ῌ ,**3

Journal of The Remote Sensing Society of Japan Vol. ,3 No. + (,**3) pp. ,3ῌ-3

RSSJ

Original Paper

Near-Global Scale Retrieval of the Optical and Microphysical
Properties of Clouds from Midori-II GLI and AMSR Data
Takashi Y. NAKAJIMA*+, ,, Hirohiko MASUNAGA*- and Teruyuki NAKAJIMA*.
Abstract
Various optical and microphysical properties of warm water-phase clouds as retrieved from the GLI and AMSR
global datasets are presented. The results indicate that the retrieved e#ective particle radius at the cloud top was
small (0 to +* mm) not only over continental and coastal ocean areas, but also over the North Paciﬁc Ocean. The
ADEOS-II GLI-AMSR coupled analysis ﬁrst yielded the vertical structure of the e#ective particle radius overmiddle-to-high latitude areas, which was not covered by TRMM TMI-VIRS. The results also show that the e#ective
particle radius at the cloud top is comparable to or larger than that in the middle to bottom layers in areas where thee#ective radius at the cloud top is small. These features are very similar to those at the Namibian, East Asian,
Californian, and Peruvian regions, which are known as non-precipitation areas. In addition, comparisons between
cloud properties retrieved from GLI and MODIS revealed that the GLI-derived e#ective particle radius was smaller
than that derived from MODIS. The GLI-derived e#ective radius had a single mode in the histogram at +, mm, while
the MODIS had a bi-modal structure at +* mm and +. mm as well as an inﬂection point at +2 mm. One of the reasonsfor this di#erence in the retrieved e#ective radii is considered to be the di#erence in wavelength used for retrieving
the cloud e#ective particle radius, where GLI and MODIS used -.1 mm and ,.+ mm, respectively.
Keywords : ADEOS-II, Global Imager (GLI), AMSR, Cloud Properties, Climate Change

carbon dioxide, which is a greenhouse gas, has a radiative+.

forcing value of ῎+.00 W/m, (warming), whereas the change

Introductionof the cloud albedo associated with the change of the aerosol+. +concentration in the atmosphere is ῏*.1 W/m, (cooling).

Signiﬁcance of cloud studies

Clouds are one of the important observation targets forThis suggests that the warming e#ects of carbon dioxide areunderstanding climate change mechanisms. The International

canceled in part by the cooling e#ects of clouds throughPanel on Climate Change (IPCC) Forth Assessment Reportchanges in the aerosol concentration. However, the estimated(AR.)+῍ pointed out that clouds are still characterized byradiative forcing due to the cloud albedo e#ect is stilllarge uncertainties in the estimation of the energy balance on

associated with large uncertainties regarding its value, which

Earth. From this point of view, it is important to reveal the

ﬂuctuates in the range of ῏+.2 to ῏*.- W/m,. This uncer-

process of the growth of cloud droplets and their interaction

tainty is mainly due to insu$cient knowledge regarding cloud-with aerosols, as well as the global distribution of clouds andaerosol interactions. Therefore, global-scale observations of

their microphysical properties. The importance of any factor

clouds and aerosols, as well as the modeling of cloud-aerosol

directly and/or indirectly a#ecting the energy balance is ex-

interactions, are important research activities in climate

pressed in terms of “radiative forcing”, which is used forstudies.

understanding how a range of human and natural factors+. ,contribute to the warming or cooling of the global climate.

The Global Imager (GLI) aboard the Midori-II satellite,

Radiative forcing is deﬁned as the di#erence in energy balance

which is known as the Advanced Earth Observing Satellite-II

between the pre-industrial and present epochs. Positive and(ADEOS-II), is one of the Earth observation sensors suitable

Advantages of the Midori-II cloud observations

negative values of radiative forcing indicate warming andfor achieving the objective mentioned above. This is because

cooling, respectively. For example, AR. has reported thatit has -0 channels in the range from the ultraviolet to the(Recievd June -*, ,**2. Accepted December -, ,**2)*+ Research and Information Center, Tokai University, ,ῌ,2ῌ. Tomigaya, Shibuya-ku, Tokyo +/+ῌ**0-, Japan*, Department of Atmospheric Science, Colorado State University, +-1+ Campus Delivery, Fort Collins, CO 2*/,-ῌ+-1+*- Hydrospheric Atmospheric Research Center, Nagoya University, F-ῌ+ (,**) Furocho, Chikusa-ku, Nagoya .0.ῌ20*+, Japan*. Center for Climate System Research, The University of Tokyo, /ῌ+ῌ/ Kashiwanoha, Kashiwa-shi, Chiba ,11ῌ2/02, Japanῌ 29 ῌ

Near-Global Scale Retrieval of the Optical and Microphysical Properties of Clouds from Midori-II GLI and AMSR Data

thermal infrared region which enables us to retrieve thetrieved the cloud properties applicable to the whole AVHRR

properties of clouds and aerosols, and a wide swath (῍+0**,***-km swath and analyzed the “Ship-Tracks” phenomenakm), which allows for frequent observations to be performed.

which appeared o# the coast of California. N&N found that

The Instantaneous Field Of View (IFOV) is + km at the nadir

the aerosol seeding from the ship resulted in a +1ῌ decrease

looking for most channels and ,/* m for the ﬁve channelsof the e#ective cloud particle radius (from +, mm to +* mm).

corresponding to the LANDSAT Thematic Mapper (TM).

Kawamoto et al. (,**+)2῎ extended the N&N algorithm so

The position and the bandwidth of each GLI channel werethat the atmospheric water vapor e#ects included in the

optimized by using radiative transfer simulations so as toradiances measured with AVHRR are explicitly corrected,῎

retrieve the physical parameters with high accuracy . The

using the e#ective water vapor proﬁle calculated from objec-

advantage of GLI with respect to cloud observations aretive analysis data, such as that from the European Centre forsummarized as follows.

Medium-Range Weather Forecasts (ECMWF), the National(+) It was scientiﬁcally optimized on the basis of radiativetransfer simulations,῎.

Center for Environmental Prediction (NCEP), and the Japan
Meteorological Agency Grid Point Value (JMA-GPV). Vali-(,) It has many channels in the range from the visible todations of the retrieved cloud properties have been performed

the infrared region enabling cloud screening to be

by comparing satellite and aircraft data in situ, as well as

performed with high accuracy-῎.

satellite data with ground-based observations1῍3῎ The es-(-) It has three short-wave infrared (SWIR) channels

timated retrieval errors were less than +*ῌ for both tc and re.

(+.0, ,.,, and -.1 mm), which allow for the precise

These previous studies demonstrated not only the possibility.῎

surveillance of cloud properties .

of performing cloud microphysics observations by satellite(.) It has an oxygen-A band channel which is used forremote sensing, but also the usefulness of the results to climate

retrieving the geometrical thickness of clouds/῎.

change studies through the analysis of snapshot images (e.g.,(/) The Advanced Microwave Scanning Radiometer

quantiﬁcation of the ship track e#ect by N & N) and/or the(AMSR) is mounted aboard the satellite together withanalysis of time series of cloud properties in remotely sensed

GLI. The coupled use of GLI and AMSR for remoteresults and subsequent comparisons of global cloud distribu-

sensing of clouds provides the vertical structure of thetions as determined from satellite remote sensing and Generalcloud droplet size (shown in this paper). The Tropical

Circulation Model (GCM) outputs+*῎. The Moderate Resolu-

Rainfall Measuring Mission (TRMM) also includes

tion Imaging Spectroradiometer (MODIS) aboard Terra

the Visible and Infrared Scanner (VIRS) and the(+333ῌ) and Aqua (,***ῌ), is a sensor similar to GLI. The

TRMM Microwave Imager (TMI).

MODIS atmosphere science team developed the cloud retriev-

However, the

GLI-AMSR observations have an advantage sinceal algorithm++῎.

they yield observations of middle-to-high latitude
areas, which are not covered by VIRS-TMI.
In this paper, we discuss (-) and focus on the advantages ofof cloud observation attempts in recent years. For example,
Masunaga et al. (,**,a, ,**,b)+,) +-῎ obtained the vertical

(/).
+. -

A synergetic use of the multispectral visible-to-infrared
imager and the microwave scanner is one of the new aspects

History of cloud observations based on satellite-borne

structure of the e#ective cloud particle radius in warm water-

imaging spectroradiometersphase clouds over low and middle latitude areas (-/ degs

A number of studies have observed the optical and micro-

north to -/ degs south) by using the visible-to-infrared imager

physical properties of clouds using satellite-borne imaging

and the microwave scanner aboard the Tropical Rainfall

0῎

spectroradiometers. For example, Han et al. (+33.) retrieved

Measuring Mission (TRMM) satellite. Matsui et al. (,**.)+.῎

the cloud optical thickness (tc) and the e#ective particle

examined the TRMM data in detail and revealed that theradius (re) near-globally using radiance data from the nadir

formation of warm rain is suppressed under highly stablelooking of the Advanced Very High Resolution Radiometeratmospheric conditions regardless of the concentration of(AVHRR) operated by the National Oceanic and Atmos-

aerosols, whereas high aerosol concentrations suppress thepheric Administration (NOAA). One of the interesting fea-

formation of warm rain in low stability conditions.

tures reported by Han et al. (+33.) is the contrast between the+. .

e#ective particle radius over continental areas and that overThis paper presents results of the microphysical properties

Contents of this paper

coastal ocean areas. In fact, the average e#ective particleof warm water-phase clouds as obtained from the GLI andradius is smaller over continental areas (῏2./ mm) than over

AMSR cloud measurement missions. The theoretical back-coastal ocean areas (῏++.2 mm). Nakajima and Nakajima

ground of the retrieval of the properties and the expected(+33/)1῎ (hereafter, N&N) developed an algorithm which re-retrieved geophysical parameters are summarized in Section

ῌ 30 ῌ

Journal of The Remote Sensing Society of Japan

Vol. ,3 No. + (,**3),. The data used in the analyses are brieﬂy summarized instandard deviation of the size distribution. Here, s*.-/ wasSection -. The typical results for the GLI cloud products andassumed for marine stratocumulus clouds in our analyses.

a comparison between the results obtained from GLI and

For the satellite signal simulation, we used an accurate and

MODIS are shown in Section . together with a discussion of

e$cient radiative transfer scheme+/+0 extended to include thethe wavelengths used in the e#ective particle radius retrievals.

thermal radiative transfer+1. We assumed a Lambert surface

The synergistic analysis of clouds performed using data fromfor the underlying surface. This assumption will not intro-

GLI and AMSR measurements, as mentioned in Sections ,duce a signiﬁcant error in the analyses if we use an equivalentand ., is one of the unique results which was not expected at

ﬂux albedo for cloudy atmospheres+2.

the beginning of the GLI science mission. This paper presents

We retrieved tc at the *./-mm wavelength and re from GLI

for the ﬁrst time the results of the coupled use of GLI andchannels +- and -* on the basis of the fact that channels +-AMSR for cloud analysis.

and -* primarily depend on the cloud optical thickness andthe e#ective particle radius, respectively. Although the con-,.

cept of the retrieval is simple, some di$culties occur when

Algorithm development

determining the cloud properties from the measured GLI,. +ATSK-r algorithm development

spectral radiance. It is necessary to remove the unexpected

The algorithm used for retrieving the microphysical andradiation components (e.g. solar radiation reﬂected by the

optical properties of clouds from GLI-measured multispectral

ground surface and thermal radiation emitted from the cloud

daytime data is named “ATSK-r (Atmospheric TaSK

layer and the ground surface) from the observed radiance.

number -, reﬂection method)” in the GLI mission ﬂow chart.

The ground reﬂectance largely contributes to the satellite

ATSK-r retrieves the cloud optical thickness, the e#ective

signal in the full range of tc in channel +-, whereas it becomes

particle radius, and the cloud top temperature (and/or cloudimportant only if tc is less than +* in channel -*. The radiance

top height), from daytime visible, short-wave infrared, andin channel -* is inﬂuenced by the ground thermal radiation inthermal infrared channels. The ATSK-r algorithm was orig-

the range of tc +*. These undesirable radiation components

inally a part of the Comprehensive Analysis Program formust be removed from the measured radiances by using the

Cloud Optical Measurement (CAPCOM) as developed fromformulations provided in the next subsection.

the N & N (+33/) algorithm. CAPCOM is distributed under(,)

Formulations of the radiative components

the Open Clustered Libraries for Atmospheric Science andAccording to the radiative transfer theory for parallel plane

Transfer of Radiation (OpenCLASTR) project (http : //layers with an underlying Lambert surface, we remove thewww.ccsr.u-tokyo.ac.jp/~clastr/index.html), operated by theunexpected radiation components, such as the solar radiation

Japanese atmospheric radiation community.

reﬂected by the ground surface and the thermal radiation(+)

Theoretical background of ATSK-r

emitted from the cloud layer and the ground surface, from thesatellite-received radiance, Lobs, in order to decouple the radi-

The solar reﬂectance method utilizes non-absorbing visible(Vis) and water-absorbing short-wave infrared (SWIR) wave-

ation component reﬂected by the cloud layer, L, as follows :lengths, such as +.0, ,., and -.1 mm, for the simultaneousfor visible wavelength (*.012 mm, GLI channel +-),

retrieval of the cloud optical thickness at the *./ mm wave-

LZc Dc tc re ; m m* f

length and the e#ective particle radius. In this paper, weLobsZc Dc tc re ; m m* f

mainly discuss the solar reﬂectance method making use of
GLI channels +- (*.0. mm), -* (-.1+/ mm), and -/ (+*.2 mm).
The e#ective particle radius of the clouds (re) is deﬁned bytZc Dc tc re ; mAg
+rZc Dc tc reAgtZc Dc tc re ; m* 

m*F*
p

 -

re

r nrdrr nrdr** ,and for SWIR wavelength (-.1+/ mm, GLI channel -*),(+)

Lwu wc tc re ; m m* f

where n (r) is the number size distribution as a function of theLobswu wc tc re ; m m* fparticle radius r. We used a log-normal size distribution in thetwu tu m+twc tc re ; mrwc tc re ; mBTc

calculations,ῌ lnrlnr* ῏
c
exp῎
ῑ
r
,s,
῍
ῐ
,

nr

(-)

twu wc tc re ; m

+Ag
BTg
+ῌrwu wc tc reAgtwu wc tc re ; mAg+ῌrwu wc tc reAg(,)

where c is a constant, r* is the mode radius, which is related totwu wc tc re ; m*

the e#ective particle radius as rer*e,./s,, and s is the log31

m*F*pNear-Global Scale Retrieval of the Optical and Microphysical Properties of Clouds from Midori-II GLI and AMSR Data

ῑLtml῎wuῌTc῏monochromatic radiance. We further introduce a process ofῑt῎wuῌtuῌm῏t῎wcῌtcῌreῌm῏Ltml῎wdῌTg῏(.)

averaging of the variables in the formulations with respect tothe wavelength.

where F* is the extraterrestrial solar ﬂux, B is the Planck

For example, t is averaged with a sub-channel response function of GLI as

function, and t, tc and tu are the optical thicknesses of theN῍ M ῎ ῒ ῏῏ ntΐnῌxnῌk tnῌk ῐῌῌ
*n῎ῌ
*nῌ
kΐ+
vΐ+
ΐ+

atmosphere, the cloud layer, and the atmosphere above the(+*)

Furthermore, m* and m are thewhere *n is the response function of the n-th subchannel

cosines of the solar and satellite zenith angles, respectively, fwavelength for each GLI channel (we used *n values obtained

cloud layer, respectively.

is the azimuthal angle of the satellite relative to the sun, Tc isfrom http : //suzaku.eorc.jaxa.jp/GLI/index.html), xn, k is thethe cloud top temperature, and wu, wc, and wd are the equiva-

weight of the k-th k-distribution, and tn, k is the transmissivity

lent water vapor amounts of above cloud (u), cloud layer (c),for the k-th k-distribution at the n-th wavelength.

and below cloud (d), which are deﬁned as

averaging process, which was applied to Eqs. (-) and (.),῏῎

wuῌcῌdΐ ῑ w῎z῏῎P῎z῏ῌPg῏*./῎TgῌT῎z῏῏῏*.3dz
ῒ῏῍

This

introduces a non-negligible error into the case of thin cloud(/)

layers in which the spectral variation of tn, k becomes large.

where, z+ and z, represent cloud top and top of atmosphere,

However, in most cases in which this process is applied, thecloud bottom and cloud top, ground level and cloud bottom,

error remains small, and it is possible to estimate the undesir-

2῏for the case of wu, wc, and wd, respectively , P (z) and T (z) areable radiation components in Eqs. (-) and (.) by using

the vertical proﬁle of the air pressure and the air temperature

spectrally averaged variables for each channel.

as a function of the altitude z, Pg and Tg are the surface,. ,pressure and the temperature. Zc and Dc are the top height

The e#ective radius retrieved from the GLI -.1+/-mm chan-

Combined GLI-AMSR algorithm

and the geometrical thickness of the cloud, respectively.

nel is the cloud droplet size at nearly the cloud top due to the

The transmissivity t, the plane albedo r, and the sphericalalbedo ῍r are given bylarge imaginary index of refraction of water droplets at thisthe other hand, contain information of cloud droplet sizet῎tcῌreῌm*῏ΐ+ ῑ ῎ῐ ῑ ῍
T῎tcῌre ; mῌm*ῌf῏mdmdfῐeῑtῌm*p ῒῌ ῒῌ

regarding the entire cloud layer. Thus, the coupled use of thevisible-to-infrared and microwave radiometers for observing(0)

warm water-phase clouds can provide signiﬁcant informationr῎tcῌreῌm῏ΐ῎ῐchannel. Contrastingly, the microwave scanner channels, onpertaining to the vertical proﬁles of cloud properties. For῍+ῑ ῑR῎tcῌre ; mῒῌmῌf῏mῒdmῒdfp ῒῌ ῒῌ

example, it is possible to calculate the ratio of two kinds of(1)

e#ective particle radii, namely re_SWIR&Vis, which is ob-

῍῍r῎tcῌre῏ΐ, ῑ r῎tcῌre ; m῏mdmῌ
ῒῌtained individually with the visible-to-infrared imager, and(2)

rc_Mic&Vis, which is obtained through the coupled use of thewhere T (tc, re ; m, m*, f) and R (tc, re ; mῒ, m, f) are bi-

microwave scanner and the visible-to-infrared imager, bydirectional transmission and reﬂection functions respectively.

using the following formulation

The second term in Eq. (-) and the fourth term in Eq. (.) are- W_Mic&Vis
re_Mic&Visῑ ῌῌ,tcground-reﬂected radiation components, and the second andthird terms in Eq. (.) are cloud and ground thermal radiation
components. The ﬁfth and sixth terms in Eq. (.) are active(++)

where W_Mic&Vis and tc are the cloud liquid water path asthermal correction components from the layers above andobtained from AMSR retrieval and the cloud optical thick-below the cloud, respectively. Multiple reﬂections between

ness obtained with GLI retrieval, respectively.

the ground surface and the upper layer are taken into consid-

Midori-II was equipped with GLI and AMSR sensors. The

eration in Eqs. (-) and (.). However, this e#ect is su$ciently
small to regard ῍r (tc, re) Ag as almost zero, especially forformer is a visible-to-infrared imager, while the latter a microwave scanner.

optically thin clouds and ground surfaces with low reﬂe-

algorithm of the GLI and AMSR analysis. First, the GLI andctance. On the contrary, with optically thick clouds and large

AMSR data are initialized in advance through a path-segment

ground albedo, this e#ect is relatively large at visible wave-

process in order to ensure the temporal and spatial registra-

lengths since the large cloud spherical albedo reﬂects radiation

tion matching of the two sensors. Next, the *.012-, -.1+/-,from the ground surface, while the relatively large trans-

and +*.2-mm channels of the GLI retrieve the cloud opticalmissivity allows this radiation component to be transmitted

thickness tc, the e#ective particle radius re_SWIR&Vis, andinto space. These formulations are exact when we consider

the cloud top temperature Tc in accordance to the algorithm

῍ 32 ῍Figure + presents the ﬂow of the coupledJournal of The Remote Sensing Society of Japan

Fig. +

Vol. ,3 No. + (,**3)

Diagram of the coupled use of GLI and AMSR
multi-spectral data used for retrieving the e#ective
radii SWIR&Vis and Mic&Vis.
Fig. ,

Table +

Cloud parameters as retrieved from GLI,AMSR, and GLI῏AMSR.

Schematic view of the GLI Atmospheric Segment(ATSEG) dataset. The upper panel represents
the global map, which has +..*ῐ1,+ boxes withequal latitude and longitude. The interval ofboxes is *.,/ degrees for both the latitude and thelongitude, and each box is composed of /ῐ/ῑ,/pixels (lower panel).

AMSR, and GLI῏AMSR retrievals.
-.

Satellite data and ancillary data

mentioned in Section ,.+. Subsequently, the ++-, +3-, and -1-

We used a reduced-volume GLI radiance dataset named

GHz channels of AMSR are used for calculating the column“Atmospheric segment (ATSEG) data”, which comprises

water vapor (CWV) and the liquid water path W_Mic&Vis bygridded data with equal longitude and latitude divisions.

using the algorithm developed by Masunaga et al. (,**,a)+,῎,

Each segment comprises a grid box with dimensions of *.,/ῐ

with the help of Tc as derived from GLI and the cloud fraction*.,/ degrees, so that the global dataset contains +..*ῐ1,+(C.F.) at every AMSR IFOV in order to obtain a precise

boxes. /ῐ/ῑ,/ pixels of the GLI image are included in eachbrightness temperature correction of the AMSR signals. Bothsegment element. Figure , illustrates the schematic view of

the GLI and the AMSR analysis use the SST data obtained

the ATSEG data. Level-+b AMSR were used for the AMSR

from the /-day running mean of the AMSR-E aboard theanalysis.

Aqua satellite daily SST dataset. Thus, Eq. (++) outputs aretrievals for the purpose of supporting the GLI cloud retriev-

di#erent e#ective particle radius (te_Mic&Vis) based on tc

al algorithm. One of them is JMA objective analysis data.

and W_Mic&Vis. Generally speaking, re_SWIR&Vis is the

The vertical proﬁles of the pressure, the humidity, and the

Two kinds of ancillary data were used in thecloud droplet size at the cloud top and re_SWIR&Vis is thetemperature for each GLI pixel were used. The other ancil-

averaged droplet size for the entire cloud layer. Therefore,

lary data is the /-day running mean daily SST dataset derived

the ratio of re_Mic&Vis to re_SWIR&Vis is assumed to be anfrom AMSR-E and provided by Keiji Imaoka from JAXA

indicator of the vertical structure of the cloud droplet size.

EORC. The SST data are needed for both the GLI and theWe deﬁne the e#ective particle radius ratio (ERR) as

AMSR analysis.

ERRῑre_Mic&Visῌre_SWIR&Visῌ

(+,)

Table + summarizes the parameters obtained from the GLI,
῍ 33 ῍Near-Global Scale Retrieval of the Optical and Microphysical Properties of Clouds from Midori-II GLI and AMSR Data

..
.. +

Results

GLI cloud properties for April ,**-

Figure - (A) (B) (C) illustrates the warm water cloudparameters obtained with GLI in April ,**-. The data aremonthly averaged at every *.,/῏*.,/ degrees in latitude andlongitude direction in the grid area. Figure - (A) shows thatthe cloud optical thickness tc is large at the east coast of North
America, the middle part of South America, the northernparts of Europe, and the northern and southeastern parts ofAsia. In these areas, the averaged tc was between ,* and -*.
Furthermore, the values of tc are relatively large over theareas o# the east coast of Asia (North Paciﬁc Ocean) andbetween North America and Europe (North Atlantic Ocean).
Figure - (B) shows that the e#ective particle radius rc issmaller over continental and coastal ocean areas than overopen ocean zones. These results are consistent with thoseobtained by Han et al. (+33.)0῎ and Kawamoto et al. (,**+)2῎.
It is found that the zones with small e#ective particle radius
are spread over a wide area extending from East Asia to theNorth Paciﬁc Ocean. This area corresponds to an area ofdense aerosols arriving from East Asia. Here, this might beindicative of the indirect e#ects of aerosols on the cloudproperties. This speculation is discussed again by using new
results obtained from the coupled analysis of GLI and AMSR
in Section /. The cloud top temperature Tc Figure - (C)shows that Tc is high in low to middle latitude areas and low
in high latitude areas.
.. , Comparison between the results obtained from MODIS
and GLI
MODIS aboard the Terra satellite was operated simultaneously with GLI throughout ,**-. Thus, we compared theresults regarding the cloud properties as obtained from GLI
and MODIS. Figure . illustrates the correlation of the cloudparameters (tc and re) obtained from GLI and MODIS asstandard products in April ,**-.

The di#erence of theobservation time between GLI and MODIS is less than +**minutes. In Figure ., we can see a good correlation in thecomparison of tc (correlation coe$cient Rῐ*.2) and re (Rῐ*.11), although the values of re are larger in MODIS than in
GLI, with a slope of +.+,. In order to analyze this di#erence,

Fig. -

we prepared a histogram of the cloud properties. Figure /shows a comparison of the histograms of GLI and MODIS

Cloud parameters averaged over one month, (A)cloud optical thickness, (B) e#ective particle
radius, and (C) cloud top temperature, as
obtained from GLI observations in April ,**-.

for both land and water areas (upper panels), land areas only
(middle), and water areas only (lower). In Figure / (a), themode values of tc in GLI and MODIS are very close to the

MODIS (῍0) for land areas, whereas GLI (῍0) is larger

values of tc up to ., and the shapes of the histograms arethan MODIS (῍.) for water areas. The shape of the tc

similar. However, the histograms in Figure / (c) and (e)histogram for water areas indicates that MODIS has moreshow that the mode value of tc from GLI (῍.) is smaller thanclouds for large tc. On the other hand, the re histogram isῌ 34 ῌ

Journal of The Remote Sensing Society of Japan

Fig. .

Vol. ,3 No. + (,**3)

Correlation between the cloud optical thickness (a) and the e#ective particle radius (b) as
obtained from GLI and MODIS (April ,**-) in the latitude domain of ./N to ./S.

quite di#erent, indicating that GLI has a single mode at +, mm.. -while MODIS has a bi-modal structure at +* mm and +. mm as

Figure 0 illustrates the ERR as obtained from GLI-AMSR

well as that MODIS has an inﬂection point at +2 mm (Fig. /(b)).

Figure / (f) shows that this unique structure was

GLI-AMSR e#ective particle radius ratioin April ,**-. It was found that the covering area of GLIAMSR is extended to῎0* degrees in latitude.

Since theintrinsic to water areas. The histogram of re from MODIS for

TRMM VIRS-TMI analysis performed by Masunaga et al.

water areas shifts to larger regions and is divided into three(,**,b)+-῍ covers a range of only ῎-/ degrees, this study

parts : /῏re῏+, mm, +,῏re῏+2 mm, and +2῏re῏,/ mm.

presents the ﬁrst ERR results which cover middle-to-highThis complex structure of the histogram for water areas

latitude areas. The investigation of clouds over middle-to-generated the bi-modal points and the inﬂection points in thehigh latitudes is important due to the large amount of strati-

re MODIS histogram.

form clouds forming over these areas+3῍. One of the charac-

So far, the possible reasons for the di#erences in the reteristics of this ﬁgure is that ERR is large over the Middle

histograms are considered to be (+) poor sensor calibration,

Paciﬁc Convergence Zone and the South Paciﬁc Convergenceespecially in the SWIR channels, (,) di#erent SWIR wave-

Zone (SPCZ), which are known as high precipitation areas.

lengths used in the measurements, (-) slight di#erences in the

Large ERR values indicate that the averaged column e#ective

algorithms utilized by GLI and MODIS, such as di#erences inparticle radius is larger than that for the cloud top. This is

the treatment of surface reﬂection, and so on. Among these,

consistent with the results obtained from the VIRS-TMI(,) is worth inspecting in detail since GLI used a -.1-mm

analysis performed by Masunaga et al. (,**,b)+-῍. On thechannel, whereas MODIS used a ,.+-mm channel for theother hand, it is found that the areas of small ERR are spread

+2῍

1῍

retrieval of re. Nakajima et al. (+33+) , N & N (+33/) , andover the North Paciﬁc Ocean, the North Atlantic Ocean, andNakajima (,**,).῍ suggested that -.1 mm and ,.+ mm wave-

the Namibian, East Asian, Californian, and Peruvian regions.

lengths might retrieve di#erent re since the e#ective depth

The e#ective particle radius of the clouds at the cloud top isfrom the cloud top for ,.+ mm is deeper than -.1 mm due to thecomparable to or larger than that at the middle to the bottom

di#erent imaginary index of refraction. Thus, the use of twolayer in these areas. The GLI-AMSR combined analysis isor three channels with the SWIR wavelength retrieves theunique since it covers middle-to-high latitude areas, which arevertical structure of the particle size in the clouds.

not covered by the VIRS-TMI analysis.

For

example, if the cloud droplet size in the middle-to-low parts of/.

the cloud layer exhibits larger ﬂuctuations than in the cloud

Discussion and conclusions

top over the water regions, the MODIS ,.+-mm channel cansense such ﬂuctuations. Thus, it is very important to investi-

The GLI atmospheric science team developed algorithms

gate the retrieved cloud microphysical properties by using

related to the atmospheric particles and their radiative quan-All possible

tity. Among those algorithms, the ATSK-r algorithm and thereasons mentioned above, including the sensitivity of the

GLI-AMSR coupled analysis algorithm, as well as the typical

wavelength to the e#ective particle radius, should be ex-

results obtained from these algorithms, were shown in thisamined in detail in future work.

paper. From the results obtained from ATSK-r, we foundactive sensors such as radars and/or lidars.

ῌ 35 ῌ

Near-Global Scale Retrieval of the Optical and Microphysical Properties of Clouds from Midori-II GLI and AMSR Data

Fig. / Histgrams of the cloud optical thickness (left) and the e#ective particle radius (right) as obtained
from GLI and MODIS. (April ,**-), for the land and water areas (upper panels), the land
areas only (middle), and water areas only (lower).

that the e#ective particle radius was smaller in continental

signiﬁcantly larger than that from GLI. The comparison ofand coastal ocean areas and larger over open ocean areas.

the respective histograms showed that the e#ective particle

The results are consistent with previous results obtained withradius as derived from GLI had a single mode at re῍+, mmother sensors. The results also showed that the areas withwhile that derived from MODIS had a bi-modal structure atsmall e#ective particle radius were spread over a wide area of+* mm and +. mm and an inﬂection point at +2 mm.

the Middle to North Paciﬁc Ocean. This area corresponds tounique shape of the histogram corresponded to measurements

This

an area rich in aerosols which come from East Asia, andover the water area. This ﬁnding has created a new opportu-

therefore it is considered that this might be due to indirect

nity for science activities since the di#erences in the values ofre as obtained from GLI and MODIS occurred despite thee#ects of environments rich in aerosols.
The e#ective particle radius as retrieved from MODIS issimilar retrieval concepts used in the two sensors.

ῌ 36 ῌ

This

Journal of The Remote Sensing Society of Japan

Fig. 0Vol. ,3 No. + (,**3)

E#ective radius ratio (ERR) obtained form the synergistic analysis using GLI and AMSR (April ,**-).

implicitly suggests that some unknown factors are still hiddenof the cloud observations for model development is the non-in the radiative transfer or the natural features of the cloudhydrostatic cloud resolving model, such as the Non-properties. Such exciting research activities are scheduled forhydrostatic Icosahedral Atmospheric Model (NICAM)

future work.

developed by Satoh et al. (,**2),,῍, who performed global-

The GLI-AMSR coupled analysis revealed the verticalstructure of the cloud e#ective particle radius over middle-to-scale simulations of the cloud distribution with horizontal
spatial resolution of 1 km and -./ km.

high latitude areas for the ﬁrst time. The results showed thatAs the model development progresses, more precise surveys

the areas of small e#ective particle radius ratio are spread overof real clouds are needed in order to derive more scientiﬁcthe North Paciﬁc Ocean and the North Atlantic Ocean. This

truths. The datasets of the vertical structure of the e#ective

indicates that the e#ective particle radius near the cloud top isparticle radius which are deﬁned by using, for example, ERR

comparable to or larger than that at the middle to bottom

and the di#erence of the e#ective particle radius obtained

layers. Since the obtained results are quite similar at non-through the -.1- and ,.,-mm channels as mentioned above inprecipitation areas, such as the Namibian, East Asian, Cali-

this paper might be useful for the further development offornian, and Peruvian regions, where the ERR values arethese models. Active remote sensing is expected to be anotherrelatively small, this constitutes evidence of the suppressed

useful technique for investigating the vertical structure ofrain formation due to the high concentration of aerosols. In

cloud properties. In fact, CloudSat,-῍ (equipped with a cloudcontrast to those non-precipitation areas, the e#ective particle

radar) and CALIPSO,.῍ (equipped with a lidar) as a series ofradius ratio was large over the Middle Paciﬁc and the SPCZ

A-Train observations are in operation today, and the launchareas, which are known as precipitation areas.

of the EarthCARE,/῍ satellite, which is equipped with a cloud

It is noteworthy that there are many extended researchradar, a lidar, an imager, and a broadband spectrometer, ise#orts regarding cloud observations which contribute toplanned for ,*+-. Such active remote sensing systems coverglobal climate studies. First, the radiation budget can be

only a very narrow area along their orbit (almost nadir), andestimated from the distribution of clouds and the optical

therefore the estimation of the cloud properties by using

properties of aerosols as obtained from GLI,*῍. Second, thepassive imagers is still important for observing the “wide-comparison of the GLI outputs with the model results can bearea” cloud parameters on Earth. Thus, the Global Change

used in assessing the process of cloud droplet growth. For

Observation Mission (GCOM)-C and GCOM-W, which are

example, Suzuki et al. (,**0),+῍ developed a non-hydrostatic

equipped with visible-to-infrared imagers and microwave

cloud droplet growth model and simulated the correlation

scanners, are planned for launch in the near future. In thispattern appearing in the NOAA/AVHRR-derived tc-rc scatter

regard, this paper suggests innovations with respect to the

1῍plots and showed the typical correlation patterns for pristine

above points.

and polluted atmospheric environments. Another application
ῌ 37 ῌ

Near-Global Scale Retrieval of the Optical and Microphysical Properties of Clouds from Midori-II GLI and AMSR Data

Acknowledgements
The authors are grateful to Keiji Imaoka from JAXA for
his work focused on providing the AMSR SST dataset, toHiroshi Murakami and the successive GLI project managers/
coordinators from EORC for their great e#orts in implementing the GLI science activities at JAXA. The authors are also
grateful to Akiko Higurashi from the National Institute forEnvironmental Studies for her comments and discussion regarding this work. Yoshitaka Muraji from EScoT provided
the pre-processed GLI and AMSR datasets. This research
was supported by JAXA (JX-PSPC-+3..-0).
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ῌ Takashi Y. Nakajimaῌ Hirohiko Masunaga

Takashi Y. Nakajima received the Doctorof Science degree in “Earth and planetary
physics” from Center for Climate System
Research (CCSR) of the University ofTokyo in ,**,. He came to the Japan
Aerospace Exploration Agency (JAXA) in+33., and moved to Tokai University in,**/. His present researches are remote sensing of clouds andaerosols by using visible and infrared multispectral radiometers,
theory of light scattering by non-spherical particles.
E-mail : nkjm῎yoyogi.ycc.u-tokai.ac.jp

Hirohiko Masunaga received the Ph.D.
degree in astrophysics from the Universityof Tokyo in +333. The present a$liation is
the Hydropheric Atmospheric ResearchCenter, Nagoya University since ,**0. His
research interests include cloud and precipitation climatology, tropical climate dynamics, satellite remote sensing, and evaluation of cloud-resolving
models.
E-mail : masunaga῎hyarc.nagoya-u.ac.jp

ῌ Teruyuki Nakajima

Teruyuki Nakajima obtained his Dr. of Science degree in geophysics from Tohoku
University in +32+. His major researchinterest is in atmospheric and climate physics. He started his professional career as ateaching assistant, Tohoku University in+311 ; Assistant professor, Tohoku University, +32+ ; Associate professor at Center for Climate System
Research (CCSR), University of Tokyo, +33+ ; now Professor
and Director of CCSR. NRC senior visiting associate at NASA/
Goddard Space Flight Center, +321-+33*. President of IAMAS
International Radiation Commission, ,**.-,**2 ; Member of
Science Council of Japan. Japan Meteorological Society Award,+33/ and Nissan Science Award, ,***.
E-mail : teruyuki῎ccsr.u-tokyo.ac.jp

ῌ 39 ῌ