JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D10, 10.1029/2001JD000743, 2002

Physical properties of maritime low clouds as retrieved by
combined use of Tropical Rainfall Measurement Mission
Microwave Imager and Visible/Infrared Scanner:
Algorithm
Hirohiko Masunaga,1 Takashi Y. Nakajima,1 Teruyuki Nakajima,2 Misako Kachi,1
Riko Oki,1 and Shunsuke Kuroda2,3
Received 13 April 2001; revised 10 August 2001; accepted 27 August 2001; published 22 May 2002.

[1] Satellite remote sensing studies on the microphysical and optical properties of clouds haveconstructed an active research field in the last decades. Clouds are observed over a wide spectral
range from the visible/infrared to the microwave, and either shortwave or microwave measurement
is used to evaluate the liquid water path (LWP). On the other hand, to date, there have been few
cloud studies based on combined measurement by a visible/infrared imager and a microwave
radiometer aboard the same platform. In this paper a physical inversion algorithm for the combined
use of visible/infrared and microwave sensors is proposed to retrieve the cloud physical quantities
such as LWP and the effective droplet radius, each of which is determined in two different ways.
The current version of the algorithm has been developed for application to the Tropical Rainfall
Measurement Mission (TRMM) sensors, i.e., Visible and Infrared Scanner (VIRS) and TRMM
Microwave Imager (TMI). The cloud top temperature obtained from the VIRS analysis is used as an
input to the TMI analysis to reduce uncertainties in estimation of LWP. Total errors in LWP are
estimated to range from 11 to 30 g /m2. In the algorithm the beam-filling efficiency of clouds for
TMI footprints is corrected by the cloud fraction evaluated from the VIRS measurements. For
application, global analysis is performed with 3-monthly data from January to March 2000. The
scatter diagram of the shortwave-retrieved LWP (LWPshrt) versus the microwave-retrieved LWP(LWPmicr) shows characteristic trends for both precipitating and nonprecipitating clouds. Verticalinhomogeneity of the cloud droplet size accounts for small excess of LWPshrt over LWPmicr for
nonprecipitating clouds, while precipitating clouds produce LWPmicr larger than LWPshrt, owing to
the presence of raindrops. These tendencies are reinforced by examination of the global
distributions of the shortwave-retrieved droplet radius R e (NV) and the microwave counterpart
defined by LWP divided by the cloud optical thickness R e (MV). The result implies that difference inthose effective radii reflects a microphysical mechanism to expedite or suppress the conversion of
the cloud water into rainfall.
INDEX TERMS: 0320 Atmospheric Composition and Structure:
Cloud physics and chemistry; 1610 Global Change: Atmosphere (0315, 0325); 1640 Global
Change: Remote sensing; 1694 Global Change: Instruments and techniques; KEYWORDS: low
clouds, effective radius, algorithm, TRMM

1. Introduction[2] Clouds are known to play significant roles in Earth’s
radiation budget, and it is necessary to clarify the global characteristics of clouds for climate system studies. Investigation of the
microphysical and optical properties of clouds by means of satellite
remote sensing has been an important subject in the last decades.
An established scheme to retrieve the effective droplet radius andthe optical thickness of clouds employs a pair of shortwave
channels, where combination of a water-absorbing channel in thenear-infrared wavelength region and a nonabsorbing channel in thevisible yields the effective droplet radius and the optical thickness

through a well-behaving inversion algorithm [Nakajima and King,
1990; Han et al., 1994; Nakajima and Nakajima, 1995; Kawamoto
et al., 2001]. The cloud optical thickness at an optical wavelengthtc, along with the effective droplet radius Re, provides the shortwave-retrieved liquid water path LWPshrt of clouds according to thewell-known formula
LWPshrt ¼

Copyright 2002 by the American Geophysical Union.
0148-0227/02/2001JD000743$09.00

AAC

ð1Þ

where Re is defined by
R1 3
r nðrÞdr
Re ¼ R01 2
0 r nðrÞdr

1

Earth Observation Research Center, National Space Development
Agency, Tokyo, Japan.
2Center for Climate System Research, University of Tokyo, Tokyo,
Japan.
3
Now at Sun Microsystems, Inc., Tokyo, Japan.

2rw
2r
Re tc ¼ w ghRe itc ;
3
3

ð2Þ

under a given droplet size distribution n(r). The mass density ofliquid water rw is constant at 106 g/m3. The correction factor g, torelate the shortwave-retrieved Re with the vertically averaged valuehRei, accounts for the vertical inhomogeneity of Re [Nakajima and

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MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

King, 1990; Brenguier et al., 2000]. If Re increases with the altitude
as typically observed in nonprecipitating cumulus clouds, g
exceeds unity because Re retrieved from satellite remote sensing
is biased toward the value near the cloud top due to the large
absorption of near-infrared radiation by cloud droplets.
[3] LWP can be retrieved from microwave brightness temperatures as well as from shortwave radiances. A large number ofalgorithms have been developed to derive LWP over oceanic
environment by means of microwave radiometers [Chang andWilheit, 1979; Wilheit and Chang, 1980; Takeda and Liu, 1987;
Petty, 1990; Greenwald et al., 1993; Lin et al., 1998a]. Comparisonof the microwave-retrieved liquid water path LWPmicr with LWPshrt
obtained by spaceborne sensors has been made by some authors.
Greenwald et al. [1993] confirmed at a local observation site that
LWP retrieved by Special Sensor Microwave/Imager (SSM/I)
measurement is consistent with that by advanced very highresolution radiometer (AVHRR). Lojou et al. [1991] improved a
regression formula for LWPmicr using LWPshrt to eliminate a bias
between scanning multichannel microwave radiometer (SMMR)
and visible and infrared spin-scan radiometer (VISSR) measurements. Lin and Rossow [1994] revealed that LWPmicr and LWPshrt
are in good agreement for warm nonprecipitating clouds on thebasis of global analysis of SSM/I and International Satellite Cloud
Climatology Project (ISCCP) data.
[4] Zuidema and Hartmann [1995] and Greenwald et al. [1995]
compiled data from SSM/I, ISCCP, and the Earth Radiation Budget
Experiment (ERBE) to derive LWP, cloudiness, and effective
droplet radius of liquid water clouds. Combined measurements
by shortwave and microwave radiometers can also be made to
detect the cloud overlapping, as demonstrated by Lin et al. [1998a,
1998b] in the use of SSM/I and ISCCP retrievals.
[5] There are some difficulties with the combined use ofmultiple sensors onboard different satellites. One of the problems
arises if the data are not matched in space and time. Furthermore,
the criterion for the presence of clouds is clearly defined in the
shortwave retrieval algorithms in terms of significantly larger
reflectance and/or substantially lower temperature than the
ground, whereas the cloud emission in the microwave is muchless distinct from the surface radiation. Lin and Rossow [1994]
implied that the arbitration in the cloud detection threshold is
responsible for a large dispersion in LWP provided by past
investigators. Microwave observations synchronized by shortwave
measurement of clouds are expected to avoid such ambiguities in
estimation of LWP.
[6] The difference in the spatial resolution between the shortwave and microwave radiometers is another obstacle to comparison of LWPmicr and LWPshrt. The typical footprint size is 1 or 2 km
for shortwave imagers, while it extends up to a few tens ofkilometers for passive microwave sensors. Therefore LWPmicr
would be smoothed out within a field of view to produce
significantly smaller values than LWPshrt when the cloud fraction
is small. The beam-filling efficiency in microwave measurement
should be corrected before comparing LWPmicr and LWPshrt.
[7] Combined use of a visible/infrared imager and a microwave
radiometer onboard the same platform overcomes these difficulties.
Moreover, the combined use also has the advantage that the cloud
top temperature, whose uncertainty is known to produce serious
errors in evaluation of LWP [Greenwald et al., 1993], is determined precisely from the visible/infrared measurement. In thispaper we propose an algorithm to retrieve the cloud properties by
the combined measurements. We apply our algorithm to global
analysis using the Tropical Rainfall Measurement Mission(TRMM) data. The present algorithm also has potential for
application to future satellite programs with multiple sensors
onboard such as the Aqua project directed by NASA, Advanced
Earth Observation Satellite (ADEOS) II by the National Space
Development Agency of Japan (NASDA), and some future programs in planning stages.

Table 1. Summary of the Retrieved Productsa
SensorChannels in Use

VIRS

0.63, 3.75,
and 10.8 mm

TMI

10.65 GHz (V),19.35 GHz (V, H), and37.0 GHz (H)Retrievalstc
Re(NV)Tc
LWPshrtfc
CWV
LWPmicr
Re(MV)c

Algorithm
GLI algorithmb

this paperaDefinitions are as follows: VIRS, Visible/Infrared Scanner; GLI, Global
Imager; TMI, Tropical Rainfall Measurement Mission Microwave Imager;
CWV, columnar water vapor.
b
From Nakajima andNakajima [1995] and Kawamoto [2001].
c
With the help of Tc.

[8] The retrieval algorithm is described and verified in section 2.
In section 3 we introduce the satellite and ancillary data that areused in the analysis. Section 4 shows results of the data analysis,
and conclusions are summarized in section 5.

2. Retrieval Method[9] In this section we describe the algorithm used to derive thecloud physical properties from visible/infrared radiances and microwave brightness temperatures, which is designed for application to
the TRMM data. The TRMM satellite has the Visible/Infrared
Scanner (VIRS) and TRMM Microwave Imager (TMI) on board,as well as precipitation radar (PR). VIRS is an AVHRR-like imager
with the five channels at the wavelengths of 0.63, 1.6, 3.75, 10.8,
and 12 mm. TMI is similar to SSM/I but has two additional lowfrequency channels at 10.65 GHz (V and H), 19.35 GHz (V and H),
21.3 GHz (V), 37.0 GHz (V and H), and 85.5 GHz (V and H), where
V and H designate vertical and horizontal polarizations, respectively. The frequency of the water vapor channel at 21.3 GHz has
been shifted from the line-centered position at 22.235 GHz as
specified by SSM/I in order to avoid saturation in the tropics.
[10] The visible/infrared technique and microwave scheme forour analysis are introduced separately, followed by the description
on the overall flow of the combined analysis. Table 1 summarizes
the retrieved products in our algorithm.
2.1. Retrieval Scheme for Visible/Infrared Measurement[11] Nakajima and Nakajima [1995] described a method for
determining the microphysical properties of water clouds by visible
and infrared measurements using AVHRR data. The original
version of the Nakajima and Nakajima algorithm used a fixedwater vapor profile to correct the atmospheric component in thethermal emission, which is justified only for regional experiments.
Kawamoto et al. [2001] improved the Nakajima and Nakajima
algorithm to make it applicable to global analysis by AVHRR withthe help of the objective analysis archives to evaluate the equivalent water vapor amount. The latest version of the algorithm,
which has been improved in technical aspects of the numerical
scheme, is incorporated in the standard analysis system for Global
Imager (GLI) to be onboard the ADEOS II satellite. We adopt thelatest GLI algorithm for VIRS analysis in the present work withmodification due to differences in the channel specifications.
[12] The visible/infrared algorithm is briefly outlined as follows. Lookup tables are made in advance on the basis of a set of
atmospheric radiative transfer simulations using the numerical
scheme presented by Nakajima and Tanaka [1986, 1988] andStamnes et al. [1988]. The optical thickness, the effective droplet
radius, and the cloud top temperature are determined by the
Newton-Raphson iteration from channel 1 (0.6 mm), channel 3(3.75 mm), and channel 4 (10.8 mm) radiances. The retrieval
scheme is designed to properly subtract the ground components

in the channel 1 and channel 3 radiances and the thermal component in the channel 3 for the solar radiation measurement to obtain
the optical thickness and the effective radius of clouds. In thisprocedure we simultaneously evaluate the cloud top temperaturefrom the channel 4 brightness temperature with given ancillary data
for the surface temperature, taking into account the cloud optical
thickness and the water vapor correction. Although the GLI
algorithm is applicable to clouds over both ocean and land, we
restrict current analysis into the marine environment for the
purpose of the data matching with TMI measurement. Nakajima
and Nakajima [1995] and Kawamoto et al. [2001] present a moredetailed description of the visible/infrared algorithm.
2.2. Retrieval Scheme for Microwave Measurement[13] We have recently developed a numerical code for atmospheric radiative transfer to make a lookup table for microwave
brightness temperatures. Fundamental strategy of the numerical
scheme follows its precursor developed for visible/infrared radiative transfer [Nakajima and Tanaka, 1983, 1986, 1988; Stamnes
et al., 1988]. In order to apply the original scheme to the microwave
version we have made some modifications, as described below.
[14] Microwave brightness temperature is sensitive to the variation of the atmospheric water vapor. We specify the verticalprofile of the water vapor content WVC(z) asWVCð zÞ ¼



CWV
z
;
exp 
Hw
Hw

ð3Þ

where Hw is the scale height of the water vapor profile and CWV iscolumnar water vapor. In our algorithm, Hw can be treated as an
input parameter in the lookup table to take into account theregional variation of the water vapor profile. We tentatively set a
constant value of Hw = 2.3 km in the present analysis. The gas
absorption coefficients are from Liebe and Layton [1987].
[15] It is known that in oceanic environment the surface
emissivity and reflectivity for microwaves vary with the nearsurface wind speed through the surface roughness, coverage offoam, and diffraction effects, in addition to change in the dielectric
constant of seawater depending on sea surface temperature andsalinity. See Nakajima and Tanaka [1983] for the formalism of thesurface roughness effect, substituting a value of the mean squareslope s2 = 0.00534u10 with a microwave counterpart proposed by
Wentz and Meissner [1999]:
sh2 ¼ 0:00522u10i
s2 ¼ 0:00522 1  0:00748ð37  vÞ1:3 u10v  37 GHz
v < 37 GHz;

ð4Þ

where u10 is wind speed (m/s) measured 10 m above the surface.
The effects of sea foam and diffraction are taken into accountaccording to the empirical formula derived by Wentz and Meissner[1999] with interpolation of the data table onto the TMI channel
frequencies.
[16] Some authors have made use of specific combinations ofbrightness temperatures to reduce uncertainties in the surface
emissivity originally developed for the CWV retrieval. Chang
and Wilheit [1979] used the ratio of (T22V  Ts)/(T19V  Ts) for
the retrieval of atmospheric parameters since it is insensitive to thesurface emissivity, where T19V and T22V designate the brightness
temperatures in vertical polarization at 19.35 and 22.235 GHz,
respectively, and Ts is the surface temperature. Prabhakara et al.
[1982] found that the brightness temperature difference between
the 18- and 21-GHz channels on SMMR is useful to derive CWV
because of insensitivity to the surface emissivity. Tjemkes et al.
[1991] pointed out that both of these two methods suffer fromsaturation for large values of CWV and alternatively proposed asemianalytic formula in terms of the brightness temperature differ-



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MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

ence at 19.35 GHz and (T19H  T )/(T19V  T ), with T approximated by the climatological sea surface temperature.
[17] Greenwald et al. [1993] derived a couple of analytic
formulae to obtain CWV and LWP, extending the formulation of
Tjemkes et al. [1991] to the 37.0-GHz brightness temperatures in
addition to the 19.35-GHz brightness temperatures. They foundthat the largest source of error in derivation of LWP is uncertainty
in the cloud temperature. Lin et al. [1998a] showed that the
combined use of T37H and T85V clearly discriminates LWP fromthe contribution of the cloud water temperature to the brightness
temperatures. They found that uncertainties in CWV are the mostimportant sources of error in estimation of LWP.
[18] Our algorithm appropriately takes into account the global
variation of the surface conditions such as sea surface temperatureand near-surface wind speed by adopting the ancillary data (seesection 3). Nevertheless, ambiguities in the dependence on nearsurface wind speed are still not negligible in estimation of LWP.
Intrinsic uncertainties in near-surface wind speed are difficult toremove partly because of our limited knowledge of the dielectric
properties of foam-covered water and partly because there is no
global-scale validation of the archived data for near-surface windspeed.
[19] Figure 1 demonstrates the calculated brightness temperatures in various combinations to examine their sensitivities to
CWV, LWP, and near-surface wind speed, where other variables
are fixed as Hw = 2 km, sea surface temperature is 288.5 K, cloud
top temperature is 282 K, and cloud base temperature is 285.25 K.
Figure 1a shows some arbitrary combinations of brightness
temperatures for reference. As Tjemkes et al. [1991] pointed out,
T21V  T19V is insensitive to the surface emissivity, and thus tonear-surface wind speed but is saturated when CWV is >50 kg/m2.
The brightness temperature difference in a dual-polarization channel such as T19V  T19H and T37V  T37H is a good tracer for
CWV or LWP but depends greatly on near-surface wind speed [seealso Tjemkes et al., 1991; Greenwald et al., 1993]. A single
brightness temperature such as T37H also has a large dependence
on both LWP and near-surface wind speed, especially in horizontal
polarization.
[20] Figures 1b and 1c propose alternative combinations ofbrightness temperatures. The brightness temperature difference
between the 19- and 10-GHz channels (T19 – 10, shown in Figure1b) is found to have sufficiently small dependence on near-surface
wind speed, in particular for the vertical polarization, without
saturation over the whole realistic range of CWV. In contrast,
T19 – 10 is almost independent of LWP. The brightness temperature difference between the 37- and 19-GHz channels (T37 – 19)is also insensitive to near-surface wind speed with satisfactory
sensitivity to LWP for the horizontal polarization as delineated in
Figure 1c, while the dependence of T37 – 19 on CWV is weak. As
a consequence, we conclude that combined use of T19V  T10V and
T37H  T19H is suitable to retrieve CWV and LWP, taking
advantage of their insensitivity to near-surface wind speed andorthogonality with respect to CWV and LWP.
[21] The inversion algorithm is based on a lookup table method,where a set of calculated brightness temperatures is tabulated inseven dimensions including five inputs, i.e., water vapor scaleheight, sea surface temperature, near-surface wind speed, cloud top
temperature, and cloud base temperature. The remaining two
dimensions are retrievals, i.e., CWV and LWP, to be determined by
Newton-Raphson iteration in terms of T19V  T10V and T37H  T19H.
To obtain a set of microwave brightness temperatures in forward
simulations, the atmospheric temperature is assumed to decreaselinearly with increasing altitude at the lapse rate of 6.5 K/km
from sea surface temperature as the lower boundary. The cosmic
background radiation of 2.7 K incident through the top of theatmosphere is included. The droplet size distribution of clouds isassumed to be the lognormal distribution with the mode radius of
10 mm, but the calculated results are equally applicable to the

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a.

90

220
200

80
70

T37V-T37H, T37H [degree]

T19V-T19H, T21V-T19V [degree]

MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

T19V-T19H

60
50
40
30
20

T21V-T19V

10
0

0

10

20

30

40

50

60

70

180
160

120
100
80

T37V-T37H

60
40
20

80

T37H

140

0

50

100 150 200 250 300 350 400 450 500

LWP [g/m2]

CWV [kg/m2]b.
100

T19V-T10V, T19H-T10H [degree]

T19V-T10V, T19H-T10H [degree]

100
90
80
70

T19H-T10H

60
50
40
30

T19V-T10V

20
10

0

10

20

30

40

50

60

70

80

90

WS=1 m/s
WS=7 m/s

8070

WS=16m/s

60

T19H-T10H

50
40
30

T19V-T10V

20
10

0

50

100 150 200 250 300 350 400 450 500

LWP [g/m2]

CWV [kg/m2]c.
T37V-T19V, T37H-T19H [degree]

T37V-T19V, T37H-T19H [degree]

50
45
40
35

T37H-T19H

30
25
20
15

T37V-T19V
0

10

20

30

40

50

60

70

80

CWV [kg/m2]

50
45

T37H-T19H
40
35
30
25

T37V-19V
20
15

0

50

100 150 200 250 300 350 400 450 500

LWP [g/m2]

Figure 1. Calculated brightness temperatures as functions of columnar water vapor (CWV) or liquid water path
(LWP). (a) T21V  T19V and T19V  T19H as functions of (left) CWV and T37V  T37H and T37H of as functions of(right) LWP. (b) T19V  T10V and T19H  T10H as functions of (left) CWV and of (right) LWP. (c) T37V  T19V and
T37H  T19H as functions of (left) CWV and (right) of LWP. Solid, dashed, and dotted lines correspond to wind speeds(WS) of 1, 7, and 16 m /s, respectively.
whole range of variations in the droplet size distributions fornonprecipitating clouds because the brightness temperatures areinsensitive to the size distribution in the microwave frequencies
considered here. This presumption would be invalid if clouds areassociated with heavy rainfall, so that scattering by large rain-

drops dominates the 37-GHz brightness temperature. The present
analysis, however, is focused on low clouds, which are notprecipitating or are associated with warm rain, and in general,warm rain is not frequently so heavy. Note that our radiativetransfer code includes multiple scattering and is readily applicableMASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

TMI

VIRS
ch.1
(0.63)

ch.3
(3.7)

ch.4
(10.8)

10G
V

19G
V,H

37G
H

GlobalAnalysisDatacloud top
temperatureTc>273K
cloud
fraction

R e(NV)

columnwater
vapor

liquidwaterpath

Re(MV)

Figure 2. Overall flow of the combined algorithm is illustrated.
VIRS and TMI denote the Visible and Infrared Scanner andTRMM Microwave imager, repectively. Re(NV) is the cloud
effective radius retrieved from near-infrared and visible radiances,and Re(MV) is that which is retrieved from the microwave and thevisible. TMISST as the TMI-retrieved sea surface temperature,
which is provided by the Earth Observation Research Center(EORC) of the National Space Development Agency of Japan(NADSA). See section 2.3 for more detail.

to heavily precipitating clouds as well if precipitating drops areincorporated in the model assumptions.
2.3. Combined Algorithm[22] Overall flow of the algorithm for combined measurements
by VIRS and TMI is outlined in Figure 2. First, we perform VIRS
analysis to retrieve the cloud optical thickness, the effective droplet
radius, the cloud top temperature, and the cloud fraction (described
below in this section). The TMI analysis algorithm is applied tocloudy pixels identified by the VIRS algorithm, with cloud top
temperature given by VIRS outputs to specify the cloud top height
and water temperature. This procedure eliminates ambiguity in thecloud water temperature (see section 2.4), which would produce
large uncertainties in estimation of LWPmicr using only microwave
channels at frequencies lower than 37 GHz [Greenwald et al.,
1993; Lin et al., 1998a]. The cloud base temperature is specified by
the atmospheric temperature at the lifting condensation level
derived from the objective analysis archives.
[23] Cold clouds with the top temperatures <273 K areomitted because we restrict our target to liquid water clouds inthe present analysis. Our lack of knowledge in the opticalproperties of ice crystals, especially in the near infrared, prevents
us from precise estimation of the effective radius for ice clouds
by remote-sensing technique to date. Ice clouds, moreover, are
not detectable by microwave radiometers unless the channel
frequency is so large that scattering of ice crystals dominatesbrightness temperature.
[24] As mentioned in section 1, the beam-filling problem arisingfrom the different spatial resolutions between VIRS and TMI

1-5

should be properly taken into account before the data matching.
In our combined algorithm the cloud fraction fc is determined by
the ratio of the number of cloudy pixels to the total pixel
number (9 2 ) within a VIRS data segment (compare section 3).
This cloud fraction is considered as the beam-filling efficiency
for LWPmicr within the corresponding TMI footprint. The simulated brightness temperatures tabulated in the lookup table arethen modified as
Tbmod ðLWPÞ ¼ fc Tborg ðLWPÞ þ ð1  fc ÞTborg ðCSÞ;TMISST

opticalthickness

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ð5Þ

where Tbmod and Tborg are modified and original brightness
temperatures, respectively, and CS is clear sky. Current retrieval
schemes for LWPmicr yield an averaged quantity over a footprint
without correction of the beam-filling efficiency. Using (5) requires
caution when fc is very small because it could enlarge noise
associated with the surface emissivity to severely degrade cloud
signals. We therefore exclude the data with fc < 1/ 3 from analysis.
Impact of the cloud fraction correction on estimation of LWPmicr isdemonstrated in section 4.
[25] LWPmicr, in combination with the optical thickness, defines
the effective droplet radius Re(MV), i.e.,ReðMVÞ ¼

3LWPmicr;
2rw tcð6Þ

in the different way from another effective radius estimated fromvisible and near-infrared measurements Re(NV). Lin et al. [1998b]
also estimated Re(MV) using tc from the ISCCP database, wheretc is retrieved under the assumption that Re is constant at 10 mm.
On the other hand, our algorithm yields tc and Re(NV) selfconsistently by the two-channel method from shortwave measurement (section 2.1). We will discuss an intercomparison of Re(NV)and Re(MV) in section 4.
2.4. Validation and Error Analysis[26] Here we concentrate the validation study on the microwave
retrievals since the shortwave part of our retrieval algorithm has
been examined in detail by Nakajima and Nakajima [1995]. For
validation of CWV we examine the correlation of the microwaveretrieved CWV with the vertically integrated water vapor content
taken from global analysis (GANAL) data. On the other hand,ground truth data to validate satellite-retrieved LWP are very
limited. Although Greenwald et al. [1993] compared ground andshipborne radiometer measurements with satellite retrievals in theuse of histograms, sampling errors in LWP are not negligible formore quantitative validation. We therefore do not demonstrate thatground measurement validates LWP, but the frequency distributionof LWP is presented under the nearly clear-sky condition to checkif there is any bias in the retrieved LWPmicr. Comparison between
LWPmicr and LWPshrt is performed in section 4 as an interconsistency check of both the LWPs.
[27] Figure 3 shows a scatter diagram for CWV estimatedfrom microwave measurement and GANAL data. TMI-CWV is
in reasonable agreement with the GANAL product although data
points are scattered around the y = x line, where the RMS error
in retrieved CWV is estimated to be 4.8 kg /m2. One of thepossible reasons for the scatter is contamination of LWP associated with heavy precipitation. Precipitation water would
enhance brightness temperatures even in the low-frequency
channels, while scattering by large precipitating hydrometeors
would influence brightness temperatures in the 37-GHz andhigher-frequency channels. In such cases our current algorithm
could produce misestimation of CWV and LWP. The water cloudcriterion (see section 2.3) saves our retrievals from significant
contamination of large frozen hydrometeors. Mismatches in space
and time between TMI-CWV and GANAL-CWV would also

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MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS8070CWV(TMI) [kg/m2]

60

50

40

30

20

10

0
0

10

20

30

40
50
CWV(GANAL) [kg/m2]

60

70

80

Figure 3. Scatterplot for the TRMM Microwave Imager (TMI)-retrieved CWV (ordinate) versus CWV calculated
from global analysis (GANAL) (abscissa).

produce, in part, the scatter in Figure 3 as well as intrinsic errors
in GANAL-CWV.
[28] The frequency distribution of LWPmicr is shown in Figure 4
under the clear-sky condition in order to check possible bias inestimation of LWPmicr according to Lin and Rossow [1994].
Retrieved LWPmicr might not be exactly zero even in cloud-freeareas because of instrumental calibration problems and/or ambiguities in the model assumptions as to the surface emissivity. The
distribution function of LWPmicr under the clear-sky condition is
expected to be centered at LWP = 0, if no bias exists, with a finite
deviation due to the ambiguities. We consider the clear-sky
criterion as the regions where the cloud optical thickness is <3
instead of cloud-free areas, in a strict sense, since cloud top
temperature is required as an input to our microwave algorithm.
Figure 4 shows no significant bias in the peak position of the
distribution function. The retrieved LWPmicr therefore is assured tosuffer from no considerable bias without any artificial adjustmentin the algorithm.
[29] We evaluate error sensitivities in estimation of LWP tovarious sources of uncertainties according to Greenwald et al.
[1993]. Major sources of errors are incorporated in the error
estimation as @LWP 2@LWP 2
s2TOT
sSST
þ sSW
@SST
@SW

 

@LWP 2@LWP 2
þ sTc
þ sTcb@Tc@Tcb
@LWP 2
þ sCWV;
ð7Þ
@CWV
where sSST, sSW, sTc, sTcb, and sCWV are RMS errors in sea surface
temperature, near-surface wind speed, cloud top temperature, cloudbase temperature, and CWV, respectively, and sTOT is the resultant
RMS error in LWP.
[30] Derivatives in the right-hand side of (7) are estimated as,
for example,
@LWP
@SST



@LWP
@T3719@T3719
;
@SST

ð8Þ

where T37 – 19 T37H  T19H is supposed here to be exclusively
responsible for evaluation of LWP, which is a reasonableapproximation to our algorithm (section 2.2).
[31] Results of error analysis are summarized in Table 2. The
square root of each term in (7) is tabulated for LWPs of 50, 100,
150, and 200 g /m2, which cover majority of nonprecipitating
clouds, with CWV of 26 and 52 kg /m2. Here we assume that sSST,
sSW, sTc ; sTcb , and sCWV are 0.68 K, 2 m /s, 1, 5, and 4.8 kg/m2(see section 2.4) in RMS errors, respectively. Although all of thesevalues except sSST and sCWV are only crude estimates, they are
considered to be plausible representatives. The largest source oferror is attributed to CWV for the smaller CWV case, but
uncertainty in near-surface wind speed makes a comparable
contribution in more moist atmospheres, particularly for smallvalues of LWP. The decreasing tendency of the error due to CWVwith increasing CWV is clearly demonstrated in Figure 1c (left)of Figure 1 where |@T37 – 19 /@CWV| decreases with increasing
CWV to reach 0 for CWV > 40 kg/m2 (see equation (8)).
Ambiguities in the cloud temperature become significant withincreasing LWP as also found by Greenwald et al. [1993].
Uncertainties in cloud base temperature are not negligible forlarge values of LWP under a possibly pessimistic assumption ofsTcb = 5. On the other hand, cloud top temperature, determined
precisely by VIRS analysis, is found to provide no serious errors.
The total error ranges from 20 to 30 g /m2 for relatively dry

MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

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30

25

Relative Frequency

20

15

10

5

0
-400

-200

0
LWP [g/m2]

200

400

Figure 4. Frequency distribution of microwave-retrieved LWP (LWPmicr) for clouds with the optical thickness <3.
The ordinate is the relative frequency in the unit of percent, and the abscissa is LWP micr .
atmospheres, whereas it decreases down to the range 11 – 18 g /m2
for more moist atmospheres.

3. Data[32] We adopt the TRMM level 1 products (version 5) for VIRS
radiances (1B01) and for TMI brightness temperature (1B11).
VIRS radiance data are selectively arrayed into data segments,
each of which is assigned on the global grid of 0.25
0.25,
where a segment consists of 9
9 neighboring pixels. TMI
brightness temperatures are assigned on the common grid after
the uncovered data with the narrower VIRS swath are omitted.
Observational time difference measured at a certain ground pointcaused by difference in the scan geometry between VIRS and TMI,
1 min, is negligibly small.
[33] A VIRS footprint size is 2.1 km, and hence the 99segment covers a 19 km
19 km area. On the other hand, theeffective field of view (EFOV) of TMI is 63.2 km (down track)
9.1 km (cross track) for the 10.65-GHz channel, 30.4 km 9.1 km
for the 19.35-GHz channel, and 16.0 km 9.1 km for the 37.0-GHz
channel [Kummerow et al., 1998].
[34] Difference in spatial resolution between VIRS and TMI
channels, which can be a factor of 10 or more, could produce
large bias when combining the retrievals by these two sensors
unless proper correction is made. In our algorithm the cloud
fraction correction is made to reduce errors as described in
section 2.3.
[35] Discrepancy in spatial resolution between 37- and 19-GHz
channels, which are mainly responsible for retrieving LWP, would
be significant if observed cloud system has considerable spatial
variation in the scale of 30 km (the resolution of 19-GHz
channel) or less. The climatological interest, however, in thepresent series of our study is focused on low clouds, e.g.,
stratocumulus and stratus, which are typically extended to a muchlarger scale showing relatively homogeneous features. We there-

fore consider that the discrepancy between 37- and 19-GHz
channels would not cause serious errors in estimation of LWP
for our purpose.
[36] Analogous discussion can be made for discrepancy inspatial resolution between 19- and 10-GHz channels, which aremainly responsible for CWV. That is, estimated CWV would not
suffer from large errors if water vapor shows no great spatial
fluctuation within 60-km scale (the resolution of 10-GHz channel). In general, this assumption is not severely ruled out.
[37] Our algorithm requires some ancillary data. We adopt thedaily mean data of TMI-retrieved sea surface temperature(TMISST) [Shibata et al., 1999; M. Kachi et al., Sea surface
temperatures retrieved from TRMM Microwave Imager and
Visible Infrared Scanner, submitted to Journal of the Meteorological Society of Japan, 2001] for the sea surface temperature

Table 2. Estimated Errors in Liquid Water Path (LWP) of 50, 100,
150, and 200 g /m2 with CWV of 26 and 52 kg /m2a
LWP

SST

WS

Tc

CWV

Total

Relative
Error, %

50
100
150
200

1.50
1.53
1.56
1.59

8.78
8.95
8.96
8.81

CWV = 26 kg/m2
0.29
2.63
17.45
0.39
3.46
20.78
0.49
4.27
24.26
0.57
5.06
27.92

19.78
22.94
26.26
29.76

39.55
22.94
17.51
14.88

50
100
150
200

1.55
1.58
1.61
1.64

9.05
8.71
8.25
7.67

CWV = 52 kg/m2
0.50
5.43
3.19
0.59
6.25
6.36
0.68
7.05
9.71
0.75
7.84
13.24

11.15
12.58
14.66
17.29

22.30
12.58
9.78
8.64

a

Tcb

See equation (7). Other independent variables are fixed. Water vapor
scale height Hw = 2.3 km, sea surface temperature SST = 295 K, nearsurface wind speed WS = 5 m/s, cloud top temperature Tc = 275.5 K, andcloud base temperature Tcb = 285.25 K.

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for both visible/infrared and microwave analyses. The use of
TMISST has some advantages compared with previous studies. In
the current algorithms the ground surface temperature has beenobtained often by objective analysis archives, infrared radiometricmeasurement, or climatological data. Objective analysis archives,
however, interpolate local meteorological data acquired byground-based measurements in space and time, and the infrared-retrieved surface temperature is restricted into cloud-freeareas. Climatological data do not include regional or temporalvariations. Sea surface temperature retrieved by a spaceborne
microwave radiometer does not suffer from these difficulties andin particular, TMISST minimizes sampling errors for our TRMM
analysis.
[38] The water vapor correction required for the VIRS analysis
and the near-surface wind speed and lifting condensation level for
the TMI analysis are determined by the GANAL data, which are
6-hourly objective analysis archives provided by the Japanese
Meteorological Agency. The GANAL data, assigned on the
1.25
1.25 grid, are linearly interpolated onto the 0.25
0.25 grid for matching satellite data. The CWV estimated fromGANAL data is used as well for validation of the TMI-retrieved
CWV (section 2.4).

4. Results[39] In this section we show the results of global analysis toobtain the cloud physical properties, applying the algorithm to
TRMM data during January to March 2000. The global distributions of LWPmicr in a 3-monthly mean are illustrated in Figure 5.
Figure 5a shows LWPmicr derived without the cloud fraction
correction (section 2.3), i.e., assuming fc = 1 anywhere for reference. The spatial structure of LWPmicr in Figure 5a has beensmoothed out within the scale of 16 – 30 km, which is limited by
the resolution of TMI footprints of the 19.35- and 37.0-GHz
channels. LWPmicr without the cloud fraction correction is comparable with the current definition of LWP derived by microwave
measurement, but it could largely underestimate the actual mixing
ratio of the cloud liquid water when the cloud fraction is small.
The global distribution of the cloud fraction (Figure 5c) has
characteristic features, showing small values corresponding to
the frequent appearance of shallow convective clouds over thetropical and subtropical oceans (note that our cloud fraction is thatof low clouds, so that information on high clouds and deep
convective clouds is not included). Figure 5b shows LWPmicr withthe cloud fraction correction, where heavily precipitating regions
such as the Intertropical Convergence Zone (ITCZ) and theSouthern Pacific Convergence Zone (SPCZ) are clearly traced
by large values of LWPmicr. While LWPmicr without the cloud
fraction correction holds information on the total amount of theatmospheric liquid water over the globe, LWPmicr with the correction reflects more directly the physical properties of individualclouds. In particular, the cloud fraction correction is inevitable for
evaluating Re (MV) by (6).
[40] One may wonder at the apparent inverse correlation
between cloud fraction and LWP observed in Figure 5. This trend
is explained by the fact that shallow convective clouds over thetropical and subtropical oceans account for smaller cloud fractions,whereas coastal stratus and stratocumulus account for larger cloud
fractions. Oceanic shallow convective clouds, often associatedwith warm rain, typically have considerably larger LWP than
nonprecipitating stratiform clouds such as stratus and stratocumulus. The inverse correlation therefore reflects the regional variation
in low-cloud properties. This tendency, however, could be inverted
if some specific types of clouds are of interest. Positive correlation
between cloud fraction and LWP was found for stratus clouds[Zuidema and Hartmann, 1995] and for the North Pacific clouds
which are dominated by northern midlatitude storms [Greenwald
et al., 1995].

[41] Equation (1), where Re = Re (NV) in combination with (6),implies that LWPshrt is connected with LWPmicr in terms of theeffective droplet radius, i.e.,ReðNVÞ
LWPshrtghRe i¼¼:
LWPmicr ReðMVÞ ReðMVÞ

ð9Þ

As mentioned in section 1, g in (9) represents verticalinhomogeneity in the spatial distribution of the effective droplet
radius. Considering that LWPmicr and tc represent the entire
physical condition throughout the vertical extent of clouds, we may
substitute hRei by Re (MV) in (9) to findReðNVÞ
LWPshrt¼¼ g:
LWPmicr ReðMVÞ

ð10Þ[42] Brenguier et al. [2000] showed that hRei is smaller by thefactor of 5/6 than the cloud top value on the basis of theadiabatic model describing the microphysical evolution of aconvective closed parcel of moist air. Nakajima and King
[1990] demonstrated that Re (NV) approaches to the cloud top
value in optically thick clouds, and in this case we may replace gby 6/5 in (10). Nonprecipitating clouds therefore are expected toshow LWPshrt slightly larger than LWPmicr. On the other hand,
LWPshrt would be exceeded by LWPmicr if a large number ofcloud droplets grow to raindrops and disappear from the cloud
top as they fall down to below the cloud base since raindrops
below a cloud layer are detectable for microwave measurement
but not for shortwave.
[43] The scatter diagram of LWPshrt versus LWPmicr over the
globe is shown in Figure 6, where each plot and the associatederror bars correspond to the average and the standard deviations,
respectively, within a grid box of 2.5
2.5. The linear fitting
curve is given by LWPshrt = 31 + 0.75LWPmicr g /m2, and thecorrelation coefficient between LWPshrt and LWPmicr is 0.62. One
finds that data plots are largely scattered around the fitting curve,
the slope of which (0.75) is considerably smaller than 6/5. Thisimplies that such clouds as represented by the adiabatic model,e.g., nonprecipitating cumuli, are contaminated by other types ofclouds having LWPmicr larger than LWPshrt. A plausible interpretation is that Figure 6 includes precipitating clouds along with
nonprecipitating ones.
[44] Figure 7 is same as Figure 6 but only in a rectangular areaof 15S – 30S and 75W – 85W in western Chile, where stratus
clouds in the boundary layer are frequently observed. The linear
fitting curve for Figure 7 is given by LWPshrt = 22 + 1.26LWPmicr
g /m2 with the correlation coefficient of 0.91, which indicates thatthe adiabatic model is a much better representative of low clouds inthis area than in the global average (Figure 6). This result is
consistent with the fact that clouds in this area rarely provide heavy
rainfall.
[45] The global trend of both Re(NV) and Re(MV) is presented in
Figure 8 (top). In the upper panel, Re(MV) in the tropics andsubtropics shows very large values exceeding 50 mm, which areconsidered as the contribution of raindrops. Surrounding areas, on
the contrary, show much smaller Re(MV) below 10 mm. Areas withlarge Re(NV) also trace precipitating regions (Figure 8, bottom) but
are less pronounced than for Re(MV). Careful examination of Figure 8
reveals that Re(MV) is smaller than Re(NV), i.e., Re(NV)/Re(MV) = g > 1,
in the areas with little precipitation, such as western California,
Chile, and southern Africa.
[46] A probable implication inferred from these results issummarized as follows. Characteristic difference between LWPshrt
and LWPmicr closely reflects vertical inhomogeneity of the effective droplet radius within or below a cloud layer, which causes
discrepancy between Re(NV) and Re(MV). Clouds with LWPshrt

MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

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Figure 5. Three-monthly mean LWPmicr (a) without the cloud fraction correction, (b) with the correction, and (c) the
cloud fraction for January to March 2000. See color version of this figure at back fo this issue.
larger than LWPmicr (or Re(NV) larger than Re(MV)) are not precipitating. In contrast, clouds are expected to be associated with
rainfall if LWPshrt (or Re(NV)) is significantly smaller than LWPmicr(or Re(MV)). Furthermore, the intermediate stage, at which clouds
are drizzling near the cloud top but are not producing considerable
amount of raindrops, would be identified as well by closer
examination of Re(NV) and Re(MV).
[47] We therefore presume that the relation between LWPshrt
and LWPmicr, or more directly, between Re(MV) and Re(NV),reflects a microphysical mechanism to expedite or suppress theconversion of the cloud water into rainfall. X. Masunaga et al.
(Physical properties of maritime low clouds as retrieved by
combined use of Tropical Rainfall Measurement Mission Microwave Imager and Visible/Infrared Scanner, 2, Warm clouds andrain, submitted to Journal of Geophysical Research, 2001) will
investigate the microphysical evolution of low clouds in terms of
the relation and difference between Re(MV) and Re(NV), alongwith other possible interpretations such as nonadiabicity ofclouds.

5. Summary[48] In this paper a physical inversion algorithm is presented in
the combined use of visible/infrared and microwave sensors toretrieve the cloud physical quantities such as LWP and the effective
droplet radius. LWP is defined in two separate ways: where
shortwave measurement yields LWPshrt according to (1) and themicrowave retrieval directly provides LWPmicr. Correspondingly,
Re(MV) is determined by (6) in a different way from the shortwaveretrieved effective radius Re(NV).
[49] Our current algorithm is based on the sensor specifications of VIRS and TMI aboard the TRMM satellite. To analyze

1 - 10

MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

350

300

LWPshrt [g/m2]

250

200

150

100

50

0
-150

-100

-50

0

50

100

150

200

250

300

350

LWPmicr [g/m2]

Figure 6. Scatterplots between LWPmicr (abscissa) and shortwave-retrieved LWP (LWPshrt; ordinate) over the globe.
Each plot and the associated error bars correspond to the average and the standard deviations, respectively, within agrid box of 2.5 2.5. The solid line delineates the linear fitting curve, given by LWPshrt = 31 + 0.75LWPmicr g /m2.
The correlation coefficient is 0.61.

350

300

250

LWPshrt [g/m2]

AAC

200

150

100

50

0
-100

-50

0

50

100

150

200

250

300

350

LWPmicr [g/m2]

Figure 7. Same as Figure 6, but with the target area a rectangular region of 15S – 30S and 75W – 85W inwestern Chile. The linear fitting curve is given by LWPshrt = 22 + 1.26 LWPmicr (g /m2) with a correlation coefficientof 0.91.

MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

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1 - 11Figure 8. Three-monthly mean (top) Re (MV) and (bottom) Re (NV) for January to March 2000. See color version of this figure at back ofthis issue.
VIRS measurements, we follow a two-channel method as incorporated in the GLI algorithm, which was originally developed by
Nakajima and Nakajima [1995] and Kawamoto et al. [2001]. The
retrieval method for TMI measurement makes combined use of
T19V T10V and T37H  T19H to evaluate CWV and LWP bymeans of a lookup table based on radiative transfer simulations
performed under a set of the free parameters of sea surface
temperature, near-surface wind speed, cloud top temperature, andcloud base temperature. The cloud top temperature required for
the TMI analysis is taken from the VIRS retrieval, which
substantially reduces uncertainties in estimation of LWP. Total
errors in estimation of LWP are within 11 – 30 g /m2 for realistic
ranges of LWP and CWV. The beam-filling efficiency of clouds
for the TMI footprints is corrected by the cloud fraction evaluatedfrom the VIRS measurements.
[ 50 ] For application, global analysis is performed with
3-monthly data from January to March 2000. The cloud fraction
correction is demonstrated to have a large impact on estimation of
LWPmicr, in particular, in the low-latitude regions (Figure 5).
[51] The scatter diagram of LWPshrt versus LWPmicr for clouds
in western Chili, which are typically nonprecipitating stratus
clouds, shows a high linear correlation with the slope of the fitting
curve of 6/5. This is explained, through the close relation
between the LWPs and the effective radii (10), by the verticalinhomogeneity of droplet size in a cloud layer, as derived by
Brenguier et al. [2000] on the basis of the adiabatic cloud model.
In contrast, the fitting curve has the slope <6/5 for the global
average with worse correlation mainly because of contamination ofclouds with LWPmicr larger than LWPshrt. Precipitating clouds
account for this excess of LWPmicr over LWPshrt due to thepresence of raindrops below the cloud base.

[52] These tendencies are more widely recognized by examination of the global distributions of Re(MV) and Re(NV), where
Re(MV) largely exceeds Re(NV) in precipitating regions such as
ITCZ and SPCZ, but the relation is inverted in the areas withlittle precipitation, e.g., western California, Chile, and southern
Africa. The result implies that the difference between Re(MV) andRe(NV) reflects a microphysical mechanism to expedite or suppress the conversion of the cloud water into rainfall. X. Masunaga et al. (manuscript in preparation, 2001) will be dedicated toclarify the origin of the discrepancy between Re(MV) and Re(NV),along with other possible interpretations such as nonadiabicity ofclouds.
[53] Acknowledgments. The authors are grateful to the anonymousreferees for helpful comments, which have improved the manuscript.
VIRS and TMI data were provided by NASA Goddard Space Flight
Center. Japan Meteorological Agency provided GANAL data. TMISST(version 2.0) was produced and supplied by NASDA/Earth Observation
Research Center.

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

M. Kachi, H. Masunaga, T. Y. Nakajima, and R. Oki, Earth
Observation Research Center/NASDA, Harumi Island Triton Square,
Office Tower X 22F, 1-8-10 Harumi, Chuo-ku, Tokyo 104-6023, Japan.
(kachi@eorc.nasda.go.jp; masunaga@eorc.nasda.go.jp; nakajima@eorc.
nasda.go.jp; oki.riko@nasda.go.jp)
S. Kuroda, Sun Microsystems, Inc., 4-10-1 Yoga, Setagaya-ku, Tokyo
158-8633, Japan. (shunsuke.kuroda@sun.co.jp)
T. Nakajima, Center for Climate System Research, University of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan. (teruyuki@ccsr.
u-tokyo.ac.jp)

MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

a.

b.

c.

Figure 5. Three-monthly mean LWPmicr (a) without the cloud fraction correction, (b) with the correction, and (c) the
cloud fraction for January to March 2000.

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MASUNAGA ET AL.: PHYSICAL PROPERTIES OF MARITIME LOW CLOUDS

R e(MV)

Re(NV)

Figure 8.

Three-monthly mean (top) Re(MV) and (bottom) Re(NV) for January to March 2000.

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