3.1.2 ATSK3_p and ATSK3_r

001

Retrieval algorithm for cloud microphysics based on a reflection method.

A. Algorithm Outline(1) Algorithm Code: ATSK3_p, ATSK3_r(2) Product Code:
CLOP_p (from ATSK3_p)
CLOP_w_r, CLER_w_r, CLTT_w_r,
CLHT_w_r, CLWP_w_r, CLOP_i_r (from ATSK3_r)
Note: Actually, ATSK3_r outputs two temporary files in a day,(work_r_w), (work_r_i). The ATSK16 algorithm reads four-day's(work_r_w) and (work_r_i) and outputs products listed above.
(3) PI names: Teruyuki NAKAJIMA(4) Overview of algorithm（Status: Standard level）
l Abstract
A method for satellite remote sensing of cloud microphysics has been developed toapply to ADEOS-II GLI multispectral radiance data. This algorithm is an enhanced
algorithm of NOAA AVHRR data analysis (Nakajima and Nakajima 1995; Kawamoto
1999), which has an active thermal collection in absorption channel. Undesirable
radiation components such as ground-reflected solar !radiation and thermal radiation are
guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and
subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of
a cloud layer which includes information about cloud microphysical properties. Thismethod can be applied to a broad range of water clouds from semi-transparent to thickclouds.
Cloud optical thickness of water and ice clouds (CLOP_p) are retrieved by
ATSK3_p. Cloud optical thickness (CLOP_w_r), cloud effective particle radius(CLER_w_r), liquid water path (CLWP_w_r), cloud top temeperature (CLTT_w_r), andclout top height (CLHT_w_r) of water clouds are retrieved by ATSK3_r. Cloud optical
thickness (CLOP_I_r) of ice clouds are retrieved by ATSK3_r.
l

Background

Cloud feedback mechanisms are recognized as a major source of uncertainty in thecurrent assessment of global climate change. Planetary boundary layer clouds of themarine subtropics affect the climate through coupling of radiative, microphysical andconvective processes with largely different time scales (Betts and Ridgway 1989).
Somerville and Remer (1984) have used a radiative-convective equilibrium model tostudy cloud optical thickness feedback and concluded that the sign of the feedback is
negative; hence, clouds can act as a thermostat and change in such a way as to reduce
the surface and tropospheric warming caused by the CO2 increase. Using recent generalcirculation models (GCM), Roeckner et al. (1987) and Mitchell et al. (1989) also
concluded that a change in cloud optical properties may result in a negative feedback
comparable in size to the positive feedback associated with a change in the cloud cover.
On the other hand, Wetherald and Manabe (1988) !were led to a conclusion that thecontribution of the negative feedback process of clouds is much smaller than the effectof the positive feedback process !induced by reduced cloud amount in the upper
troposphere and increased cloudiness around the tropopause. In order to improve thetreatment of clouds in GCM simulations of climate change, observations of global cloud
GLI Algorithm Description

3.1.2-1

radiative and microphysical properties are very important. The International Satellite
Cloud Climatology Project (ISCCP) was initiated to develop and coordinate basic
research on techniques for measuring the physical properties of clouds !by satellite
remote sensing and to apply the resulting techniques to derive a global cloud
climatology for improving the parameterization of clouds in climate models (Schiffer
and Rossow 1983).
An important possibility that increasing anthropogenic aerosols may change thecloud radiative properties has been pointed out recently by many researchers (e.g.,
Coakley et al. 1987; Radke et al. 1989). Twomey et al. (1984) studied the effect ofanthropogenic aerosols of continental origin on the cloud albedo and concluded that theloss of sunlight may compensate for the expected warming by CO2. An interesting
example of the effect of anthropogenic cloud condensation nuclei (CCN) on cloudreflectivity is the so-called ship track phenomenon caused by seeding of CCN fromships. Ship tracks appear frequently in satellite imagery as a signature of modificationof! low-lying stratus and stratocumulus clouds (Radke et al. 1989). Durkee et al. (1986)
and Kaufman and Nakajima (1993) noted that the simultaneous study of both cloud andaerosol properties is important for better understanding of cloud-aerosol interaction
processes.
Stimulated by the importance of cloud microphysical change problems as discussed
above, there has been an increasing number of studies pertaining to the retrieval theories
and methods for obtaining cloud optical thickness and particle size from multispectral
radiometers on aircraft (Hansen and Pollack 1970; Twomey and Cocks 1982; King
1987; Foot 1988, Rawlins and Foot 1990; Nakajima et al. 1991) and on satellites(Curran and Wu 1982; Arking and Childs 1985; Rossow et al. 1989; Kaufman andNakajima 1993; Platnick and Twomey 1994; Han et al. 1994, Nakajima and Nakajima
1995, Kawamoto 1999).! The soundness of aircraft remote sensing retrievals of cloud
microphysical properties has been extensively studied by comparisons between in situ
and remote sensing-derived values. We need more effort, on the other hand, to establish
the satellite!remote sensing method, since there are many sources of uncertainty in themethod, such as removal of undesirable radiation components in the satellite-received
spectral radiance and methods of assuming the model atmosphere which we need tosimulate the satellite-received radiances. There are not so many comparisons of satellite
remote sensing results with in situ measurements to study these problems. There also isa room for developing techniques to improve the performance of the method. Han et al.
(1994), for example, have surveyed the effective radius for near-global area by using anadir remote sensing method, in which case the reflected radiation is independent of
azimuthal angle and the effect of non-plane parallel features of clouds may be small,
making use of the ISCCP CX dataset that prepares !ISCCP-derived cloud optical
thickness. It will be useful to study merit and demerit of off-nadir analyses.
The principal intent of this ATBD, therefore, is to introduce an efficient method forsatellite remote sensing retrievals of cloud optical thickness, effective particle radius,
cloud top temperature, and some related products. The present method can be applied towide area as large in size as the view of the ADEOS-II polar orbital satellite.

GLI Algorithm Description

3.1.2-2001

B. Theoretical Description

001(1) Methodology and Logic flowl Approach and Methodology
The ATSK3_p (the pixel by pixel analysis corresponding GLI Lebel-1b area) and
ATSK3_r (the segment analysis for near-global area) uses LUT (Look up Table)Iteration Method (LIM) to retrieve the target geophysical parameters from satellitederived radiance data.
In the ATSK3_r, a non-absorption channel (channel 13), an absorption channel(channel 30), and a thermal channel (channel 35) are used to derive cloud optical
thickness (CLOP), cloud effective particle radius (CLER), and cloud top temperature(CLTT). Adding to these radiance data, some ancillary input data, such as the verticalprofile of the temperature, pressure, water vapor, ground albedo are also used to
calculate related geophysical parameters; cloud top height (CLHT) and cloud top
pressure (CLTP) are retrieved by comparing cloud top temperature and temperature
profile from ancillary data; liquid water path (CLWP) is calculated by cloud optical
thickness and effective particle radius (Eq.9). Additional flag which identifies cirrus
cloud will be output from ATSK3_r. The cirrus flag is set by the brightness temperature
difference (BDT) between channel 35 and 36 larger than 1.5 K (see ATBD of ATSK16
in detail).
On the contrary, ATSK3_p uses only a non-absorption channel (channel 13) toretrieve cloud optical thickness (CLOP), because 3-channel LIM takes more computing
time. Theoretical background and important formulas used in the ATSK3_p and
ATSK3_r are shown in the next subsection.
l Logic Flow
Basically, ATSK3_p and ATSK3_r is the same program. However, the calculation
modes, mentioned above, can be selected by user-defined process control datafile. Four
LUTs are prepared for this purpose, i. e., LUT of cloud-reflected radiance in channels
13 and 30, transmissivities and reflectivity in channels 13 and 30, channel 35
transmissivity, and the active thermal correction. Table 1 summarizes the grid systemof the LUTs and Fig. 1 illustrates the flow of the analysis. We used Newton-Raphson
method to iterate a main loop in the program. Some related parameters, such as; cloud
geometrical thickness, liquid water path, and; cloud top height are also calculated in thealgorithm with temperature slicing data from objective analysis such as JMA GANAL
dataset. We found that the iteration does not converge in some cases of optically thinclouds when the removed radiation significantly dominates over the signal. In this case
we cancel the analysis.
Table 1. The grid system of the look up tables; Wu and Wc are the equvalent water vapor
amount of the layer of above cloud(wu) and cloud(wc); q , q0 , f are the satellite
and solar zenith angle, and relative azimuth angle. t c and re are cloud optical
thickness and effective particle radius.

Quantities
WuWc
q (degrees)q0 (degrees)f (degrees)t c (Water cloud)

Grid point values
50, 5000, 10000, 20000, 30000, 40000, 50000
50, 5000, 10000, 20000, 30000, 40000, 50000
0, 5, 10, 20, 30, 35, 50, 45, 50, 55, 60
0, 5, 10, 20, 30, 35, 50, 45, 50, 55, 60, 65, 70
0 to 180 (divided by every 10 degrees)
1,2,4,6,9,14,12,20,30,50,70

GLI Algorithm Description

3.1.2-3

t c (Ice cloud)re (Water cloud)re (Ice cloud)0.1, 0.5, 1,2,4,8,16,32,48,64
4, 6, 9, 12, 15, 20, 25, 30
5, 10, 20, 40, 60, 80, 110, 140

GLI Algorithm Description

3.1.2-4

001

FIG. 1. Flow chart of the ATSK3_r. In the ATSK3_p process, only non-absorbing
channel (Lch13) and ground albedo Ag are used to calculate only
(CLOP_p). Pre-process and post-process program are required to selecttarget pixels from segment dataset and select suitable results from all
outputs. Please see subsection B-(3) in this section about pre-process andpost-process.

GLI Algorithm Description

3.1.2-5

001(1) Physical and Mathematical aspects of the algorithml Concept
The solar reflectance method uses non-absorbing visible and water-absorbing nearinfrared wavelengths, such as 1.6, 2.2 and 3.7 µm, for simultaneous retrievals of cloudoptical thickness and effective particle radius. In this paper we mainly discuss the solarreflectance method making use of channels 13 (0.64 µm), 30 (3.715 µm) and 35 (10.8µm) of GLI aboard ADEOS-II polar orbiter. Figure 2 illustrates a simulation ofreflected solar radiances in channels 13 and 30 of GLI as a function of the cloud optical
thickness ( t c ) and effective cloud particle radius ( re ) as defined by•r n(r )drÚ,r ≡
Ú r n(r)dr
3

e

(1)

0
•

2

0

where n(r ) is the number size distribution as a function of particle radius r. We
used a!log-normal size distribution in the calculation,n( r) =

È (ln r - ln r0 ) 2 ˘N
exp Í˙,
2ps
2s 2
ÍÎ
˙˚

(2)

FIG. 2. !Simulation of reflected solar radiances in GLI channels 13 and 30 as a function of cloudoptical thickness ( t c =1, 2, 4, 8, 16, 32 and 64) and effective radius ( re =2, 4, 8, 16 and 32) with theocondition of q = 40 , q0 = 60 o and f = 60 o . Near-vertical and near-horizontal lines illustrate isooptical thickness and iso-effective radius radiances, respectively. Ground-reflected and thermalradiations are not taken into account in this result. LOWTRAN-7 and U. S. Standard model wereassumed to model gaseous absorption.
2

where r0 is the mode radius, which is related to the effective radius as re = r0e 3.5s , ands is the log-standard deviation of the size distribution. s = 0.35 was assumed formarine stratocumulus clouds in our analyses. For the satellite signal simulation, weused an accurate and efficient radiative transfer scheme (Nakajima and Tanaka, 1986,
1988; Nakajima and King, 1992) extended to include thermal radiative transfer as
GLI Algorithm Description

3.1.2-6

proposed by Stamnes et al. (1988). In the radiative transfer calculation we used the kdistribution of absorption coefficient from LOWTRAN-7 (Kneizys et al., 1988) for a
gas absorption model. The Midlatitude Summer atmosphere model was divided into
four plane-parallel layers with interfaces at 1, 2 and 12 km and the top at 120 km in thisexample. The cloud is inserted into the third layer from the top of the model
atmosphere with saturated humidity in the cloud layer. As for the underlying surface,
we assumed a Lambert surface. This assumption will not introduce a significant error in
the analyses if we use an equivalent flux albedo as suggested by Nakajima et al. (1991)
for cloudy atmospheres.
We recognize in Fig. 1 that the reflected solar radiance in channel 13, 30 primarily
depends on the cloud optical thickness whereas the reflected radiance in channel 30
primarily depends on the effective radius. This fact can be explained by the differencein the magnitude of the imaginary index of refraction of liquid water in channel 13 ( ~10-9) and channel 30 (~ 10-3). Since the solar radiation in channel 13 penetrates moredeeply into the cloud layer than that in channel 30, larger optical thicknesses (~ 100) canbe retrieved from channel 13 radiance than optical thicknesses (~ 10) from channel 30.
!The moderately large imaginary index of refraction in channel 30 makes the radiancesensitive to particle size. The sensitivity is larger than those of 1.6 µm or 2.2 µm at
which measured radiances do not include thermal radiation. In channel 30, instead, we
have to take into account the effect of thermal radiation as discussed in the following
subsection.
As t c and re decrease, multiple solutions of t c and re are possible as noted by
Nakajima and King (1990). The reason for this phenomenon is that the optical
thickness in channel 30 takes the maximum value at re ~ 4 µm when the optical
thickness in channel 13 is fixed constant. When the cloud is optically thin, the ratio ofchannel 30 radiance to channel 13 radiance (i. e., the slope of iso-effective radius lines
in FIG. 2) becomes a good index of particle size. Wielicki et al. (1990) used thoseslopes at 1.6 and 2.2 µm !bands of LANDSAT TM for cirrus-particle sizing.
1.

Removal of undesirable radiation components
Although the concept of the retrieval is simple, some difficulties will occur when we
determine the cloud microphysical properties from measured spectral radiances of GLI.
We have to decouple the radiation reflected by the cloud layer, which depends on cloudoptical thickness and particle size, from other undesirable radiation components, such as
solar radiation reflected by the ground surface, especially for optically thin clouds, andthermal radiation emitted from cloud layer and ground surface in channel 30. Figures 2
and 3 illustrate simulated radiances (W m-2 sr-1 µm-1) in channels 13 and 30 as afunction of cloud optical thickness t c and ground surface albedo Ag . Similar figures
were presented by King (1987) at wavelengths of 0.75 µm and 1.63 µm. We see, inthese figures, that ground reflectance largely contributes to the satellite signal in the fullrange of t c in channel 13 whereas it becomes important only for t c less than 10 inchannel 30.
Figure 5 illustrates simulated radiances in channel 30 as a function of t c in cases
containing 1) no thermal radiation, 2) ground thermal radiation, 3) cloud thermalradiation, and 4) both ground and cloud thermal radiation. In this simulation, thesurface albedo Ag , ground Tg and cloud temperatures Tc are set to =0.0, =288.2 K and=275.2 K, respectively. This figure indicates that the radiance in channel 30 is
influenced by the ground thermal radiation in the range of t c <10, whereas it is affected
GLI Algorithm Description

3.1.2-7001

by the cloud thermal radiation in the range of t c >2 Even for optically thin clouds( t c <2), we can see an effect of thermal radiation.
These undesirable radiation components have to be removed from the measured
radiances. Rossow (1989) noted that the success of this kind of analysis depends on the
fidelity of the radiative transfer model used in the data analyses and the accuracy ofspecifying the other properties of atmosphere and ground surface that affect themeasured radiance. Kaufman and Nakajima (1993) have adopted a simple method ofsubtracting thermal radiation by using an effective temperature derived from channel 35
and the optical thickness guessed from channel 13, by which we can calculate the cloudreflectivity in channel 30 without thermal radiation. Of course, this approximation isadequate for optically thick clouds because their neglected small transmissivity prevents
ground-reflected radiation and ground thermal radiation transmitting to the space. Kingand Harshvardhan (1986) noted that the transmissivity of cloud layers can be neglected
for t c >10. As an alternative method, thermal radiation in channel 30 has been
determined from radiance in channel 35 that is fitted as a function of radiance in channel
30 using night time measurements (Coakley and Davies, 1986). A ground-reflectedradiation and ground thermal radiation, however, are not so small as to neglect for
optically thin clouds. Also, the fitting method does not handle well dual-source thermal

FIG. 3 (Upper left) Simulated radiances in channel 13 (0.678 µm) as a function of cloud optical thickness
and ground surface albedo under the same condition as in FIG. 2. The effective particle radius isfixed at 8 µm.
FIG. 4 (Upper right) As in FIG. 3 except for channel 30 (3.715 µm).
FIG. 5. (Lower left) Simulated thermal emission contributions in channel 30 under the same condition as
in FIG. 2.

GLI Algorithm Description

3.1.2-8001

radiation that is frequently observed when clouds are optically thin.
Thus, it will be very useful to find an effective method to infer the cloud reflectivity
by decoupling it from the undesirable radiation components by making explicit use ofradiative transfer theory and all of the information of channels 13, 30 and 35. Ou et al.
(1993) developed such a method for their cirrus cloud retrievals, but they used thethermal radiation information for their purpose by subtracting the reflected radiationfrom the satellite-received radiances.
2.

Expression of the radiation components
According to the radiative transfer theory for plane parallel layers with an underlying
Lambert surface, we can decouple the radiance reflected by the cloud layer, L, from thesatellite-received radiance, Lobs, by removing the undesirable radiance components asfollows:
for visible wavelength (0.678µm, channel 13),
L( w u , wc ,t ,re ; m, m0 , f) = Lobs (w u , w c ,t , re ; m, m0 , f )-t (w u , w c , t, re ; m )Ag
m F , (3)t( w u ,w c ,t , re ; m0 ) 0 0
1 - r ( wu , w c , t, re ) Agp

and for near-infrared wavelength (3.715µm, channel 30),
L( w u , wc ,t ,re ; m, m0 , f) = Lobs (w u , w c ,t , re ; m, m0 , f )[]-t (w u ,t u , m) 1 - t( w c , t c , re ; m) - r( w c ,t c , re ; m ) B( Tc )-t (w u , w c , t , re ; m )1 - Ag
B Tg
1 - r ( wu , w c , t, re ) Ag

( )

Ag
m F
-t (w u , w c , t , re ; m )t( w u ,w c ,t , re ; m0 ) 0 0
1 - r ( wu , w c , t, re ) Ag
p

, (4)

-Ltml ( w u ,Tc )(-t(w u , t u , m)t( w c , t c , re ; m) L tml w d , Tg)

where F0 is the extraterrestrial solar flux and B is the Planck function; t , t c and t u
are respectively the optical thicknesses of atmosphere, cloud layer and the atmosphereabove the cloud layer; m0 and m are respectively the cosines of solar and satellite zenith
angles, and f is the azimuthal angle of the satellite relative to the sun. wu, wc, and wd are
the equivalent water vapor amount, which is defined asz2

Ê P( z) ˆw u,c,d =w (z )Á
˜zË Pg ¯Ú10.5

Ê Tg ˆ
ÁT z ˜Ë ( )¯

0.9

(5)

dz

where, z1 and z2 are, ground level and cloud bottom, cloud bottom and cloud top,
cloud top and top of atmosphere, for the case of wu, wc, and wd. P(z) and T(z) are thevertical profile of the air pressure and air temperature as a function of altitude z, Pg and
Tg is the surface pressure and temperature.
The transmissivity t , plane albedo r and spherical albedo r are given !by,t(w u , wc , t ,re ; m0 ) =1p

GLI Algorithm Description

2p1

Ú Ú T(w , w ,t ,r ; m, m ,f )mdmdf + e
u

0

c

e

0

3.1.2-90-t / m0

,

(6)

001

1
r (wu , wc , t , re ; m) =
p

2p

1

Ú Ú R(w , w ,t , r ; m' , m,f )m' dm' df ,e(7)

r (w u ,w c ,t , re ) = 2 r( w u ,w c ,t ,re ; m)mdm ,

(8)

u

0

c

0

and
1

Ú

0

where T ( wu , wc ,t ,re ; m, m0 , f ) and R(wu , wc, t, re; m' , m,f ) are bi-directional transmission andreflection functions respectively. The second term of Eq. (3) and the fourth term of Eq.
(4) are ground-reflected radiation components, the second and third terms of Eq. (4) arecloud and ground thermal radiation components. The fourth and fifth terms of Eq. (4)are active thermal correction components from the layer of above the cloud, and below
the cloud respectively. These quantities are parameterized by wu Tc1.5 .
Multiple reflection between ground surface and the upper layer is taken into
consideration in Eqs. (3) and (4). This effect is very small, however, enough to regardr (w u ,w c ,t , re )Ag as almost zero, especially for an optically thin cloud and for groundsurface of low reflectance. On the contrary, with optically thick cloud and large ground
albedo, this effect is relatively large at visible wavelength because the large cloudspherical albedo reflects radiation!from the ground surface and the relatively largetransmissivity allows this radiation component transmitting to the space.
These formulations are exact apart from the neglected thermal radiation from theatmosphere other than the cloud layer, when we treat monochromatic radiances. We
further introduce a wavelength averaging of variables in the formulations. For example,
t is averaged with sub-channel response function of GLI as,
N

ÏÔ M
¸
jnÌ
(x n,k ¥ tn,k ) Ô˝ÔÓ k =1
Ô˛
n=1

Â Â
t=
Âj
n

,

(10)

n

v =1

where j n is the response function of nth subchannel wavelength for each GLI
channel (We used j n values obtained from http://www.eorc.nasda.go.jp/ADEOSII/GLI/index.html) ; xn,k is the weight of kth k-distribution and tn,k is transmissivity forthe kth k-distribution at nth wavelength. In the calculation, n is 22 for channel 13 and35, 21 for channel 30, and k is 3. This averaging, applied to Eqs. (3) and (4), brings a
non-ngligible error in the case of thin cloud layers in which spectral variation of tn,kbecomes large. However, for most cases of our application, the error remains small andwe can estimate undesirable radiation components in Eqs. (3) and (4) with variables
spectrally averaged for each channel.
3.

Liquid water path calculated from optical thickness and effective radius.
The liquid water path is calculated byW@

GLI Algorithm Description

2t c re.
3(11)

3.1.2-10001

(2) Pre-process and post-process of ATSK3_r.
The ATSK3_r analyses atmospheric segment data. The atmospheric segment data
has 25 pixels in one segment (0.25 x 0.25 degrees square area), and not all pixels will beidentified to clouds in most cases. Therefore, the ATSK3_r needs pre-process program
which choose target pixels analyzed by ATSK3_r and post-process which calculates
statistic values from four-day output (work_r_w and work_r_I) of ATSK3_r.
Two options are available when the pre-process program of ATSK3_r chooses
target cloud pixel(s) from one segment. One is method which choose one "suitable"
pixel from 25 pixels in one segment (we call this method "saving-time method"), theother method choose all pixels in one segment that are identified cloud referring(CLFLG) obtained by ATSK1+2 algorithm (see ATBD of ATSK1+2 in detail) (we callthis method "full-specification method"). Median selection, mode selection, andmaximum selection, of non-absorption channel (channel 13) can be selected to obtain"suitable" one from 25 pixels. If saving-time method is chosen, the post-processprogram of ATSK3_r (that is ATSK16) simply averages four-day output (work_r_w
and work_r_I) of every segment. If full-specification method is chosen, ATSK16 simply
averages four-day output (work_r_w and work_r_I) of every segment for roughclassification, and classify all output from one segment into 11 cloud types in order toobtain cloud fraction (CLFR) for detailed-classification (see ATBD of ATSK16 indetail). The normal operation of ATSK3_r, we will choose full-specification method.
However, during the checkout period for GLI project, saving-time method with mode
selection option will be chosen to check the optical performance of GLI sensor, andcomputing performance of data analysis system.

C.

Practical Considerations(1) Programming, Procedural, Running Considerations
Program Requirements: The following table shows information about the expectedsoftware generated from this algorithm.
Program Memory
Program Size
Required Channels
Necessary/Ancillary Data
Expected Disk Volume
Special Programs or
Subroutines

53.9 MBytes
3509 lines
Channel 13, 30, 35
Objective Analysis data, Look up tables
221.4 MBytes
Pre-process and post-process program are required to
select target pixels from Level-1B or segment dataset,and select suitable results from all outputs.

(2) Calibration and validation
Under construction.
(3) Quality Control and Diagnostic Information
Under construction.
(4) Exception Handling
Under construction.
(5) Constraints, Limitations, Assumptions
Under construction.
(6) Publications and Papers
Nakajima, T. Y. and T. Nakajima, 1995: Wide-area determination of cloud
GLI Algorithm Description

3.1.2-11001

microphysical properties from NOAA AVHRR measurements for FIRE andASTEX regions. J. Atmos. Sci., 52, 4043-4059
Kawamoto, K. 1999: On the global distribution of the water cloud microphysics derived
from AVHRR remote sensing, Ph.D. dissertation University of Tokyo, Tokyo,
Japan.
Kawamoto, K., T. Nakajima, and T. Y. Nakajima, 1999: A global determination ofcloud microphysics with AVHRR remote sensing. J. Climate, Submitted.

D. References
Arking, A., and J. D. Childs, 1985: Retrieval of cloud cover parameters frommultispectral satellite images. J. Clim. Appl. Meteor., 24, 322-333.
Betts, A. K., and W. Ridgway, 1989: Climatic equilibrium of the atmospheric
convective boundary layer over a tropical ocean. J. Atmos. Sci., 46, 26212641.
Coakley, J. A., Jr., and R. Davies, 1986: The effect of cloud sides on reflected solar
radiation as deduced from satellite observations. J. Atmos. Sci., 43, 10251035.
Coakley, J. A., Jr., R. L. Bernstein and P. A. Durkee, 1987: Effect of ship-stack
effluents on cloud reflectivity. Science, 237, 1020-1022.
Curran, R. J., and M. L. C. Wu, 1982: Skylab near-infrared observations of clouds
indicating supercooled liquid water droplets. J. Atmos. Sci., 39, 635-647.
Durkee, P. A., D. R. Jensen, E. E. Hindman and T. H. V. Haar, 1986: The relationshipbetween marine aerosol particles and satellite detected radiance. J. Geophys.
Res., 91, 4063-4072.
Foot, J. S., 1988: Some observations of the optical properties of clouds. I: Stratocumulus.
Quart. J. R. Met. Soc., 114, 129-144.
Han, Q., W. B. Rossow and A. A. Lacis, 1994: Near-Global survey of effective droplet
radii in liquid water clouds using ISCCP data. J. Climate, 7, 465-497.
Hansen, J. E., and J. B. Pollack ,1970: Near-infrared light scattering by terrestrial clouds.
J. Atmos. Sci., 27, 265-281.
Kaufman, Y. J., and T. Nakajima, 1993: Effect of amazon smoke on cloud microphysics
and albedo - Analysis from satellite imagery. J. Appl. Meteor., 32, 729-744.
King, M. D., and Harshvardhan, 1986: Comparative accuracy of the albedo,transmission and absorption for selected radiative transfer approximations.
NASA Reference Publication, 1160, 41 pp.
King, M. D., 1987: Determination of the scaled optical thickness of clouds fromreflected solar radiation measurements. J. Atmos. Sci., 44, 1734-1751.
Kneizys, F. X., E. P. Shettle, L. W. Arbeu, J. H. Chetwynd, G. P. Anderson, W. O.
Gallery, J. E. A. Selby, and S. A. Clough, 1988: Users guide to LOWTRAN-7.
AFGL-TR-88-0177, 137 pp.
Mitchell, J. F. B., C. A. Senior and W. J. Ingram, 1989: CO2 and climate: A missing
feedback? Nature, 341, 132-134.
Nakajima, T., and M. Tanaka, 1986: Matrix formulation for the transfer of solar
radiation in a plane-parallel scattering atmosphere. J. Quant. Spectrosc. Radiat.
Transfer, 35, 13-21.
Nakajima, T., and M. Tanaka, 1988: Algorithms for radiative intensity calculations inmoderately thick atmospheres using a truncation approximation. J. Quant.
Spectrosc. Radiat. Transfer , 40, 51-69.
GLI Algorithm Description

3.1.2-12001

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GLI Algorithm Description

3.1.2-13001