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Satellite Data Simulator Unit (SDSU): A multi-sensor, multi-spectral satellite simulator

package

Hirohiko Masunaga ∗

Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan

Toshihisa MatsuiWei-kuo Tao

and Arthur Y. HouNASA Goddard Space Flight Center, Greenbelt, Maryland

Christian D. KummerowDepartment of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Teruyuki NakajimaCenter for Climate System Research, University of Tokyo, Chiba, Japan

Peter BauerEuropean Centre for Medium-range Weather Forecasts, Reading, United Kingdom

William S. OlsonNASA Goddard Space Flight Center, Greenbelt, Maryland

Miho SekiguchiFaculty of Marine Technology, Tokyo University of Marine Science and Technology, Tokyo, Japan

Takashi Y. NakajimaResearch and Information Center, Tokai University, Tokyo, Japan

To appear in the In Box section of Bull. Amer. Meteor. Soc.

Since earliest meteorological satellites were sent into theorbit in the 1960s, satellite remote sensing has been the vi-tal means to monitor clouds and precipitation uniformlyacross the Earth. Spaceborne remote sensors at presentday are of a great variety in terms of spectral range (visi-ble, infrared, and microwave) and measuring principle (ac-tive and passive), each of which has its own strengths andlimitations. Satellite imagers equipped with visible and in-frared channels are an optimal instrument to derive cloudoptical thickness and top height, while microwave radiome-try is sensitive deeper to the whole cloud column, providingmore of a physical link to the underlying rainfall structure.Microwave radiometers, however, typically have a spatialresolution as low as 50 km at the lowest microwave fre-quencies (e.g., 6 and 10 GHz) and do not resolve the verti-cal structure of atmospheric constituents. Two spaceborneradars, the TRMM PR and CloudSat CPR (all acronymsare listed at the end of the article), launched within thelast decade literally added a new dimension to cloud and

precipitation measurements from the space. The increasingvariety of satellite sensors has greatly expanded the appli-cability of satellite data, particularly when different sensorsare combined to exploit the information content beyond thecapability of an individual sensor alone. Multi-sensor dataanalyses vastly enrich the quality (and quantity) of data tobe processed, requiring a sophisticated analysis softwarethat helps us interpret the observations. Potentially use-ful for this purpose is a multi-sensor satellite simulator, ora computer program to derive synthetic measurements forvarious satellite instruments computed with given meteoro-logical parameters virtually representing the atmosphericand ground state.

Several multi-sensor simulator packages are being de-veloped by different research groups across the world. Suchsimulator packages, e.g., COSP (cfmip.metoffice.com/COSP.html), CRTM (www.star.nesdis.noaa.gov/smcd/spb/CRTM),ECSIM (Voors et al. 2007), RTTOV (Matricardi et al.2004; Bauer et al. 2006), ISSARS (under development,

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Table 1. SDSU components and applicable multi-sensor satellite missions

Simulator Microwave radiometer Radar Visible/IR ImagerNumerical scheme Kummerow (1993) Masunaga and Kummerow

(2005)Nakajima et al. (2003)

Missions/sensorsapplicableTRMM TMI PR VIRSA-Train Aqua AMSR-E CloudSat CPR Aqua MODISGPM GMI DPREarthCARE CPR MSI

Tanelli 2009), and SDSU (this article) among others, sharethe overall aims although some are targeted more on partic-ular satellite programs or specific applications (for researchpurposes or for operational use) than others. The SDSU orSatellite Data Simulator Unit is a general-purpose simula-tor comprised of Fortran 90 codes and applicable to space-borne microwave radiometer, radar, and visible/infraredimagers including, but not limited to, the sensors listed inTable 1. Table 1 shows satellite programmes particularlysuitable for multi-sensor data analysis: some are singlesatellite missions carrying two or more instruments whileothers are constellations of satellites flying in formation.The TRMM and A-Train are ongoing satellite missionscarrying diverse sensors that observe cloud and precipita-tion, and will be continued or augmented by future multi-sensor missions such as the GPM and EarthCARE withinthe decade to come. The ultimate goals of these presentand proposed satellite programs are not restricted to cloudsand precipitation but are to better understand their in-teractions with atmospheric dynamics/chemistry and feed-back to climate. The SDSU’s applicability is not techni-cally limited to hydrometeor measurements either, but maybe extended to air temperature and humidity observationsby tuning the SDSU to sounding channels. As such, theSDSU and other multi-sensor simulators would potentiallycontribute to a broad area of climate and atmospheric sci-ences.

The SDSU is not optimized to any particular orbitalgeometry of satellites. The SDSU is applicable not onlyto low Earth orbiting platforms as listed in Table 1 butalso to geostationary meteorological satellites. Althoughno geosynchronous satellite carries microwave instrumentsat present or in the near future, the SDSU would be use-ful for future geostationary satellites with a microwave ra-diometer and/or a radar aboard, which could become morefeasible as engineering challenges are met.

In this short article, the SDSU algorithm architectureand potential applications are reviewed in brief.

Microphysical model setting

Microwave

Simulator

Radar

Simulator

Vis/IR

Simulator

Beam convolution

Output synthetic measurements

Mie LUT

Generator

Mie LUT

Ingest CRM profiles

PSD library

General simulator setting

Fig. 1. SDSU flowchart.

SDSU structureFigure 1 outlines the SDSU algorithm flow. General

simulator settings including sensor specifications such aschannel frequencies/wavelengths are defined first by theuser, followed by the microphysical model setting wherethe particle size distribution (PSD) models are customizedusing the PSD library (see below). Input meteorologicalparameters, e.g., snapshots from a CRM simulation con-taining temperature, humidity, and hydrometeor profiles,are then ingested and passed on to individual simulatorcomponents. As such, the I/O routines and each simula-tor are designed to be strictly modular so that the user canflexibly customize the SDSU, e.g., modify the I/O interfacein a way compatible with the user-provided input data for-mat and/or add a new simulator component to expand theSDSU capability. Technical details on each simulator com-ponent are documented in the papers cited in Table 1 andin the SDSU User’s Guide. Antenna pattern convolutionis applied to the simulator outputs, that is, simulated syn-

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x [km] x [km] x [km]

x [km]

y [km]

y [km]

y [km

]

y [km

]

z [km

]z [km

]

Fig. 2. Synthetic observations computed with the SDSU, applied to a snapshot of a GCE-simulated tropical squall line.(a) Plan view of 19-GHz microwave brightness temperature [K] in horizontal polarization. Dashed line 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) Verticalcross 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 of thermal infrared (12 µm) brightnesstemperature [K].

thetic measurements are made “out of focus” so the spatialresolution matches the field of view (FOV) of satellite sen-sors.

The PSD library is a feature unique to the SDSU thatallows the user to set arbitrary PSD models for individ-ual hydrometeor species, facilitating the flexible implemen-tation of various bulk microphysical schemes in radiativetransfer calculations. As discussed later, remote sensingmeasurements are often sensitive to the microphysical prop-erties of clouds and precipitation such as, for example,hydrometeor particle size. The SDSU PSD library offerstemplates of typical PSD functions such as a single- anddouble-moment exponential and gamma distributions forusers’ convenience. The users can either choose from thesetemplate PSDs or create their own PSD and add them tothe library. Given the PSD specified by the user, the ra-diative properties of hydrometeors are computed assuming

that all particles are spherical. In a future version of theSDSU, a more sophisticated radiative transfer model willbe implemented in which the non-sphericity of hydromete-ors is taken into account.

A lookup table (LUT) generator creates pre-computedtables of the hydrometeor radiative properties, or hereaftercalled the Mie LUTs. The simulator works several times oreven an order of magnitude faster when the radiative prop-erties are calculated off-line in advance and stored in LUTsso that the simulator quickly refers to the LUTs without re-peating time-consuming computations. Since the hydrom-eteor radiative properties depend on microphysics, the MieLUT generator is designed to be directly linked with thePSD library when it is compiled for execution.

These features make the SDSU unique compared toconventional satellite simulators used by spacecraft design-ers and manufacturers. While these simulators are well

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Fig. 3. A-Train multi-sensor observations (left column) and synthetic measurements computed with the Goddard SDSUapplied to a WRF simulation (right column). The time stamp is approximately 7:50 UTC, 20 June, 2006. Top) AMSR-E36.5 GHz brightness temperature [K], Middle) MODIS 11 µm brightness temperature [K], and Bottom) CloudSat radarreflectivity [dBZ]. Red, dashed line crossing the top and middle panels indicates the CloudSat overpass.

specialized for testing engineering details of instruments,the SDSU is targeted more on meteorological applicationsand is flexibly tunable to realistic atmospheric and surfacemodels. Examples of synthetic satellite measurements sim-ulated with the SDSU are presented in the next section.

Synthetic satellite observationsThe SDSU is applied to a CRM-generated mesoscale

convective system to illustrate how they would be observedif the TRMM satellite flew over the scene. The exampleshown in Fig. 2 is a mature tropical squall line simulatedby the GCE model and virtually observed by the threeprimary TRMM sensors: TMI, PR, and VIRS (see Ta-ble 1). Synthetic microwave brightness temperature at alow frequency of 19 GHz (Fig. 2a) shows thermal emission

from liquid clouds and rainfall that form the main body ofthe convective system. Deep convective cores are identifiedas brightness temperature depressions in the map of high-frequency (85 GHz) microwave brightness temperature, re-sulting from microwave scattering by frozen precipitatingparticles (Fig. 2b). The spatial distribution of brightnesstemperatures is smoother at 19 GHz than 85 GHz, as theantenna pattern convolution works more aggressively atlower frequencies (note that the radiometer FOV size isinversely proportional to the channel frequency when ob-served with the same antenna).

Shown in figure 2c is the vertical cross section of simu-lated TRMM PR (14-GHz) reflectivity in a vertical planeat x = 160 km. Some of the deep convective cores identified

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by 85-GHz scattering signals are clearly captured as areaswith distinctly large PR echoes. The SDSU, with a simplemelting-particle model installed, is able to reproduce theradar bright band, observed around the height of 4 km. Forcomparison, CloudSat (94-GHz) radar reflectivity is simu-lated with the same snapshot (Fig. 2d). This frequency issufficiently high to thoroughly map non-precipitating clouddecks extending into the upper troposphere, whereas lowerportions of deep convective cores are left undetected as aresult of the severe attenuation of radar echo by the thicklayer of condensate above.

While microwave radiometry is a rough proxy for thetotal liquid and ice water constituted of millimeter-size pre-cipitation particles, visible imagery is sensitive to smallercloud droplets which may or may not accompany precipita-tion. When observed in the visible (Fig. 2e), the convectivesystem in this particular case exhibits a complicated tex-ture including a line of shallow clouds newly spawned alonga bow-shaped convective leading edge. Infrared bright-ness temperatures (Fig. 2f) delineate the top temperature,nearly as cold as 200 K, of extensive high clouds spread-ing entirely over the convective system. A series of syn-thetic observations shown by Figs. 2 implies that the threeTRMM instruments provide different pieces of complemen-tary information, and that a thorough picture of the con-vective system emerges only when observations from all thesensors are combined together.

Another example of synthetic measurements is presentedin Fig. 3 together with corresponding observations by theAMSR-E, MODIS, and CloudSat. In this particular case,the A-Train constellation follows a track from the Bayof Bengal to the Tibetan plateau at roughly 7:50 UTCon 20 June, 2006. For the input model, a WRF simula-tion forced by the NCEP Global Forecast System was runwith the GCE one-moment bulk microphysics and Goddardradiation schemes. Synthetic measurements of AMSR-E36.5-GHz brightness temperature, MODIS 11-µm bright-ness temperature, and CloudSat radar reflectivity were com-puted using the Goddard SDSU (see the last section of thisarticle for a description). A tropical disturbance observedby the AMSR-E and MODIS over the Bay of Bengal is gen-erally captured by the WRF simulation, although the dis-turbance consists of numerous cloud cells scattered acrossthe area in the model simulation rather than organizedinto an arch-like band as clearly delineated by the AMSR-E observation. A cloud deck covering the Tibetan Plateaudiscernible in the AMSR-E and MODIS observations is rea-sonably well reproduced by the model simulation. Thesesimilarities and discrepancies are readily identified also in avertical cross section of the observed and simulated Cloud-Sat radar echoes.

The examples shown in Figs. 2 and 3 imply that theSDSU, or any satellite simulator applicable to a varietyof sensors with different spectral ranges, is expected to be

a powerful diagnostic tool to study the three-dimensionalstructure of cloud systems in depth. Potential applicationsof the SDSU are next summarized.

Application 1: model evaluationA primary application of the SDSU is to diagnose the

performance of CRMs in comparison with satellite observedradiances and back-scattered electromagnetic echoes. Modelevaluation studies with a satellite simulator have an ad-vantage over the more traditional approach based on satel-lite retrievals; e.g., comparing surface rain rates. This isbecause, as depicted in Fig. 4, a satellite retrieval algo-rithm is an inverse model, i.e., satellite measurements areinverted to find consistent input to the radiative transferproblem, based upon a number of assumptions with theircharacteristic underlying uncertainties. A major source ofuncertainty for cloud and/or precipitation retrieval is thehydrometeor PSD. CRM simulations are not guaranteed to(or almost guaranteed not to) agree with any satellite dataproduct even if applied to the same precipitation event asobserved, since the conventional PSD assumptions in re-trieval algorithms generally have different historical rootsfrom the assumptions underlying current CRM microphys-ical schemes (e.g., Masunaga et al. 2002a). Besides thePSD, radiative emission from the surface and atmosphericgases can be an additional source of uncertainty if not prop-erly modeled in the satellite retrieval algorithm.

These difficulties are avoided when the CRM is evalu-ated not with a satellite-based external data product butin terms of direct measurables such as radiances. Theradiance-based model evaluation only needs a satellite sim-ulator or a forward model to compute synthetic measure-ments (Fig. 4), requiring no inverse model to be invoked.While the PSD assumptions and other implicit assump-tions that are built into retrieval algorithms are generallynot perfectly consistent with the CRM physics, the sur-face and atmospheric parameters, including the PSD, arefully under the control of users in the SDSU. The SDSUuser can specify the surface and/or atmospheric character-izations exactly as given by the CRM output, or make amodification for testing the model sensitivity to the inter-nal physics such as the microphysical scheme. As such,satellite simulators offer a useful testbed to validate CRMperformance.

By combining data from multiple satellite instruments,the applicability of satellite simulators for evaluating CRMperformance is greatly expanded, as illustrated by somerecent studies, as follows. Model biases in macrophysicaland microphysical quantities may be separately identifiedwhen the TRMM PR and CloudSat CPR are combined,exploiting the difference in the radar sensitivity to parti-cle size between different microwave frequencies (Masunagaet al. 2008). The combination of microwave and infraredbrightness temperatures, together with radar reflectivities,

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Satellite Simulator

(Forward model)

Satellite Retrieval

(Inversion model)

Observation

Synthetic Observation

Compare

Agree?

Cloud model

Data product

Fig. 4. Schematic that illustrates the roles of a satellite simulator and a retrieval algorithm. A satellite simulator is aforward model in which synthetic measurements are obtained uniquely from the radiative transfer equation once all therequired inputs are provided. On the other hand, a satellite retrieval algorithm is an inversion model, where the solutionis retrieved by finding the input to the radiative transfer model that is compatible with a given set of measurements. Sincesatellite remote sensing generally involves under-constrained inversion problems, a retrieval algorithm is built on internalerror models that characterize the uncertainties associated with the unmeasurables (and statistical noise associated withthe measurables as well). The unmeasurables, in contrast, are controllable parameters for satellite simulators. Theretrieval algorithm output (or data product) would agree with the simulator input (or cloud model) only when the errormodels are defined in a physically consistent manner between the satellite algorithm and simulator.

provide a good test of CRM reproducibility in the simu-lation of tropical convective clouds (Matsui et al. 2009; Liet al. 2010). A similar technique utilizing TRMM PR andTMI data has been also applied to a cold-frontal rainbandsimulation (Han et al. 2010).

Application 2: algorithm developmentThe usefulness of satellite simulators is not limited to

model evaluation. The SDSU can serve as a tool to sup-port retrieval algorithm development for current and fu-ture multi-sensor satellite programs. Examples of such al-gorithms include combined use of passive and active mi-crowave instruments to derive precipitation profiles (Had-dad et al. 1997; Grecu et al. 2004; Masunaga and Kum-merow 2005), passive microwave and visible/infrared sen-

sors to detect drizzle in maritime low clouds (Masunagaet al. 2002b; Shao and Liu 2004), and millimeter-waveradar and visible imagery to determine cloud microphysicalproperties (Austin and Stephens 2001).

While inversion schemes vary from one algorithm toanother, typical modern satellite algorithms involve for-ward radiative transfer simulations to create a database orlookup table for the algorithm to search for the solution (oran ensemble of solutions). Alternatively, an inverse modelmay be designed using a set of forward radiative trans-fer simulations to train a neural network that establishesthe connection between the measurements and solutions.As such, satellite simulators can act as a forward modelengine at work in the processes of constructing satellitealgorithms. The core of the SDSU is indeed a collection

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of routines that were originally constructed for algorithmdevelopment purposes. A database of candidate solutionsand neural network are efficient and generally useful, butare based only on a finite number of forward simulationsperformed off-line. The anticipated advance of computercapabilities could allow a forward radiative transfer modelto be run online within satellite algorithms. The direct im-plementation of a forward model also facilitates numericalweather prediction schemes that assimilate satellite radi-ance data.

Another application of the SDSU is to assist in algo-rithm development by creating synthetic satellite observa-tions used for testing algorithm performance. Figure 4 mayalso be viewed as a schematic illustrating the potential roleof satellite simulators as a testbed for satellite algorithms.Satellite simulators are particularly helpful for prospectivesatellite projects that involve new instrumental technolo-gies unavailable at present, for which synthetic observa-tions provide the unique opportunity to test the retrievalalgorithm with virtual data.

Future improvementsThe SDSU will be kept updated in an effort to meet

the requests of users, fix bugs, and to improve utility. Animportant upgrade being planned on a long-term basis isthe implementation of more realistic hydrometeor radia-tive properties for frozen particles, including the effects ofparticle non-sphericity and inhomogeneity. Currently inthe SDSU, the radiative properties such as the extinctionand scattering coefficients are based on Mie solutions, forwhich all particles are assumed to be spherical. In reality,while cloud droplets and rain drops can be thought of ashomogeneous liquid water spheres to a reasonable extent,frozen hydrometeors such as cloud ice crystals and snowflakes have highly complicated crystal structure varyingdramatically with a number of factors including ambienttemperature and humidity. It is almost impossible to es-tablish a single tractable theoretical framework applicableto arbitrarily shaped ice particles, but there are practi-cal strategies using different approximations to model theradiative properties of inhomogeneous, non-spherical par-ticles. In a future version of the SDSU, the present MieLUTs will be replaced with a more realistic database ofthe radiative properties for frozen hydrometeors.

Another long-term plan for the upgrade is to expand theSDSU to include additional satellite sensors. The constantrefrain from existing users is a request to add a lidar simula-tor to the SDSU. The CALIPSO lidar, when combined withother A-Train instruments such as the CloudSat CPR, hasproven its capability for measuring the vertical structureof ice cloud microphysical properties that were previouslyundetectable from satellites. It is expected that researchinterest in lidar, utilized as a component of multi-sensorspaceborne observatories, will continue to grow. This in-

terest will be stimulated by the planned EarthCARE mis-sion, which will include a lidar as part of a suite of satelliteinstruments.

SDSU users are encouraged to modify the distributedsource code, written in the standard Fortran 90 format,and to implement new subroutines for their own researchpurposes if necessary. A notable example is the “spin-off”Goddard SDSU being developed at NASA GSFC. In ad-dition to the existing three simulator components of theSDSU, lidar and broadband radiometer simulator compo-nents have been included in the Goddard version. Thesenew instrument simulators, utilized in combination withthe visible/infrared simulator, enhance the SDSU capabil-ity for analyzing aerosols as well as clouds and precipita-tion. The Goddard SDSU’s core interface has been rein-vented 1) to include parallel computational capability and2) to support the NASA multi-scale modeling system (Taoet al. 2009). With these additional features, the applicabil-ity of the Goddard SDSU is extended to support NASA’swide variety of ongoing and planned satellite missions in-cluding TRMM, Terra, A-Train, GPM, and possible futuremissions under study, such as ACE.

Download the SDSUThe SDSU package is available for download from

http://precip.hyarc.nagoya-u.ac.jp/sdsu/index.html

for registered users. Registrants are requested to providetheir name and email address so that they will be notifiedof major upgrades. The SDSU package consists of sourcecodes for the simulator components, Mie LUT generatorwith sample pre-computed LUTs, and all ancillary routinesrequired to run the SDSU. Sample CRM inputs as demon-strated by Figure 2 are optionally available. The SDSUUser’s Guide provides comprehensive instructions for usersand is available in the PDF format from the WWW siteabove.

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Appendix: glossary

ACE Aerosol-Cloud-EcosystemsAMSR-E Advanced Microwave Scanning Radiometer for EOSCALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite ObservationCERES Clouds and the Earth’s Radiant Energy SystemCFMIP Cloud Feedback Model Intercomparison ProjectCOSP CFMIP Observational Simulator PackageCPR Cloud Profiling RadarDPR Dual-frequency Precipitation RadarCRM Cloud-resolving modelCRTM Community Radiative Transfer ModelEarthCARE Earth Clouds, Aerosols, and Radiation ExplorerECSIM EarthCARE SimulatorEOS Earth Observing SystemGCE Goddard Cumulus Ensemble modelGSFC Goddard Space Flight CenterGMI GPM Microwave ImagerGPM Global Precipitation MeasurementISCCP International Satellite Cloud Climatology ProjectISSARS Instrument Simulator Suite for Atmospheric Remote SensingLUT Lookup tableMODIS Moderate Resolution Imaging SpectroradiometerMSI Multi-Spectral ImagerNASA National Aeronautics and Space AdministrationNCEP National Centers for Environmental PredictionPR Precipitation RadarPSD Particle size distributionRTTOV Radiative Transfer model for the TIROS Operational Vertical SounderSDSU Satellite Data Simulator UnitTIROS Television Infrared Observation SatelliteTMI TRMM Microwave ImagerTRMM Tropical Rainfall Measuring MissionVIRS Visible/Infrared ScannerWRF Weather Research and Forecasting model

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