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Remote Sensing: Cloud Properties
P Yang, Texas A&M University, College Station, TX, USA
BA Baum, University of Wisconsin–Madison, Madison, WI, USA
Ó 2015 Elsevier Ltd. All rights reserved.
Synopsis
Clouds constitute a unique and important component of the atmosphere. This article briefly reviews the methods of inferring
cloud-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 passive
spaceborne sensor (the Moderate Resolution Imaging Spectroradiometer) and an active spaceborne sensor (the Cloud–
Aerosol Lidar with Orthogonal Polarization) are illustrated.
Introduction
On any given day, clouds cover about 65% of the planet. In
a fairly stable atmosphere, clouds may be cellular in appearance (i.e., cumuliform) or may appear in sheets (i.e., stratiform) that may extend over large horizontal distances. While
these clouds may extend over wide areas, their typical
geometric thickness is less than 1 km. In unstable atmospheres,
clouds may extend from near the planet’s surface to the upper
troposphere. As most of the tropospheric water vapor resides
near the surface, where temperatures tend to be relatively
warm, low-level clouds tend to be composed of water droplets
and are generally opaque to the viewer. The opacity is denoted
in terms of a quantity known as optical thickness, or optical
depth, and is a dimensionless measure of light attenuation
caused by the scattering and absorption of energy by atmospheric particles. Clouds forming near the tropopause reside at
very cold temperatures and are typically composed of ice
particles. For clouds at intermediate heights between the
planetary boundary layer (w1 km above the surface) and the
middle troposphere, clouds may be composed of a mixture of
supercooled water and ice particles. Water and ice clouds
interact with solar radiation differently and have a large influence on the Earth’s radiative energy budget. The energy budget
is composed of both solar and terrestrial radiation components. Solar radiation spans from ultraviolet (l < 0.4 mm,
where l is the wavelength) to infrared (IR) wavelengths
(l > 5 mm). A portion of the incoming solar radiation may be
absorbed at the surface and within the atmosphere by clouds,
aerosols, water vapor, and other trace gases such as carbon
dioxide and methane. Subsequently, absorbed solar radiation
is reemitted at longer wavelengths ranging from 5 to 100 mm.
Data from operational polar-orbiting and geostationary
meteorological satellites are analyzed routinely for global
cloud macrophysical properties such as cloud height, phase
(water, ice, or some mixture of both), and microphysical and
optical properties such as optical thickness and the effective
particle size. Global cloud observations based on satellite
measurements serve many uses. In numerical weather models,
where the time scale of interest is on the order of hours to
days, satellite-derived cloud and clear-sky properties from the
geostationary satellites can serve as initial conditions for the
models, that is, where the clouds are at some given time, their
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height, and other properties. Numerical weather models may
be regional in extent, covering a specific area such as North
America, or global, in which case global and near real-time
clouds and clear-sky properties are required for initialization
of the models. Monthly, annual, or decadal averages of
satellite-derived cloud properties are also useful for comparing
with results from global climate models where the time scale
of interest is much longer than for weather prediction models.
For this type of use, cloud properties need to be collected,
analyzed, and ultimately reduced to a global-gridded and
time-interpolated product. An example of such a product
would be one where each of the cloud properties retrieved
during the course of a month is reduced to a monthly average
with a time resolution of every 3–6 h. One of the primary
issues in building a decadal climatology based on satellite
observations is that the satellite sensor calibration needs to be
very accurate. Since the advent of meteorological satellites,
beginning around 1980, a long line of weather satellites have
come into or out of service. Once in space, the platforms are
subject to very harsh environments that can modify the sensor
calibration over time, and for polar-orbiting platforms, the
orbit can degrade over time. The derivation of a decadal record
of cloud properties requires constant attention to sensor
calibration.
To date, meteorological satellites have recorded information over the Earth at a limited number of wavelengths through
the use of specially designed filter radiometers. The filters only
allow radiation over a very narrow wavelength range to pass
through to the detectors. Such narrowband wavelengths are
typically chosen in atmospheric ‘windows,’ where the atmospheric constituents such as water vapor and carbon dioxide
least attenuate the energy along the path to/from the surface,
through the atmosphere, and finally to the satellite. At
a minimum, operational satellite data are recorded at a visible
(VIS) wavelength (e.g., 0.65 mm), a medium-wave-infrared
(MWIR) wavelength (3.82 mm), and an IR wavelength
(11 mm). Radiances at VIS and near-IR wavelengths are often
converted to reflectances whose values range from 0 to 1. IR
radiances are often converted to brightness temperatures (BTs)
through application of the Planck function. Because of the
huge volumes of data collected by satellites, the data reduction
effort can become quite complex. In this article, we will discuss
some of the available methods to infer cloud properties such as
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cloud-top pressure, phase, optical thickness, and the effective
particle size.
Cloud-Top Pressure–Height–Temperature
Over the past several decades, a number of approaches have
been developed to infer cloud-top heights from satellite
multispectral data. Actually, the literature provides a wealth
of different research-grade algorithms, but very few have been
fully developed and adopted for routine, operational processing of global data. For operational data processing, the
assumption is made that only a single cloud layer is present
in any individual field of view (FOV). Both surface observations and spaceborne lidar or radar measurements indicate
that multilayered clouds occur frequently. If the uppermost
cloud layer is optically thick, then a passive satellite sensor
cannot sense the presence of lower level cloud layers. If,
however, the upper cloud layer is optically thin, such as
cirrus, then there is some potential for the presence of a lower
level cloud layer to modify the radiances observed by the
satellite sensor, causing errors in the assessment of the cloud
properties for that FOV.
Another assumption generally made when inferring the
cloud height is that there is a well-defined cloud-top boundary.
For low-level water clouds, such as stratocumulus or cumulus,
the cloud-top boundary is well defined. For high-level clouds,
such as cirrus, this assumption is more problematic as the cirrus
layer can be geometrically thick but with very sparse ice particles throughout the layer, which is another way of saying the
cloud is optically thin.
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The clouds that require the most attention in operational
retrievals are those that reside either near the tropopause (highlevel clouds) or near the surface. Some low-level clouds occur
in atmospheres with temperature inversions. Proper placement
of cloud-top heights requires that there be some knowledge of
the atmospheric temperature profile, and numerical models are
somewhat deficient on this in many cases. Given the many
assumptions that need to be made, e.g., that an FOV contains
only a single-layered cloud, is not optically thin at the top of
the cloud layer, and that the temperature profile contains no
surprises, there are some general approaches to inferring cloud
height that are in use.
On many satellite platforms, measurements are obtained
at wavelengths located in the 15-mm wavelength region,
a region in which atmospheric transmission is dominated by
atmospheric CO2. As the wavelength increases from 13.3 to
15 mm, the atmosphere becomes more opaque due to CO2
absorption, thereby causing each channel to be sensitive to
a different portion of the atmosphere. This sensitivity is
demonstrated in Figure 1, which shows weighting functions
at several Moderate Resolution Imaging Spectroradiometer
(MODIS) channels located at wavelengths ranging from 12 to
14 mm. Each channel has a peak in its weighting function that
occurs at a different pressure level than the other channels.
The 12-mm channel is shown for comparison – note that its
weighting function peaks at the surface. This is a ‘window’
channel that is insensitive to CO2. In the 1970s, Moustafa
Chahine, William Smith Sr., and Martin Platt developed
a technique known as CO2 slicing to infer cloud-top pressure
from radiances measured at wavelengths between 13.3 and
14.2 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 14.2 mm. The weighting function is the derivative of the
transmittance profile as a function of pressure. The peak in the weighting function provides an indication of what levels in the atmosphere provide most of
the upwelling radiance that will be measured by a satellite.
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following relation derived from the theory of radiative
transfer:
RP
N10 c GðP; n1 ÞfvB½TðPÞ; n1 =vPgdP
Rðy1 Þ Rclear ðy1 Þ
P
¼
; [1]
R P0
Rðy2 Þ Rclear ðy2 Þ
N 0 c GðP; n2 ÞfvB½TðPÞ; n2 =vPgdP
2 P
0
where R(y1) and R(y2) are the radiances measured at two
channels centered at wave numbers y1 and y2, whereas Rclear(y1)
and Rclear(y2) are the corresponding clear-sky radiances. The
terms G, T, and P indicate the transmissivity, temperature, and
pressure, respectively. P0 and Pc indicate the pressure values at
the surface and cloud top, respectively. N0 denotes the effective
cloud amount that is the product of the cloud fraction and the
cloud emissivity. If the two channels are selected to be sufficiently close in wave number, the corresponding effective cloud
amount values are approximately the same. In this case, it is
straightforward to find an appropriate value for the cloud-top
pressure Pc by assuring the equality in eqn [1].
The pressure at cloud level is converted to cloud height and
cloud temperature through the use of gridded meteorological
products that provide temperature profiles at some nominal
vertical resolution every 6 h. One benefit to this algorithm is
that cloud properties are derived similarly for both daytime
and nighttime conditions as the IR method is independent of
solar illumination. This approach is very useful for the analysis
of midlevel to high-level clouds and even optically thin clouds
such as cirrus. The drawback to the use of the 15-mm channel is
that the signal-to-noise ratio becomes small for clouds occurring in the lowest 3 km of the atmosphere, making retrievals
problematic for low-level clouds. When low clouds are present,
the 11-mm channel (also a window channel) is used to infer
cloud height.
Cloud Thermodynamic Phase
While the cloud phase is extremely important in radiative
transfer simulations of clouds and the retrieval of cloud properties, it is not always straightforward to determine a cloud’s
phase. If the cloud is located in the upper troposphere where
the temperatures are extremely cold, it is assumed to be
composed of ice. Conversely, if the cloud is located in the
boundary layer over warm surfaces, it is assumed to be water.
The difficulty lies in the inference of phase when the cloud-top
temperature lies between 233 and 273 K. If the cloud temperature is below 233 K, the homogeneous nucleation temperature, it will be composed of ice. If the cloud temperature is
above 273 K, it will be composed of water. If the cloud has
a temperature between 233 and 273 K, it could be ice, water, or
some mixture of both. In the high-latitude storm tracks in
either hemisphere, large-scale stratiform cloud decks tend to
form with cloud-top temperatures in the 250–265 K range, and
cloud phase is quite difficult to discern.
At temperatures below 273 K, the supersaturation of ice is
much higher than the supersaturation with respect to water. If
water vapor is present in an atmospheric layer at a temperature
in this range, say 260 K, and both water and ice particles are
present in this layer, the water vapor will preferentially
condense on the ice particles rather than the water particles.
As the ice particles become larger, which occur over the course
of seconds to minutes, the growing ice particles will begin to
fall through the cloud layer. In this situation, the top of the
cloud layer tends to be populated primarily by very small water
droplets, while ice particles fall through the cloud base. The
cloud layer may contain both ice and water particles, so inference of the cloud phase from satellite data under these conditions is quite challenging.
Two simple approaches are discussed here to infer cloud
phase from the radiometric observations made by a passive
sensor. One method involves IR radiances measured at 8.5 and
11 mm. The radiances are converted to BTs through the Planck
function, and the phase is inferred from the brightness
temperature difference (BTD) between the 8.5 and 11 mm BTs
(BTD[8.5–11]) as well as the 11 mm BT. Ice clouds exhibit
positive BTD[8.5–11] values, whereas water clouds tend to
exhibit highly negative values. There are three contributing
factors to the behavior of the BTD[8.5–11] for ice and water
clouds. First, the imaginary component of the index of
refraction (mi) differs for ice and water at these two wavelengths. Second, while the atmosphere is relatively transparent
to gaseous absorption, absorption by water vapor in the
atmospheric column above the cloud can still exert a considerable effect on the BTD values. As most of the atmospheric
water vapor resides in the lower layers of the atmosphere near
the surface, the BTD[8.5–11] values will be most affected in
moist atmospheres rather than high-level clouds that reside
above most of the water vapor. Third, while a small effect,
cloud particles scatter radiation even at the IR wavelengths, and
clouds with smaller particles will tend to scatter more radiation
than those with larger particles. Multiple-scattering radiative
transfer calculations show that for ice clouds, the BTD[8.5–11]
values tend to be positive in sign, whereas for low-level water
clouds, the BTD[8.5–11] values tend to be very negative
(<2 K).
This simple BTD approach with IR channels can be
improved for optically thin ice cloud discrimination by calculating cloud emissivity ratios. In the simplest terms, the cloud
emissivity for a channel is based on three numbers: the
measured cloud radiance, the black cloud radiance, and the
calculated clear-sky radiance. The term ‘black’ here means that
the cloud radiates as a blackbody, which implies that it is
opaque at the wavelength of the observation. This is more
complicated than a simple BTD approach above because it
requires the use of a radiative transfer model (RTM) to provide
the clear-sky and black cloud radiances. However, what this
approach provides is much more sensitive to optically thin ice
clouds. The IR methods are not very useful when supercooled
water clouds are present, however, since it is problematic to
discriminate between water and ice as discussed previously.
One way to improve the discrimination between water and
ice clouds is to analyze reflectances obtained at a VIS wavelength and a shortwave-infrared (SWIR) wavelength (e.g., 0.65
and 1.64 mm, respectively). At wavelengths less than about
0.7 mm, clouds composed of either liquid or ice tend to absorb
very little solar radiation. However, at 1.64 mm (and 2.15 mm),
the mi values for both water and ice increase in comparison
with those at the VIS wavelength and diverge, with mi for ice
being greater than the value of mi for water. From this line of
reasoning, one might expect that for two different clouds (one
ice and one water) of similar particle size and habit (or particle
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shape) distributions, the cloud reflectance at 0.65 mm would
not depend on thermodynamic phase, whereas the cloud
reflectance at 1.64 mm would. In theory and in practice, the
1.64 mm (and 2.15 mm) reflectances are much lower for a cloud
composed of ice than water particles.
The observations made by an active spaceborne sensor, for
example, the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud–Aerosol Lidar and Infrared
Pathfinder Satellite Observations (CALIPSO) platform can be
used to effectively determine the cloud thermodynamic phase.
The CALIOP 532-nm channel measurements offer polarization
capabilities. Two quantities, the layer-integrated backscatter
(g0 ) and the layer-integrated depolarization ratio (d) can be
employed to effectively discriminate cloud thermodynamic
phase, which are defined as follows:
0
g ¼
Z
cloud base h
cloud top
R
d ¼ R
b0t ðzÞ þ
b0k ðzÞ
i
dz;
[2]
cloud base
b0 ðzÞdz
cloud top t
;
cloud base
0
b ðzÞdz
cloud top k
[3]
where b’t ðzÞ and b’k ðzÞ indicate the vertical backscatter profiles
associated with the perpendicular and parallel components,
respectively. For a given cloudy scene, the g0 –d relationship can
be used to distinguish cloud phase. As illustrated in the right
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panel of Figure 2 (the physical concept was originally developed by Yongxiang Hu at NASA Langley Research Center),
water cloud pixels correspond to a g0 –d relationship with
a positive slope, whereas a g0 –d relationship with a negative
slope is related to ice cloud pixels. Furthermore, in the case of
ice cloud pixels, the upper left branch of the g0 –d curve corresponds to ice clouds containing horizontally oriented ice
crystals, whereas the lower right branch of the g0 –d curve is
related to ice clouds composed of randomly oriented ice
particles. The right panel of Figure 2 shows the frequency of
occurrence of the g0 –d relations of ice clouds based on the
CALIOP data collected from July through December 2006.
Cloud Optical Thickness and Particle Size
The basic retrieval methodology for inferring the optical
thickness and effective particle size is to (1) employ a RTM to
develop a lookup table (LUT) for a wide range of assumed
cloud properties and viewing geometries and subsequently
(2) compare the measured radiances for selected wavelength
channels to values in the LUT. The RTM requires a set of singlescattering properties for the cloud layer, which includes the
single-scattering albedo, the scattering phase function, the
scattering–absorption–extinction efficiencies, and the asymmetry factor. These parameters essentially determine how
much incident radiation is reflected or absorbed by the cloud.
The single-scattering albedo is defined as the ratio of the
portion of energy scattered by a particle to the total extinction
Figure 2 Left panel: schematic diagram showing the g0 d relationships for water and ice cloud pixels. Right panel: g0 d relationships based on
the CALIOP measurements in the case when the lidar beam was pointed within 0.3 from the nadir.
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(scattering þ absorption) of energy by the particle. The phase
function specifies the percentage of radiative energy that is not
absorbed but is instead redistributed by the action of scattering
by cloud particles when radiation impinges on clouds. The
asymmetry factor describes the ratio of forward scattered to
backscattered energy and is a quantity often used in radiative
flux calculations. In practice, the single-scattering albedo and
the asymmetry factor are parameterized in terms of analytical
functions (normally polynomials) that depend on particle
effective size for both water and ice clouds. In many RTMs, the
radiative properties of clouds are described in terms of particle
effective size and either liquid or ice water content (LWC or
IWC), depending on the cloud phase. Cloud optical thickness
and particle effective size are critically dependent on the accurate determination of the cloud bulk radiative properties, and
a focus of recent research has been to improve the description
of ice clouds in RTMs.
Various methods have been suggested to derive the optical
thickness and effective particle size based on narrowband
radiometer measurements by airborne- or satellite-based
imagers. Operational methods tend to rely on IR bands or
a combination of VIS and SWIR bands. The IR approach
depends on the spectral information from thermal emission of
clouds, whereas the VIS–SWIR approach is based on the
reflection of solar radiation. Teruyuki Nakajima and Michael
King were among the first to use reflected solar radiation to
simultaneously retrieve cloud optical thickness and effective
particle size for water clouds. The typical IR technique employs
the BT or BTD values based on window channels at 8.5, 11, and
12 mm. Regardless of the detailed spectral information involved
in these two methods, they are similar in that both depend on
comparison of measured radiance data with simulated radiances derived for similar viewing and atmospheric conditions.
The first step in this process is to discuss the generation of
reliable libraries of simulated cloud and clear-sky radiances.
Single-scattering calculations must be carried out regarding
how individual cloud particles interact with incident radiation.
For water clouds, the liquid droplets can be well approximated
as spheres for light scattering. The scattering properties of an
individual liquid sphere can be calculated by using the wellknown Lorenz-Mie theory that has been documented in
many texts. James Hansen and Larry Travis have extensively
discussed the effect of size distribution on single-scattering
properties of spheres. Their work provides a theoretical framework for using and applying the bulk radiative properties of
liquid droplet distributions which is briefly recaptured here.
Within a given water cloud, liquid water droplets span
a range of sizes that may be represented mathematically in
terms of the Gamma distribution, given by
V 1 V
N0 reff Veff ð eff Þ= eff ð13Veff Þ=Veff
r
exp r=reff Veff ;
nðrÞ ¼
G 1 2Veff Veff
[4]
where N0 is the total number of the droplets in a unit volume;
reff and Veff are the effective radius and effective variance that
are defined, respectively, as follows:
R r2 3
r r nðrÞdr
;
[5]
reff ¼ R r12 2
r1 r nðrÞdr
R r2 Veff ¼
r1
2
r reff r 2 nðrÞdr
R
:
r
reff2 r12 r 2 nðrÞdr
[6]
In a plot of the Gamma distribution, the peak of the
distribution defines the reff, while Veff affects the width of the
distribution. Typical values of the effective variance for water
clouds range from 0.05 to 0.1. For a given size distribution, the
bulk-scattering properties of cloud droplets may be calculated.
For example, the phase function averaged over a size distribution is given by
R r2
r ss ðrÞPðq; rÞnðrÞdr
< PðqÞ > ¼ 1 R r2
;
[7]
r1 ss ðrÞnðrÞdr
where ss is scattering cross section of droplets and P(q,r) is the
phase function for droplets with radii of r, which describes the
angular distribution of scattered radiation versus scattering
angle q.
Figure 3 shows the phase functions averaged for size
distributions for water clouds at wavelengths 0.65, 1.63, and
11 mm. For the 0.65-mm wavelength, the phase function
displays scattering maxima at 140 and 180 . Physically, the two
maxima are due to mechanisms associated with the rainbow
and glory, both characteristic features of Mie scattering. The
phase functions at the SWIR wavelength (1.63 mm) are similar
to those at 0.65 mm, but the rainbow and glory maxima are
somewhat reduced by absorption within the particle. At the IR
wavelength of 11 mm, the scattering maxima of the phase
function are largely smoothed out due to absorption within the
water droplets.
Another measure of the relative amounts of scattering versus
absorption is provided by the single-scattering albedo. At
0.65 mm, the scattering of incident radiation by cloud droplets
is conservative, meaning that energy may be scattered, but not
absorbed, by the particles. Thus, the single-scattering albedo is
unity at 0.65 mm but less than unity at 1.63 mm. The particle
size also affects the single-scattering albedo at 1.63 mm.
For example, for effective sizes 4 and 32 mm, the particle
single-scattering albedo is unity at 0.65 mm, whereas the
corresponding values at 1.63 mm are 0.9976 and 0.9824,
respectively. Because of the difference in single-scattering
albedo at the two wavelengths, reflection by an optically
thick cloud at 0.65 mm is essentially a function of optical
thickness. At 1.63 mm, however, cloud reflectance is sensitive to
droplet effective size. This feature of cloud reflectance provides
a mechanism to retrieve cloud optical thickness and particle
sizes using two channels at VIS and SWIR wavelengths, as will
be further explained later in this section.
Ice clouds are almost exclusively composed of nonspherical
ice particles with various sizes and habits (i.e., shapes). Ice
particles can consist of relatively simple shapes such as bullet
rosettes, columns, and plates or more complex shapes such as
aggregates of columns or plates. Most of the columnar particles
can have hollow intrusions at the ends, which is caused by
preferential molecular deposition onto a growing particle. In
an environment where supercooled water droplets are present,
the ice particles can also become rimed, which increases an
individual particle’s surface roughness. An increasing amount
of research is showing that the consistency of inferred ice cloud
properties improves between algorithms using solar, IR, or
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121
Figure 3 Scattering phase function of water droplets calculated at three wavelengths at 0.65, 1.63, and 11 mm for effective radii of 4, 8, and 16 mm.
polarized measurements if an assumption of ice particle severe
surface roughening is adopted.
Research is underway to determine how to accurately
calculate the single-scattering properties of a limited set of
idealized ice habits. In practice, methods such as the discrete
dipole approximation, finite-difference time domain technique, or the T-matrix method are used to calculate the scattering properties of a given habit for which the ratio of the
particle circumference to the wavelength (also known as the
size parameter) is small, i.e., less than 30. For ice particles
with larger size parameters, scattering calculations are performed using a ray-tracing technique based on the principles
of geometric optics.
Figure 4 shows the phase matrices at 0.65-mm wavelength
for two types of ice crystals: a solid column with smooth
surfaces and aggregates of plates with rough surface. The phase
function of smooth hexagonal columns displays a strong
scattering peak at 22 and is produced by the hexagonal
structure typical of ice crystals. In addition to the peak at 22 ,
the phase function of solid columns also displays a small peak
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Figure 4
Satellites and Satellite Remote Sensing j Remote Sensing: Cloud Properties
The scattering phase matrices of hexagonal ice crystals with smooth surface and aggregates of plates with rough surfaces.
corresponding to a 46 halo. Compared to the phase function
for pristine crystal habits, the phase function for aggregates of
plates is essentially featureless due to the severely roughened
surface texture. The rougher the particle, the more featureless is
the phase function. The other nonzero elements of the phase
matrix are related to the polarization state of the scattered light.
The impact of surface roughness on the polarization state is
significant. Some recent studies have demonstrated that
polarization measurements, for example, by the Polarization
and Anisotropy of Reflectances for Atmospheric Sciences
coupled with Observations from a Lidar (PARASOL) offer
unprecedented capabilities to infer ice crystal habit and associated particle roughness. In particular, the comparison
between the polarized reflectance observed by PARASOL and
the relevant theoretical simulations illustrates that the closest
match occurs when assuming the presence of ice crystals with
severely roughened surfaces.
In reality, ice clouds are composed of many different crystal
habits. To derive the bulk radiative properties of cirrus clouds,
we need to consider not only a particle size distribution but
also the percentages of the various particle habits that comprise
the cloud. For this reason, the derivation of accurate radiative
transfer simulations of ice clouds is considered more difficult
than for water clouds. For a given size distribution, a number of
definitions have been suggested for the effective size. If the
effective size is defined as the ratio of total volume to total
projected area, however, the bulk optical properties are insensitive to the detailed structure of the size distribution. The
effective radius is then
RP
fi Vi ðDÞnðDÞdD
3 i
;
[8]
reff ¼ R P
4
fi Ai ðDÞnðDÞdD
i
where D is the maximum dimension of an ice particle, fi is the
habit fraction, V and A are the volume and projected area for
individual particle, and n is the particle number concentration.
Based on in situ measurements within ice clouds, a modified
gamma distribution is used most often to describe the particle
size distribution.
In situ ice cloud measurements are now available from
numerous field campaigns based at locations around the
world. For example, Table 1 (data courtesy of Andrew
Heymsfield, National Center for Atmospheric Research) lists
a number of the particle size distributions obtained at various
field campaigns and the instruments used for the microphysical
measurements. This is by no means a complete list. A new
generation of sensors is beginning to provide measurements of
the smallest particles in a given particle population and even
a sense of the particle roughening. In situ measurements indicate that the effective radius of ice crystals in cirrus clouds may
range from about 5 mm (small ice particles near the tropopause) to more than 100 mm (deep convection). Larger particle
radii might be expected for ice clouds formed in convective
situations where the updraft velocity is much higher (m s1)
than that found under conditions where optically thin cirrus
tend to form (cm s1). The in situ measurements provide
insight for the development of an appropriate ice cloud model
in terms of the ice crystal habit and size distributions. As an
example, the upper left panel of Figure 5 illustrates an ice
model based on two habits (hexagonal columns and aggregates
of plates) with surface roughness. The lower left panel of
Figure 5 shows the comparisons of the computed medium
mass diameter (where half the mass is in smaller particles and
half in larger particles) versus in situ measurements, whereas the
lower right panel shows the corresponding comparison for
IWC. Apparently, the two-habit model can reasonably represent in situ microphysical measurements. The upper right panel
of Figure 5 shows the phase function based on the two-habit
model in comparison with the MODIS Collection 5 counterpart. Note that the asymmetry factors associated with the two
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Table 1
Number of the ice particle size distributions obtained during various field campaigns and the instruments for the
microphysical measurements
Field campaign
Year
Location
Probes
ARM-IOP
TRMM KWAJEX
CRYSTAL-FACE
Pre-AVE
MidCiX
ACTIVE-Hector
ACTIVE-Monsoon
ACTIVE-Squall Line
SCOUT
TC-4
MPACE
2000
1999
2004
2004
2004
2005
2005
2005
2005
2006
2004
Oklahoma, USA
Kwajelein, Marshall Islands
Florida area, over ocean
Houston, Texas
Oklahoma
Darwin
Darwin
Darwin
Darwin, Australia
Costa Rica
Alaska
2D-C, 2D-P, CPI
2D-C, 2D-P, CPI
CAPS, VIPS
VIPS
CAPS, VIPS
CAPS
CAPS
CAPS
FSSP, 2D-C
CAPS, CPI
2D-C, 2D-P, CPI
The data are filtered such that the in situ measurement occurs at a cloud temperature T 40 C.
Notes: (1) The table is from: http://www.ssec.wisc.edu/ice_models/microphysical_data.html. (2) The data sets currently include a total of 14 406
particle 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 distribution.
Lower left panel: comparison of the theoretical median mass diameter versus in situ measurements associated with the data sets listed in Table 1.
Lower right panel: comparison of the theoretical IWC versus in situ measurements associated with the data sets listed in Table 1. Upper right panel:
the phase function computed with the two-habit model in comparison with the MODIS Collection 5 phase function.
phase functions are quite different; particularly, the asymmetry
factor for the two-habit model is approximately 0.76, whereas
the MODIS Collection 5 counterpart is 0.82.
Given the single-scattering properties, radiative transfer
computations can be carried out for various cloud optical
thickness and effective particle sizes for a number of solar and
viewing geometry configurations. To calculate the bidirectional
radiance of clouds, one can use well-established discrete ordinate or adding–doubling methods. Figure 6 shows the correlation of 2.13-mm reflectance and 0.86-mm reflectance of cirrus
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Figure 6 The theoretical relationship between the reflection function at 0.86 and 2.13 mm for various values of cloud optical thickness and effective
particle size.
clouds for a number of optical thickness and effective sizes for
a given incident-view geometry. At higher optical thicknesses
(meaning the cloud is more opaque), there is a ‘quasi-orthogonality’ between the optical thickness and particle size curves.
As we have mentioned previously, the cloud reflectance at
0.86 mm is primarily sensitive to cloud optical thickness,
whereas the reflectance at 2.13 mm is sensitive to both the
particle size and cloud optical thickness. This orthogonality
forms the underlying principle for application of the twochannel correlation technique for retrieving cloud optical
thickness and effective size. For example, assume the symbol ‘X’
in Figure 6 to represent the (0.86 and 2.13 mm) reflectivity pair.
One may infer that the corresponding optical thickness is
approximately 14, whereas the effective particle size is 25 mm. It
should be pointed out that, in practice, the (0.86 and 2.13 mm)
reflectivity combination is usually used for retrieval over ocean,
the (0.64 and 2.13 mm) reflectivity combination is used for over
land, and the (1.24 and 2.13 mm) reflectivity combination is
used over snow or ice. In addition to the 2.13-mm channel,
a channel located at 1.64 or 3.7 mm can be used as the SWIR or
MWIR channel involved in the aforementioned bispectral
method.
As an alternative or as a complement to the VIS–SWIR bispectral retrieval algorithm, IR channels in the window region
(8–12 mm) may be used for retrieving cloud properties. The
window region is an important part of the IR spectrum because
terrestrial thermal emission peaks within this spectral region.
IR-based methods are useful because a single approach may be
used for both daytime and nighttime conditions, thereby
simplifying the data reduction effort and also the comparison
between daytime and nighttime cloud properties. IR methods
are insensitive to sun glint over water that is often present in
operational data. Interpretation of data over reflective surfaces
is often performed more readily using IR methods rather than
those that involve VIS–SWIR wavelengths. The underlying
principle for IR retrievals is based on the sensitivity of the BT or
the cloud emissivity (related to blackbody or graybody emission) to cloud optical thickness and particle size. The BT is the
temperature that, when applied to the calculation of Plank
function for blackbody radiation, gives the same value as the
satellite measured IR radiance. Figure 7 illustrates the sensitivity of the BTD between the 11- and 12-mm channels as
a function of the BT at the 11-mm channel for various cloud
optical thickness and the effective particle size. Evidently,
comparing the measurements of the BTD–BT relation with the
theoretical computations permit simultaneous retrieval of
cloud optical thickness and the effective particle size. However,
the IR technique is more sensitive to the atmospheric profile
(particularly, the temperature profile) and the surface emissivity than the VIS–SWIR technique.
In addition to the use of BDT and BT, a quantity known as
the cloud emissivity has been widely used to infer cloud
properties. In practice, the cloud emissivity can be calculated as
follows:
εðlÞ ¼
RðBÞ R
;
RðBÞ RðCÞ
[9]
where R is the upwelling radiance at the cloud top, R(B) is the
upwelling radiance at the cloud bottom, and R(C) is the
upwelling blackbody radiance corresponding to the cloud
temperature. In practice, for a given scene, the radiance at cloud
base can be obtained by the noncloudy (i.e., clear sky) pixels.
Furthermore, the IR techniques for retrieving ice cloud
properties are less sensitive than their VIS–SWIR counterparts
to ice crystal habits assumed in the forward light-scattering and
radiative transfer simulations. To illustrate this point, panels
(a) and (b) of Figure 8 show the phase functions of two ice
crystal habits (hexagonal columns and hollow bullet rosettes)
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Figure 7
125
The variation of the BTD between the 8.5- 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 0.86 mm. The gamma
distribution is used to simulate the size distribution. Panel (b): similar to panel (a) except for a wavelength of 11 mm. Panel (c): comparison of cloud
optical thickness retrievals based on the VIS–SWIR retrieval on the basis of a solid column habit model and a hollow bullet rosette habit model.
Panel (d): similar to panel (c) except that an IR technique is used.
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at wavelengths of 0.86 and 11 mm, respectively. Substantial
differences are noticeable at the 0.86-mm near IR wavelength,
whereas the two phase functions are quite similar at the 11-mm
wavelength. Panel (c) of Figure 8 compares the optical thickness values retrieved with the VIS–SWIR bispectral method
based on MODIS Band 2 (0.86 mm) and Band 7 (2.13 mm)
measurements. The impact of the assumed ice crystal habit on
the retrieval is obvious from panel (c). The optical thickness
values retrieved from the IR technique are shown in panel (d)
based on the MODIS Band 29 (8.5 mm), Band 31 (11 mm), and
Band 32 (12 mm) observations. The effect of ice crystal habit on
the IR-based technique is negligible.
Cloud radiative and microphysical properties are cloud
inherent properties that should be independent of a specific
retrieval algorithm employed to infer the cloud properties. In
this sense, the VIS–SWIR and IR retrievals should be consistent.
The spectral consistency of cloud property retrievals is critical to
some analyses, particularly, the study of the diurnal variations
of cloud properties based on a VIS–SWIR algorithm for daytime
and an IR algorithm for nighttime. In the case of ice cloud,
recent studies have demonstrated that the ice cloud optical
model involved in the forward radiative transfer simulation is
essential for achieving spectral consistency.
Future Challenges in Cloud Property Retrieval
Current efforts to derive a global cloud climatology from
satellite data generally do not account properly for multiple
cloud layers in pixel-level imager data. To date, operational
algorithms are designed to infer cloud properties for each
imager pixel under the assumption that only one cloud layer is
present. Climatologies of retrieved cloud properties do not
address the effect of an optically thin upper cloud layer, such as
cirrus, that may overlay a lower cloud layer such as a cumuliform cloud deck. Surface observations show that clouds often
occur in multiple layers simultaneously in a vertical column,
i.e., cloud layers often overlap. Multiple cloud layers occur in
about half of all cloud observations and are generally present in
the vicinity of midlatitude fronts and in the tropics where cirrus
anvils may spread hundreds of kilometers from the center of
convective activity. When multilayered clouds are present, the
retrieval algorithms will generally place the cloud layer at
a height between the two (or more) actual layers present in the
FOV. Currently, available satellite cloud climatologies provide
a horizontal distribution of clouds but need improvement in
the description of vertical distribution of clouds. At this point,
a reliable method has not been developed for the retrieval of
cloud properties (optical thickness, cloud thermodynamic
phase, and effective particle size) when multilayered, overlapping clouds are present.
Even for a single-layered cloud, satellite retrieval algorithms
do not account for the effect of a likely vertical variation of
cloud microphysical properties, which in turn will decrease the
ability of radiative transfer calculations to accurately simulate
the cloud. It is unlikely that cloud particles are homogeneously
distributed throughout any given cloud. For example, ice
crystal size and habit are typically quite different for midlatitude cirrus at cloud top from at cloud base. A common
assumption in satellite imager–based cirrus retrieval algorithms
is 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. However,
observations of synoptic cirrus clouds with low updraft velocities have shown that pristine small ice crystals with hexagonal
shapes having an aspect ratio close to unity (length and width
are approximately equal) are predominant in top layers. The
middle layers of cirrus are often composed of well-defined
columns and plates, while irregular polycrystals or aggregates
are dominant near cloud base. This picture is quite different
from ice particles that form in deep convection; in this case, the
population of ice particles may be dominated by complex
aggregates.
Another interesting area of complexity in satellite remote
sensing is caused by mixed-phase clouds. Single-layered
clouds composed of mixtures of supercooled water droplets
and ice particles have been observed frequently during various
field campaigns. Recent analyses of these data and MODIS
satellite cloud property retrievals highlight the difficulty of
ascertaining phase. If mixed-phase clouds are present in the
data, one might expect larger errors in retrieved properties
such as optical thickness and particle size than clouds that are
primarily of a single phase. From the perspective of satellite
remote sensing, the working assumption is that any imager
pixel contains either ice or water but not a mixture. There is no
rigorous method available for determining the single-scattering properties of mixed-phase clouds. From the microphysical cloud process perspective that is important for
developing cloud model parameterizations, the presence of
both ice particles and supercooled water droplets will affect
cloud lifetime. Why? It is likely that the ice particles will grow
much more quickly from vapor deposition than the water
droplets as the environment may be supersaturated with
respect to ice. The result of this process is that the ice particles
will rime, grow quickly in size, and fall through the cloud, and
the available water will be depleted quickly. The process of
glaciation is very important for modelers because the water–
ice conversion rates affect cloud lifetime. Details of cloud
microphysics, such as cloud water amount, cloud ice amount,
snow, graupel, and hail, are important for improving cloud
retrieval.
While approaches exist to retrieve a variety of cloud properties from satellite imager data, it is not an easy problem to
compare the satellite retrievals with ground-based measurements of the same cloud. Comparisons are often attempted
between a surface-based measurement at a fixed location over
a long temporal period and satellite measurements that
provide an instantaneous measurement over a wide area. While
difficult and often creative, confidence in retrievals is often
gained through painstaking comparison between the two. For
some cloud properties, it may be possible to compare properties derived from two or more different satellite instruments.
This will be one of the more active areas in future research.
Acknowledgments
The authors are grateful to several individuals for their assistance in the preparation of the diagrams in this article, particularly, Lei Bi (for Figure 4), Chao Liu (for Figure 5), Chenxi
Wang (for Figures 6–8), and Chen Zhou (for Figure 2).
Encyclopedia of Atmospheric Sciences, Second Edition, 2015, 116–127
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Satellites and Satellite Remote Sensing j Remote Sensing: Cloud Properties
See also: Aerosols: Aerosol–Cloud Interactions and Their
Radiative Forcing. Clouds and Fog: Classification of Clouds;
Climatology; Contrails; Measurement Techniques In Situ;
Lidar: Backscatter. Radiation Transfer in the Atmosphere:
Cloud-Radiative Processes; Scattering. Satellites and Satellite
Remote Sensing: Research.
127
Mishchenko, M.I., Hovenier, J.W., Travis, L.D. (Eds.), 1999. Light Scattering by
Nonspherical Particles: Theory, Measurements, and Geophysical Applications.
Academic Press, San Diego.
Stephens, G.L., 1994. Remote Sensing of the Lower Atmosphere. Oxford University
Press, Oxford.
Wendisch, M., Yang, P., 2012. Theory of Atmospheric Radiative Transfer – A
Comprehensive Introduction. Wiley-VCH Verlag GmbH & Co., KGaA, Weinheim,
Germany.
Further Reading
Kidder, S.Q., Vonder Haar, T.H., 1995. Satellite Meteorology: An Introduction.
Academic Press.
Liou, K.N., 1992. Radiation and Cloud Processes in the Atmosphere. Oxford University
Press, Oxford.
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Remote Sensing: Cloud Properties