validation of cloud property retrievals from mtsat-1r imagery using modis observations

Validation of cloud property retrievals from MTSAT-1R imagery usingMODIS observations

YONG-SANG CHOI†‡ and CHANG-HOI HO‡*

†Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of

Technology, Cambridge, Massachusetts, USA

‡School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea

(Received 21 September 2007; in final form 17 February 2008)

A cloud property retrieval algorithm optimized for five channels (centred at 0.6,

3.7, 6.7, 10.8, and 12.0 mm) has been explored for application to onboard meteor-

ological radiometers on geostationary satellites; however, its validity remains to be

established. Here, we present validation results for the cloud properties retrieved

by the developed algorithm from the full-disk imagery of the Multi-functional

Transport Satellite (MTSAT-1R) for August 2006. The considered cloud proper-

ties include cloud phase (CP), cloud optical thickness (COT), effective radius (ER)

and cloud top pressure (CTP). Their one-month averages, daily variations, and

respective collocated values are compared with the Moderate Resolution Imaging

Spectroradiometer cloud data. Our validation results show that an additional

6.7 mm brightness temperature test in CP retrieval identifies water and ice phases

that may be overlooked in the 10.8- and 12.0-mm bands. Our method to extract

cloud-reflected radiances at the 0.6- and 3.7-mm bands contributes to the accuracy

of the COT for values between 5 and 60, and the ER for values less than 40 mm.

Estimating high-cloud top pressure from the radiance ratio in the 6.7- and 10.8-mm

bands remarkably reduces (by up to 70%) large uncertainties in the CTP, which

may be found in the presence of high thin cirrus clouds.

1. Introduction

Atmospheric parameters retrieved from satellite algorithms contain inevitable uncer-

tainties that may originate from both modelling and physical sources. Reflection or

emittance function look-up libraries generated from radiative transfer (RT) models

for the retrieval algorithm have model-induced biases; further, physical instrument

errors and various atmospheric effects cause significant uncertainties (King et al.

1997). The uncertainties are even amplificatory, particularly in temporally and spa-

tially heterogeneous fields of measurement, such as cloud properties that include the

cloud phase (CP), cloud optical thickness (COT), effective radius (ER), cloud toppressure (CTP), etc. Considering that clouds play a crucial role in the Earth’s hydro-

logical cycle due to their large and varying areal coverage as well as their complex

microphysical properties, the validation of retrieved cloud properties is an essential

task for improving our understanding of the Earth’s weather/climate and guarantee-

ing a reliable product.

*Corresponding author: Email: hoch@cpl.snu.ac.kr

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2009 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160902791887

International Journal of Remote Sensing

Vol. 30, No. 22, 20 November 2009, 5935–5958

Validation of the cloud property retrievals has been performed on a case-by-case

basis by using in situ measurements onboard aircraft and/or by intercomparison

with other collocated data from satellite instruments (e.g. Nakajima and Nakajima

1995, King et al. 1997, Baum et al. 2000). Ground-based measurements of cloud

radiative and microphysical properties using lidars and radars are useful for valida-tion (e.g. Mace et al. 2005). However, these methods for the identification of the

cloud top and optical thickness have significant limitations, such as their insensi-

tivity to small-sized particles at the cloud top and/or the attenuation of the light

beam by low thick clouds (Clothiaux et al. 2000); accordingly, such ground mea-

surements of cloud properties contain additional uncertainty. The Moderate

Resolution Imaging Spectroradiometer (MODIS) instrument, a reliable source of

satellite data, measures various cloud properties via 36 visible and infrared (IR)

spectral bands (King et al. 1997). The prominent capacity for retrieval by MODISdue to the advent of the informative bands has made the MODIS cloud properties

useful references for the validation of the cloud properties retrieved using other

satellites.

To create a weather service with a higher temporal resolution using the

Communication, Ocean and Meteorological Satellite (COMS) imager scheduled to

be launched in 2009 in Korea, a new cloud analysis algorithm (CLA) was explored on

the basis of five channels (centred at 0.6, 3.7, 6.7, 10.8 and 12.0 mm) from weather

imagers in geosynchronous earth orbit (Choi et al. 2007). The algorithm introducesthe following retrieval methods: (i) CP based on spectral absorptivity in the 6.7m m

band in addition to the 10.8- and 12.0-mm bands; (ii) COT and ER using a combina-

tion of cloud-reflected radiances in the 0.6- and 3.7-mm bands through thermal

correction; and (iii) CTP using the brightness temperature in the 10.8-mm band for

optically thick clouds (COT . ,10), and the radiance ratio in the 6.7- and 10.8-mm

bands for optically thin high clouds (CTP � 400 hPa). These three methods were

developed based on RT theory.

The retrieval methods have been previously validated by comparing MODIS clouddata and the collocated cloud properties from CLA by using the MODIS level-2

calibrated radiances (Choi et al. 2007); however, these validation results may indicate

only the uncertainty induced by RT modelling. Because the MODIS radiances have

spectral information within finer bandwidths than those of most five-channel meteor-

ological imagers, the results may not indicate the substantial physical uncertainties

that would emerge from the coarser bandwidths of these imagers. The Japanese

Advanced Meteorological Imager (JAMI) onboard the Multi-functional Transport

Satellite (MTSAT-1R) is also a five-channel weather imager and has been in operationsince 28 June 2005 (Puschell et al. 2006). JAMI has almost the same response func-

tions as the COMS imager, and its orbit and field-of-view (FOV) are similar to those

of the COMS satellite. Therefore, the imagery of JAMI can be used as an input for the

CLA, which produces the cloud properties to be validated.

The present paper attempts to validate the pre-performed CLA algorithm by using

hourly MTSAT-1R imagery via comparison with the MODIS cloud data. A detailed

description of the data and methods is provided in §2. We begin with large-scale and

time-series analyses of cloud products derived for August 2006 (§3). The duration ofone month is determined by considering the limited computational space; however,

hemispheric FOV for the duration considered retains all kinds of sufficient condi-

tions, including surface and cloud types, atmospheric gas profiles, geometry, etc.

Since one of the objectives of the COMS imager is severe weather forecast, we

5936 Y.-S. Choi and C.-H. Ho

subsequently examine the performance of the algorithm during the lifespans of

tropical cyclones (Saomi and Bopha in this study) on the pixel level. The scenes of

tropical cyclones varying dynamically from their generation to decay are overcast by

various types of clouds; this allows investigation of the capacity of the algorithm to

diagnose the stage of a tropical cyclone based on the changes in cloud properties.Finally, a summary and discussion are provided in §4.

2. Data and methods

2.1 MTSAT-1R radiance and MODIS cloud products

We used the hemispheric full-disk images of hourly JAMI/MTSAT-1R calibrated

radiance (or converted brightness temperature) for August 2006 as inputs for the CLA

developed by Choi et al. (2007). The spatial resolution of the JAMI radiances and the

corresponding geometrical angles used in this study are 4 km · 4 km and 0�, 20�, 40�,60� and 80�. The five JAMI onboard spectral channels are centred at 0.725 (hereafter

VIS), 10.8 (IR1), 12.0 (IR2), 6.75 (IR3), and 3.75 mm (IR4). The full-disk image covers

East Asia, West Pacific, Australia, and a part of the Antarctic (80.5� S-80.5� N, 60.4�

E-139.4� W), which may be similar to the COMS imagery, although its final location

has not yet been determined (figure 1).

Figure 1. JAMI/MTSAT-1R radiance imagery for the five spectral channels centred at 0.725(VIS) (a), 10.8 (IR1) (b), 12.0 (IR2) (c), 6.75 (IR3) (d), and 3.75 mm (IR4) (e) for 0333 UTC 7August 2006. Except for the VIS channel, the brighter colour corresponds to a relatively lowvalue in W m-2 sr-1 mm-1. The full-disk imagery covers East Asia, West Pacific, Australia, and apart of the Antarctic region (80.5� S-80.5� N, 60.4� E-139.4� W).

Validation of MTSAT-1R cloud property 5937

To compare with the cloud products from the JAMI imagery on the pixel level, we used

the MODIS cloud product (MOD06, collection 5) data that contain various retrieval

results of cloud properties such as the CP and CTP (COT and ER) at a 5 km · 5 km

(1 km · 1 km) nadir resolution (Platnick et al. 2003). Details of the improvements in the

collection 5 data over those of previous collections can be found elsewhere (Baum et al.

2005, King et al. 2006, Yang et al. 2007). In this study, we selected the MOD06 granules in

the north-western Pacific (10�–30� N, 113�–149� E) during 5–11 August 2006.

The recent CP product in MOD06 is derived in two ways: the bispectral IR test,

and the shortwave IR test. The bispectral IR technique using the 8.5- and 11-mm

bands is a simplified version of the trispectral IR technique (Strabala et al. 1994)

using the 8.5-, 11- and 12-mm bands (Menzel et al. 2006). The shortwave IR test uses

visible, shortwave IR and thermal bands, and is a part of the cloud optical property

module (King et al. 2006). Since the shortwave IR test uses more information, ityields more reliable estimates. However, this is only available during the daytime

and the capability cannot be applied to MTSAT-1R. Thus, we used the MODIS CP

data derived from the bispectral IR test. This CP product includes the following

categories: water, ice, mixed, and uncertain phases.

The COT and ER in MOD06 are the total-column values determined by the

combination of visible channels and a near-IR channel (0.6, 0.8, 1.2, and 2.1 mm).

The minimum detectable COT in MOD06 is about 0.1 (Choi et al. 2005), and the

maximum is set to 100. For ER, the valid retrieval range is within 2 and 30 mm (5 and90 mm) for liquid (ice) clouds. Both COT and ER are retrieved to the second decimal

place. To compare with the MODIS data, the retrieval extent of the COT and ER in

the CLA was set to be the same.

On the other hand, the CTP in MOD06 is retrieved by a CO2 slicing method (also

called radiance ratioing method) using CO2 absorption bands within 13.2–14.4 mm

(Menzel et al. 1983, 2006). The CTP has an interval larger than 10 hPa between 95 hPa

and 1040 hPa, which is finer than the obtainable CLA products (i.e., 50 hPa within

100 hPa and 1000 hPa).The MODIS gridded level-3 daily atmospheric product (MOD08, collection 5) is also

collected for the analysis period. MOD08 contains the 1� · 1� gridded values calculated

from the MOD06 cloud properties. We used MOD08 to calculate the averages for the

analysis period and the time-series of the cloud properties for the given grids.

2.2 Cloudy scene identification

We identified ‘cloudy’ pixels from the images by a bispectral test using the VIS and

IR1 channels before using the CLA to retrieve the cloud properties. The bispectral test

for cloud detection is based on the International Satellite Cloud Climatology Project

(ISCCP) algorithm (Rossow and Garder 1993a), but it is simplified as in the followingrelations:

Clear :ðTclrIR1 � TIR1Þ � �IR and ðLVIS � Lclr

VISÞ � �VIS (1a)

Cloudy :ðT clrIR1 � TIR1Þ>�IR or ðLVIS � Lclr

VISÞ � �VIS; (1b)

where TIR1, TclrIR1, LVIS, and Lclr

VIS are, respectively, the IR1 total-sky brightness

temperature, the IR1 clear-sky brightness temperature, the VIS total-sky radiance,

and the VIS clear-sky radiance. Note that LVIS is a scaled radiance in percentage, as

used in the ISCCP algorithm. The values of dIR and dVIS are 12.0 K and 6.0% for land

(3.5 K and 3.0% for ocean), respectively. The validity of the cloud detection must be

judged primarily by the accuracy of the clear-sky radiances (see Rossow and Garder

1993b). In this work, T clrIR1 (Lclr

VIS) is obtained as the maximum (minimum) observed

value for each UTC for the 31 days of August 2006. dVIS is the same as that of theISCCP, but dIR is higher than the values given by Rossow and Garder (1993a) (by 6 K

for land and 1 K for ocean) due to higher IR clear-sky values. At night, only the IR

conditions in equation (1) are used.

The cloud fraction in the FOV obtained by this method is 57.3% on average for

August 2006, which is comparable with other measurements of global cloud fractions:

62.7%, 61.2%, 61.4% and 51.8% in the ISCCP C2 dataset (1984–1988), gridded surface

weather station reports (SOBS) (1971–1981), METEOR (1976–1988), and Nimbus-7

(1980–1984) images, respectively (Rossow et al. 1993). It is noted that the cloud fractionidentified using the five channels of MTSAT-1R is much less than that from MODIS

(77.6% for the same period and domain of this study), since the MODIS cloud

discrimination, which includes thin cirrus clouds, is much more robust due to the use

of about 18 different bands and a smaller FOV. The underlying uncertainties in the

cloud mask may exist in the derived MTSAT-1R cloud properties; however, the thin

clouds overlooked by our simple thresholds will have little impact on COT and ER.

2.3 Algorithm description and adjustment

Since MTSAT-1R imagery is used as the input for the CLA, all the look-up libraries

and thresholds in the CLA should be set for the MTSAT-1R spectral response

functions and images (see Puschell et al. 2006). The libraries and thresholds were

recalculated in the same manner as described in Choi et al. (2007) by using the discrete

ordinates radiative transfer (DISORT) models (Stamnes et al. 1988): Streamer

(Key and Schweiger 1998) and Santa Barbara DISORT Atmospheric Radiative

Transfer (SBDART) (Ricchiazzi et al. 1998). In brief, the model limitations to beaddressed are the following. The parameterization of the scattering of nonspherical

ice crystals is imperfect in the two DISORT models. The only gases considered are

water vapour, oxygen, carbon dioxide and ozone. No atmospheric refraction or

spherical geometry is included; therefore, the results for solar zenith angles over

about 70� are subject to errors in Streamer. Further, Streamer exhibits a number of

problems in simulating radiance for near-IR bands (3.4–4.0 mm), since both the

reflected and emitted energy values are factored in (Key 2002). These problems may

affect the CLA retrievals in various ways.The CP algorithm consists of brightness temperature threshold tests using the IR1,

IR2 and IR3 channels. Choi et al. (2007) noted that the IR3 band is a useful alter-

native when the 8.7-mm band is absent; this should be validated in the present study.

Our RT modelling for the MTSAT-1R spectral response function reveals that some

clouds with a mixed phase can have a brightness temperature in the IR3 band (TIR3)

between 228 K and 239K. Thus, the IR3 threshold for ice phase discrimination is

changed to the more rigorous values of 228 K and 239 K from the original 234 K and

250 K in order to prevent false identification of an ice phase from the MTSAT-1Rradiance (compare with table 2 in Choi et al. 2007). The changed criteria and test logic

for determining a cloud phase are shown in table 1.

The COT and ER values are derived simultaneously by using the cloud-reflected

radiance components in the VIS and IR4 channels – the sun reflection method. The

desired cloud-reflected components can be decoupled from the undesired ones such as

the ground-reflected components, and the cloud and ground thermal radiances can be

resolved by the rapid method using only one look-up library, which should also be

validated in this study. The COT and ER look-up library comprises the VIS and IR4

radiances, various geometric angles (0�, 20�, 40�, 60� and 80�), and ground albedo

(Ag = 0 and 0.5). Here, we used Ag = 0.5 instead of unity suggested by Choi et al.(2007) because most values of Ag in nature are lower than 0.5 in the MTSAT

FOV. Theoretically, the ground-reflected radiance changes almost linearly in propor-

tion to Ag under the assumption of very small multiple reflections between the ground

and the upper layer (Choi et al. 2007). Once the angular variables and Ag are known

for a given pixel, the simulated thermal-free radiance (i.e. the sum of the cloud- and

ground-reflected radiances) for Ag = 0 is subtracted from that for Ag = 0.5 in the look-

up table and multiplied by a given Ag. In this study, the Ag database used for

individual pixels is the space-dependent (in 2.5�) one-month average from theEuropean Centre for Medium-Range Weather Forecasts, while MODIS uses a

quite sophisticated Ag database with higher temporal (16 day) and spatial (2 km)

resolutions (Moody et al. 2005, 2007).

To increase computational efficiency, the COT and ER libraries were calculated by

using a single wavelength of the peak of the spectral bandwidth, i.e. 0.725 and 3.75

mm. King et al. (1997) described that possible model uncertainties can be induced by

using four factors – a single wavelength, spectrally averaged radiance, a shift in the

central wavelengths, and false optical constants. Among these four factors, the errorin the COT and ER retrievals caused by using a single wavelength is known to be

relatively small if the spectral centre is chosen accurately. While not shown in the

figure, the spectrally averaged radiances weighted with several wavelengths for the

IR4 (or VIS) band have an almost exact linear relationship with the radiance of a

single wavelength. However, this linear relationship is not well represented for solar

and satellite zenith angles greater than or equal to 60�. Therefore, we retrieved the

COT and ER only in the limited daytime FOV with solar and satellite zenith angles

less than 60� (see the second and third panels in figure 2).The CTP is retrieved by a combination of two methods: the IR-window channel

(simply IR1) estimate and the radiance ratioing method. The IR1 estimate is a typical

method in which the TIR1 value is compared with the vertical temperature profile in

the area of interest. This method is valid only for thick clouds (COT� 10) (Choi et al.

2007). In the radiance ratioing method, the observed radiance ratio defined by

LIR1 � LclrIR1

� �� LIR3 � Lclr

� �where the subscripts and superscripts have similar

meanings to those used in equation (1) is compared with the simulated radiance

ratio. Since the radiance ratio is a function of the CTP regardless of the opticaldepth, the method compensates for the limitation of the IR1 estimate for thin clouds

(COT , 10). The radiance ratioing method using the IR1 and IR3 bands must be

Table 1. Criteria (required tests) for determining cloud phase.

Ice Mixed Water

TIR1 , 238 K or For no ice For no ice/mixedTIR1-TIR2 � 4.5 K or 238 K � TIR1 , 268 K and TIR1 � 285 K orTIR3 , 228 K 228 K � TIR3 , 239 K TIR3 � 239 K

100 1000hPa

IceMixedWater

Saomai

0 64µm

(i ) Base products (ii ) Final products

100 1000hPa

ptical th

IceMixedWater

ressure

Saomai

0 64µm

ective

Figure 2. Cloud properties derived by the CLA from the JAMI level-1b calibrated radiancesshown in figure 1. Cloud phase (a), cloud optical thickness (b), effective radius (c), and cloud toppressure (d) are shown. Base products (i) are the results of conventional methods or withoutcorrection methods, and final products (ii) from improved methods or with the correctionmethods developed in the present study.

validated in this study. The CTP library that includes the relation between the

radiance ratio and the CTP is calculated for the standard profiles of the tropics,

mid-latitude summer, and mid-latitude winter (Ellingson et al. 1991) and applied to

the ranges of latitudes 30� S-30� N, 30–60� N, and 30–60� S, respectively.

2.4 Validation methods

Since we wish to validate the methods, two types of cloud products are retrieved,

namely, base products and final products, in a stepwise manner from the previous or

conventional algorithm and the present algorithm, respectively. In detail, the base CP

is retrieved using the IR1 and IR2 channels only, and the final CP using the IR1, IR2

and IR3 channels in order to determine the role of the IR3 test. Similarly, the base

COT and ER are retrieved without using the decoupling method, and the final COTand ER are retrieved using the decoupling method. The CTP is retrieved simply by

IR1 estimate (base CTP), and corrected by the radiance ratioing method (final CTP).

The terms of the products used in this analysis are summarized in table 2.

We compared the base, final and MODIS products by the following four-fold

procedure: (i) scene analysis, (ii) large-scale climatology comparison, (iii) time-series

comparison and (iv) pixel-by-pixel comparison. Scene analysis was the first step to

validate the cloud products with the JAMI imagery. Then, we analysed the large-scale

climatology of the cloud products for August 2006 in comparison with the MOD08data. The climatology was calculated for many restricted conditions such as August

2006, daytime (solar zenith angle , 80�), night-time, liquid clouds, ice clouds, north-

ern and southern hemispheres, and polar (latitude 60–90�), tropical (equator-30�),and mid-latitude regions (30–60�). These conditions are necessary because the average

values of the parameters do not reveal the underlying factors that cause biases.

Next, the daily variation in the cloud products was examined in 1� · 1� regions of

interest for August 2006. The regions were selected considering various surface condi-

tions including land, ocean, desert and snow/ice, as well as different latitudes includingtropics, mid-latitudes and polar regions; the selected nine regions were Seoul, the north-

east China plain, the Gobi Desert, the Tibetan Plateau, the South China Sea, the

Philippine Sea, the East Pacific, the Bering Sea and the Antarctic region.

Table 2. Definitions of terms used in this analysis.

Term Unit Definition

Base CP unitless Cloud phase is determined by TIR1 and TIR1-TIR2 tests intable 1.

Final CP unitless Cloud phase is determined by TIR1, TIR1-TIR2, and TIR3

tests in table 1.Base COT/ER unitless/mm Cloud optical thickness and effective radius are roughly

retrieved by using measured VIS and IR4 radiances thatremain to include both thermal and reflected components.

Final COT/ER unitless/mm Cloud optical thickness and effective radius are retrieved bythe sun reflection method that uses the decoupledradiances, i.e. cloud-reflected components.

Base CTP hPa Cloud top pressure is retrieved by the IR1 window estimate.Final CTP hPa Cloud top pressure is retrieved by both the IR1 window

estimate and the radiance ratioing method using IR1 andIR3 radiances.

Finally, the retrieved cloud properties were also analysed on the pixel-level scale to

comprehend their error ranges. We compared the MOD06 collection 5 cloudy pixels

and the derived cloud properties from the JAMI images. In this analysis, we selected

the north-western Pacific (10–30� N, 113–149� E) during 5–11 August 2006. Various

types of clouds can be detected since many tropical cyclones passing the area arecharacterized by well-organized deep convection with intense cyclonic winds and

spiral rain bands (c.f. Kim et al. 2006). In order to avoid the possible mismatch due

to the temporal and spatial differences between the bi-daily MODIS and hourly JAMI

images, we allowed a 50-km distance within 30 min, considering the wind trajectories.

Under this condition, we obtained about 2 160 000 cloudy pixels for the analysis (half

for COT/ER). Note that the cloud properties were produced for each given cloudy

pixel at a 4-km resolution by the CLA, while the MOD06 contains the CTP and CP

(COT and ER) at a 5-km (1-km) resolution.

3. Results

3.1 Scene analysis

Figure 1 shows an example of JAMI radiance imagery for 0333 UTC 7 August 2006.Clouds are clearly shown along the intertropical convergence zone (ITCZ) as are

tropical cyclones (Saomi and Bopha centred at 18� N, 139� E, and 23� N, 129� E,

respectively). In the VIS image, optically thicker clouds appear brighter due to solar

light reflection, and they are well distinguished from darker surfaces (e.g., the ocean)

(figure 1(a)). In the IR images, the brighter colour corresponds to relatively lower

values (figures 1(b)–1(e)). Thus, higher clouds appear brighter due to low emitted IR

radiance from the cloud top. It is noted that only high clouds (,,400 hPa) appear

brighter in the IR3 channel due to the strong water vapour absorption in the low- andmid-troposphere, while low clouds are distinguishable in the atmospheric window

channels, i.e. IR1 and IR2. The IR4 radiance is generally higher for smaller-sized

cloud particles and liquid clouds than for larger particles and ice clouds if the other

conditions are identical.

Allowing for the spectral characteristics of the five images, we may infer three major

features in the example: (i) the presence of very high and optically thick clouds in the

tropical West Pacific, including the known tropical cyclones Saomai and Bopha

(characterized by high VIS, low IR1, and low IR2 radiances); (ii) the presence ofhigh thin clouds in the East Pacific (characterized by low VIS, low IR1, and low IR2

radiances); and (iii) the presence of widely spread low thin clouds and coexistent high

thick clouds over the south-western seas of Australia (characterized by the widespread

region of low VIS, high IR1, and high IR2 radiances, and the branch-shaped area of

high VIS and low IR3 radiances running through the widespread cloud fields). Later,

we will relate the inferred features in points (i) to (iii) with the cloud properties

retrieved by the CLA.

Figure 2 shows the cloud properties retrieved by the CLA for the same period as infigure 1. The CP (a), COT (b), ER (c), and CTP (d) are retrieved in a stepwise fashion

from the base products (i) to the final products (ii). Overall, noticeable differences

exist between the base and final products. The final CP shows more water clouds and

fewer uncertain clouds (white) over the globe than the base CP (the first panel). The

final COT (ER) values in the centres of tropical cyclones and ITCZ are larger than the

base COT (ER) (the second and third panels). More clouds have low CTP (i.e. higher

top) in the final product in the East Pacific than in the base product (the fourth panel).

These differences make the three abovementioned features inferred from the radiance

scenes in figure 1 more noticeable in the final products than in the base products. Very

high and thick clouds are found more clearly in the tropical West Pacific in the final

products than in the base products. Although the optical properties of some clouds

are not retrieved due to high geometric angles, the final CTP and COT more obviouslyshow the high thin clouds in the East Pacific and the branch-shaped high thick clouds

over the south-western seas of Australia than the base products.

3.2 Large-scale climatology comparison

In this section, the retrieved cloud properties are compared quantitatively with the

MODIS retrievals for August 2006. Table 3 shows the one-month climatology of the

final, base, and MODIS CP. Overall, there are large differences between the derived CPand the MODIS CP. Water and ice clouds are noticeably more prevalent in the final CP

than the base CP, and their proportion is closer to that in the MODIS CP. This indicates

that the additional water and ice clouds in the final CP are identified by adding the IR3

test to the conventional IR1 and IR2 tests. Without the IR3 test, a large number of

cloudy pixels remain undecided. This improvement is actually seen regardless of the

local time and region, as shown in table 3. Meanwhile, mixed clouds are relatively

smaller in the final CP than in the base CP. This reduction in mixed clouds should be

due to the additional IR3 test. Despite these improvements in the final CP, a largeamount – to the extent in the MODIS CP (roughly 15%) – of clouds remain unknown.

The characteristics of the CP in the polar regions where the averaged temperature is

less than -30�C at all levels (-40�C over 700 hPa) are noteworthy. Our final CP has a

large portion of ice and mixed phases in this region (54.8 + 36.3 = 91.1%) and a

negligible portion of water phase (0.7%). Interestingly, the MODIS bispectral IR

algorithm provides a much larger number of water clouds (28.4%) and large unknown

clouds (17.4%). Some middle-level clouds with temperatures greater than about

-20�C can contain supercooled water droplets that coexist with ice particles(Liou 2002). Recent reports from field/aircraft-based experiments in the Arctic also

indicate that cloud particles can exist in the liquid phase at temperatures down to

-30�C (Shupe et al. 2006, Verlinde et al. 2007). However, this remains to be clarified in

further analysis with more observational evidence.

Table 3. Comparison of cloud phases from the JAMI/MTSAT-1R and the MODIS algorithm inAugust 2006. The numbers indicate the percentage of cloud phase (water/ice/mixed/uncertain)

over the total cloud fraction. All results are calculated in the FOV of JAMI.

MTSAT CP

Domain Final Base MODIS CP

GlobalAugust 39.4/24.6/20.5/15.5 20.9/19.1/29.2/30.8 50.3/29.5/5.6/14.7Day 34.2/28.2/18.2/19.4 20.9/21.6/26.0/31.4 53.2/26.7/5.1/15.0Night 34.5/29.2/21.1/15.2 16.0/20.4/32.9/30.8 46.1/33.4/6.4/14.2

Northern hemisphere 30.2/29.1/22.3/18.3 21.8/23.7/29.4/25.1 48.1/32.4/4.4/15.1Southern hemisphere 50.6/19.0/18.2/12.2 19.9/13.5/29.0/37.6 52.5/26.5/6.7/14.2Polar 0.7/54.8/36.3/8.2 0.2/31.5/59.8/8.5 28.4/40.3/14.1/17.4Mid-latitude 28.9/19.6/29.8/21.7 8.8/12.9/40.4/37.9 52.7/23.8/6.8/16.7Tropical 46.1/25.7/15.3/12.9 27.7/21.6/22.5/28.2 55.1/30.6/2.0/12.3

In the mid-latitudes and tropics, a similar contrast is also revealed (i.e. large ice and

mixed clouds in the derived CP versus large water clouds in MODIS). Moreover,

mixed clouds hold a larger portion in the derived CP than in the MODIS CP, and

clouds of this type were possibly flagged as being in the water phase in MODIS. The

high proportion of mixed phase in the derived CP as compared with that inthe MODIS CP is perhaps because the key channel (e.g. 8.5 mm), which is used in

the MODIS algorithm to identify a mixed phase, is absent in our algorithm. Since it is

known that the MODIS CP algorithm fails to identify high or cirrus clouds as

belonging to the ice phase (Menzel et al. 2006), the comparison in this study recon-

firms that contemporary CP algorithms from passive IR radiometers are still

imperfect.

Despite large differences between the final CP and the MODIS CP, the differences

are comparable to some extent under reciprocal conditions. The night-time CP hasmore clouds of the ice phase than the daytime CP in both the final and the MODIS

data, indicating the presence of a diurnal variation in CP. Further, the northern

hemispheric CP has more of the ice phase than the southern hemispheric CP in both

products. This is because the ITCZ is located in the northern hemisphere in August

(see figure 2). However, these differences cannot be seen in the base CP.

The relative frequency of the one-month climatology of the COT is examined for all

the clouds for various conditions (figure 3). The bin size is set to a value based on the

criteria of the COT for the ISCCP cloud classification (Rossow and Schiffer 1999).The dotted, solid and thick solid lines indicate the base, final and MODIS products,

respectively. The derived COT is generally underestimated in comparison with the

MODIS COT. The highest frequency is seen in bins 4–9 and 9–25 in the final and

MODIS COT, respectively. The frequency of ‘COT , 4’ is greater in the derived data

than in the MODIS COT data, while the frequency of ‘COT = 9–25’ is less in the

derived data than in the MODIS COT data in all cases. This systematic bias may

result from the modelling calculation, and it should be minimized by tuning the COT

library.The bias can be also induced by the different reflectivity between liquid and ice

clouds. In figure 3, the bias is much larger for liquid clouds than for ice clouds. Water

droplets reflect solar insolation more effectively than ice crystals; consequently, the

reflected radiance from the liquid clouds is greater than that from the ice clouds for

the same COT. Accordingly, the CLA can underestimate the COT if ice clouds are

falsely identified as liquid clouds. This indicates the important role of the CP classi-

fication result in the COT retrieval.

A similar analysis result is shown for the ER in figure 4. Our derived ER is slightlyless than the MODIS ER. However, the bias between them is relatively small in

comparison with that of the COT (compare figures 3 and 4). In comparison with

the liquid and ice phases, the frequency of ER in both the datasets shows consistently

smaller liquid particles with ER between 2 mm and 30 mm and larger ice crystals with

ER between 5 mm and 64 mm. The highest frequency is shown in the bins 0–20 mm and

20–30 mm for liquid and ice clouds, respectively. On the other hand, no noticeable

difference is found between the ER values of the northern and southern hemispheres.

While the bias is relatively large in the tropics, we note that in the mid-latitudes, therelative frequencies in the final and MODIS data show fairly good agreement. Since

ER retrieval is highly sensitive to the IR4 reflected radiance, the results show that our

method may be particularly effective in the mid-latitudes to decouple the thermal and

surface-reflected components from the observed IR4 radiance (Choi et al. 2007).

With regard to the comparison between the final and the base products, the one-

month climatology of the COT and ER agrees with those of the MODIS products to

some extent due to the decoupling method employed in the algorithm (compare the

dotted and solid lines). By using the decoupling method, the frequency of thin clouds

(COT , 4) decreases and that of thick clouds (COT � 4) increases, except for iceclouds (figure 3). Similarly, the frequency of clouds of small particles (ER , 10)

decreases and that of clouds of large particles (ER � 10) increases (figure 4). Since

clouds with optical depths between 1 and 9 are particularly significant in the tropical

energy balance (Choi and Ho 2006), the change in the corresponding cloud amounts

by the decoupling method can give rise to a change in the estimation of the cloud

radiative effect in the tropics. Clearly, the decoupling method contributes to improv-

ing not only the COT and ER retrievals but also our understanding of cloud radiative

effects.

0~1 1~2 2~3 3~4 4~9 9~25 25~60 60~

(a) August

Cloud optical thickness

Simple

Decoupled

(b) Liquid

0~1 1~2 2~3 3~4 4~9 9~25 25~60 60~

(c) Ice

0~1 1~2 2~3 3~4 4~9 9~25 25~60 60~

(d) NH

0~1 1~2 2~3 3~4 4~9 9~25 25~60 60~

(e) SH

0~1 1~2 2~3 3~4 4~9 9~25 25~60 60~

( f ) Mid-latitude

0~1 1~2 2~3 3~4 4~9 9~25 25~60 60~

( g ) Tropical

0~1 1~2 2~3 3~4 4~9 9~25 25~60 60~

Figure 3. Relative frequency (in %) of cloud optical thickness without using the decouplingmethod (i.e. base products), using the decoupling method (i.e. final products), and MODIS datato the total clouds for the corresponding conditions. SH and NH stand for the northern andsouthern hemispheres, respectively.

The one-month climatology of the CTP is also compared with the MODIS CTP for

the analysis period (figure 5). It is noted that our CTP and the MODIS CTP are

produced in the units of 50 and 25 hPa, respectively. In the analysis of the MODIS

CTP (figure 5(a)), the CTP frequency is highest at the 300–400, 700–800 and 900–1000

hPa levels in August. This distribution varies between day and night, northern and

southern hemispheres, and tropical and polar regions. The base CTP from the IR1

estimate reveals a similar shape of CTP distribution to that of MODIS. However, no

high frequencies at the high- and mid-levels are found, and low clouds (CTP = 900–1000hPa) are more frequent in the IR1-estimated CTP (figure 5(b)). When the CTP is

corrected by the radiance ratioing method, the frequency of high clouds (300–400 hPa)

increases by 4% and that of low clouds (900–1000 hPa) decreases by 7% (figure 5(c)).

Consequently, the values from the final CTP at both levels become fairly close to those of

MODIS (23.5% versus 20.7% at the low level, 12.2% versus 12.2% at the high level).

Interestingly, the regional characteristics of the CTP distribution are present in both the

final CTP and the MODIS CTP data; in the tropics, very high (200–300 hPa) and very

(a) AugustSimple

Decoupled

(b) Liquid (c) Ice

(d) NH (e) SH (f) Mid-latitude

(g) Tropical

(a) August

Effective radius (µm)

2~5 5~1010~20

20~3030~40 60~

50~6040~50

ency (

2~5 5~1010~20

20~3030~40 60~

50~6040~50

ency (

2~5 5~1010~20

20~3030~40 60~

50~6040~50

ency (

2~5 5~1010~20

20~3030~40 60~

50~6040~50

ency (

2~5 5~1010~20

20~3030~40 60~

50~6040~50

ency (

2~5 5~1010~20

20~3030~40 60~

50~6040~50

ency (

2~5 5~1010~20

20~3030~40 60~

50~6040~50

ency (

60Simple

Decoupled

(b) Liquid (c) Ice

(d) NH (e) SH ( f ) Mid-latitude

(g) Tropical

Figure 4. Same as figure 3 but for cloud effective radius (in mm).

low clouds (� 1000 hPa) are the most common, while middle clouds (600–800 hPa) are

the most common in the polar regions. Therefore, the radiance ratioing method using the

IR1 and IR3 channels is found to be useful in identifying high clouds above 400 hPa that

are falsely flagged as low clouds using the IR1 estimate alone. It should be noted that

some middle clouds identified by the MODIS CO2 slicing method cannot be discrimi-nated by our method, since the radiance ratioing method is valid only above 400 hPa.

In order to quantitatively estimate the uncertainty, we calculated the difference

between the derived MTSAT CTP and the MODIS CTP (figure 6). The derived CTP

includes the CTP from the IR1 estimate (i.e. base CTP), and its corrected CTP

IR1 only

100 km

220 km

MTSAT − MODIS distribution (%)

–20 –10 0 10 20

IR1 only

100 km

220 km

( f ) Polar (h ) Tropical(g ) Mid-latitude

(a) August

(e) SH

(b) Day (c) Night (d) NH

Figure 6. The difference between MTSAT-1R (both base and final) CTP and MODIS CTPvalues (in %) shown in figure 5. The radiance ratios are calculated by using clear-sky radiancesobtained within various spatial resolutions: 12 (3 · 3), 60 (15 · 15), 100 (25 · 25), and 220 km(55 · 55 pixels).

Distribution (%)

0 5 10 15 20 25 30 35

Distribution (%)

0 5 10 15 20 25 30 35

August

Tropical

Midlatitude

Distribution (%)

0 5 10 15 20 25 30 35

(a) MODIS CTP (b) Base CTP (c) final CTP

Figure 5. Relative frequency distribution (in %) of MODIS CTP (a), base CTP retrieved bythe IR1 estimate only (b), and final CTP corrected by the radiance ratioing method (c) for thetotal clouds. The results are shown for August 2006, daytime, night-time, northern and south-ern hemispheres, and the polar, tropical and mid-latitude regions.

(i.e. final CTP) using the radiance ratio in various spatial domains. For the polar

region, the radiance ratioing method cannot be applied (Choi et al. 2007). In the

MODIS CTP algorithm, the radiance ratio using CO2 channels is calculated by using

the clear-sky radiance obtained from 5 · 5 km, i.e. 5 · 5 MODIS pixels. As the

spatial resolution of a pixel in JAMI is 4 km in the IR bands, the minimal domain-to-domain clear-sky radiance for a given cloudy pixel should be 12 · 12 km (i.e. 3 · 3

pixels). Taking a larger domain would cause larger uncertainties in the CTP due to

false clear-sky radiances; however, more clear-sky values would be obtained. Thus,

the uncertainty of the final CTP can be more than that of the base CTP when the size

of the domain is large (e.g. 220 km). Figure 6 shows clearly that the final CTP using a

12-km domain has the frequencies nearest to those of the MODIS CTP. The absolute

value of the CTP from the 12-km domain minus the corresponding MODIS CTP

distribution is highest at the 100–200 hPa and 700–800 hPa levels (3% and 7%,respectively). Therefore, the accuracy of the obtained clear-sky radiance is one of

the major factors that determine the CTP uncertainties.

3.3 Time-series comparison

We have investigated the averaged values of cloud properties for various conditions

during our analysis period. Although the analysis of the averages is important for

validating the retrieved products, it cannot be guaranteed that the results representactual coherency with the MODIS products, which are considered as true reference

values in this study. Therefore, we examine the time series of cloud properties in the

nine selected regions. Since the MTSAT data are hourly at a 4 km · 4 km resolution,

the cloud properties were averaged for a 1� grid for each hour in order to compare it

with MOD08. MODIS/Terra passes at about 10:30 am local time over all the

regions. Therefore, the time series of the hourly MTSAT data is expected not to

be the same as that of MODIS, only retaining similar daily variations with hourly

fluctuation.As expected, the time series of the ratio of ice clouds to the total clouds in the base

CP (i) and the final CP (ii) includes large hourly fluctuations, indicating a rapid

change in the cloud properties in nature (figure 7). The figure also indicates that the

effect of adding the IR3 test to the bispectral CP test is dependent on region, on

comparing (i) and (ii). The time series of the ratios in most regions shows that

the derived and the MODIS data are nearly in phase. The ratios become higher

when the IR3 test is incorporated; therefore, the ratios in the final CP are closer to

the MODIS daily values. However, in the Antarctic region, the ratios in the final CPare relatively high in comparison with the MODIS values (11–20 and 25–31 August).

Considering that our analysis period corresponds to wintertime in this region, this

high ratio of ice clouds may be additional evidence showing the effect of the IR3 test to

identify ice clouds in the cold air of the polar region. As we will show the time series of

the CTP in the same regions later, the frequent occurrence of high clouds above 500

hPa in this region also strongly supports the high ratio of ice clouds.

The COT and ER are retrieved for the FOVs with solar and satellite zenith angles

less than 60�. Thus, the time series of the base (i) and final products (ii) are obtained inonly five of the nine regions at daytime (figures 8 and 9); better agreement with the

MODIS data is found for the final data in most regions than for the base data

(compare (i) and (ii) in both figures). This improvement in the COT and ER retrievals

via the decoupling method is seen regardless of the region. However, large hourly

variations in the COT and ER retrievals exist. This is perhaps related to the influence

of the change in the solar zenith angle on the retrieval; large bias must occur around

sunrise or sunset.

The time series of the derived CTP generally follow that of the MODIS values in all

the analysis regions (figure 10). The effect of applying the ratioing method may be to

change the overestimated base CTP values (i) into more realistic, lower final CTP

values (ii). In comparison with the MODIS values, the final CTP values may be

1 4 7 10 13 16 19 22 25 28 31

Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

1 4 7 10 13 16 19 22 25 28 31Ic

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

Day of monthDay of month

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

(a) Seoul (37°–38°N, 126°–127°E) (b) Northeast China Plain (32°–33°N, 115°–116°E) (c) Gobi desert (40°–41°N, 104°–105°E)

MTSAT MODIS (i)

1 4 7 10 13 16 19 22 25 28 31

Day of month1 4 7 10 13 16 19 22 25 28 31

1 4 7 10 13 16 19 22 25 28 31

Day of month1 4 7 10 13 16 19 22 25 28 31

MTSATMODIS

1 4 7 10 13 16 19 22 25 28 31

Day of month

Day of month Day of month Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSATMODIS

(d) Tibetan Plateau (35°–36°N, 90°–91°E) (e) The South China Sea (10°–11°N, 115°–116°E) ( f ) The Philippine Sea (10°–11°N, 135°–136°E)

o o o o

o o o o(g) The East Pacific (0 −1 N, 150 −151 W) (h) The Bering Sea (59 −60 N, 179 −180 W) (i) The Antarctic (70 −71 S, 159 −160 E)

1 4 7 10 13 16 19 22 25 28 31

Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

louds (

Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

Day of month

1 4 7 10 13 16 19 22 25 28 31

Figure 7. Time series of the ratio of ice clouds to the total clouds at nine selected sites; base CPfrom IR1 and IR2 (i), and final CP from IR1, IR2, and IR3 (ii).

1 4 7 10 13 16 19 22 25 28 31

Optical th

Day of month1 4 7 10 13 16 19 22 25 28 31

Optical th

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

tical th

ickness

Day of month1 4 7 10 13 16 19 22 25 28 31

Optica

MTSAT MODIS

Day of month

Day of monthDay of month

MTSAT MODIS (i )

MTSAT MODIS

(a) Seoul (37°–38°N, 126°–127°E) (b) Northeast China Plain (32°–33°N, 115°–116°E)

1 4 7 10 13 16 19 22 25 28 31

Optical th

Day of month1 4 7 10 13 16 19 22 25 28 31

Optical th

MTSAT MODIS

Day of month

MTSAT MODIS

(c) Gobi desert (40°–41°N, 104°–105°E)°

1 4 7 10 13 16 19 22 25 28 31

Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

Optical th

ickness

Optical th

ickness

Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

(d) The South China Sea (10°–11°N, 115°–116°E)

(e) The Philippine Sea (10°–11°N, 135°–136°E)

MTSAT MODIS

–11 –116

Figure 8. Same as figure 7 but for base COT using the VIS and IR4 radiances (i), and finalCOT corrected using the decoupling method in order to have a reflected component fromclouds only in the radiances (ii).

1 4 7 10 13 16 19 22 25 28 31

ective r

Day of month1 4 7 10 13 16 19 22 25 28 31

ective r

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

ective r

Day of month1 4 7 10 13 16 19 22 25 28 31

ective r

MTSAT MODIS

Day of month Day of month

°–38°N, 126°–127°E) (b) Northeast China Plain (32°–33°N, 115°–116°E)

1 4 7 10 13 16 19 22 25 28 31

(a) Seoul (37

1 4 7 10 13 16 19 22 25 28 31

ective r

Day of month

1 4 7 10 13 16 19 22 25 28 31

ective r

MTSAT MODIS

(c) Gobi desert (40°–41°N, 104°–105°E)

1 4 7 10 13 16 19 22 25 28 31

ective r

Day of month

1 4 7 10 13 16 19 22 25 28 31

ective r

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

ective

Day of month

1 4 7 10 13 16 19 22 25 28 31

ective

MTSAT MODIS

(d ) The South China Sea (10°–11°N, 115°–116°E)

(e ) The Philippine Sea (10°–11°N, 135°–136°E)

Figure 9. Same as figure 8 but for base ER (i) and final ER (ii).

underestimated to some extent in Seoul, the north-east China plain, the East Pacificand the Bering Sea. There are several reasons for this underestimation: (i) middle clouds

are not commonly identified; (ii) the radiance ratio library for the mid-latitudes is

applied to the 30–60� latitudes, while the atmospheric conditions over the Bering Sea

(located between 59� and 60� N) may be far from the model profile; and (iii) in the mid-

latitudes, the radiance ratio is not distinctly sensitive to the CTP (refer to figure 7 of

Choi et al. 2007). Despite these several sources of uncertainties, the final CTP agrees

fairly well with the MODIS values in the other regions, including the Gobi desert,

Tibetan plateau, South China Sea, Philippine Sea and Antarctic region.

1 4 7 10 13 16 19 22 25 28 31

Day of month1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

Day of month1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

(a) Seoul (37°–38°N, 126°–127°E) (b) Northeast China Plain (32°–33°N, 115°–116°E) (c) Gobi desert (40°–41°N, 104°–105°E)

1 4 7 10 13 16 19 22 25 28 31

Day of month1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

Day of month1 4 7 10 13 16 19 22 25 28 31

MTSATMODIS

1 4 7 10 13 16 19 22 25 28 31

Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSATMODIS

(d ) Tibetan Plateau (35°–36°N, 90°–91°E) (e) The South China Sea (10°–11°N, 115°–116°E) ( f ) The Philippine Sea (10°–11°N, 135°–136°E)

(g) The East Pacific (0o−1oN, 150o−151oW)

1 4 7 10 13 16 19 22 25 28 31

Day of month1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

(h) The Bering Sea (59o−60oN, 179o−180oW)

1 4 7 10 13 16 19 22 25 28 31

Day of month

Day of monthDay of month Day of month

1 4 7 10 13 16 19 22 25 28 31

MTSAT MODIS

(i) The Antarctic (70o−71oS, 159o−160oE)

1 4 7 10 13 16 19 22 25 28 31

(i ) MTSAT MODIS

1 4 7 10 13 16 19 22 25 28 31

Figure 10. Same as figure 7 but for base CTP (i) and final CTP (ii).

(a) CTP difference (b) Error in retrieved CTP

(c) COT difference (d) Error in retrieved COT

(e) ( f)ecnereffidRE Error in retrieved ER

FinalBase

Figure 11. Relative frequency of MTSAT minus MODIS CTP (a), COT (c), and ER (e) for themaximum values. Errors in the retrieved CTP (b), COT (d), and ER (f) (in %) with respect to thecorresponding parameters. The solid and dotted lines indicate values from the final (corrected)and base (uncorrected) products, respectively.

Over the South China and Philippine seas, strong convection systems appear to

occur between 7 and 13 August, as high ratio of ice clouds, high COT, and low CTP

are consistently revealed (figures 7, 8, and 10). It should be noted that the convective

clouds over the South China Sea are generally more developed vertically than those

over the Philippine Sea (Park et al. 2007). This may be indicated by consistentresults: higher ratio of ice clouds, higher COT, and lower CTP over the South

China Sea.

3.4 Pixel-by-pixel comparison

The results of the pixel-by-pixel comparison of cloud properties are presented in this

subsection. Figure 11 shows the relative frequency of the differences between the

derived MTSAT and the MODIS retrievals to the maximum values, and the errors inthe retrievals with respect to the corresponding MODIS values. The error is expressed

in terms of the ratio (in percentage) of the difference between the MTSAT and

MODIS retrievals to the MODIS retrievals.

Most (90%) of the final CTP and MODIS CTP values are in agreement with each

other (figure 11). A large difference in CTP over 300 hPa also occurs for about 0.1%

of the total pixels. This uncertainty may result from a number of factors such as the

discrepancies in the retrieval resolutions, radiance ratios, constructed clear-sky

radiances and the numerical atmospheric profiles employed. A comparison of thebase and final CTP values clearly reveals that our correction method (i.e. the

radiance ratioing method for high-cloud top pressure) is effective particularly

when the MTSAT CTP is higher than the MODIS CTP (figure 11(a)). Large errors

for low clouds (CTP � 700 hPa) are dramatically decreased by this method (figure

11(b)). The error in the final CTP is found to be less than roughly 10%, and it is

lowest for high clouds between 200 and 300 hPa. These results confirm the effect of

the radiance ratioing method using the IR1 and IR3 bands in the present CTP

algorithm.The final COT and the MODIS COT values are mostly identical to within a

difference of � 5. Only a few pixels (,2% of the total pixels) are in disagreement

with the MODIS COT (figure 11(c)). The disagreement and the error in the final COT

values are much lower than those for the base COT values, especially for clouds with

COT , 60 (figures 11(c) and 11(d)). The errors in both the base and the final COT

values are very small for very (optically) thick clouds with COT � 60. This is some-

what contradictory when considering that the optical thickness of thick clouds may

have much more uncertainty due to physical reasons such as the high sensitivity of theCOT value to VIS radiance (Choi et al. 2007). Our further analysis shows that the

occurrence of very thick clouds is not sufficient in nature. In addition, the collocation

problems are immanent in any case. These reasons would lead to false errors, and

often to low errors by chance.

Unlike the COT, the final ER values still have large uncertainty when compared to

the MODIS values. The disagreement between the final and MODIS ER values

occurs for a considerable portion of the pixels, and it is likely to have originated

from the relatively large ER values (. 40 mm) (figures 11(e) and 11(d)). This uncer-tainty probably results from the insensitivity of IR4 radiance to large-sized cloud

particles (Choi et al. 2007). Therefore, reliable ER retrieval from MTSAT-1R images

can be guaranteed only up to a value of approximately 40 mm. We note that the ER

from MODIS provides estimates for liquid (ice) clouds only up to 30 (90 mm).

4. Summary and discussion

We have presented validation results of the CLA (Choi et al. 2007) by comparing the

base, final and MODIS cloud properties. The base and final cloud properties are

defined as the products from the hourly MTSAT five-channel imagery by the previous

and present algorithm, respectively (table 2). For the validation, we carried out scene

analysis, large-scale climatology comparison, time-series comparison, and pixel-by-

pixel comparison. Based on our analysis, the final CP, COT, ER and CTP values are

consistently closer to the MODIS reference data than the base products. This

indicates that the present developed schemes for the CLA are, in effect, of meritdespite the limitation of the channel number. The major results are summarized as

follows.

The final CP has considerably more water and ice clouds (or fewer mixed and

unknown clouds) than the base CP, which indicates the role of the additional IR3 test.

Compared with the MODIS CP, however, the proportion of water (mixed) clouds

across the globe is still very low (high); it is lower (higher) by about 11% (15%). This

uncertainty between the water and mixed phases remains as the limitation of the

present IR CP algorithm due to the absence of the 8.5-mm channel in the MODISalgorithm. It is noted that the proportion of ice clouds is comparable with that in the

MODIS CP data, but is very high (by about 15%) only in the polar region.

The final COT and ER values have less bias against the MODIS values than the

base products, particularly in the mid-latitudes, which indicates the effect of the

radiance decoupling method to extract cloud-reflected radiances in the VIS and IR4

channels. When the decoupling method is incorporated, the bias is dramatically

reduced for 5 � COT , 60 and for ER , 40 mm. The large uncertainty in the COT

and ER retrievals due to high solar zenith angles and very large COT (.. 60) and ERvalues (.. 40 mm) remains to be resolved.

Reliable CTP values can be retrieved by correcting the high-cloud top pressure

(, 400 hPa) via the radiance ratio obtained from the clear-sky radiance, which is

defined as the maximal radiance in the domain of 12 km · 12 km. The accuracy of

CTP retrievals largely depends on the accuracy of the obtained clear-sky radiance.

Bias against the MODIS CTP still exists in the mid-troposphere between the 700 and

800 hPa levels.

The cloud properties retrieved from the validated CLA in this study can be used inmany fields of study such as cloud microphysics, severe weather forecast, and climate

research. The CLA is intended for use in the COMS meteorological data processing

system in Korea. The present study details the physical uncertainties and limitations

underlying the algorithm, based on intercomparison with the MODIS cloud data. In

fact, by using the imagery from onboard five-channel passive radiometers on present

and past geostationary satellites, we believe that this CLA can also be operated via an

off-line system to construct a global map of cloud properties with a high temporal and

spatial resolution. The accumulated and reconstructed cloud properties would beuseful in investigating numerous natural phenomena of interest, including general

circulation, cloud-radiative feedback, global/regional climate changes, etc.

Acknowledgements

This study was funded by the Korea Meteorological Administration Research and

Development Program under Grant CATER 2006–4204. The MODIS data were

obtained from LAADS at the Goddard Space Flight Center.

References

BAUM, B.A., SOULEN, P.F., STRABALA, K.I., KING, M.D., ACKERMAN, S.A., MENZEL, W.P. and

YANG, P., 2000, Remote sensing of cloud properties using MODIS airborne simulator

imagery during SUCCESS, 2, Cloud thermodynamic phase. Journal of Geophysical

Research, 105, pp. 11781–11792.

BAUM, B.A., YANG, P., HEYMSFIELD, A.J., PLATNICK, S., KING, M.D., HU, Y.X. and BEDKA, S.T.,

2005, Bulk scattering properties for the remote sensing of ice clouds. Part II:

Narrowband models. Journal of Applied Meteorology, 44, pp. 1896–1911.

CHOI, Y.-S. and HO., C.-H., 2006, Radiative effect of cirrus with different optical properties

over the tropics in MODIS and CERES observations. Geophysical Research Letters, 33,

L21811, doi:10.1029/2006GL027403.

CHOI, Y.-S., HO, C.-H. and SUI, C.-H., 2005, Different optical properties of high cloud in GMS

and MODIS observations. Geophysical Research Letters, 32, L23823, doi:10.1029/

2005GL024616.

CHOI, Y.-S., HO, C.-H., AHN, M.-H. and KIM, Y.-M., 2007, An exploratory study of cloud

remote sensing capabilities of the Communication, Ocean and Meteorological Satellite

(COMS) Imagery. International Journal of Remote Sensing, 28, pp. 4715–4732.

CLOTHIAUX, E.E., ACKERMAN, T.P., MACE, G.G., MORAN, K.P., MARCHAND, R.T., MILLER,

M.A. and MARTNER, B.E., 2000, Objective determination of cloud heights and radar

reflectivities using a combination of active remote sensors at the ARM CART Sites.

Journal of Applied Meteorology, 33, pp. 645–665.

ELLINGSON, R.G., ELLIS, J. and FELS, S., 1991, The intercomparison of radiation codes used in

climate models: long wave results. Journal of Geophysical Research, 96, pp. 8929–8953.

KEY, J.R., 2002, Streamer Version 3.0 User’s Guide (Madison, WI: NOAA/NESDIS). Available

online at: stratus.ssec.wisc.edu (accessed 23 July 2009).

KEY, J.R. and SCHWEIGER, A., 1998, Tools for atmospheric radiative transfer: Streamer and

FluxNet. Computers and Geosciences, 24, pp. 443–451.

KIM, J.-H., HO, C.-H., LEE, M.-H., JEONG, J.-H. and CHEN, D., 2006, Large increase in heavy

rainfall associated with tropical cyclone landfalls in Korea after the late 1970s.

Geophysical Research Letters, 33, L18706, doi:10.1029/2006GL027430.

KING, M.D., TSAY, S.C., PLANTNICK, S.E., WANG, M. and LIOU, K.N., 1997, Cloud retrieval

algorithms for MODIS: optical thickness, effective particle radius, and thermodynamic

phase. MODIS Algorithm Theoretical Basis Document, NASA.

KING, M.D., PLATNICK, HUBANKS, P.A., ARNOLD, G.T., MOODY, E.G., WIND, G. and WIND, B.,

2006, Collection 005 Change Summary for the MODIS Cloud Optical Property

(06_OD) Algorithm. Available online at: http://modis-atmos.gsfc.nasa.gov/

C005_Changes/C005_CloudOpticalProperties_ver311.pdf (accessed 23 July 2009).

LIOU, K.N., 2002, An Introduction to Atmospheric Radiation, 2nd ed. (San Diego: Academic Press).

MACE, G.G., ZHANG, Y., PLATNICK, S., KING, M.D., MINNIS, P. and YANG, P., 2005, Evaluation

of cirrus cloud properties derived from MODIS data using cloud properties derived

from ground-based observations collected at the ARM SGP site. Journal of Applied

Meteorology, 44, pp. 221–240.

MENZEL, W.P., SMITH, W.L. and STEWART, T.R., 1983, Improved cloud motion wind vector and

altitude assignment using VAS. Journal of Climate and Applied Meteorology, 22,

pp. 377–384.

MENZEL, W.P., FREY, R.A., BAUM, B.A. and ZHANG, H., 2006, Cloud top properties and cloud

phase algorithm theoretical basis document. MODIS Algorithm Theoretical Basis

Document, NASA.

MOODY, E.G., KING, M.D., PLATNICK, S., SCHAAF, C.B. and GAO, F., 2005, Spatially complete

global spectral surface albedos: Value-added datasets derived from Terra MODIS land

products. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 144–158.

MOODY, E.G., KING, M.D., SCHAAF, C.B., HALL, D.K. and PLATNICK, S., 2007, Northern

Hemisphere five-year average (2000–2004) spectral albedos of surfaces in the presence

of snow: Statistics computed from Terra MODIS land products. Remote Sensing of

Environment, 111, pp. 337–345.

NAKAJIMA, T.Y. and NAKAJIMA, T., 1995, Wide-area determination of cloud microphysical

properties from NOAA AVHRR measurements for FIRE and ASTEX regions.

Journal of the Atmospheric Sciences, 52, pp. 4043–4059.

PARK, M.-S., CHOI, Y.-S., HO, C.-H., SUI, C.-H., PARK, S.-K. and AHN, M.-H., 2007,

Regional cloud characteristics over the tropical northwestern Pacific as revealed

by TRMM PR and TMI. Journal of Geophysical Research, 112, D05209,

doi: 10.1029/2006JD007437.

PLATNICK, S., KING, M.D., ACKERMANN, S.A., MENZEL, W.P., BAUM, B.A., RIEDI, J.C. and

FREY, R.A., 2003, The MODIS cloud products: Algorithms and examples from Terra.

IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 456–473.

PUSCHELL, J.J., OSGOOD, R., AUCHTER, J., HURT, W.T., HITOMI, M., SASAKI, M., TAHARA, Y.,

TADROS, A., FALLER, K., MCLAREN, M., SHEFFIELD, J., GAISER, J., KAMEL, A. and

GUNSHOR, M., 2006, In-flight performance of the Japanese Advanced Meteorological

Imager, SPIE Proceedings, 6296, 62960N.

RICCHIAZZI, P., YANG, S., GAUTIER, C. and SOWLE, D., 1998, SBDART: A research and teaching

software tool for plane-parallel radiative transfer in the earth’s atmosphere. Bulletin of

the American Meteorological Society, 79, pp. 2101–2114.

ROSSOW, W.B. and GARDER, L.C., 1993a, Cloud detection using satellite measurements of

infrared and visible radiances for ISCCP. Journal of Climate, 6, pp. 2341–2369.

ROSSOW, W.B. and GARDER, L.C., 1993b, Validation of ISCCP cloud detections. Journal of

Climate, 6, pp. 2370–2393.

ROSSOW, W.B. and SCHIFFER, R.A., 1999, Advances in understanding clouds from ISCCP.

Bulletin of the American Meteorological Society, 80, pp. 2261–2287.

ROSSOW, W.B., WALKER, A.W. and GARDER, L.C., 1993, Comparison of ISCCP and other cloud

amounts. Journal of Climate, 6, pp. 2394–2418.

SHUPE, M.D., MATROSOV, S.Y. and UTTAL, T., 2006, Arctic mixed-phase cloud properties

derived from surface-based sensors at SHEBA. Journal of the Atmospheric Sciences,

63, pp. 697–711.

STAMNES, K., TSAY, S.C., WISCOMBE, W. and JAYAWEERA, K., 1988, Numerically stable algo-

rithm for discrete-ordinate-method radiative transfer in multiple scattering and emit-

ting layered media. Applied Optics, 27, pp. 2502–2509.

STRABALA, K.I., ACKERMAN, S.A. and MENZEL, W.P., 1994, Cloud properties inferred from

8–12-mm data. Journal of Applied Meteorology, 33, pp. 212–229.

VERLINDE, J., HARRINGTON, J.Y., MCFARQUHAR, G.M., YANNUZZI, V.T., AVRAMOV, A.,

GREENBERG, S., JOHNSON, N., ZHANG, G., POELLOT, M.R., MATHER, J.H., TURNER, D.D.,

ELORANTA, E.W., ZAK, B.D., PRENNI, A.J., DANIEL, J.S., KOK, G.L., TOBIN, D.C., HOLZ, R.,

SASSEN, K., SPANGENBERG, D., MINNIS, P., TOOMAN, T.P., IVEY, M.D., RICHARDSON, S.J.,

BAHRMANN, C.P., SHUPE, M., DEMOTT, P.J., HEYMSFIELD, A.J., and SCHOFIELD, R., 2007,

The mixed-phase arctic cloud experiment. Bulletin of the American Meteorological

Society, 88, pp. 205–221.

YANG, P., ZHANG, L., HONG, G., NASIRI, S.L., BAUM, B.A., HUANG, H.L., KING, M.D. and

PLATNICK, S., 2007, Differences between collection 4 and 5 MODIS ice cloud optical/

microphysical products and their impact on radiative forcing simulations. IEEE

Transanctions on Geoscience and Remote Sensing, 45, pp.2886–2899.

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