 K, of extensive
high clouds spreading entirely over the convective
system. A series of synthetic observations shown by
Fig. 2 implies that the three TRMM instruments provide different pieces of complementary information,and that a thorough picture of the convective systememerges only when observations from all the sensors
are combined together.
Another example of synthetic measurements ispresented in Fig. 3 together with cor­responding observations by the AMSR-E, MODIS, and CloudSat. In
this particular case, the A-Train constellation follows
a track from the Bay of Bengal to the Tibetan plateau
at roughly 0750 UTC on 20 June 2006. For the input
model, a WRF simulation forced by the NCEP Global
Forecast System was run with the GCE one-momentbulk microphysics and Goddard radiation schemes.
Synthetic measurements of AMSR-E 36.5-GHz
brightness temperature, MODIS 11-µm brightness
temperature, and CloudSat radar reflectivity werecomputed using the Goddard SDSU (see the lastsection of this article for a description). A tropicaldisturbance observed by the AMSR-E and MODIS
over the Bay of Bengal is generally captured by the
WRF simulation, although the disturbance consistsof numerous cloud cells scattered across the area inthe model simulation rather than organized into anarch-like band, as clearly delineated by the AMSR-E
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observation. A cloud deck covering the Tibetan Plateau discernible in the AMSR-E and MODIS observations is reasonably well reproduced by the model
simulation. These similarities and discrepancies arealso readily identified in a vertical cross section of theobserved and simulated CloudSat radar echoes.
The examples shown in Figs. 2 and 3 imply that theSDSU, or any satellite simulator applicable to a variety
of sensors with different spectral ranges, is expected to
be a powerful diagnostic tool to study the three-dimensional structure of cloud systems in depth. Potential
applications of the SDSU are next summarized.
Application 1: Model Evaluation.
A primary application of the SDSU is to diagnose theperformance of CRMs in com­parison with satelliteobserved radiances and backscattered electromagnetic echoes. Model evaluation studies with a satellite
simulator have an advantage over the more traditional
approach based on satellite retrievals (e.g., comparing surface rain rates). This is because, as depicted
in Fig. 4, a satellite retrieval algorithm is an inverse
model—that is, satellite measure­ments are inverted
to find consistent input to the radiative transfer problem, based upon a number of assumptions with theircharacteristic underlying uncertainties. A major
source of uncertainty for cloud and/or precipitation
retrieval is the hydrometeor PSD. CRM sim­u lations
rarely agree with any satellite data product even if
applied to the same precipitation event as observed,
since the conventional PSD assumptions in retrieval
algorithms generally have different historical roots
from the assumptions underlying current CRM
microphysical schemes. Besides the PSD, radiativeemission from the surface and atmospheric gases canbe an ad­ditional source of uncertainty if not properly
modeled in the satellite retrieval algorithm.
These difficulties are avoided when the CRM isevaluated not with a satellite-based exter­nal data
product but in terms of direct measurables such asradiances. The radiance-based model evaluation
only needs a satellite simulator or a forward model
to compute synthetic measurements (Fig. 4), requiring no inverse model to be invoked. While the PSD
assump­tions and other implicit assumptions that are
built into retrieval algorithms are generally not perfectly consistent with the CRM physics, the surface
and atmospheric parameters, including the PSD, arefully under the control of users of the SDSU. The
SDSU user can specify the surface and/or atmosphericcharacterizations exactly as given by the CRM out­put,
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| 1629simulation of tropical convective
clouds. Han et al. apply a similartech­n ique utilizing TRMM PR and
TMI data to a cold-frontal rainband
simulation.
Application 2: Algorithm
Development. The usefulnessof satellite simulators is not limited tomodel evaluation. The SDSU can serve
as a tool to support retrieval algorithm
development for current and future
multisensor satellite programs. Suchalgorithms have combined use of passive and active microwave instruments
to derive precipitation profiles, passive
microwave and visible/infrared sensors to detect drizzle in maritime low
Fig . 4. Schematic that illustrates the roles of a satellite simulator clouds, and millimeter-wave radar
and a retrieval algorithm. A satellite simulator is a forward model in and visible imagery to determine
which synthetic mea­surements are obtained uniquely from the radia- cloud microphysical properties.
tive transfer equation once all the required inputs are provided. On
While inversion schemes vary fromthe other hand, a satellite retrieval algorithm is an inversion model, one algorithm to another, typicalwhere the solution is retrieved by finding the input to the radiative modern satellite algorithms involve
transfer model that is compatible with a given set of measurements.
forward radiative transfer simulaSince satellite remote sensing generally involves underconstrained
inversion problems, a retrieval algorithm is built on internal error tions to create a database or lookup
models that characterize the uncertainties associated with the un- table for the algorithm to search formeasurables (and statistical noise associated with the measurables as the solution (or an ensemble of soluwell). The unmea­surables, in contrast, are controllable parameters tions). Alternatively, an inverse model
for satellite simulators. The retrieval algorithm output (or data may be designed using a set of forward
product) would agree with the simulator input (or cloud model) only radiative transfer simulations to train
when the error models are defined in a physically consistent mannera neural network that establishes the
between the satellite algorithm and simulator.
connection between the measurements and solutions. As such, satellite
or make a modification for testing the model sensitiv- simulators can act as a forward model engine at work
ity to the internal physics such as the microphysical in the processes of constructing satellite algorithms.
scheme. As such, satellite simulators offer a useful The core of the SDSU is indeed a collection of routines
testbed to validate CRM performance.
that were originally constructed for algorithm develBy combining data from multiple satellite instru- opment purposes. A database of candidate solutions
ments, the applicability of satellite sim­u lators for and neural networks are efficient and generally useful,evaluating CRM performance is greatly expanded, as but are based only on a finite number of forward simillustrated by some recent studies, as follows. Model ulations performed offline. The anticipated advancebiases in macrophysical and microphysical quantities of computer capabilities could allow a forward radiamay be sep­arately identified when the TRMM PR and tive transfer model to be run online within satellite
CloudSat CPR are combined, exploiting the differ- algorithms. The direct implementation of a forward
ence in the radar sensitivity to particle size between model also facilitates numerical weather prediction
different microwave frequencies (Masunaga et al. schemes that assimilate satellite radiance data.
2008). Papers by Matsui et al. and Li et al. show that
Another application of the SDSU is to assist in
the combination of microwave and infrared bright- algorithm development by creating syn­thetic satellite
ness tempera­tures, together with radar reflectivities, observations used for testing algorithm performance.
provides a good test of CRM reproducibility in the Figure 4 may also be viewed as a schematic illustrating
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the potential role of satellite simulators as a testbed
for satellite algorithms. Satellite simulators are particularly helpful for prospective satel­lite projects thatinvolve new instrumental technologies unavailable
at present, for which synthetic observations provide
the unique opportunity to test the retrieval algorithmwith virtual data.
Future Improvements. The SDSU will be
kept updated in an effort to meet the requests of users,
fix bugs, and improve utility. An important upgrade
being planned on a long-term basis is the implemen­tation of more realistic hydrometeor radiative properties for frozen particles, including the effects of particle
nonsphericity and inhomogeneity. Currently in theSDSU, the radiative properties such as the extinction
and scattering coefficients are based on Mie solutions,for which all particles are assumed to be spherical.
In reality, while cloud droplets and raindrops can bethought of as homogeneous liquid water spheres to areasonable extent, frozen hydrometeors such as cloud
ice crystals and snowflakes have highly complicatedcrystal structures varying dramatically with a numberof factors, including ambient temperature and humidity. It is almost impossible to establish a single tractable
theoretical framework ap­plicable to arbitrarily shaped
ice particles, but there are practical strategies using different approximations to model the radiative properties
of inhomogeneous, nonspherical particles. In a futureversion of the SDSU, the present Mie LUTs will be
replaced with a more realistic database of the radiative
properties for frozen hydrometeors.
Another long-term plan for the upgrade is toexpand the SDSU to include additional satellite
sensors. The constant refrain from existing users isa request to add a lidar simulator to the SDSU. The
CALIPSO lidar, when combined with other A-Train
instruments such as the CloudSat CPR, has provenits capability for measuring the vertical structure of
ice-cloud microphysical properties that were previously undetectable from satellites. It is expected that
research interest in lidar, utilized as a componentof multisensor spaceborne observatories, will continue to grow. This interest will be stimulated by theplanned EarthCARE mission, which will include alidar as part of a suite of satellite instruments.
SDSU users are encouraged to modify the distributed source code, written in the standard Fortran 90
format, and to implement new subroutines for theirown research purposes if nec­essary. A notable example
is the “spin off” Goddard SDSU being developed at
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NASA GSFC. In addition to the existing three simulator components of the SDSU, lidar and broadband
radiometer simulator components have been includedin the Goddard version. These new instrument simulators, utilized in combination with the visible/infrared
simulator, enhance the SDSU capability for analyzing
aerosols as well as clouds and precipitation. The Goddard SDSU’s core interface has been reinvented 1) toinclude parallel computational capability, and 2) tosupport the NASA multiscale modeling system. With
these additional features, the applicability of the Goddard SDSU is extended to support NASA’s wide varietyof ongoing and planned satellite missions, including
TRMM, Terra, A-Train, GPM, and possible future
missions under study, such as ACE.
Download the SDSU. The SDSU package
is available for download from http://precip.hyarc.
nagoya-u.ac.jp/sdsu/index.html for registered users.
Registrants are requested to provide their name ande-mail address so that they will be notified of major
upgrades. The SDSU package consists of source codes
for the simulator components, Mie LUT generator
with sample pre-computed LUTs, and all ancillaryroutines required to run the SDSU. Sample CRM
inputs as demonstrated by Fig. 2 are optionally available. The SDSU User’s Guide provides comprehensive
instructions for users and is available in the PDF
format from the Web site above.
Appendix: Glossary
ACE
Aerosol-Cloud-Ecosystems
AMSR-E Advanced Microwave ScanningRadiometer for EOS
CALIPSO		 Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observation
CERES Clouds and the Earth’s Radiant Energy
System
CFMIP Cloud Feedback Model Intercomparison
Project
COSP
CFMIP Observational Simulator Package
CPR
Cloud Profiling Radar
DPR
Dual-frequency Precipitation Radar
CRM
Cloud-resolving model
CRTM Community Radiative Transfer Model
EarthCARE Earth Clouds, Aerosols, andRadiation Explorer
ECSIM EarthCARE Simulator
EOS
Earth Observing System
GCE
Goddard Cumulus Ensemble model
GSFC
Goddard Space Flight Center
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GMI
GPM
ISCCP

GPM Microwave Imager
Global Precipitation Measurement
International Satellite Cloud Climatology
Project
ISSARS Instrument Simulator Suite for
Atmospheric Remote Sensing
LUT
Lookup table
MODIS Moderate Resolution Imaging
Spectroradiometer
MSI
MultiSpectral Imager
NASA
National Aeronautics and Space
Administration
NCEP
National Centers for Environmental
Prediction
PR
Precipitation Radar
PSD
Particle size distribution
RTTOV Radiative Transfer model for the TIROS
Operational Vertical Sounder
SDSU
Satellite Data Simulator Unit
TIROS Television Infrared Observation Satellite
TMI
TRMM Microwave Imager
TRMM Tropical Rainfall Measuring Mission
VIRS
Visible/Infrared Scanner
WRF
Weather Research and Forecasting model

For Further Reading
Austin, R., and G. L. Stephens, 2001: Retrieval of stratus
cloud microphysical parameters using millimeterwave radar and visible optical depth in preparation
for CloudSat. J. Geophys. Res., 106, 28 233–28 242.
Bauer, P., E. Moreau, F. Chevallier, and U. O’Keefe, 2006:
Multiple-scattering microwave radiative transfer fordata assimilation applications. Quart. J. Roy. Meteor.
Soc., 132, 1259–1281.
Grecu, M., W. S. Olson, and E. N. Anagnostou, 2004:
Retrieval of precipitation profiles from multiresolution, multifrequency active and passive microwave
observations. J. Appl. Meteor., 43, 562–575.
Haddad, Z. S., E. A. Smith, C. D. Kummerow, T. Iguchi,
M. R. Farrar, S. L. Durden, and M. Alves, 1997: The
TRMM ‘day-1’ radar/radiometer combined rain-profiling algorithm. J. Meteor. Soc. Japan, 75, 799–809.
Han, M., S. Braun, W. S. Olson, P. O. G. Persson, and
J.-W. Bao, 2010: Application of TRMM PR and TMI
measurements to assess cloud microphysical schemesin the MM5 model for a winter storm. J. Appl. Meteor.
Climatol., 49, 1129–1148.
Kummerow, C. D., 1993: On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies. J. Geophys. Res., 98, 2757–2765.

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December 2010

Li, X., W.-K. Tao, T. Matsui, C. Liu, and H. Masunaga,
2010: Improving a spectral bin microphysical scheme
using TRMM satellite observations. Quart. J. Roy.
Meteor. Soc., 136, 382–399.
Masunaga, H., and C. D. Kummerow, 2005: Combined
radar and radiometer analysis of precipitation profiles for a parametric retrieval algorithm. J. Atmos.
Oceanic Technol., 22, 909–929.
——, T. Iguchi, R. Oki, and M. Kachi, 2002a: Comparisonof rainfall products derived from TRMM Microwave
Imager and Precipitation Radar. J. Appl. Meteor., 41,
849–862.
——, T. Y. Nakajima, T. Nakajima, M. Kachi, R. Oki, andS. Kuroda, 2002b: Physical properties of maritime
low clouds as retrieved by combined use of TropicalRain­fall Measurement Mission Microwave Imager
and Visible/Infrared Scanner: Algorithm. J. Geophys.
Res., 107, doi:10.1029/2001JD000743.
——, M. Satoh, and H. Miura, 2008: A joint satellite andglobal cloud-resolving model analysis of a MaddenJulian Oscillation event: Model diagnosis. J. Geophys.
Res., 113, D17210, doi:10.1029/2008JD009986.
Matricardi, M., F. Chevallier, G. Kelly, and J.-N. Thépaut,2004: An improved general fast radiative transfer
model for the assimilation of radiance observations.
Quart. J. Roy. Meteor. Soc., 130, 153–173.
Matsui, T., X. Zeng, W.-K. Tao, H. Masunaga, W. S.
Olson, and S. Lang, 2009: Evaluation of long-term
cloud-resolving model simulations using satellite
radiance observations and multifrequency satellite simulators. J. Atmos. Oceanic Technol., 26,
1261–1274.
Nakajima, T. Y., H. Murakami, M. Hori, T. Nakajima,
T. Aoki, T. Oishi, and A. Tanaka, 2003: Efficient useof an improved radiative transfer code to simulate
near-global distri­butions of satellite-measured radiances. Appl. Optics, 42, 3460–3471.
Shao, H. and G. Liu, 2004: Detecting drizzle in marine warm clouds using combined visible, infraredand microwave satellite data. J. Geophys. Res., 109,
D07205.
Tanelli, S., 2009: Development of NASA’s integrated
instrument simulator suite for atmo­spheric remotesensing. 34th AMS Conference on Radar Meteorology, Williamsburg, VA, Amer. Meteor. Soc.
Tao, W.-K., and Coauthors, 2009: Goddard multi-scalemodeling systems with unified physics. Annales
Geophys., 27, 3055–3064.
Voors, R., and Coauthors, 2007: ECSIM: the simulator framework for EarthCARE. Proc. SPIE, 6744,
67441Y, doi:10.1117/12.737738.