Author's personal copystc-se.com/data/bbaum/PDF/Encyclopedia_Yang_2015.pdf · issues in building a decadal climatology based on satellite observations is that the satellite sensor

Remote Sensing Cloud PropertiesP Yang Texas AampM University College Station TX USABA Baum University of WisconsinndashMadison Madison WI USA

2015 Elsevier Ltd All rights reserved

Synopsis

Clouds constitute a unique and important component of the atmosphere This article briefly reviews the methods of inferringcloud-top height determining cloud thermodynamic phase and retrieving cloud microphysical and optical properties(specifically the effective particle size and optical thickness) Some examples based on observations made by a passivespaceborne sensor (the Moderate Resolution Imaging Spectroradiometer) and an active spaceborne sensor (the CloudndashAerosol Lidar with Orthogonal Polarization) are illustrated

Introduction

On any given day clouds cover about 65 of the planet Ina fairly stable atmosphere clouds may be cellular in appear-ance (ie cumuliform) or may appear in sheets (ie strati-form) that may extend over large horizontal distances Whilethese clouds may extend over wide areas their typicalgeometric thickness is less than 1 km In unstable atmospheresclouds may extend from near the planetrsquos surface to the uppertroposphere As most of the tropospheric water vapor residesnear the surface where temperatures tend to be relativelywarm low-level clouds tend to be composed of water dropletsand are generally opaque to the viewer The opacity is denotedin terms of a quantity known as optical thickness or opticaldepth and is a dimensionless measure of light attenuationcaused by the scattering and absorption of energy by atmo-spheric particles Clouds forming near the tropopause reside atvery cold temperatures and are typically composed of iceparticles For clouds at intermediate heights between theplanetary boundary layer (w1 km above the surface) and themiddle troposphere clouds may be composed of a mixture ofsupercooled water and ice particles Water and ice cloudsinteract with solar radiation differently and have a large influ-ence on the Earthrsquos radiative energy budget The energy budgetis composed of both solar and terrestrial radiation compo-nents Solar radiation spans from ultraviolet (l lt 04 mmwhere l is the wavelength) to infrared (IR) wavelengths(l gt 5 mm) A portion of the incoming solar radiation may beabsorbed at the surface and within the atmosphere by cloudsaerosols water vapor and other trace gases such as carbondioxide and methane Subsequently absorbed solar radiationis reemitted at longer wavelengths ranging from 5 to 100 mm

Data from operational polar-orbiting and geostationarymeteorological satellites are analyzed routinely for globalcloud macrophysical properties such as cloud height phase(water ice or some mixture of both) and microphysical andoptical properties such as optical thickness and the effectiveparticle size Global cloud observations based on satellitemeasurements serve many uses In numerical weather modelswhere the time scale of interest is on the order of hours todays satellite-derived cloud and clear-sky properties from thegeostationary satellites can serve as initial conditions for themodels that is where the clouds are at some given time their

height and other properties Numerical weather models maybe regional in extent covering a specific area such as NorthAmerica or global in which case global and near real-timeclouds and clear-sky properties are required for initializationof the models Monthly annual or decadal averages ofsatellite-derived cloud properties are also useful for comparingwith results from global climate models where the time scaleof interest is much longer than for weather prediction modelsFor this type of use cloud properties need to be collectedanalyzed and ultimately reduced to a global-gridded andtime-interpolated product An example of such a productwould be one where each of the cloud properties retrievedduring the course of a month is reduced to a monthly averagewith a time resolution of every 3ndash6 h One of the primaryissues in building a decadal climatology based on satelliteobservations is that the satellite sensor calibration needs to bevery accurate Since the advent of meteorological satellitesbeginning around 1980 a long line of weather satellites havecome into or out of service Once in space the platforms aresubject to very harsh environments that can modify the sensorcalibration over time and for polar-orbiting platforms theorbit can degrade over time The derivation of a decadal recordof cloud properties requires constant attention to sensorcalibration

To date meteorological satellites have recorded informa-tion over the Earth at a limited number of wavelengths throughthe use of specially designed filter radiometers The filters onlyallow radiation over a very narrow wavelength range to passthrough to the detectors Such narrowband wavelengths aretypically chosen in atmospheric lsquowindowsrsquo where the atmo-spheric constituents such as water vapor and carbon dioxideleast attenuate the energy along the path tofrom the surfacethrough the atmosphere and finally to the satellite Ata minimum operational satellite data are recorded at a visible(VIS) wavelength (eg 065 mm) a medium-wave-infrared(MWIR) wavelength (382 mm) and an IR wavelength(11 mm) Radiances at VIS and near-IR wavelengths are oftenconverted to reflectances whose values range from 0 to 1 IRradiances are often converted to brightness temperatures (BTs)through application of the Planck function Because of thehuge volumes of data collected by satellites the data reductioneffort can become quite complex In this article we will discusssome of the available methods to infer cloud properties such as

116 Encyclopedia of Atmospheric Sciences 2nd Edition Volume 5 httpdxdoiorg101016B978-0-12-382225-300503-X

Encyclopedia of Atmospheric Sciences Second Edition 2015 116ndash127

Authors personal copy

cloud-top pressure phase optical thickness and the effectiveparticle size

Cloud-Top PressurendashHeightndashTemperature

Over the past several decades a number of approaches havebeen developed to infer cloud-top heights from satellitemultispectral data Actually the literature provides a wealthof different research-grade algorithms but very few have beenfully developed and adopted for routine operational pro-cessing of global data For operational data processing theassumption is made that only a single cloud layer is presentin any individual field of view (FOV) Both surface observa-tions and spaceborne lidar or radar measurements indicatethat multilayered clouds occur frequently If the uppermostcloud layer is optically thick then a passive satellite sensorcannot sense the presence of lower level cloud layers Ifhowever the upper cloud layer is optically thin such ascirrus then there is some potential for the presence of a lowerlevel cloud layer to modify the radiances observed by thesatellite sensor causing errors in the assessment of the cloudproperties for that FOV

Another assumption generally made when inferring thecloud height is that there is a well-defined cloud-top boundaryFor low-level water clouds such as stratocumulus or cumulusthe cloud-top boundary is well defined For high-level cloudssuch as cirrus this assumption is more problematic as the cirruslayer can be geometrically thick but with very sparse ice parti-cles throughout the layer which is another way of saying thecloud is optically thin

The clouds that require the most attention in operationalretrievals are those that reside either near the tropopause (high-level clouds) or near the surface Some low-level clouds occurin atmospheres with temperature inversions Proper placementof cloud-top heights requires that there be some knowledge ofthe atmospheric temperature profile and numerical models aresomewhat deficient on this in many cases Given the manyassumptions that need to be made eg that an FOV containsonly a single-layered cloud is not optically thin at the top ofthe cloud layer and that the temperature profile contains nosurprises there are some general approaches to inferring cloudheight that are in use

On many satellite platforms measurements are obtainedat wavelengths located in the 15-mm wavelength regiona region in which atmospheric transmission is dominated byatmospheric CO2 As the wavelength increases from 133 to15 mm the atmosphere becomes more opaque due to CO2

absorption thereby causing each channel to be sensitive toa different portion of the atmosphere This sensitivity isdemonstrated in Figure 1 which shows weighting functionsat several Moderate Resolution Imaging Spectroradiometer(MODIS) channels located at wavelengths ranging from 12 to14 mm Each channel has a peak in its weighting function thatoccurs at a different pressure level than the other channelsThe 12-mm channel is shown for comparison ndash note that itsweighting function peaks at the surface This is a lsquowindowrsquochannel that is insensitive to CO2 In the 1970s MoustafaChahine William Smith Sr and Martin Platt developeda technique known as CO2 slicing to infer cloud-top pressurefrom radiances measured at wavelengths between 133 and142 mm In principle the CO2 slicing method is based on the

Figure 1 Weighting functions that are derived for MODIS wavelengths ranging from 12 to 142 mm The weighting function is the derivative of thetransmittance profile as a function of pressure The peak in the weighting function provides an indication of what levels in the atmosphere provide most ofthe upwelling radiance that will be measured by a satellite

Satellites and Satellite Remote Sensing j Remote Sensing Cloud Properties 117



following relation derived from the theory of radiativetransfer

Rethy1THORN Rclearethy1THORNRethy2THORN Rclearethy2THORN

frac14N0

1

R Pc

P0

GethP n1THORNfvBfrac12TethPTHORN n1=vPgdPN0

2

R Pc

P0

GethP n2THORNfvBfrac12TethPTHORN n2=vPgdP [1]

where R(y1) and R(y2) are the radiances measured at twochannels centered at wave numbers y1 and y2 whereas Rclear(y1)and Rclear(y2) are the corresponding clear-sky radiances Theterms G T and P indicate the transmissivity temperature andpressure respectively P0 and Pc indicate the pressure values atthe surface and cloud top respectively N0 denotes the effectivecloud amount that is the product of the cloud fraction and thecloud emissivity If the two channels are selected to be suffi-ciently close in wave number the corresponding effective cloudamount values are approximately the same In this case it isstraightforward to find an appropriate value for the cloud-toppressure Pc by assuring the equality in eqn [1]

The pressure at cloud level is converted to cloud height andcloud temperature through the use of gridded meteorologicalproducts that provide temperature profiles at some nominalvertical resolution every 6 h One benefit to this algorithm isthat cloud properties are derived similarly for both daytimeand nighttime conditions as the IR method is independent ofsolar illumination This approach is very useful for the analysisof midlevel to high-level clouds and even optically thin cloudssuch as cirrus The drawback to the use of the 15-mm channel isthat the signal-to-noise ratio becomes small for clouds occur-ring in the lowest 3 km of the atmosphere making retrievalsproblematic for low-level clouds When low clouds are presentthe 11-mm channel (also a window channel) is used to infercloud height

Cloud Thermodynamic Phase

While the cloud phase is extremely important in radiativetransfer simulations of clouds and the retrieval of cloud prop-erties it is not always straightforward to determine a cloudrsquosphase If the cloud is located in the upper troposphere wherethe temperatures are extremely cold it is assumed to becomposed of ice Conversely if the cloud is located in theboundary layer over warm surfaces it is assumed to be waterThe difficulty lies in the inference of phase when the cloud-toptemperature lies between 233 and 273 K If the cloud temper-ature is below 233 K the homogeneous nucleation tempera-ture it will be composed of ice If the cloud temperature isabove 273 K it will be composed of water If the cloud hasa temperature between 233 and 273 K it could be ice water orsome mixture of both In the high-latitude storm tracks ineither hemisphere large-scale stratiform cloud decks tend toform with cloud-top temperatures in the 250ndash265 K range andcloud phase is quite difficult to discern

At temperatures below 273 K the supersaturation of ice ismuch higher than the supersaturation with respect to water Ifwater vapor is present in an atmospheric layer at a temperaturein this range say 260 K and both water and ice particles arepresent in this layer the water vapor will preferentiallycondense on the ice particles rather than the water particlesAs the ice particles become larger which occur over the course

of seconds to minutes the growing ice particles will begin tofall through the cloud layer In this situation the top of thecloud layer tends to be populated primarily by very small waterdroplets while ice particles fall through the cloud base Thecloud layer may contain both ice and water particles so infer-ence of the cloud phase from satellite data under these condi-tions is quite challenging

Two simple approaches are discussed here to infer cloudphase from the radiometric observations made by a passivesensor One method involves IR radiances measured at 85 and11 mm The radiances are converted to BTs through the Planckfunction and the phase is inferred from the brightnesstemperature difference (BTD) between the 85 and 11 mm BTs(BTD[85ndash11]) as well as the 11 mm BT Ice clouds exhibitpositive BTD[85ndash11] values whereas water clouds tend toexhibit highly negative values There are three contributingfactors to the behavior of the BTD[85ndash11] for ice and waterclouds First the imaginary component of the index ofrefraction (mi) differs for ice and water at these two wave-lengths Second while the atmosphere is relatively transparentto gaseous absorption absorption by water vapor in theatmospheric column above the cloud can still exert a consid-erable effect on the BTD values As most of the atmosphericwater vapor resides in the lower layers of the atmosphere nearthe surface the BTD[85ndash11] values will be most affected inmoist atmospheres rather than high-level clouds that resideabove most of the water vapor Third while a small effectcloud particles scatter radiation even at the IR wavelengths andclouds with smaller particles will tend to scatter more radiationthan those with larger particles Multiple-scattering radiativetransfer calculations show that for ice clouds the BTD[85ndash11]values tend to be positive in sign whereas for low-level waterclouds the BTD[85ndash11] values tend to be very negative(lt2 K)

This simple BTD approach with IR channels can beimproved for optically thin ice cloud discrimination by calcu-lating cloud emissivity ratios In the simplest terms the cloudemissivity for a channel is based on three numbers themeasured cloud radiance the black cloud radiance and thecalculated clear-sky radiance The term lsquoblackrsquo here means thatthe cloud radiates as a blackbody which implies that it isopaque at the wavelength of the observation This is morecomplicated than a simple BTD approach above because itrequires the use of a radiative transfer model (RTM) to providethe clear-sky and black cloud radiances However what thisapproach provides is much more sensitive to optically thin iceclouds The IR methods are not very useful when supercooledwater clouds are present however since it is problematic todiscriminate between water and ice as discussed previously

One way to improve the discrimination between water andice clouds is to analyze reflectances obtained at a VIS wave-length and a shortwave-infrared (SWIR) wavelength (eg 065and 164 mm respectively) At wavelengths less than about07 mm clouds composed of either liquid or ice tend to absorbvery little solar radiation However at 164 mm (and 215 mm)the mi values for both water and ice increase in comparisonwith those at the VIS wavelength and diverge with mi for icebeing greater than the value of mi for water From this line ofreasoning one might expect that for two different clouds (oneice and one water) of similar particle size and habit (or particle

118 Satellites and Satellite Remote Sensing j Remote Sensing Cloud Properties



shape) distributions the cloud reflectance at 065 mm wouldnot depend on thermodynamic phase whereas the cloudreflectance at 164 mm would In theory and in practice the164 mm (and 215 mm) reflectances are much lower for a cloudcomposed of ice than water particles

The observations made by an active spaceborne sensor forexample the CloudndashAerosol Lidar with Orthogonal Polariza-tion (CALIOP) on the CloudndashAerosol Lidar and InfraredPathfinder Satellite Observations (CALIPSO) platform can beused to effectively determine the cloud thermodynamic phaseThe CALIOP 532-nm channel measurements offer polarizationcapabilities Two quantities the layer-integrated backscatter(g0) and the layer-integrated depolarization ratio (d) can beemployed to effectively discriminate cloud thermodynamicphase which are defined as follows

g0 frac14Z cloud base

cloud top

hb0tethzTHORN thorn b0kethzTHORN

idz [2]

d frac14

R cloud base

cloud topb0tethzTHORNdzR cloud base

cloud topb0kethzTHORNdz

[3]

where brsquotethzTHORN and brsquokethzTHORN indicate the vertical backscatter profilesassociated with the perpendicular and parallel componentsrespectively For a given cloudy scene the g0ndashd relationship canbe used to distinguish cloud phase As illustrated in the right

panel of Figure 2 (the physical concept was originally devel-oped by Yongxiang Hu at NASA Langley Research Center)water cloud pixels correspond to a g0ndashd relationship witha positive slope whereas a g0ndashd relationship with a negativeslope is related to ice cloud pixels Furthermore in the case ofice cloud pixels the upper left branch of the g0ndashd curve corre-sponds to ice clouds containing horizontally oriented icecrystals whereas the lower right branch of the g0ndashd curve isrelated to ice clouds composed of randomly oriented iceparticles The right panel of Figure 2 shows the frequency ofoccurrence of the g0ndashd relations of ice clouds based on theCALIOP data collected from July through December 2006

Cloud Optical Thickness and Particle Size

The basic retrieval methodology for inferring the opticalthickness and effective particle size is to (1) employ a RTM todevelop a lookup table (LUT) for a wide range of assumedcloud properties and viewing geometries and subsequently(2) compare the measured radiances for selected wavelengthchannels to values in the LUT The RTM requires a set of single-scattering properties for the cloud layer which includes thesingle-scattering albedo the scattering phase function thescatteringndashabsorptionndashextinction efficiencies and the asym-metry factor These parameters essentially determine howmuch incident radiation is reflected or absorbed by the cloudThe single-scattering albedo is defined as the ratio of theportion of energy scattered by a particle to the total extinction

Figure 2 Left panel schematic diagram showing the g0d relationships for water and ice cloud pixels Right panel g0d relationships based onthe CALIOP measurements in the case when the lidar beam was pointed within 03 from the nadir




(scattering thorn absorption) of energy by the particle The phasefunction specifies the percentage of radiative energy that is notabsorbed but is instead redistributed by the action of scatteringby cloud particles when radiation impinges on clouds Theasymmetry factor describes the ratio of forward scattered tobackscattered energy and is a quantity often used in radiativeflux calculations In practice the single-scattering albedo andthe asymmetry factor are parameterized in terms of analyticalfunctions (normally polynomials) that depend on particleeffective size for both water and ice clouds In many RTMs theradiative properties of clouds are described in terms of particleeffective size and either liquid or ice water content (LWC orIWC) depending on the cloud phase Cloud optical thicknessand particle effective size are critically dependent on the accu-rate determination of the cloud bulk radiative properties anda focus of recent research has been to improve the descriptionof ice clouds in RTMs

Various methods have been suggested to derive the opticalthickness and effective particle size based on narrowbandradiometer measurements by airborne- or satellite-basedimagers Operational methods tend to rely on IR bands ora combination of VIS and SWIR bands The IR approachdepends on the spectral information from thermal emission ofclouds whereas the VISndashSWIR approach is based on thereflection of solar radiation Teruyuki Nakajima and MichaelKing were among the first to use reflected solar radiation tosimultaneously retrieve cloud optical thickness and effectiveparticle size for water clouds The typical IR technique employsthe BT or BTD values based on window channels at 85 11 and12 mm Regardless of the detailed spectral information involvedin these two methods they are similar in that both depend oncomparison of measured radiance data with simulated radi-ances derived for similar viewing and atmospheric conditions

The first step in this process is to discuss the generation ofreliable libraries of simulated cloud and clear-sky radiancesSingle-scattering calculations must be carried out regardinghow individual cloud particles interact with incident radiationFor water clouds the liquid droplets can be well approximatedas spheres for light scattering The scattering properties of anindividual liquid sphere can be calculated by using the well-known Lorenz-Mie theory that has been documented inmany texts James Hansen and Larry Travis have extensivelydiscussed the effect of size distribution on single-scatteringproperties of spheres Their work provides a theoretical frame-work for using and applying the bulk radiative properties ofliquid droplet distributions which is briefly recaptured here

Within a given water cloud liquid water droplets spana range of sizes that may be represented mathematically interms of the Gamma distribution given by

nethrTHORN frac14 N0reffVeff

ethVeff1THORN=Veff

G1 2Veff

Veff

reth13VeffTHORN=Veff exp r=reffVeff

[4]

where N0 is the total number of the droplets in a unit volumereff and Veff are the effective radius and effective variance thatare defined respectively as follows

reff frac14R r2r1r3nethrTHORNdrR r2

r1r2nethrTHORNdr [5]

Veff frac14R r2r1

r reff

2r2nethrTHORNdrr 2eff

R r2r1r2nethrTHORNdr [6]

In a plot of the Gamma distribution the peak of thedistribution defines the reff while Veff affects the width of thedistribution Typical values of the effective variance for waterclouds range from 005 to 01 For a given size distribution thebulk-scattering properties of cloud droplets may be calculatedFor example the phase function averaged over a size distribu-tion is given by

lt PethqTHORN gtfrac14R r2r1ssethrTHORNPethq rTHORNnethrTHORNdrR r2r1ssethrTHORNnethrTHORNdr

[7]

where ss is scattering cross section of droplets and P(qr) is thephase function for droplets with radii of r which describes theangular distribution of scattered radiation versus scatteringangle q

Figure 3 shows the phase functions averaged for sizedistributions for water clouds at wavelengths 065 163 and11 mm For the 065-mm wavelength the phase functiondisplays scattering maxima at 140 and 180 Physically the twomaxima are due to mechanisms associated with the rainbowand glory both characteristic features of Mie scattering Thephase functions at the SWIR wavelength (163 mm) are similarto those at 065 mm but the rainbow and glory maxima aresomewhat reduced by absorption within the particle At the IRwavelength of 11 mm the scattering maxima of the phasefunction are largely smoothed out due to absorption within thewater droplets

Another measure of the relative amounts of scattering versusabsorption is provided by the single-scattering albedo At065 mm the scattering of incident radiation by cloud dropletsis conservative meaning that energy may be scattered but notabsorbed by the particles Thus the single-scattering albedo isunity at 065 mm but less than unity at 163 mm The particlesize also affects the single-scattering albedo at 163 mmFor example for effective sizes 4 and 32 mm the particlesingle-scattering albedo is unity at 065 mm whereas thecorresponding values at 163 mm are 09976 and 09824respectively Because of the difference in single-scatteringalbedo at the two wavelengths reflection by an opticallythick cloud at 065 mm is essentially a function of opticalthickness At 163 mm however cloud reflectance is sensitive todroplet effective size This feature of cloud reflectance providesa mechanism to retrieve cloud optical thickness and particlesizes using two channels at VIS and SWIR wavelengths as willbe further explained later in this section

Ice clouds are almost exclusively composed of nonsphericalice particles with various sizes and habits (ie shapes) Iceparticles can consist of relatively simple shapes such as bulletrosettes columns and plates or more complex shapes such asaggregates of columns or plates Most of the columnar particlescan have hollow intrusions at the ends which is caused bypreferential molecular deposition onto a growing particle Inan environment where supercooled water droplets are presentthe ice particles can also become rimed which increases anindividual particlersquos surface roughness An increasing amountof research is showing that the consistency of inferred ice cloudproperties improves between algorithms using solar IR or




polarized measurements if an assumption of ice particle severesurface roughening is adopted

Research is underway to determine how to accuratelycalculate the single-scattering properties of a limited set ofidealized ice habits In practice methods such as the discretedipole approximation finite-difference time domain tech-nique or the T-matrix method are used to calculate the scat-tering properties of a given habit for which the ratio of theparticle circumference to the wavelength (also known as thesize parameter) is small ie less than 30 For ice particles

with larger size parameters scattering calculations are per-formed using a ray-tracing technique based on the principlesof geometric optics

Figure 4 shows the phase matrices at 065-mm wavelengthfor two types of ice crystals a solid column with smoothsurfaces and aggregates of plates with rough surface The phasefunction of smooth hexagonal columns displays a strongscattering peak at 22 and is produced by the hexagonalstructure typical of ice crystals In addition to the peak at 22the phase function of solid columns also displays a small peak

Figure 3 Scattering phase function of water droplets calculated at three wavelengths at 065 163 and 11 mm for effective radii of 4 8 and 16 mm




corresponding to a 46 halo Compared to the phase functionfor pristine crystal habits the phase function for aggregates ofplates is essentially featureless due to the severely roughenedsurface texture The rougher the particle the more featureless isthe phase function The other nonzero elements of the phasematrix are related to the polarization state of the scattered lightThe impact of surface roughness on the polarization state issignificant Some recent studies have demonstrated thatpolarization measurements for example by the Polarizationand Anisotropy of Reflectances for Atmospheric Sciencescoupled with Observations from a Lidar (PARASOL) offerunprecedented capabilities to infer ice crystal habit and asso-ciated particle roughness In particular the comparisonbetween the polarized reflectance observed by PARASOL andthe relevant theoretical simulations illustrates that the closestmatch occurs when assuming the presence of ice crystals withseverely roughened surfaces

In reality ice clouds are composed of many different crystalhabits To derive the bulk radiative properties of cirrus cloudswe need to consider not only a particle size distribution butalso the percentages of the various particle habits that comprisethe cloud For this reason the derivation of accurate radiativetransfer simulations of ice clouds is considered more difficultthan for water clouds For a given size distribution a number ofdefinitions have been suggested for the effective size If theeffective size is defined as the ratio of total volume to totalprojected area however the bulk optical properties are insen-sitive to the detailed structure of the size distribution Theeffective radius is then

reff frac14 34

R PifiViethDTHORNnethDTHORNdD

R PifiAiethDTHORNnethDTHORNdD [8]

where D is the maximum dimension of an ice particle fi is thehabit fraction V and A are the volume and projected area for

individual particle and n is the particle number concentrationBased on in situ measurements within ice clouds a modifiedgamma distribution is used most often to describe the particlesize distribution

In situ ice cloud measurements are now available fromnumerous field campaigns based at locations around theworld For example Table 1 (data courtesy of AndrewHeymsfield National Center for Atmospheric Research) listsa number of the particle size distributions obtained at variousfield campaigns and the instruments used for the microphysicalmeasurements This is by no means a complete list A newgeneration of sensors is beginning to provide measurements ofthe smallest particles in a given particle population and evena sense of the particle roughening In situ measurements indi-cate that the effective radius of ice crystals in cirrus clouds mayrange from about 5 mm (small ice particles near the tropo-pause) to more than 100 mm (deep convection) Larger particleradii might be expected for ice clouds formed in convectivesituations where the updraft velocity is much higher (m s1)than that found under conditions where optically thin cirrustend to form (cm s1) The in situ measurements provideinsight for the development of an appropriate ice cloud modelin terms of the ice crystal habit and size distributions As anexample the upper left panel of Figure 5 illustrates an icemodel based on two habits (hexagonal columns and aggregatesof plates) with surface roughness The lower left panel ofFigure 5 shows the comparisons of the computed mediummass diameter (where half the mass is in smaller particles andhalf in larger particles) versus in situmeasurements whereas thelower right panel shows the corresponding comparison forIWC Apparently the two-habit model can reasonably repre-sent in situmicrophysical measurements The upper right panelof Figure 5 shows the phase function based on the two-habitmodel in comparison with the MODIS Collection 5 counter-part Note that the asymmetry factors associated with the two

Figure 4 The scattering phase matrices of hexagonal ice crystals with smooth surface and aggregates of plates with rough surfaces




phase functions are quite different particularly the asymmetryfactor for the two-habit model is approximately 076 whereasthe MODIS Collection 5 counterpart is 082

Given the single-scattering properties radiative transfercomputations can be carried out for various cloud optical

thickness and effective particle sizes for a number of solar andviewing geometry configurations To calculate the bidirectionalradiance of clouds one can use well-established discrete ordi-nate or addingndashdoubling methods Figure 6 shows the corre-lation of 213-mm reflectance and 086-mm reflectance of cirrus

Table 1 Number of the ice particle size distributions obtained during various field campaigns and the instruments for themicrophysical measurements

Field campaign Year Location Probes

ARM-IOP 2000 Oklahoma USA 2D-C 2D-P CPITRMM KWAJEX 1999 Kwajelein Marshall Islands 2D-C 2D-P CPICRYSTAL-FACE 2004 Florida area over ocean CAPS VIPSPre-AVE 2004 Houston Texas VIPSMidCiX 2004 Oklahoma CAPS VIPSACTIVE-Hector 2005 Darwin CAPSACTIVE-Monsoon 2005 Darwin CAPSACTIVE-Squall Line 2005 Darwin CAPSSCOUT 2005 Darwin Australia FSSP 2D-CTC-4 2006 Costa Rica CAPS CPIMPACE 2004 Alaska 2D-C 2D-P CPI

The data are filtered such that the in situ measurement occurs at a cloud temperature T 40 CNotes (1) The table is from httpwwwssecwisceduice_modelsmicrophysical_datahtml (2) The data sets currently include a total of 14 406particle size distributions and the list will increase over time

Figure 5 Upper left panel a two-habit ice cloud model based on hexagonal columns and aggregate of plates in conjunction with the Gamma distributionLower left panel comparison of the theoretical median mass diameter versus in situ measurements associated with the data sets listed in Table 1Lower right panel comparison of the theoretical IWC versus in situ measurements associated with the data sets listed in Table 1 Upper right panelthe phase function computed with the two-habit model in comparison with the MODIS Collection 5 phase function




clouds for a number of optical thickness and effective sizes fora given incident-view geometry At higher optical thicknesses(meaning the cloud is more opaque) there is a lsquoquasi-orthog-onalityrsquo between the optical thickness and particle size curvesAs we have mentioned previously the cloud reflectance at086 mm is primarily sensitive to cloud optical thicknesswhereas the reflectance at 213 mm is sensitive to both theparticle size and cloud optical thickness This orthogonalityforms the underlying principle for application of the two-channel correlation technique for retrieving cloud opticalthickness and effective size For example assume the symbol lsquoXrsquoin Figure 6 to represent the (086 and 213 mm) reflectivity pairOne may infer that the corresponding optical thickness isapproximately 14 whereas the effective particle size is 25 mm Itshould be pointed out that in practice the (086 and 213 mm)reflectivity combination is usually used for retrieval over oceanthe (064 and 213 mm) reflectivity combination is used for overland and the (124 and 213 mm) reflectivity combination isused over snow or ice In addition to the 213-mm channela channel located at 164 or 37 mm can be used as the SWIR orMWIR channel involved in the aforementioned bispectralmethod

As an alternative or as a complement to the VISndashSWIR bi-spectral retrieval algorithm IR channels in the window region(8ndash12 mm) may be used for retrieving cloud properties Thewindow region is an important part of the IR spectrum becauseterrestrial thermal emission peaks within this spectral regionIR-based methods are useful because a single approach may beused for both daytime and nighttime conditions therebysimplifying the data reduction effort and also the comparisonbetween daytime and nighttime cloud properties IR methodsare insensitive to sun glint over water that is often present inoperational data Interpretation of data over reflective surfacesis often performed more readily using IR methods rather than

those that involve VISndashSWIR wavelengths The underlyingprinciple for IR retrievals is based on the sensitivity of the BT orthe cloud emissivity (related to blackbody or graybody emis-sion) to cloud optical thickness and particle size The BT is thetemperature that when applied to the calculation of Plankfunction for blackbody radiation gives the same value as thesatellite measured IR radiance Figure 7 illustrates the sensi-tivity of the BTD between the 11- and 12-mm channels asa function of the BT at the 11-mm channel for various cloudoptical thickness and the effective particle size Evidentlycomparing the measurements of the BTDndashBT relation with thetheoretical computations permit simultaneous retrieval ofcloud optical thickness and the effective particle size Howeverthe IR technique is more sensitive to the atmospheric profile(particularly the temperature profile) and the surface emis-sivity than the VISndashSWIR technique

In addition to the use of BDT and BT a quantity known asthe cloud emissivity has been widely used to infer cloudproperties In practice the cloud emissivity can be calculated asfollows

εethlTHORN frac14 RethBTHORN RRethBTHORN RethCTHORN [9]

where R is the upwelling radiance at the cloud top R(B) is theupwelling radiance at the cloud bottom and R(C) is theupwelling blackbody radiance corresponding to the cloudtemperature In practice for a given scene the radiance at cloudbase can be obtained by the noncloudy (ie clear sky) pixels

Furthermore the IR techniques for retrieving ice cloudproperties are less sensitive than their VISndashSWIR counterpartsto ice crystal habits assumed in the forward light-scattering andradiative transfer simulations To illustrate this point panels(a) and (b) of Figure 8 show the phase functions of two icecrystal habits (hexagonal columns and hollow bullet rosettes)

Figure 6 The theoretical relationship between the reflection function at 086 and 213 mm for various values of cloud optical thickness and effectiveparticle size




Figure 7 The variation of the BTD between the 85- and 12-mm channels as a function of the BT at the 11-mm channel

Figure 8 Panel (a) bulk phase functions of solid columns and hollow bullet rosette with an effective particle size of 50 mm at 086 mm The gammadistribution is used to simulate the size distribution Panel (b) similar to panel (a) except for a wavelength of 11 mm Panel (c) comparison of cloudoptical thickness retrievals based on the VISndashSWIR retrieval on the basis of a solid column habit model and a hollow bullet rosette habit modelPanel (d) similar to panel (c) except that an IR technique is used




at wavelengths of 086 and 11 mm respectively Substantialdifferences are noticeable at the 086-mm near IR wavelengthwhereas the two phase functions are quite similar at the 11-mmwavelength Panel (c) of Figure 8 compares the optical thick-ness values retrieved with the VISndashSWIR bispectral methodbased on MODIS Band 2 (086 mm) and Band 7 (213 mm)measurements The impact of the assumed ice crystal habit onthe retrieval is obvious from panel (c) The optical thicknessvalues retrieved from the IR technique are shown in panel (d)based on the MODIS Band 29 (85 mm) Band 31 (11 mm) andBand 32 (12 mm) observations The effect of ice crystal habit onthe IR-based technique is negligible

Cloud radiative and microphysical properties are cloudinherent properties that should be independent of a specificretrieval algorithm employed to infer the cloud properties Inthis sense the VISndashSWIR and IR retrievals should be consistentThe spectral consistency of cloud property retrievals is critical tosome analyses particularly the study of the diurnal variationsof cloud properties based on a VISndashSWIR algorithm for daytimeand an IR algorithm for nighttime In the case of ice cloudrecent studies have demonstrated that the ice cloud opticalmodel involved in the forward radiative transfer simulation isessential for achieving spectral consistency

Future Challenges in Cloud Property Retrieval

Current efforts to derive a global cloud climatology fromsatellite data generally do not account properly for multiplecloud layers in pixel-level imager data To date operationalalgorithms are designed to infer cloud properties for eachimager pixel under the assumption that only one cloud layer ispresent Climatologies of retrieved cloud properties do notaddress the effect of an optically thin upper cloud layer such ascirrus that may overlay a lower cloud layer such as a cumuli-form cloud deck Surface observations show that clouds oftenoccur in multiple layers simultaneously in a vertical columnie cloud layers often overlap Multiple cloud layers occur inabout half of all cloud observations and are generally present inthe vicinity of midlatitude fronts and in the tropics where cirrusanvils may spread hundreds of kilometers from the center ofconvective activity When multilayered clouds are present theretrieval algorithms will generally place the cloud layer ata height between the two (or more) actual layers present in theFOV Currently available satellite cloud climatologies providea horizontal distribution of clouds but need improvement inthe description of vertical distribution of clouds At this pointa reliable method has not been developed for the retrieval ofcloud properties (optical thickness cloud thermodynamicphase and effective particle size) when multilayered over-lapping clouds are present

Even for a single-layered cloud satellite retrieval algorithmsdo not account for the effect of a likely vertical variation ofcloud microphysical properties which in turn will decrease theability of radiative transfer calculations to accurately simulatethe cloud It is unlikely that cloud particles are homogeneouslydistributed throughout any given cloud For example icecrystal size and habit are typically quite different for midlati-tude cirrus at cloud top from at cloud base A commonassumption in satellite imagerndashbased cirrus retrieval algorithmsis that the radiative properties of a cirrus cloud may be

represented by those associated with a specific ice crystal shape(or habit) and a single particle size distribution Howeverobservations of synoptic cirrus clouds with low updraft veloc-ities have shown that pristine small ice crystals with hexagonalshapes having an aspect ratio close to unity (length and widthare approximately equal) are predominant in top layers Themiddle layers of cirrus are often composed of well-definedcolumns and plates while irregular polycrystals or aggregatesare dominant near cloud base This picture is quite differentfrom ice particles that form in deep convection in this case thepopulation of ice particles may be dominated by complexaggregates

Another interesting area of complexity in satellite remotesensing is caused by mixed-phase clouds Single-layeredclouds composed of mixtures of supercooled water dropletsand ice particles have been observed frequently during variousfield campaigns Recent analyses of these data and MODISsatellite cloud property retrievals highlight the difficulty ofascertaining phase If mixed-phase clouds are present in thedata one might expect larger errors in retrieved propertiessuch as optical thickness and particle size than clouds that areprimarily of a single phase From the perspective of satelliteremote sensing the working assumption is that any imagerpixel contains either ice or water but not a mixture There is norigorous method available for determining the single-scat-tering properties of mixed-phase clouds From the micro-physical cloud process perspective that is important fordeveloping cloud model parameterizations the presence ofboth ice particles and supercooled water droplets will affectcloud lifetime Why It is likely that the ice particles will growmuch more quickly from vapor deposition than the waterdroplets as the environment may be supersaturated withrespect to ice The result of this process is that the ice particleswill rime grow quickly in size and fall through the cloud andthe available water will be depleted quickly The process ofglaciation is very important for modelers because the waterndashice conversion rates affect cloud lifetime Details of cloudmicrophysics such as cloud water amount cloud ice amountsnow graupel and hail are important for improving cloudretrieval

While approaches exist to retrieve a variety of cloud prop-erties from satellite imager data it is not an easy problem tocompare the satellite retrievals with ground-based measure-ments of the same cloud Comparisons are often attemptedbetween a surface-based measurement at a fixed location overa long temporal period and satellite measurements thatprovide an instantaneous measurement over a wide area Whiledifficult and often creative confidence in retrievals is oftengained through painstaking comparison between the two Forsome cloud properties it may be possible to compare proper-ties derived from two or more different satellite instrumentsThis will be one of the more active areas in future research

Acknowledgments

The authors are grateful to several individuals for their assis-tance in the preparation of the diagrams in this article partic-ularly Lei Bi (for Figure 4) Chao Liu (for Figure 5) ChenxiWang (for Figures 6ndash8) and Chen Zhou (for Figure 2)




See also Aerosols AerosolndashCloud Interactions and TheirRadiative Forcing Clouds and Fog Classification of CloudsClimatology Contrails Measurement Techniques In SituLidar Backscatter Radiation Transfer in the AtmosphereCloud-Radiative Processes Scattering Satellites and SatelliteRemote Sensing Research

Further Reading

Kidder SQ Vonder Haar TH 1995 Satellite Meteorology An IntroductionAcademic Press

Liou KN 1992 Radiation and Cloud Processes in the Atmosphere Oxford UniversityPress Oxford

Mishchenko MI Hovenier JW Travis LD (Eds) 1999 Light Scattering byNonspherical Particles Theory Measurements and Geophysical ApplicationsAcademic Press San Diego

Stephens GL 1994 Remote Sensing of the Lower Atmosphere Oxford UniversityPress Oxford

Wendisch M Yang P 2012 Theory of Atmospheric Radiative Transfer ndash AComprehensive Introduction Wiley-VCH Verlag GmbH amp Co KGaA WeinheimGermany




cloud-top pressure phase optical thickness and the effectiveparticle size

Cloud-Top PressurendashHeightndashTemperature

Over the past several decades a number of approaches havebeen developed to infer cloud-top heights from satellitemultispectral data Actually the literature provides a wealthof different research-grade algorithms but very few have beenfully developed and adopted for routine operational pro-cessing of global data For operational data processing theassumption is made that only a single cloud layer is presentin any individual field of view (FOV) Both surface observa-tions and spaceborne lidar or radar measurements indicatethat multilayered clouds occur frequently If the uppermostcloud layer is optically thick then a passive satellite sensorcannot sense the presence of lower level cloud layers Ifhowever the upper cloud layer is optically thin such ascirrus then there is some potential for the presence of a lowerlevel cloud layer to modify the radiances observed by thesatellite sensor causing errors in the assessment of the cloudproperties for that FOV

Another assumption generally made when inferring thecloud height is that there is a well-defined cloud-top boundaryFor low-level water clouds such as stratocumulus or cumulusthe cloud-top boundary is well defined For high-level cloudssuch as cirrus this assumption is more problematic as the cirruslayer can be geometrically thick but with very sparse ice parti-cles throughout the layer which is another way of saying thecloud is optically thin

The clouds that require the most attention in operationalretrievals are those that reside either near the tropopause (high-level clouds) or near the surface Some low-level clouds occurin atmospheres with temperature inversions Proper placementof cloud-top heights requires that there be some knowledge ofthe atmospheric temperature profile and numerical models aresomewhat deficient on this in many cases Given the manyassumptions that need to be made eg that an FOV containsonly a single-layered cloud is not optically thin at the top ofthe cloud layer and that the temperature profile contains nosurprises there are some general approaches to inferring cloudheight that are in use

On many satellite platforms measurements are obtainedat wavelengths located in the 15-mm wavelength regiona region in which atmospheric transmission is dominated byatmospheric CO2 As the wavelength increases from 133 to15 mm the atmosphere becomes more opaque due to CO2

absorption thereby causing each channel to be sensitive toa different portion of the atmosphere This sensitivity isdemonstrated in Figure 1 which shows weighting functionsat several Moderate Resolution Imaging Spectroradiometer(MODIS) channels located at wavelengths ranging from 12 to14 mm Each channel has a peak in its weighting function thatoccurs at a different pressure level than the other channelsThe 12-mm channel is shown for comparison ndash note that itsweighting function peaks at the surface This is a lsquowindowrsquochannel that is insensitive to CO2 In the 1970s MoustafaChahine William Smith Sr and Martin Platt developeda technique known as CO2 slicing to infer cloud-top pressurefrom radiances measured at wavelengths between 133 and142 mm In principle the CO2 slicing method is based on the

Figure 1 Weighting functions that are derived for MODIS wavelengths ranging from 12 to 142 mm The weighting function is the derivative of thetransmittance profile as a function of pressure The peak in the weighting function provides an indication of what levels in the atmosphere provide most ofthe upwelling radiance that will be measured by a satellite






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Documents

Author's personal copystc-se.com/data/bbaum/PDF/Encyclopedia_Yang_2015.pdf · issues in building a decadal climatology based on satellite observations is that the satellite sensor