Published by Center for Environmental Remote Sensing,
Chiba University,
1-33 Yayoi-cho, Inage, Chiba, 263-8522 Japan
This compilation ©2005, Center for Environmental Remote Sensing, Chiba University
Authors retain all rights to Individual manuscript.
Cover layout by T. Ishiyama
and cover image was provided by NASA-JSC

INDEX (Click the Article No. to jump to
the manuscript)
Article 1st Author(Family Name

Article 1st Author(Family Name

No.

First Name), Coauthor

No.

00

PREFACE

Session 4

01

PROGRAM

4-1

Wang

02

CONTENTS

4-2

Wandinger U.

4-3

Xie

First Name), Coauthor

Y.
P.-H.

et al.

Keynote Talk
KN

Nakajima

Teruyuki

Session 1

Session 5
5-1

Pinker R.

5-2

Hayasaka T.

et al.

5-3

Kinoshita K.

et al.

1-1

Zhang

Y.-C.

et al.

5-4

Rajan

D.

et al.

1-2

Oku

Y.

et al.

5-5

Asano

S.

et al.

1-3

Dim

J. R.

et al.

5-6

Schutgens N.

et al.

1-4

Yang

K.

et al.

5-7

Sano

I.

et al.

1-5

Nakajima Takashi et al.

5-8Takano T.

et al.

5-9

Kuji

et al.

M.

Session 2
2-1

Raschke E.

et al.

Poster Session

2-2

Kawata Y.

et al.

P-1

Okayama H.

2-3

Kozai

et al.

P-2

Hu

B.

et al.

2-4

Takenaka H.

et al.

P-3

Fukagawa S.

et al.

2-5

Minomura M.

et al.

P-4

Bagtasa G.

et al.

P-5

Batbayar J.

et al.

P-6

Kimura T.

et al.

K.

Session 3
3-1

Liu

Z.-S.

et al.

P-7

Sudiana D.

et al.

3-2

Murayama T.

et al.

P-8

Asakuma K.

et al.

3-3

Qiu

P-9

Guo

et al.

3-4

Shiobara M.

P-10

Kikuchi N.

3-5

Khatri P. W. Jr. et al.

3-6

Jugder Dulam et al.

J.-H.
et al.

J.-J.

et al.

CEReS International Symposium 2005

CEReS International Symposium on
Radiation Budget and Atmospheric Parameters
Studied by Satellite and Ground Observation Data
― Toward the Understanding of Long Term Trend in Asia

February 17 (Thur.) & 18 (Fri.), 2005
Keyaki-Hall, Chiba University, Chiba, Japan

Editors
N.Takeuchi, T.Takamura and H. Kuze

Hosted by
CEReS, Chiba University
http://www.cr.chiba-u.jp/

PREFACE

Center for Environmental Remote Sensing (CEReS), Chiba University wasestablished in 1995 for the purpose of developing remote sensing technology and its
application to environmental science. Since then, CEReS has hosted an international
symposium every year in a variety of fields in remote sensing(2003); vegetation (1995,
1998), oceanography (1999), atmosphere (1998, 2001), hydrology (1997), arid land
(1997) and cryosphere (2003). In the field of atmosphere, a symposium entitled“Atmospheric correction of satellite data and its application to global environment” washeld in 1998, and another one “Remote sensing of the atmosphere and validation ofsatellite data” in 2001. For the title of the coming symposium in 2005, we have selected
the topic of radiation budget and related validation activities. Radiation budget is of vital
importance in the consideration of global warming and climate. Remote sensing (RS)provides the capability of global-scale monitoring; in particular, the atmospheric RS has aconsiderable potential for future development. At the present stage, we still need
intensive validation activities in order to establish the relationship between the satellite
monitoring and true atmospheric/ground conditions. Furthermore, long-term satellite andground observations will provide us with the precious data concerning the environmental
trend. This symposium is organized to present and discuss these state-of-the-art issues inthis fast developing field of the atmospheric RS.
This symposium consists of a keynote talk, 5 oral sessions, 1 poster session and 1
session for introducing the center projects. This covers a wide field including aerosol andcloud and their validation on the ground.
If this symposium will become useful in promoting the relating study, then it is thegreatest pleasure as an organizer.
Nobuo Takeuchi
Symposium Chair
Prof. of Chiba University
Director of CEReSCEReS Symposium Program
CEReS International Symposium on
Radiation Budget and Atmospheric Parameters Studied by Satellite and Ground Observation Data
― Toward the Understanding of Long Term Trend in Asia
February 17 (Thu) and 18 (Fri), 2005,
Keyaki-Hall, Chiba University, Japan
February 17
9:30-9:40
Opening Remark

Nobuo Takeuchi

9:40-10:10
Keynote Talk
Invited (30) Teruyuki Nakajima
On the recent progress of atmospheric satellite remote sensing and radiation budget studies
Session 1
Long-term trend in the radiation budget and atmospheric parameters from satellite observations
10:10-10:40
1-1. Invited (30) Yuanchong Zhang, William B. Rossow and Paul W. Stackhouse Jr.
ISCCP-FD’s surface radiation flux datasets: characteristics and comparison with GEWEX SRB
10:40-11:00
1-2. (20) Yuichiro Oku, Hirohiko Ishikawa and Zhongbo Su
Estimation of land surface energy fluxes over the Tibetan Plateau using GMS data
11:00-11:20 BREAK
11:20-11:40
1-3. (20) Jules R. Dim, Tamio Takamura, Itaru Okada, Hideaki Takenaka
Comparative Study of cloud parameters derived from Terra-MODIS and GMS-VISSR
11:40-12:00
1-4. (20) Kun Yang, Toshio Koike
Development and validation of a general model for estimating global solar radiation from hourly, daily
and monthly surface meteorological data
12:00-12:20
1-5. (20) Takashi Y. Nakajima, Teruyuki Nakajima, Shuichiro Katagiri
The characteristics of the cloud properties retrieved from Global Imager aboard the ADEOS-II(Midori-II) Earth observation satellite
12:20-13:30 LUNCH BREAK
Session 2
Interpretation of satellite data for atmospheric analysis and meteorological applications
13:30-14:00
2-1. Invited (30) Ehrhard Raschke, Makoto Wada and Takashi Yamanouchi
Measurement of clouds, and radiation from space for climate studies
14:00-14:20
2-2. (20) Y. Kawata , T. Umeki and K. Takemata
Reflectance band ratios in Japan using satellite and sky observation data
14:20-14:40
2-3. (20) Katsutoshi Kozai, Anna Sasaki
SeaWiFS and MODIS-derived product verification using normalized water-leaving radiance model in thewestern equatorial Pacific Oceani14:40-15:00
2-4. (20) Hideaki Takenaka, Tamio Takamura
Uncertainty in cloud optical thickness estimation from GMS-5 VISSR algorithm, explained byquantization noise and, it's Influence on the estimated radiative budget
15:00-15:20
2-5. (20) Mitsuo Minomura, Yoshiyasu Todate, Hiroaki Kuze, Nobuo Takeuchi
Retrieval of aerosol optical properties over Chiba land area from Landsat/TM imagery― Part I: Determination of spatial distribution of aerosol optical thickness
BREAK 15:20-15:30
CEReS Projects
15:30-15:45
Project-1 (15) Ryutaro Tateishi
Monitoring and analysis of global surface environmental changes by satellite data
15:45-16:00
Project-2 (15) Yoshiaki Honda and Koji Kajiwara
Study on earth surface, vegetation and land cover change with changing of surface 3D structure on
Eurasian continent and satellite data analysis, processing method, development of data verification
methods
16:00-16:15
Project-3 (15) Hiroaki Kuze
Evaluation of radiation budget on the basis of satellite data and ground observation network, and study oflong-term changes in atmospheric parameters
16:15-16:30
Project-4 (15) Akihiko Kondoh
Application of remote sensing methods to local communities ― enlightenment activities by means of
the synergy effect of various spatial data
BREAK 16:30-16:40
16:40-17:40
Poster Session
P-1. Hiroshi Okayama
Laboratory test of atmospheric turbulence and its implication in the satellite observations
P-2. Hu Bo, Wang Yuesi, Liu Guangren, Ma Zhiqiang
Comprehensive study on photosynthetically active radiation in Beijing
P-3. Shunsuke Fukagawa, Ikue Kouga, Hiroaki Kuze, Nobuo Takeuchi, Makoto Sasaki, Yoichi Asaoka, ,Satoru Ogawa
Environmental application of the all-sky survey high-resolution air-shower(ASHRA) telescope ―aerosol distribution measurement using a bistatic, imaging lidar
P-4. Gerry Bagtasa, Nofel Lagrosas, Hiroaki Kuze, Nobuo Takeuchi, Shunsuke Fukagawa, Yotsumi
Yoshii, Suekazu Naito, Masanori Yabuki
Mie-scattering simulation and measurement of mass extinction efficiency from portable automated lidar
and suspended particulate matter measurements
P-5. J. Batbayar, S.Tuya, N. Tugjsuren
Net radiation estimation using MODIS-TERRA data for clear sky days over homogeneous areas inMongolia
P-6. Toru Kimura, Toyofumi Umekawa, Si Fuqi, Hiroaki Kuze, Nobuo Takeuchi
Measurement of NO2, SO2, O3, H2O and aerosol in the troposphere using differential optical absorption
spectroscopy (DOAS)ii

P-7. Dodi Sudiana, Mitsuo Minomura, Hiroaki Kuze, Nobuo Takeuchi
Analysis of the Asian dust aerosol optical properties over the ocean
P-8. Koji Asakuma, Mitsuo Minomura, Hiroaki Kuze, Nobuo Takeuchi
Retrieval of aerosol optical properties over land in Chiba area from Landsat/TM imagery ― Part II:
Determination of aerosol size distribution
P-9. Jin-jia Guo, Zhi-shen Liu, Zhao-ai Yan
Micro Pulse Lidar Observation of Low Relative Humidity Layer
P-10 Nobuhiro Kikuchi, Hiroshi Kumagai, Hiroshi Kuroiwa, Teruyuki Nakajima, Akihide Kamei,
Ryosuke Nakamura
Cloud optical thickness and effective particle radius derived from transmitted solar radiation
measurements: Comparison with cloud radar observations
BANQUET 17:45-19:15
February 18
Session 3
Observation of aerosols and their impact on atmospheric radiation
9:00-9:30
3-1. Invited (30) Zhi-shen Liu, Zhao-ai Yan Bing-yi Liu Zhao-bin Sun
Characters of marine atmospheric boundary layer structure and aerosol profile observed by HSRL
9:30-9:50
3-2. (20) Toshiyuki Murayama, Miho Sekiguchi, Detlef Mueller, Katsuya Wada, and Yasuharu Saito
Characterization of Asian tropospheric aerosols with multi-wavelength Mie-Raman lidar andskyradiometer
9:50-10:20
3-3. Invited (30) Qiu Jinhuan
A study of optical properties of urban aerosols in China
10:20-10:40
3-4. (20) M. Shiobara, M. Yabuki, H. Kobayashi, and K. Hara
Optical, physical and chemical properties of aerosols around Japan based on the R/V Shirase shipboard
measurements
BREAK 10:40-10:50
10:50-11:10
3-5. (20) Pradeep Khatri, Yutaka Ishizaka, and Tamio Takamura
Observation on radiative properties of aerosol particles over the urban area of Nagoya
11:10-11:30
3-6. (20) Jugder Dulam and Erdenetsetseg Baasandai
Dust storm observations in Mongolia in spring 2004
Session 4
Network observation of the atmosphere
11:30-12:00
4-1. Invited (30) Yuesi Wang
The radiation monitoring network of Chinese ecosystem research: (CERN)LUNCH BREAK 12:00-13:00
13:00-13:30
4-2. Invited (30) Ulla Wandinger
EARLINET: the first continental-scale lidar network for vertical aerosol profilingiii

13:30-13:50
4-3. (20) Pinhua Xie, Yihuai Lu, Yujun Zhang, Ang Li, Jianguo Liu and Wenqing Liu
Ultraviolet radiation measurement in the south of Sinkiang using a compact zenith-sky spectrometer
Session 5
Improved determination of radiation and atmospheric parameters from satellite and ground observations
13:50-14:20
5-1. Invited (30) Rachel Pinker
Progress and outstanding challenges in estimating surface radiation budgets by methods of remotesensing
14:20-14:50
5-2. Invited (30) Tadahiro Hayasaka, Kazuaki Kawamoto, Jianqing Xu and Guangyu Shi
Seasonal and long-term variations of shortwave radiation in China
14:50-15:10
5-3. (20) Kisei Kinoshita, Hiroyuki Kikukawa, Naoko Iino, Wang Ning, Zhang Gang, Jugder Dulam,
Tsatsaral Batmunkh, and Satoshi Hamada
Properties of long-time digital camera records in Changchun and Ulaanbaatar
BREAK 15:10-15:20
15:20-15:40
5-4. (20) D. Rajan and GR. Iyengar
Analysis and impact study of global positioning system radio occultation precipitable water vapor
obtained from Chiba University over East Asia region
15:40-16:00
5-5. (20) Shoji Asano, Masaya Kojima, Tamio Takamura
Optical and microphysical properties of the 2003 Yamase clouds estimated from satellite remote sensing
and shipboard observation
16:00-16:20
5-6. (20) Nick Schutgens, Hiroshi Kumagai
Improving along-beam spatial resolution of radar measurements
16:20-16:40
5-7. (20) Itaru Sano, Sonoyo Mukai
Aerosol properties over Asia with ADEOS-1 & -2/POLDER
16:40-17:00
5-8. (20) Toshiaki Takano, Ken-ichi Akita, Hiroshi Kubo, Youhei Kawamura, Hiroshi Kumagai, Tamio
Takamura, Yuji Nakanishi and Teruyuki Nakajima
Observations of cloud properties using the developed millimeter-wave FM-CW radar at 95 GHz
17:00-17:20
5-9. (20) Makoko Kuji, Nobuyuki Kikuchi and Akihiro Uchiyama
Retrieval of precipitable water using ADEOS-II / GLI near infrared data
CLOSING REMARK Tamio Takaumuraiv

CEReS Symposium Contents
CEReS International Symposium on Radiation Budget and AtmosphericParameters Studied by Satellite and Ground Observation Data
― Toward the Understanding of Long Term Trend in Asia
Keynote Talk
On the recent progress of atmospheric satellite remote sensing and radiation budget studies
Teruyuki NAKAJIMA ··································································································································

1

Session 1
Long-term trend in the radiation budget and atmospheric parameters from satellite
observations
1-1.
ISCCP-FD’s surface radiation flux datasets: characteristics and comparison with GEWEX SRB
Yuanchong ZHANG, William B. ROSSOW, Paul W. STACKHOUSE Jr. ················································ 5
1-2.
Estimation of land surface energy fluxes over the Tibetan Plateau using GMS data
Yuichiro OKU, Hirohiko ISHIKAWA, Zhongbo SU ················································································ 12
1-3.
Comparative Study of cloud parameters derived from Terra-MODIS and GMS-VISSR
Jules R. DIM, Tamio TAKAMURA, Itaru OKADA, Hideaki TAKENAKA ·········································· 19
1-4.
Development and validation of a general model for estimating global solar radiation from hourly,
daily and monthly surface meteorological data
Kun YANG, Toshio KOIKE ······················································································································· 26
1-5.
The characteristics of the cloud properties retrieved from Global Imager aboard the ADEOS-II(Midori-II) Earth observation satellite
Takashi Y. NAKAJIMA, Teruyuki NAKAJIMA, Shuichiro KATAGIRI ················································ 32

Session 2
Interpretation of satellite data for atmospheric analysis and meteorological applications
2-1.
Measurement of clouds, and radiation from space for climate studies
Ehrhard RASCHKE, Makoto WADA, Takashi YAMANOUCHI ···························································
2-2.
Reflectance band ratios in Japan using satellite and sky observation data
Y. KAWATA , T. UMEKI, K. TAKEMATA ······························································································
2-3.
SeaWiFS and MODIS-derived product verification using normalized water-leaving radiance modelin the western equatorial Pacific Ocea
Katsutoshi KOZAI, Anna SASAKI ···········································································································

v

35

43

48

2-4.
Uncertainty in cloud optical thickness estimated by GMS-5S-VISSR algorithm, and its influenceon the estimated radiative budget
Hideaki TAKENAKA, Tamio TAKAMURA, I. OKADA, T. Y. NAKAJIMA, J. R. DIM ····················
2-5.
Retrieval of aerosol optical properties over Chiba land area from Landsat/TM imagery― Part I: Determination of spatial distribution of aerosol optical thickness
Mitsuo MINOMURA, Yoshiyasu TODATE, Hiroaki KUZE, Nobuo TAKEUCHI ·······························

51

58

Session 3
Observation of aerosols and their impact on atmospheric radiation
3-1.
Characters of marine atmospheric boundary layer structure and aerosol profile observed by HSRL
Zhi-shen LIU, Zhao-ai YAN, Bing-yi LIU, Zhao-bin SUN ······································································ 65
3-2.
Characterization of Asian tropospheric aerosols with multi-wavelength Mie-Raman lidar andskyradiometer
Toshiyuki MURAYAMA, Miho SEKIGUCHI, Detlef MUELLER, Katsuya WADA, Yasuharu SAITOH············································································· 73
3-3.
A study of optical properties of urban aerosols in China
Jinhuan QIU ················································································································································ 83
3-4.
Optical, physical and chemical properties of aerosols around Japan based on the R/VShirase shipboard measurements
M. SHIOBARA, M. YABUKi, K. HARA, H. KOBAYASHI ·································································· 85
3-5.
Observation on radiative properties of aerosol particles over the urban area of Nagoya
Pradeep KHATRI, Yutaka ISHIZAKA, Tamio TAKAMURA ································································· 91
3-6.
Dust storm observations in Mongolia in spring 2004
Jugder DULAM, Erdenetsetseg BAASANDAI ························································································ 100

Session 4
Network observation of the atmosphere4-1.
The radiation monitoring network of Chinese ecosystem research: (CERN)
Yuesi WANG ·············································································································································· 109
4-2.
EARLINET: the first continental-scale lidar network for vertical aerosol profiling
Ulla WANDINGER ···································································································································· 115

vi

4-3.
Ultraviolet radiation measurement in the south of Sinkiang using a compact zenith-sky spectrometer
Pinhua XIE, Yihuai LU, Yujun ZHANG, Ang LI, Jianguo LIU, Wenqing LIU ······································· 118

Session 5
Improved determination of radiation and atmospheric parameters from satellite andground observations
5-1.
Progress and outstanding challenges in estimating surface radiation budgets by methodsof remote sensing
R. T. PINKER ············································································································································· 123
5-2.
Seasonal and long-term variations of shortwave radiation in China
Tadahiro HAYASAKA, Kazuaki KAWAMOTO, Jianqing XU, Guangyu SHI ······································ 132
5-3.
Properties of long-time digital camera records in Changchun and Ulaanbaatar
Kisei KINOSHITA, Hiroyuki KIKUKAWA, Naoko IINO, Wang NING, Zhang GANG,
Jugder DULAM, Tsatsaral BATMUNKH, Satoshi HAMADA ······························································· 136
5-4.
Analysis and impact study of global positioning system radio occultation precipitable water vapor
over East Asia region
D. RAJAN and GR. IYENGAR ················································································································ 142
5-5.
Optical and microphysical properties of the 2003 Yamase clouds estimated from satellite
remote sensing and shipboard observation
Shoji ASANO, Masaya KOJIMA, Tamio TAKAMURA ········································································· 150
5-6.
Improving along-beam spatial resolution of radar measurements
Nick SCHUTGENS, Hiroshi KUMAGAI ································································································ 154
5-7.
Aerosol properties over Asia with ADEOS-1 & -2/POLDER
Itaru SANO, Sonoyo MUKAI, Yasuhiko OKADA, Masayoshi YASUMOTO ······································ 158
5-8.
Observations of cloud properties using the developed millimeter-wave FM-CW radar at 95 GHz
Toshiaki TAKANO, Ken-ichi AKITA, Hiroshi KUBO, Youhei KAWAMURA, Hiroshi KUMAGAI,
Tamio TAKAMURA, Yuji NAKANISHI, Teruyuki NAKAJIMA ·························································· 160
5-9.
Retrieval of precipitable water using ADEOS-II / GLI near infrared data
Makoko KUJI, Nobuyuki KIKUCHI, Akihiro UCHIYAMA ··································································· 166

vii

Poster Session
P-1.
Laboratory test of atmospheric turbulence and its implication in the satellite observations
Hiroshi OKAYAMA ··································································································································· 175
P-2.
Comprehensive study on photosynthetically active radiation in Beijing
HU Bo, WANG Yuesi, LIU Guangren, MA Zhiqiang ·············································································· 185
P-3.
Environmental application of the all-sky survey high-resolution air-shower (ASHRA) telescope― aerosol distribution measurement using a bistatic, imaging lidar
Shunsuke FUKAGAWA, Ikue KOUGA, Hiroaki KUZE, Nobuo TAKEUCHI, Makoto SASAKI,
Yoichi ASAOKA, Satoru OGAWA ··········································································································· 196
P-4.
Mie-scattering simulation and measurement of mass extinction efficiency from portable
automated lidar and suspended particulate matter measurements
Gerry BAGTASA, Nofel LAGROSAS, Hiroaki KUZE, Nobuo TAKEUCHI, Shunsuke FUKAGAWA,
Yotsumi YOSHII, Suekazu NAITO, Masanori YABUKI ········································································· 200
P-5.
Net radiation estimation using MODIS-TERRA data for clear sky days over homogeneous areasin Mongolia
J. BATBAYAR, S.TUYA, N. TUGJSUREN ····························································································· 206
P-6.
Measurement of NO2, SO2, O3, H2O and aerosol in the troposphere using differential
optical absorption spectroscopy (DOAS)
Toru KIMURA, Toyofumi UMEKAWA, SI Fuqi, Hiroaki KUZE, Nobuo TAKEUCHI ······················· 214
P-7.
Analysis of the Asian dust aerosol optical properties over the ocean
Dodi SUDIANA, Mitsuo MINOMURA, Hiroaki KUZE, Nobuo TAKEUCHI ··································· 220
P-8.
Retrieval of aerosol optical properties over Chiba land area from Landsat/TM imagery― Part II: Determination of aerosol size distribution ―Koji ASAKUMA, Mitsuo MINOMURA, Hiroaki KUZE, Nobuo TAKEUCHI ···································· 228
P-9.
Micro Pulse Lidar Observation of Low Relative Humidity Layer
Jin-jia GUO, Zhi-shen LIU, Zhao-ai YAN ································································································ 232
P-10
Cloud optical thickness and effective particle radius derived from transmitted
solar radiation measurements: comparison with cloud radar observations
Nobuhiro KIKUCHI, Hiroshi KUMAGAI, Hiroshi KUROIWA, Teruyuki NAKAJIMA, Akihide
KAMEI, Ryosuke NAKAMURA ·············································································································· 235

Author index ··············································································································································· 241

viii

KN

On the Recent Progress of Atmospheric Satellite Remote Sensing andRadiation Budget Studies
Teruyuki Nakajima (teruyuki@ccsr.u-tokyo.ac.jp)
Center for Climate System Research, The University of Tokyo
1. Introduction
In the last two decades the atmospheric satellite remote sensing and radiation budget studies havemade a great progress partly because of the strong demand of accurate evaluation of the radiative forcingof anthropogenic climate change factors such as direct and indirect climate effects of man-made aerosols.
IPCC (e.g., IPCC, 2001) has devoted a considerable effort on accessing these various radiative forcings.
Another important key to the progress is that the modeling of atmospheric radiative transfer processes has
become more comprehensive and accurate to describe the processes in the real earth's atmosphere. Opticalproperties of aerosols and clouds have been extensively measured and modeled in several large-scale field
experiments of aerosols and radiation as in INDOEX (Ramanathan et al., 2001), IGAC/ACE-Asia (Hubert
et al., 2003), Japan Science and Technology Corporation/APEX (Nakajima et al., 2003), etc. Surface
networks, such as NASA AERONET (Holben et al., 2001), WCRP/BSRN, and JAXA SKYNET, also havestarted providing quantitative data useful for validating the satellite remote sensing and model simulation.
In this paper I like to discuss the recent progress in satellite remote sensing of atmosphere andradiation budget studies, especially those related with radaiative forcing evaluation issue.
2. Studies on satellite remote sensing of atmosphere and radiation budget
A variety of satellite remote sensing techniques of atmospheric aerosols and clouds have been
proposed in the last two decades. ADEOS-II/GLI, EOS/MODIS, ENVISAT/MERIS are newsatellite-borne sensors to explore the utilities of information included in multi-wavelength spectral
radiances. Radiances at wavelengths from UV to near infrared spectral regions have provided good
information to retrieve the aerosol optical thickness, size index or Ångström exponent, and aerosol types.
Further use of bidirectional angular information and polarization are also found to be useful to stabilize
the inversion process such as aerosols over land area. Figure 1 shows an example of aerosol typeclassification using the four channel algorithm of Higurashi and Nakajima (2002) with MODIS radiancesfrom UV to near infrared spectral region. Comparing with surface-measured values of optical thickness
contributions from sulfate, carbonaceous, mineral dust, and sea salt aerosols, it is found that the technique
can provide useful information about the dominant aerosol type that determines satellite-received
radiances. Our study found that the most popular aerosol type in the East Asian region is of carbonaceousthat absorb strongly the solar radiation in blue spectral region. The single scattering albedo (SSA) of
aerosols from AERONET also shows a typical value about 0.9 in most areas where anthropogenic aerosols
are dominant.
It is interesting to study what is the globally averaged clear sky radiative forcing of anthropogenic
aerosols with such low SSA. Kaufman et al. (2003) derived the clear sky direct radiative forcing ofanthropogenic aerosols as -0.46 W/m2 from MODIS global measurements of aerosols as summarized inFig. 2. On the other hand, -0.70 W/m2 is our value of the clear sky direct radiative forcing ofanthropogenic aerosols estimated by CCSR/NIES AGCM combined with SPRINTARS aerosol chemicaltransport model (Takemura et al., 2005). These values show that the anthropogenic aerosols have verysmall forcing even in the clear sky condition due to their strong absorption of solar radiation. It is also
important to find that the direct forcing of anthropgenic aerosols in the whole sky condition is as small as-0.02 W/m2. This large reduction of the magnitude of forcing is a result of large positive forcing due tostrong absorption by aerosols in cloudy regions. Since the MODIS clear sky forcing is smaller inmagnitude than that from GCM, the real value of the radiative forcing in the whole sky condition may be1near zero or positive. This discussion suggests that measurements of solar radiation absorption by aerosols
in cloud regions will be needed for better estimation of the aerosol direct forcing.
On the other hand, the recent evaluation of indirect forcing is still in confusion. Even with the same
SPRINTARS model with CCSR/NIES AGCM, there have been several estimates ranging from -0.94W/m2
to -2.4 W/m2 as summarized in Fig. 2, depending on what parameterization is adopted for conversion ofaerosol particle number to cloud particle number (Takemura et al., 2005) and for autoconversion of cloudwater to precipitation (Suzuki et al., 2003). A lower value of of -0.94 W/m2 is obtained when they take
into a updraft velocity effect and a large background aerosol number (Takemura et al., 2005). A key
parameter for the magnitude of the indirect forcing is the increase rate of number of anthropogenic
aerosols after industrial revolution, i.e., ν= Na,present/Na,pre-industrial-1. The ν-value takes 1.3 in Takemura et al.
(2003) and 0.3 in Takemura et al. (2005) suggesting a large uncertainty in the estimation of aerosol
number increase after the industrial revolution. This suggests that a study of natural source aerosols is
important for better estimation of anthropogenic aerosol forcings.
Satellite remote sensing techniques can also be used to estimate the aerosol indirect forcing. Nakajima
et al. (2001) used AVHRR-retrieved aerosol and cloud optical thickness and effective radius (or Ångström
exponent) to derive column numbers of aerosols and clouds. They found a decrease in the effective
particle radius and an increase in the aerosol optical thickness with increasing column aerosol number. By
assuming ν= 0.30, they found the indirect forcing over ocean as -1.3 W/m2. Sekiguchi et al. (2003)
re-examined this analysis more carefully and found that for the forcing is similar as -1.4 W/m2. They also
newly found that there is a cloud fraction increase with increasing aerosol number. A contradiction,
however, is that the forcing becomes very small as -0.18 W/m2 over ocean and -0.14 W/m2 over land when
they use POLDER-retrieved parameters. It is suggested that we need a more careful study of remotesensing data for evaluation of the indirect radiative forcing.
3. Conclusions
As discussed in the previous section, the direct forcing of anthropogenic aerosols is close to zero due
to a large absorption of solar radiation by aerosol particles. This conclusion consequently raises aquestion: how large is the surface forcing by aerosol particles. Large absorption causes a large reduction in
the solar radiative flux reaching the earth's surface, but at the same time the evaluation of forcing becomesdifficult. So far, our GCM simulation produces surface radiative forcing values of -1 W/m2 and -2.3 W/m2
over ocean and land, respectively, so that there is a large diabatic forcing between ocean and land areas todrive secondary general circulation. More studies should be devoted for simultaneous measurements ofsurface radiative flux and aerosol absorption. Key parameters to be studied for better understanding of theradiative budget in cloudy regions are the single scattering albedo of aerosols and also the CCN ability ofaerosols. Especially water activity of carbonaceous aerosols and size distribution should be studied more
intensively. Considerable efforts are now being planned in a new project of UNEP/Atmospheric Brown
Cloud (ABC) for comprehensive understanding the aerosol effects to climate, agriculture, and publichealth (Ramanathan et al., 2003).
References

Higurashi, A., and T. Nakajima, 2002: Detection of aerosol types over the East China Sea near Japan fromfour-channel satellite data. Geophys. Res. Lett., 29(17), 1836, doi:10.1029/2002GL015357.
Holben, B.N., D. Tanré, A. Smirnov, T.F. Eck, I. Slutsker, N. Abuhassan, W.W. Newcomb, J.S.Schafer, B. Chatenet, F.
Lavenu, Y.J. Kaufman, J.V. Castle, A. Setzer, B. Markham, D. Clark, R. Frouin, R. Halthore, A. Karneli, N.T.
O'Neill, C. Pietras, R.T. Pinker, K. Voss, and G. Zibordi , 2001: An emerging ground-based aerosol climatology:
Aerosol optical depth from AERONET. J. Geophys. Res., 106, 12067-12097.
Huebert, B.T., T. Bates, P.B. Russell, G.Y. Shi, Y.J. Kim, K. Kawamura, G. Carmichael, and T. Nakajima, 2003: An
overview of ACE-Asia: Strategies for quantifying the relationships between Asian aerosols and their climatic
impacts. J. Geophys. Res., 108, No. D23, 8633, doi:10.1029/2003JD003550.
Kaufman, Y.J., A., 2003: The global aerosol system and its direct forcing of climate results from MODIS, AERONET
and GOCART. IUGG General Assembly, Sapporo, June 30-July 4.
IPCC, 2001: Climate Change 2001-The Scientific Basis J. T. Houghton, Ed., Cambridge Univ. Press.

2Nakajima, T., A. Higurashi, K. Kawamoto, and J. E. Penner, 2001: A possible correlation between satellite-derived
cloud and aerosol microphysical parameters. Geophys. Res. Lett., 28, 1171-1174.
Nakajima, T., M. Sekiguchi, T. Takemura, I. Uno, A. Higurashi, D.H. Kim, B.J. Sohn, S.N. Oh, T.Y. Nakajima, S.
Ohta, I. Okada, T. Takamura, and K. Kawamoto, 2003: Significance of direct and indirect radiative forcings ofaerosols in the East China Sea region. J. Geophys. Res. , 108(D23), 8658, doi: 10.1029/2002JD003261.
Nozawa, T., S. Emori, A. Numaguti, Y. Tsushima, T. Takemura, T. Nakajima, A. Abe-Ouchi, and M., Kimoto, 2001:
Projections of future climate change in the 21st century simulated by the CCSR/NIES CGCM under the IPCC
SRES scenarios, In 'Present and Future of Modeling Global Environmental Change-Toward Integrated Modeling',
Matsuno, T. and H. Kida eds., Terra Scientific Publishing Company, Tokyo, pp.15-28.
Ramanathan, V., P.J. Crutzen, J. Lelieveld, A.P. Mitra, D. Althausen, J. Andersen, M.O. Andreae, W. Cantrell, G.R.
Cass, C.E. Chung, A.D. Clarke, J.A. Coalkey, W.D. Collins, W.C. Conant, F. Dulac, J. Heinzenberg, A.J.
Heymsfield, B. Holben, S. Howell, J. Hudson, A. Jayaraman, J.T. Kiehl, T.N. Krishnamurti, D. Lubin, G.
McFarquhar, T. Novakov, J.A. Ogren, I.A. Podgorny, K. Prather, K. Priestley, J.M. Prospero, P.K. Quinn, K.
Rajeev, P. Rasch, S. Rupert, R. Sadourny, S.K. Satheesh, G.E. Shaw, P. Sheridan, and F.P.J. Valero, 2001: Indian
Ocean Experiment: An integrated analysis of the climate forcing and effects of the great Indo-Asian haze, J.
Geophys. Res., 106, 28371-28398.
Ramanathan, V., and P.J. Crutzen, 2003: New directions: Atmospheric Brown Clouds, Atmos. Environ., 37,4033-4035.
Sekiguchi, M., T. Nakajima, K. Suzuki, K. Kawamoto, A. Higurashi, D. Rosenfeld, I. Sano, and S. Mukai, 2003: A
study of the direct and indirect effects of aerosols using global satellite datasets of aerosol and cloud parameters.
J. Geophys. Res., in press.
Suzuki, K., T. Nakajima, A. Numaguti, T. Takemura, K. Kawamoto, and A. Higurashi, 2003: A study of the aerosol
effect on a cloud field with simultaneous use of GCM modeling and satellite observation. J. Atmos. Sci., in press.
Takemura, T., T. Nakajima, O. Dubovik, B. N. Holben, and S. Kinne, 2002: Single scattering albedo and radiative
forcing of various aerosol species with a global three-dimensional model. J. Climate, 15, 333-352.
Takemura, T., 2003: Private communication regarding SPRINTARS simulations with different aerosol-cloudinteraction parameterizations.
Takemura, T., T. Nozawa, S. Emori, T.Y. Nakajima, and T. Nakajima, 2005: Simulation of climate response toaerosol direct and indirect effects with aerosol transport-radiation model. J. Geophys. Res.,
doi:10.1029/2004JD005029.

Fig. 1. Satellite classification of aerosol chemical properties by the four channel algorithm of Higurashi
and Nakajima (2002) applied to a East Asian region. The panel (a) shows a comparison of aerosol optical
thickness contributions of sulfate (green), carbonaceous (yellow), mineral dust (red), and sea-salt (blue)3

compositions as estimated by the satellite remote sensing (Sat.) and surface chemical measurements(Grnd.) at Fukue-jima Island and Amami-Oshima Island on 3 April 2003. RGB composite (b), aerosol
types (c), and optical thickness (d) are also shown.

Fig. 2. Radiative forcing of anthropogenic greenhouse gases and aerosols since 1850 as estimated by IPCC
(2001), Takemura et al. (2002; 2003; 2005); Kaufman et al. (2003); Nozawa et al. (2001); Nakajima et al.
(2001); Sekiguchi et al. (2003). νthe inrease rate of aerosol number since 1850.

4

1-1

♣

ISCCP-FD’s Surface Radiation Flux Dataset:
Characteristics and Comparison with GEWEX SRB
Yuanchong Zhang1, William B. Rossow2 and Paul W. Stackhouse Jr.3
1

Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA
2
NASA Goddard Institute for Space Studies, New York, NY, USA
3
Atmospheric Science Division, NASA Langley Research Center, Hampton, VA,,USA

Abstract. Two decades-long global surface radiative flux products, ISCCP-FD and GEWEX/SRB, have beencharacterized and their 85-89 annual means are compared. Although their global-mean values agree quite well,their regional differences are larger, reflecting differences in their input datasets and radiative transfer treatments,
mainly for surface albedo, aerosols and clouds for SW and temperature/humidity profiles and surface skintemperature for LW, respectively. Studying and resolving these differences are necessary to further improvesurface radiation budget estimates so that the long-term climate trend may be derived in the future.
Key Words: Surface radiation budget (SRB), ISCCP-FD, GEWEX/SRB, radiative transfer model.

1. INTRODUCTION
Radiation is the primary forcing that drives the weather and climate within the Earthatmosphere system. Since Simpson (1929), estimating Earth’s radiation budget has long been
pursued. But it is only the advent of satellites that has made it feasible to produce global
radiation budget at top of the atmosphere (TOA) and various datasets measuring the physicalproperties of the atmosphere, clouds and surface that can be put into a radiative transfer
model to calculate the radiation budget at the surface (SRF), TOA, and in the atmosphere(ATM). Such calculations are also essential for numerical modeling of the weather andclimate. In the past two decades or so, numerous authors have produced satellite-based
radiative flux datasets at TOA/SRF; but only recently have there appeared global, decadeslong radiative flux datasets produced using more sophisticated radiative transfer models andimproved input datasets. Among them are the two flux products from ISCCP-FD(International Satellite Cloud Climatology Project Flux product using the ISCCP D-seriesdata) and GEWEX/SRB (Global Energy and Water Cycle Experiment Surface Radiation
Budget project).

2. ISCCP-FD PRODUCTISCCP-FD is a self-consistent and integrated radiative flux profile (PRF) product. It
contains radiative flux profiles defined by fluxes at five levels: SRF, 680 mb, 440 mb, 100
mb and TOA, and the input atmospheric and surface physical quantities used to calculate
them. All of this information is collected into four datasets: FD-TOA, FD-SRF, FD-PRF and
FD-INP, where FD-INP is a complete input dataset with virtually all the parameters used in
the flux calculation. There is also an additional fifth monthly-mean FD-PRF dataset. Table 1
shows the definition of the five datasets of the ISCCP-FD product.
The FD product has complete global coverage with a spatial resolution of 280 km andtime interval of 3-hour and monthly. At the time of writing, it covers a time period from July
1983 to June 2001 but will soon be extended to 2004.
The version of the radiative transfer model used in the flux calculation is 2001 NASA
Goddard Institute for Space Studies (GISS) model. The most important characteristics of the♣

Extended abstract presented at CEReS International Symposium on Radiation Budget and AtmosphericParameters Studied by Satellite and Ground Observation Data Toward the Understanding of Long Term Trend
in Asia, February 17 (Thur.) & 18 (Fri.), 2005, Chiba, Japan.

5

Table 1. Five Datasets of the ISCCP-FD ProductDataset Name
Definition
A
FD-TOAAll the radiative flux components at TOAa
All the radiative flux components at SRFa
B
FD-SRFC
FD-PRFAll the radiative flux components for PRF (TOA and SRF, inclusive) aD
FD-INP
Complete input variables used in flux calculation
FD-MPF
Radiatively linearly averaged monthly-mean FD-PRF
Ea
Including summary of the most important input variables for it.

new GISS model are: (1) higher spectral resolution employing 15 non-contiguous correlated
k-intervals to model overlapping cloud-aerosol and gaseous absorption for the shortwave(SW) (nominally 0.2-5.0 :m) with UVA and UVB treatment incorporated; (2) 33 noncontiguous correlated k-intervals for the longwave (LW) (nominally 5.0-200.0 :m, including
one for a “window” wavelength: 11.1-11.3 :m) to match line-by-line fluxes to within 1 Wm!2
and provide a significant improvement for upper stratospheric cooling rates due to water
vapor over the old 1983 GISS model [Oinas et al., 2001]; (3) improved surface visible albedo(0.2-0.7 :m) values and a more precise 5-band near-infrared (NIR: 0.7-5.0 :m) albedo
representation for 11 “vegetation” type; (4) more comprehensive set of atmospheric gaseous
absorbers and aerosols with realistic spatio-temporal variations; (5) explicit treatments of
non-spherical ice cloud microphysics and cloud macro-inhomogeneity; (6) realistic non-unit
spectral emissivities for surface and cloud LW emission.
With the model, the 5-level downwelling and upwelling SW and LW radiative flux
profiles are calculated using the following datasets to specify the properties of the Earth's
atmosphere and surface: ISCCP-D1 (Rossow and Schiffer, 1999) cloud dataset with astatistical cloud vertical structure (CVS) model, the TIROS Operational Vertical Sounder(TOVS) temperature/humidity profile products, the Total Ozone Mapping Spectrometer(TOMS) ozone products, a climatology of near-surface air temperature (and surface skintemperature) diurnal cycle constructed from NOAA National Weather Service National
Meteorological Center (NMC) surface weather reports and the first NCEP reanalysis, aclimatology of cloud particle sizes from Han et al. (1994), and a climatology of stratosphericand upper tropospheric water vapor and stratospheric aerosols from Stratospheric Aerosol
and Gas Experiment II (SAGE-II), a climatology of tropospheric aerosols used in the current
NASA GISS climate model.
The complete description of the model, input data, and the sensitivity-study/validation
results for FD product may be referred to Zhang et al. (2004). The information to obtain thedatasets can be found at the ISCCP website (http://isccp.giss.nasa.gov/projects/flux.html),

3. ISCCP FD-SRF DATASETAs the bottom boundary part of the integrated FD flux profiles, the FD-SRF dataset
has all the characteristics described above. In Zhang et al. (2004), we have repeated virtually
all the validation studies for FD-SRF that were done for FD’s precursor FC (C for ISCCP Cseries data, the precursor of D-series) (Zhang et al., 1995). Overall, FD decreases
uncertainties about 5 Wm!2 over FC. In addition, we have validated FD using new and moreaccurate ground ‘truth’ from the Baseline Surface Radiation Network (BSRN). The BSRN
started operations in 1992 with 9 stations and has 35 sites currently. The original target
accuracies for BSRN were ±10 Wm!2 for downwelling SRF SW (S9s) and ±20 Wm!2 for
downwelling SRF LW (L9s). But significant improvements in procedures, instrument
calibration and knowledge have led to estimated accuracies of ±5 Wm!2 for S9s and ±10
Wm!2 for L9s (Ohmura et al., 1998). The comparisons of monthly, regional mean values from6

FD with BSRN values suggests that we have been able to reduce the overall uncertainties to
10-15 W/m2 at SRF from 20-25 W/m2 for FC (Zhang et al., 2004).
Table 2a summarizes the statistics of the comparison of all the available and matched,monthly mean FD and BSRN values, totaling 1970 and 1831 data points for S9s and L9s,
respectively. For S9s, the mean (rms) difference (FD ! BSRN) is 2.0 (18.5) Wm!2 and thecorrelation coefficient between the two sets is 0.98. For L9s, these statistics are 2.2 (19.0)
Wm!2 and 0.97, respectively. Tables 2b and 2c show the same comparison statistics but forseven separate latitudinal zones. For the SW, most zones exhibit differences # 10 Wm!2; thelargest mean differences appear in the tropical zone (21.3 Wm!2), where biomass burning
aerosol effects have not been (completely) accounted for in our calculations, and at southernhigh latitudes (-20.0 Wm!2), where the sample size at the surface is very small (23). The
largest rms differences appear in the southern and northern polar regions (20.6 and 21.8 Wm!2,
respectively) with the rest of the zones exhibiting values # 16 Wm!2. The correlation
coefficients in all zones are above 0.97 except in the tropical zone (0.89). For the LW, all thezones have mean differences # 10 Wm!2, except at southern high latitudes (18.8 Wm!2) where
the sample size is very small. Generally, the LW flux rms differences are slightly larger thanfor the SW and the correlation coefficients are lower, but still ∃ 0.81, except again at southernhigh latitudes.
Table 2a. SRF Downwelling SW and LW Fluxes for all FD-SRF and BSRN data♦
Quantity

FD

BRSN

S9s
L9s

168.20302.23166.19300.01meandifference
2.017
2.219

Stdv

corr
coefficient
0.98250.970618.49119.042Slopeintercept

0.96
1.05

3.90
-17.40

Norm
dev
13.07
12.89

sample#1970
1831

Table 2b. SRF Downwelling SW from FD-SRF and BSRN Separated into Latitudinal Zones♦
Lat. Zone

FD

BRSN

mean
difference

Stdv

corr
coefficientSlopeintercept

Norm
dev

sample90Ε S 6 65Ε S
65Ε S 6 35Ε S
35Ε S 6 15Ε S
15Ε S 6 15Ε N
15Ε N 6 35Ε N
35Ε N 6 65Ε N
65Ε N 6 90Ε N

114.23145.18217.11
247.72210.87168.3486.64

122.36165.15219.53226.40200.61
168.2386.63-8.133-19.972-2.41221.31810.2620.116
0.005

20.59915.37011.72813.96316.092
14.18021.7980.99070.98220.98470.89280.97420.98470.9724

1.05
1.03
1.00
0.95
0.97
0.95
0.97

2.31
15.08
2.32
-9.03
-4.65
7.96
3.01

13.38
10.53
8.29
10.07
11.45
9.88
15.51

302
23
144
218
243
819
221

#

Table 2c. SRF Downwelling LW between Latitudinal-zonal FD-SRF and BSRN♦
Lat. Zone

FD

BRSN

mean
difference

Stdv

corr
coefficientSlopeintercept

Norm
dev

sample90Ε S 6 65Ε S
65Ε S 6 35Ε S
35Ε S 6 15Ε S
15Ε S 6 15Ε N
15Ε N 6 35Ε N
35Ε N 6 65Ε N
65Ε N 6 90Ε N

194.11
316.67357.99414.65360.45305.72251.83184.12297.85360.82415.33356.36307.05244.619.994
18.820-2.828-0.6804.096-1.3277.217

19.12717.13222.6638.797
19.95617.68820.0800.94780.29160.81220.80940.86340.92090.9293

1.14
0.60
1.09
0.80
1.00
0.98
1.33

-36.49107.75-30.75
84.20
-4.25
7.50
-91.49

11.90
14.40
15.18
6.49
14.11
12.62
10.18

276
23
141
136
237
814
204

♦

#

Regression statistics are from a linear least squares fit to the scatter of points, X/Y for FD-SRF/BSRN. All
values are in Wm!2, except the correlation coefficients and slopes, which are unitless. “Norm Dev” is the rms
distance of all the points from the regression line.

4. GEWEX SRB DATASETGEWEX-SRB (Release 2) dataset is also a global, long-term set with a higher spatial
resolution of 1º X 1º at 3-hour temporal interval. It covers a time period of 12 years (July
1983 - October 1995) (Stackhouse et al., 2001, 2004) but will soon be extended to 2004. Its
7

radiative transfer algorithms are from Pinker and Laszlo (1992) and Fu et al. (1997) for
broadband SW (0.2-4.0 :m) and LW (4.5-∞ :m), respectively. Among the input datasets,
cloud information is from ISCCP-DX (Rossow et al., 1996), the atmospherictemperature/humidity profiles are from the Goddard Earth Observing System (version1,
GEOS-1) reanalysis, surface skin temperature is from GEOS-1 (originally from Reynolds,
1988) over oceans and land with more than 50% cloud cover and from ISCCP for land withless than 50% cloud cover and snow/ice covered surfaces. Ozone data is from TOMS
integrated with TOVS when needed. The surface albedo is obtained from Pinker/Laszlo
algorithm (Pinker and Laszlo, 1992) and the emissivity map is adapted from the CERESSARB surface emissivity map (Wilbur et al., 1999). A background aerosol is assumed based
upon surface type in the Pinker/Laszlo model adapted from the WCP-55 (1983) aerosol types.
The effective optical depths are scaled in the algorithm to account for differences between
composite clear-sky and observed clear radiances. The monthly averaged mean (RMS)
difference of GEWEX SRB compared against all BSRN site measurements from 1992 – 1995
is -3.4 (23) Wm-2 for SW fluxes and –5.2 (15.5) Wm-2 for LW fluxes, respectively. That
exclusion of polar sites reduces SW monthly mean (rms) difference to –0.2 (18.5) Wm-2
indicates a need for further study in the polar regions (Stackhouse, et al., 2004).

5. SURFACE FLUX COMPARISON BETWEEN FD-SRF AND SRB

Table 3 shows statistical results from the comparison between FD-SRF and SRB for
global averages of all the primary and derived surface flux components based on 5-year (8589) annual means (with SRB map regridded to ISCCP’s standard 280-km equal-area map).
Table 3. Comparison of Global Mean for 85-89 Annual Mean between FD-SRF and SRB ††Quantity
S↓s
S↑s
ALBs
CLR-S↓s
CLR-S↑s
CLR-ALBs
NSs
CLR-NSsL↓s
L↑s
CLR-L↓s
CLR-L↑s
NLs
CLR-NLs
Ns
CLR-Ns
CFC-NSs
CFC-NLs
CFC-Ns

FD
189.2
24.0
15.0
248.3
29.8
14.3
165.2
218.5
344.7
395.6
313.6
394.1
-50.9
-80.5
114.3
137.9
-53.3
29.6
-23.7

SRB
186.9
21.8
13.9
244.3
28.6
14.1
165.1
215.7
343.2
393.7
306.2
393.2
-50.5
-87.0
114.6
128.7
-50.6
36.5
-14.2

mean diff.
2.34
2.21
1.13
3.98
1.22
0.25
0.13
2.76
1.49
1.90
7.43
0.96
-0.40
6.48
-0.28
9.24
-2.63
-6.88
-9.51

STDV.
10.50
7.01
3.47
11.76
7.96
3.15
9.91
13.12
10.16
11.15
11.30
11.26
13.04
13.38
16.48
19.93
6.55
3.95
7.56

Cor.Coef.
0.9832
0.95420.9846
0.9766
0.9571
0.98430.9876
0.98210.98940.98890.98880.98880.78780.72300.96120.96230.9544
0.95150.9356

SLOPE
1.007
0.812
0.876
0.990
0.826
0.907
0.976
0.964
1.035
0.980
1.002
0.973
0.733
0.823
1.041
0.987
0.942
0.929
0.902

Intercept
-3.69
2.30
0.74
-1.39
3.96
1.07
3.86
5.17
-13.69
6.19
-8.16
9.81
-13.17-20.73
-4.42
-7.42
-0.45
8.98
7.19

norm dev
7.39
4.38
2.04
8.35
5.04
2.03
7.01
9.27
6.88
7.89
7.98
7.94
9.53
10.08
11.31
14.17
4.68
2.82
5.39

Cell #
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596
6596

In addition to the symbols/acronyms similar to those in Table 2 (X/Y for FD/SRB now), CLR=clear-sky,
CFC=cloud flux change (“cloud forcing”), ALB=albedo, N=net, and ↑ is for those upwelling fluxes.

Generally speaking, the two’s agree reasonably well: for all the primary flux
components (i.e., S↓s, S↑s, L↓s and L↑s and their clear-sky counterparts, see footnotes inTable 3 for symbol definitions), their mean differences are < 4 Wm-2 (except CLR-L↓s) withrms < 12 Wm-2, and their spatial correlation coefficients are > 0.95.
However, regional differences are evident and there seem to be some systematic
patterns associated with specific meteorological, cloud and aerosol conditions in someregions. Figure 1 shows global difference maps of S↓s, S↑s, CLR-S↓s and CLR-S↑s,respectively. The three big yellow circular patterns in CLR-S↑s map are obviously related toview angle of individual geostationary satellites, primarily from the surface albedo retrieval

8method of SRB. Comparing CLR-S↓s and CLR-S↑s maps shows FD has lower surface albedo
than SRB in most of the northern part of South America and the southern part of Africa. In
the polar regions, FD has generally higher surface albedo than SRB. There is an area in

Fig. 1. 85-89 annual-mean global difference (FDSRF minus SRB) maps, from top to bottom: S↓s,
S↑s, CLR-S↓s and CLR-S↑s, respectively, with
global statistics (of regional difference) in the titles.

9Fig. 2. 85-89 annual-mean global difference (FDSRF minus SRB) maps, from top to bottom: L↓s,
L↑s, CLR-L↓s and CLR-L↑s, respectively, with
global statistics (of regional difference) in the titles.

central China where FD has up to > 40 Wm-2 higher SW than SRB (for both CLR-S↓s and
CLR-S↑s). This is because FD’s aerosols are much less than SRB in that area, where FD’s
absorbed atmospheric SW is up to 30 Wm-2 less than SRB (not shown). In the top panel (S↓s)
of Fig. 1, there are several oceanic areas (two in the eastern Pacific, two in the eastern
Atlantic, and one in the northwestern Pacific) where FD is as much as 18 Wm-2 less than SRB,
likely caused by two different treatments of clouds: although both data products draw theircloud-related information from ISCCP, the FD product uses all of the cloud properties
reported by ISCCP whereas SRB uses only cloud cover and visible reflectance. Figure 2
shows the LW counterparts of Fig. 1. The first and third panels (L↓s and CLR-L↓s) suggestthat there are some fundamental differences (with a global mean difference > 7 Wm-2 for
CLR-L↓s) in the input temperature/humidity profiles from TOVS and GEOS-1 that the mostof the larger differences of FD’s values in west coast areas in eastern Pacific and Atlantic
oceans and the belt from northern Africa to mid-east areas but a reverse difference in centralwestern China. Such large systematic differences need further study. The upwelling LW(panels 2 and 4 in Fig. 2) differences are primarily caused by different input surface skintemperature data sets: for ocean area and land areas with > 50% cloud cover, it reflects thedifferences between ISCCP and Reynolds, and for the rest land areas, it reflects FD’s diurnal
adjustment (Zhang et al., 2004) that makes its surface skin temperature different from theoriginal ISCCP’s.

6. CONCLUSIONS
Two decades-long global surface radaitive flux data products are introduced and their
85-89 annual means are compared. Although their global mean values agree reasonably well,there are regional differences that require further investigation. The main comparable causes
are likely the two’s different input datasets and treatments for surface albedo, aerosols andclouds for SW, and temperature/humidity profiles and surface skin temperature for LW.
Nevertheless, the overall agreement of these two products, to within 10-20 Wm-2, indicatesthat they both can be usefully applied to study the factors controlling the weather-scalevariations of surface fluxes. Resolution of some of the differences will allow these data
products to be used to study interannual variations as well.

7. REFERENCES
Fu. Q., K.-N. Liou and A. Grossman, 1997, Multiple scattering parameterization inthermal infrared radiative transfer, J. Atmos. Sci., 54, 2799-2812.
Han, Q., W.B. Rossow, and A.A. Lacis (1994), Near-global survey of effective
droplet radii in liquid water clouds using ISCCP data, J. Climate, 7, No. 4, 465-497.
10

Ohmura, A., E.G. Dutton, B. Forgan, C. Fröhlich, H. Gilgen, H. Hegner, A. Heimo, G.
König-Langlo, B. McArthur, G. Müller, R. Philipona, R. Pinker, C.H. Whitlock, K. Dehne,and M. Wild (1998), Baseline surface radiation network (BSRN/WCRP): new precision
radiometry for climate research, Bull. Amer. Meteor. Soc., 79, No. 10, 2115-2136.
Oinas, V., A.A. Lacis, D. Rind, D.T. Shindell and J.E. Hansen (2001), Radiative
cooling by stratospheric water vapor: Big differences in GCM results, Geophys. Res. Lett., 28,2791-2794.
Pinker, R.T. and I. Laszlo (1992), Modeling surface solar irradiance for satellite
applications on global scale, J. Appl. Meteor., 31, 194-211.
Reynolds, C.D. (1988), A real time global sea surface temperature analisys, J. Climate,
1, 75-86.
Rossow, W.B., A.W. Walker, D.E. Beuschel and M.D. Roiter (1996), International
Satellite Cloud Climatology Project (ISCCP) documentation of new cloud datasets,
WMO/TD-No. 737, World Climate Research Programme (ICSU and WMO), 115 pp.
Rossow, W.B. and R.A. Schiffer (1999), Advances in understanding clouds from
ISCCP, Bull. Amer.Meteor. Soc., 80, 2261-2287.
Simpson, G.C. (1929): The distribution of terrestrial radiation. Mem. Roy. Meteor.
Soc., 3, 53-78.
Stackhouse Jr., P.W., Stephen J. Cox, Shashi K. Gupta, Marc Chiacchio, and J.
Colleen, Mikovitz (2001), The WCRP/GEWEX surface radiation budget project release 2:
An assessment of surface fluxes at 1 degree resolution. International Radiation Sysposium,
St.-Petersburg, Russia, July 24-29, 2000. IRS 2000: Current Problems in Atmospheric
Radiation, W.L. Smith and Y. Timofeyev (eds.), A. Deepak Publishing, 147.
Stackhouse Jr., P.W., S.K. Gupta, S.J. Cox, J.C. Mikovitz, T. Zhang, and M.
Chiacchio, and (2004), 12-Year Surface Radiation Budget Data Set, GEWEX News,
November,10-12.
WCP-55 (1983). World Climate Research Report of the experts meeting on aerosols
and their climatic effects, Williamsburg, Virginia, 28-30 March 1983, Eds: A. Deepak and H.
E. Gerber, 107 pp.
Wilber, A. C., D. P. Kratz, S. K. Gupta, 1999: Surface emissivity maps for use insatellite retrievals of Longwave Radiation, NASA Technical Publication, 1999-209362, 35
pp.
Zhang, Y.-C., W.B. Rossow and A. A. Lacis (1995), Calculationof surface and top of
atmosphere radiative fluxes from physical quantities based on ISCCP data sets, 1. Method
and sensitivity to input data uncertainties, J. Geophys. Res., 100, 1149-1165.
Zhang, Y-C., W.B. Rossow, A.A. Lacis, V. Oinas and M.I. Mishchenko (2004),
Calculation of radiative fluxes from the surface to top-of-atmosphere based on ISCCP andother global datasets: Refinements of the radiative transfer model and the input data, J.
Geophys. Res., 109, D19105, doi:10.1029/2003JD004457.

11

1-2

Estimation of Land Surface Energy Fluxes over the
Tibetan Plateau using GMS data
Yuichiro Oku1, Hirohiko Ishikawa1,
1
2

Zhongbo Su2Disaster Prevention Research Institute, Kyoto University, Japan

International Institute for Geo-Information Science and Earth Observation, the Netherlands

A Surface Energy Balance System (SEBS) originally developed for NOAA/AVHRR is applied to GMS-5/VISSR
data in combination with meteorological information. Land surface temperature obtained from GMS data are
input to SEBS to estimate hourly regional distribution of land surface heat fluxes over the Tibetan Plateau. In
SEBS, latent heat flux is obtained as the residual of the energy budget. Estimated fluxes are validated bycorresponding field observations measured by sonic anemometer-thermometer with using the eddy correlation
methodology at Amdo. Diurnal cycle of estimated fluxes is in good agreement with the field measurement. For
example, diurnal range of estimated sensible heat flux decreases from June to August, which reflects the changeof surface characteristics from dry to wet due to frequent precipitation in the summer monsoon. Over the Tibetan
Plateau, the diurnal range of land surface temperature is as large as the annual range, so that the resultant
sensible heat flux has large diurnal variation. Hence, the hourly estimation with GMS data may contribute better
understanding of land surface-atmosphere interaction of this important area.
Key Words: land surface energy flux, surface energy balance, Tibetan Plateau, GMS.

1.

Introduction

The Tibetan Plateau has been thought to play an important role in the progress of the Asiansummer monsoon through thermal effects on the surrounding mid-troposphere. In order tounderstand the role of the Tibetan Plateau as an elevated heat source in this region, intensive
meteorological observations were conducted in the Global Energy and Water Cycle
Experiment (GEWEX) Asian Monsoon Experiment (GAME) Tibet project. During
GAME/Tibet, surface sensible and latent heat fluxes were measured at a set of sites together
with monitoring of relevant surface parameters. Using the data obtained in these experiments,
the land surface interaction was observed experimentally at these sites, it only represents flux
over a patch scale around the measurement sites. Patch-scale knowledge needs to be
integrated with a regional-scale understanding of the plateau. Remote sensing from satellites
12offers the possibility to derive regional distributions of land surface energy fluxes incombination with sparsely distributed set of field experiment stations. Ma (2003) estimatedthe distributions of land surface variables over the GAME/Tibet enhanced observation area by
combining NOAA/AVHRR data with field observations. According to Tanaka et al. (2001),
however, strong diurnal variations are one of the outstanding features of the plateau surface,which cannot be detected in the data of polar-orbiting satellites. In this study, a method for theland surface energy fluxes from geostationary satellite data is presented. The retrieval
algorithm SEBS (Surface Energy Balance System) used for NOAA/AVHRR data (Su, 2002)
is applied to GMS/VISSR data.
2.

Surface Energy Balance System (SEBS)

The net radiation flux Rn is estimated as
Rn = (1 −where RS↓

)RS ↓ + air RL ↓ − sfc Tsfc 4(1)

is the downward shortwave radiation which is a function of the solar constant,atmospheric transmittance at the location and time.

is surface albedo,

the emissivity of the air and the surface, respectively. RL

↓

air

and

sfc

are

is downward longwave radiationis Stephan-Boltzmann constant and Tsfc isparameterized by atmospheric temperature,

land surface temperature. The soil heat flux G0 is estimated by net radiation as
G0 = Rn [Γ c + (1 − f c )(Γ s − Γ c )](2)

where Γs and Γc are the ratios between G0 and Rn for bare soil and fully covered
vegetation surface, respectively. The fractional vegetation cover f c is determined by NDVI .
In order to derive the sensible heat flux H , use is made of the similarity theory. Over acomplex landscape there is a height, the blending height, where the distinct impacts of theunderlying surfaces diminish. At the blending height z , wind speed u and air temperature

Tair satisfy the general conditions given by Monin-Obukhov’s similarity theory. They aredescribed asu=Tsfc − Tair =u∗ ⎡ ⎛ z − d0 ⎞⎛ z − d0 ⎞⎛ z ⎞⎤⎟⎟ − Ψm ⎜⎟ − Ψm ⎜ 0m ⎟⎥⎢ln⎜⎜k ⎣ ⎝ z 0m ⎠⎝ L ⎠⎝ L ⎠⎦(3)⎡ ⎛ z − d0 ⎞⎛ z 0h ⎞⎤⎛ z − d0 ⎞
⎟
⎜
−
Ψ
ln
−
Ψ
⎟⎥
⎜
⎟
⎜
⎢
h
h
ku ∗ C p ⎣ ⎜⎝ z 0h ⎟⎠⎝ L ⎠⎦
⎝ L ⎠

(4)

H

is air density, C p is specific heat constant,where k = 0.4 is von Karman’s constant,u ∗ is the friction velocity, d 0 is the zero plane displacement height, z 0m and z 0h are theroughness heights for momentum and heat transfer, Ψm and Ψh are the stability correction
13

functions for momentum and sensible heat transfer, respectively. L Is the Monin-Obukhov
stability length defined as

L=

C pTair u ∗
kgH

(5)

where g is the acceleration due to gravity. Derivation of the sensible heat flux H using
Eqns. (3–5) requires only the wind speed u and temperature Tair at the blending height z
as well as the surface temperature Tair . The latent heat flux

E is derived as the residual of

the energy budget theorem for the land surface, i.e.

R n = G0 + H + E .

(6)

3. DataLand surface temperature Tsfc is one of the most important parameter in diurnal flux
retrieval. According to Oku and Ishikawa (2004), hourly Tsfc is estimated by using infrared
split-window technique from GMS-5/VISSR data. Other surface parameters, such as albedo
and emissivity

sfc, do not vary in inter-diurnal scale severely, but change seasonal scale.

These are estimated from visible reflectance measured in NOAA-14/AVHRR channels 1 and
2, as 1 month mean. All available GMS and NOAA images from May 1998 to April 1999 are
prepared to estimate flux distribution over the Tibetan Plateau.

However, neither GMS nor NOAA data could derive wind speed u and air temperature Tairat the blending height z , ECMWF 40 years re-analysis data (ERA40) is used. To assess thereality of ERA40 data, u and Tair are compared with independent data sets of sonde
observations at Amdo (32.24N, 91.63E, 4,700mASL). At the 500hPa level, it corresponds tonearly 1,000mAGL at Amdo, root mean square error (RMSE) between ERA40 and sonde dataof Tair and u are 1.29K and 3.44m/s, respectively. These RMSEs have a possibility to
induce several-ten W/m2 error in daytime and several W/m2 in nighttime in sensible heat flux
retrieval. Study area is defined as the area higher than 4,000m ASL. The data in grid format
over longitude and latitude having a resolution of 0.1 degree are interpolated from original
image data that are archived in line-pixel format.

Surface energy fluxes measured at Amdo during GAME/Tibet intensive observation period
are used as ground truth for verification fluxes estimated by presented algorithm. The net
radiation flux is obtained using data from the 4-component radiation system. The soil heat
flux is computed from a thermal conductivity equation with using soil temperature andmoisture profiles (Tanaka et al., 2003). The sensible heat flux is calculated form turbulence
14Fig. 1: A scatter diagram of surface energy fluxes observed at Amdo (horizontal axis) and fluxes
estimated by SEBS (vertical axis). Upper left panel is net radiation Rn, upper right is soil heat flux
G0, lower left is sensible heat flux H and lower right is latent heat flux lE.

data measured by sonic anemo-thermometer with using eddy correlation method. A problem
is identified in the latent heat flux data calculated by eddy correlation method, attributed tothe performance of the infrared hygrometer, the latent heat flux is derived as the residual of
the energy budget theorem for the land surface as described in Eq. (6)

15

4.

Validation and Remarks

Surface energy fluxes retrieved by presented algorithm are compared with corresponding
ground truth at Amdo, as is shown in Fig. 1 and Table 1. High correlation coefficient implies
diurnal cycles of retrieved flux are a good match to observed values. Overestimation in net
radiation seems to be attributed to the difference in the spatial resolution between GMS
images and ground based observations. The pixel size of GMS images is about 50km2 at
Amdo, since GMS views the plateau from geostationary orbit at 140E. Surface measurements
are not always representative of the typical value over a GMS pixel, particularly in that
subpixel-scaled cloud formations affect the surface measurement would be missed by GMS.
Estimated soil heat fluxes exhibit considerable RMSE with respect to observed value. Thiscan be partially explained that Eq. (2) does not take account for soil moisture conditions. Wet
condition in ground soil makes thermal conductance large. This process might be needed toinclude soil heat flux retrieval.

Table. 1: Statistics of measured versus retrieved surface energy fluxes at Amdo. R is correlation
coefficient, RMSE is root mean square error in W/m2 and N is number of data used. Rn is net
radiation, G0 is soil heat flux, H is sensible heat flux, and lE is latent heat flux.

Rn

G0

H

lE

R

0.96450.82280.8866

0.8999

RMSE

67.45

46.63

52.78

88.43

N

6477

5588

945

743

16

Fig. 2: Time series of surface energy fluxes estimated by SEBS (net radiation Rn, soil heat flux G0,
sensible heat flux H and latent heat flux lE from upper to lower) at Amdo from May 1998 to April
1999. Unit is W/m2.

Fig. 2 displays seasonal variation of retrieved fluxes. The sensible heat flux decreases and thelatent heat flux increases from June to July. This remarkable change can be seen in observed
fluxes, because the plateau surface becomes wet due to daily precipitation as the monsoon
progress. Fig.3 shows spatial distributions of retrieved fluxes. The greatest advantage of GMS
is that observations of the earth are made from a stationary position which meteorologicalphenomena can be monitored continuously, making the analysis intra-diurnal variations overlarge scale possible.
Acknowledgements
This research was partially supported by the Ministry of Education, Science, Sports and Culture,
Grant-in-Aid for the Japan Society for the Promotion of Science Fellows and Joint Research on Center
for Environmental Remote Sensing, Chiba University. The surface energy fluxes data at Amdo wasprovided by Dr. Kenji Tanaka of Kumamoto University.
References
Ma, Y., 2003: Remote sensing parameterization of regional net radiation over heterogeneous land17

Fig. 3: Horizontal distribution of available retrieved surface energy fluxes (net radiation Rn, soil
heat flux G0, sensible heat flux H and latent heat flux lE from upper to lower) from 0000UTC(0600LST at 90E) to 0600UTC 25 Apr. 1998. Blank white region over the Tibetan Plateau iscloudy area. Unit is W/m2.
surface of Tibetan Plateau and arid area. Int. J. Remote Sens., 24(15), 3137-3148.
Oku, Y. and Ishikawa, H., 2004: Estimation of land surface temperature over the Tibetan Plateau using
GMS data. J. Appl. Meteorol., 43(4), 548-561.
Su, Z. 2002: The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol.
Earth Syst. Sci., 6(1), 85-99.
Tanaka, K, Ishikawa, H., Hayashi, T., Tamagawa, I., and Ma, Y., 2001: Surface energy budget at Amdo
in the Tibetan Plateau using GAME/Tibet IOP98 data. J. Meteorol. Soc. Jpn., 79, 505–517.
Tanaka, K., Tamagawa, I., Ishikawa, H., Ma, Y. and Hu, Z., 2003: Surface energy budget and closureof the eastern Tibetan Plateau during GAME-Tibet IOP 1998. J. Hydrol, 283, 169-183.

18

1-3

Comparative Study of Cloud Parameters Derived
From Terra-MODIS and GMS-VISSR
Jules R. Dim, Tamio Takamura, Itaru Okada, Hideaki Takenaka
Center for Environmental Remote Sensing, Chiba University
1-33 Yayoi-cho, Inage-ku
CHIBA 263-8522, JAPAN(E-mail: dim@ceres.cr.chiba-u.ac.jp, takamura@ceres.cr.chiba-u.ac.jp, iokada@ceres.cr.chibau.ac.jp, takenaka_ceres@graduate.chiba-u.jp)
Abstract
Top cloud reflected solar radiance measurements directly recorded by satellites at various spectral bands andobservation conditions are used to understand clouds’ optical and physical characteristics. The magnitude of theseradiance measurements modulates the energy balance of the Earth through absorption and scattering. The accuracyof the data obtained highly depends on the sensitivity of the recording sensors. The Visible and thermal infrared
channels are mainly employed to extract, through various inversion techniques, cloud microphysical parameters suchas the brightness temperature, optical depth, effective radius, top cloud temperature etc. This study examines
reflectance and optical depth retrievals derived from cloud radiative measurement data collected by the SVISSR(Stretched Visible Infrared Spin-Scan Radiometer) and the MODIS (MODerate resolution Imaging
Spectroradiometer) sensors onboard the geostationary satellite (GMS-5) and the polar-orbiting satellite (Terra)
respectively. MODIS cloud optical depth as well as reflectance appears systematically higher than the SVISSR
corresponding data. And, the largest differences are mostly seen in thick cloud areas. To understand these
discrepancies, a cloud classification based on the split-window method, has been conducted and, the influence ofvarious parameters on the measurements and retrievals among which, dimensional radiative effects (asymmetry of
the clouds) and the sun-earth-satellite viewing geometry are examined.
Key Words: MODIS, SVISSR, cloud optical depth, reflectance

1. INTRODUCTION
Retrieval of cloud properties from passive remote sensing has extensively relied on satellite data.
The cloud characteristics therefore retrieved from radiance measurements are mainly the clear or
cloudy scenes distribution, the cloud fraction, reflectance and emissivity, liquid water content,phase (water or ice clouds), optical depth, effective radius, top cloud temperature etc. Various
factors such as the solar altitude, the view angle geometry and the cloud inhomogeneity influence
the accuracy of the cloud properties measurements. The degree to which satellite measurements
are influenced by cloud inhomogeneities depends on the spatial resolution of the instrument, thesun-earth-satellite viewing geometry, and whether the observations are analyzed at the local pixel
scale or over larger regions by averaging pixel-level values (Loeb and Coakley, 1998).
Various cloud detection methods have been adopted among which the maximum temperature(retaining of the highest temperature of a given area), the infrared variability, the two-wavelength
infrared (split-window method), the two-wavelength visible-infrared (using reflected sunlight todetect clouds).
The GMS-5 satellite, launched in 1995 into a geostationary orbit above 0o N, 140oE, carries a
Stretched Visible and Infrared Spin-Scan Radiometer (SVISSR) with four channels, one in thevisible part of the spectrum (0.5 – 0.75 µm) and two in the infrared (10.5 - 11.5 µm and 11.5 –19

12.5 µm) then one water vapor channel (6.5 – 7.0 µm). The ground resolution at the subsatellite
point is 1.25 km in the visible channel and 5 Km in the infrared. It is not possible to derive cloudparticle size with the SVISSR sensor due to the non-existence of the 3.9 µm channel (therefore,
for optical parameters’ retrievals, a fixed value of 10 µm was used as effective radius). The
satellite however has a good time resolution with scanning intervals at least every hour. The
SVISSR instrument noise is around 0.5 K (MSC, 1997).
MODIS (MODerate resolution Imaging Spectroradiometer) sensor onboard The Terra polar
orbiting satellite is a 36-band spectrometer (ranging from 0.41 to 14.38 µm) providing a global
data set every 1-2 days with a 16-day repeat cycle. The spatial resolution of MODIS (pixel size at
nadir) is 250m for channels 1 and 2 (0.6 µm - 0.9 µm), 500m for channels 3 to 7 (0.4 µm - 2.1µm) and 1000 m for channels 8 to 36 (0.4 µm - 14.4 µm), respectively. The sensitivity at the
11µm channel permits the detection of temperature differences as small as 0.01 K and the noise
equivalent temperature difference is about 0.05 K. Because of this thermal detection capacity,
adjacent pixels from MODIS data show higher variability than those from SVISSR data.
In the present study, we would like to examine cloud mocrophysical parameters retrievals derived
from cloud radiative measurements collected by the SVISSR and the MODIS sensors. Theninvestigations will be conducted on the effects that the random geometry of the clouds would
have on the accuracy of the cloud reflected radiation. Beyond the comparison of satellite data,
this study was motivated by the search of reasonable explanations and corrections of some
important differences noticed in the optical and radiative properties retrievals and, measurementsfrom SVISSR and MODIS sensors. The area of study chosen to conduct this work is mainly
centered on the southern part of the Sea of Japan and limited by the following geographicalcoordinates 25N - 35N latitude and 125E - 135E longitude. The SVISSR and MODIS matching
images on this area, have time differences of 3 to 28 minutes. A grid resolution of 0.05o or 5 Km
pixel’s length is used for data processing.

2. METHODOLOGY AND OBSERVATIONS
Data from the MODIS sensor almost temporally coincident with the SVISSR images areanalyzed. Visible and infrared channels are used to retrieve cloud microphysical properties suchas the brightness temperature, optical depth, top cloud temperature, particle effective radius etc.
Using the split-window technique (Brightness temperature difference at 11µm and 12 µm) on the
SVISSR IR data, clouds were classified as cumulus, cumulo-nimbus and dense cirrus and, cirrus.
This classification was attempted in order to understand the sensors’ sensitivity to themorphological structure of the clouds encountered in the region. Large discrepancies in the
microphysical properties (reflectance, optical depth mainly) of the clouds, generally in areas ofthick clouds were observed. The MODIS data appear systematically higher than the SVISSR datain such areas. The best correlation between MODIS and SVISSR data is found with the relatively
thin mid- and low-level clouds i.e. at cloud optical depth (COD)<20. Figure 1 is the illustration of
the frequency distribution of the differences in cloud optical properties between MODIS andSVISSR. The split-window classification following these observations is made to identify thetypes of clouds associated with the strongest differences in cloud properties between both sensors.
The last part of this study is devoted to investigations on possible causes of the discrepancies
noticed. Among the reasons generally accounted for as influential in the accuracy of cloud20

microphysical properties: size, geometry and differentiation of cloud phases (water and ice
clouds), differences in the resolution and spectral channels’ separation, accuracy of thecharacterization of the cloud scenes, threshold for cloud detection, calibration errors of thesensors, misalignment of images (geolocation problem) and time differential in the scanning of
the sensors, solar zenith and satellite view angles, scattering patterns and cloud geometries.
COD Differences (2001/07/04)

70.00%

40.00%

60.00%

35.00%

Frequency of occurrences

Frequency of occurrences

COD Differences (2001/02/04)

50.00%
40.00%
30.00%
20.00%
10.00%
0.00%

-50

-40

-30

-20

-10

0

10

20

30

40

30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%

50

COD 0.02% 0.04% 0.10% 0.29% 2.41% 62.05 30.90 3.03% 0.93% 0.22% 0.02%-60 -50-40 -30

-20

-10

0

10

20

30

40

50

60

COD 0.04 0.04 0.09 0.47 0.35 0.96 3.21 35.6736.65 12.55 6.81 2.75 0.41

Difference (MODIS - SVISSR)

Difference (MODIS - SVISSR)

Fig. 1. Frequency histograms of the differences between the cloud optical properties obtained
from SVISSR and MODIS

3. DATA ANALYSISAs stated earlier, cloud optical properties data retrieved from MODIS appear systematicallyhigher than those of SVISSR (See Figure 1). In order to understand these discrepancies, the firststep of our work consisted in adopting a classification method permitting the differentiation ofclouds based on their morphological structure. The method used in this study takes advantage ofthe existence of the 11µm and 12µm channels on the GMS-5/SVISSR. This method is based on
split-window data (11µm-12µm) as suggested by Inoue et al. (1987). The cloud types identified
are: Cirrus, dense cirrus, cumulo-nimbus and cumulus.
It was observed that the largest discrepancies in the cloud properties between both sensors mostly
occur in the vertically well-developed cumulo-nimbus/dense cirrus type of clouds.
Most of the differences noticed in the optical properties between MODIS and SVISSR mainly
occur in the vertically developing cloud structures represented by the cumulo-nimbus and dense
cirrus clouds. The smallest differences are found in the cumulus type. It appears therefore that themorphology of the clouds may play an important role in the accurate determination of radiation
measurements by both sensors. The second step of this work consisted in reviewing andevaluating the other possible causes of the optical properties differences. Particular emphasis
will be placed among others on the relation between the cloud surfaces and the sun radiationgeometry.

21

4. RESULTS AND DISCUSSION
For the visible channels, the radiance measurements made by the SVISSR are converted intoreflectance products; the MODIS data available for this study are calibrated geolocated radianceproducts (at 0.86µm wavelength). These MODIS radiances can be translated into reflectance byusing the following formula:R(µ, µ0 ,φ ) =π I (π , µ0 ,φ )
µ0 F

(

)
F = Solar irradiance (W m µ m )With I = radiance W m −2 sr −1 µ m −1−2−1

µ = Cosine of the observer’s zenith angleµ 0 = Cosine of solar zenith angle
φ = Azimuth angle relative to the solar plane
In this section, observed reflectance data are discussed together with retrieved optical depths inorder to evaluate factors likely to influence measurements and retrievals of satellite data. Amongthese factors:
1) Differences in resolution and spectral channels:
MODIS has a higher resolution (1 km at subsatellite) than SVISSR (1.25 km for visible and 5 km
for IR). Lower accuracies expected for the SVISSR data could be most likely due the poorer
spatial (5 km pixel against 25 pixel averages for the MODIS data) and lower digitization (8-bit
against 10-bit for the MODIS). The higher resolution MODIS data should allow for better
discrimination between clear and cloudy pixels than SVISSR but, as cloud thermal infrared
brightness comparisons show relatively good matches, the resolution alone can not explain the
differences in reflectance and optical depth noticed between both sets of data.
2) Improper thresholds for cloud detection and, accuracy in the characterization of the clear
scenes (algorithm problem):
Equivalent assumptions were made in the algorithms used for the retrievals of both sets of data,
this, minimizing possible retrieval errors. Furthermore, MODIS Cloud reflectance data appear too,
generally higher than the SVISSR derived reflectance and show a trend consistent with that of
COD retrievals from both satellite measurements.
3) Errors in calibrations:Though, the calibration tables for IR channels 1 and 2 have errors of ±0.2 K due to quantization(Tanahashi et al. 2000), a consistent error trend is not visible in either IR data or reflectance datafrom both sensors.
4) Geolocation problem and Time differential in the scanning of both sensors:
A simple comparison of the SVISSR IR channel 1 images at 02 UTC and 03 UTC shows that insome areas, large temperature differences (therefore cloud movements) can occur in just an hour22time interval separating both measurements. This suggests that some of the wide temperature
differences between MODIS and SVISSR IR data may be much more due to the time differential(3 to 28 minute-difference) than to possible geolocation problems. Figure 2 shows the images of
the SVISSR (at 02 UTC and 03 UTC) and MODIS (around 45 mn after and 15 mn before the
SVISSR 02 UTC and 03 UTC respectively) cloud movements and the corresponding IR
temperature cross-section at 30N, on April 2, 2001.

IR temperature at 30N (2001/02/04)
300
IR Temperature (K)

290
280

SVISSR 03 UTC

270

MODIS

260

SVISSR 02 UTC

250
240
230
124

126

128

130

132

134

136

Longitude E

Fig. 2. Cloud movements as illustrated by the optical depth images (upper) and equivalent IR
brightness temperature at 30o N latitude (lower diagram).

235) Size, geometry and cloud phase (water and ice clouds):
Due to the lack of channel 3.9 µm, cloud effective radius (reff) is not available from SVISSR; aconstant cloud effective radius of 10 µm was used in the SVISSR retrieval of cloud
microphysical data. The reff from MODIS retrievals vary widely from around 2 to 40 µm. Thisvariation follows a unimodal distribution whose peak is generally around 10 µm. Regarding thecloud phases, it was noticed that the large cloud properties differences occur as much in the water
as in the ice phase.
6) Influence of the solar zenith angle:
The solar zenith angles from the satellite images used are generally below 35o. Analyses made sofar, didn’t allow us to give enough evidence of the existence of cloud properties’ dependence on
this parameter.
7) Dimensional radiative effects (cloud asymmetry) and relation with satellite viewing geometry:
The effects of finite cloud geometry have been analyzed with satellite observations mainly at
infrared wavelengths. To understand the cloud asymmetry effect on the measurements of cloudproperties, we’ll use a technique of detection of these radiative effects over a chosen area, as
proposed by Varnai and Marshak (2002). The method’s basic idea is to estimate, for each basic
pixel in the area, whether 3D effects are likely to have increased or decreased relatively to 1D
theory, the pixel’s brightness at the visible channel. The technique assumes that the question ofwhether 3D effects enhance or reduce the brightness of a pixel depends mainly on whether the
pixel is on a slope tilted toward (illuminated slopes) or away from the sun (shadowy slopes). An
area of (50 Km)2 size is chosen with the goal that the area contains many pixels for statistical
calculations but that they do not contain clouds from different cloud fields too often. Significant
3D effects can be revealed from the average brightness difference between the brightened(illuminated) pixels and darkened (shadowy) pixels. The implementation of the technique follows
the steps below:
Step1: use the geolocation data to determine from which direction the sun illuminates the area ofa pixel, and thus, which neighboring pixels are closest to the solar azimuth in front of it andbehind it. Only cloudy pixels are examined. In our study area, the solar azimuth angle varies
between 150 and 180o, the sun incident direction adopted will be therefore from South to North.
Step2: determine whether the pixel is on an illuminated slope or shadowy slope. To detect this,the local gradient of brightness temperature ( g ) is calculated from the pixels in front and behind:g = (T front − Tbehind ) d
Where d is the distance separating the pixels in front and behind (10 km)-If g > 0 , the pixel is on an illuminated slope-If g < 0 , the pixel is a shadowy slope
Step 3: After all cloudy pixels in a (50 km)2 area are designated as either illuminated or shadowy
the mean brightness (reflectance) of the illuminated ( Ri ) and shadowy ( Rs ) pixels is calculated.
If the 2 mean values are close to each other, this indicates that 3D effects do not make much a24difference in the (50 km)2 area. If however, Ri is much larger than Rs , 3D effects (asymmetry)are expected to be strong.
Application of this technique to the data of this study shows that for the SVISSR Cloud opticaldepth and reflectance, the illuminated pixels are much more brighter than shadow pixels. Thiseffect is less strong for MODIS data. This supposes that 3D effects due to the asymmetry of thecloud faces relative to the exposition to solar radiation influence both the cloud properties
measurements and retrievals. High differences in cloud properties between both sensors are
generally found when the SVISSR thermal gradient is negative i.e. the cloud face is on the
shadow side relative to the solar radiation. This implies that the SVISSR data measurements andretrievals would be strongly affected and therefore lowered at the shadowy faces.

5. CONCLUSION AND FUTURE WORK
Comparison of MODIS and SVISSR derived cloud properties made in this study shows large
discrepancies between the data retrievals and measurements from both sensors. Possible reasonsof these discrepancies were discussed. It appears that time difference (in the scanning of both
sensors) and the asymmetry effects of the clouds are the factors mostly accounted for as being at
the origin of the differences noticed. Many more uncertainties related to the accuracy of the dataacquired remain to be clarified. Future work would therefore consist of identifying these
uncertainties (view angle differences and other aspects of the specific geometry of the clouds)
and their quantification.

Acknowledgments
The MODIS data used in this study were kindly provided by Dr. T. Nakajima of JAXA-EORC,
Japan, whom we’d like to thank here.
References
Inoue, T., 1987. A cloud type classification with NOAA 7 split-window measurements. J.
Geophys. Res., 92, 3991-4000.
Loeb N. G., and J. A., Jr. Coakley, 1998. Inference of marine stratus cloud optical depths fromsatellite measurements: does 1D theory apply? J. Climate, 11, 215-232.
MSC, 1997. The GMS user’s guide. Meteorological satellite Center of Japan, 190 pp.
Tanahashi S., Kawamura H., Matsuura T., Tomoyuki T., and H. Yusa, 2000. Improved estimatesof wide-ranging sea surface temperature from GMS S-VISSR data. J. Oceanogr., 56, 345-358.
Varnai T., and A. Marshak, 2002. Observations of three-dimensional radiative effects that
influence MODIS cloud optical thickness retrievals. J. Atmos. Sci., 59, 1607-1618.

25

1-4

Development and validation of a general model for
estimating global solar radiation from hourly, daily
and monthly surface meteorological data
Kun Yang and Toshio Koike
River Lab., Dept. of Civil Engineering, University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan

Abstract: Long-term recorded data at world-widely distributed meteorological stations provides an opportunity
to construct historical solar radiation for studies on crop productivity, hydrology, climate change, and solar
power-relevant designs The Ångström–Prescott model has been widely used to estimate global solar radiationfrom measured sunshine duration. However, this model has site-dependent coefficients, which have to becalibrated for different climate zones and elevations, because this model does not explicitly take into accountradiative extinction processes in the atmosphere. In this study, we developed a general radiation model available
with global data sets for turbidity and ozone from remote sensing. Its development followed the four principles
below: (1) it keeps the simple form of Angstrom model while can explicitly and accurately deal with radiative
extinction processes in the atmosphere; (2) model inputs are surface meteorological data (sunshine duration, airtemperature, and relative humidity) so that the model can be applied easily; (3) under clear skies, the estimatedsolar radiation is equal to the value calculated by sophisticated spectral models; (4) at the top of atmosphere, theestimated solar radiation is exactly equal to the theoretical value of solar radiation. The model is calibrated withdata in Japan, while it is successfully applied to China, Saudi Arab, and America, where have distinct climate
zones and elevations. We expect the radiation output of this model could be used for cross-validation withsatellite products.
Key words: surface solar radiation, Ångström–Prescott model, general radiation model, turbidity data set, ozone
data set.

1. Introduction
Solar radiation reaching the Earth drives most of physical and biological cycle in the
Earth system. The surface solar radiation is an indispensable input for many studies on
agriculture, hydrology, and climate change. Also, it is a crucial index in designing solar
devices. However, direct measurements of solar radiation are still too sparse to supportscientific and technological research. The situation is particularly true in remote areas andmountainous areas, where snowmelting and glacier retreat become hot topics in hydrologicaland climate change studies, due to global warming and water resources crisis. A feasible
26

approach to provide solar radiation for these studies is to estimate it from other surface
meteorological data, such as temperature, humidity, precipitation, sunshine duration, and soon. They often have a long record at meteorological and hydrological stations and thus
provide a possibility to construct historical solar radiation for relevant studies.
There exist two major methods to estimate global solar radiation from the surface
meteorological data. The first one is the so-called Ångström–Prescott model, which estimate
surface solar radiation from measured bright sunshine duration. This model has been widely
applied to agricultural meteorology and hydrology for nearly one century. However, it does
not consider radiative extinction processes in the atmosphere, so model parameters are
site-dependent, and have to be calibrated locally. Although a number of studies have focusedon how to tune the parameters, but their success is still limited. The second one is so-called
Bristow-Campbell model, which uses daily temperature (minimum and maximum) and
precipitation records for the estimation. In general, the first method can provide betterradiation estimation than the second one. Also, the second cannot be applied to estimate
hourly solar radiation.
In this study, we made efforts to develop a more general model for solar radiation
estimation from surface meteorological variables and available global data set for turbidity
and ozone thickness. This model follows the simple form of Ångström–Prescott approach
while it can account for the radiative extinction processes. The input data are sunshine
duration, air temperature, and relative humidity. The output are hourly, daily and monthly
global solar radiation, depending on the temporal resolution of the input.
The paper is organized as follows: First, we develop a sub-model to calculate solar
radiation from surface air temperature and relative humidity under clear skies. Second, several
globally covered data sets are introduced into the solar radiation model to calculate Ångström
turbidity coefficient and ozone. Third, the global solar radiation under cloudy conditions isscaled by the surface solar radiation under clear skies, rather scaled by than radiation at theextraterrestrial level. Fourth, model parameters are tuned for estimation of hourly, daily, ormonthly-mean daily radiation respectively, using data at 53 stations of Japan in 1995. The
Ångström–Prescott model is calibrated by the same data sets. Finally, we apply the newmodel and Ångström–Prescott model to world-widely distributed stations. These stations havedistinct climate from humid to dry zones and surface elevation varying from the sea-level toas high as near 4000 m.

2. Global solar radiation model under clear sky conditions
The global solar radiation is affected by a number of extinction processes in theatmosphere. They are Rayleigh scattering, aerosol extinction, ozone absorption, water vapor
absorption and permanent gas absorption. A spectral model can be used for calculating global
solar radiation under clear skies:27

Rb 0 ≡ sin h ∫

λ max

λ min

Rd 0 ≡ 0.5 sin h ∫

λ max

λ min

I 0i (λ )τ oz (λ )τ w (λ )τ g (λ )τ r (λ )τ a (λ )dλ ,I 0i (λ )τ oz (λ )τ w (λ )τ g (λ )[1 − τ r (λ )τ a (λ )]dλ .

(2.1)
(2.2)

where Rb 0 (W m-2) is solar beam and Rd 0 (W m-2) is solar diffuse radiation at ground level
under clear skies.
We then simplify the spectral model as follows
Rb 0 = I 0τ b 0 cos Z ,(2.2a)

Rd 0 = 0.5 I 0τ d 0 cos Z ,(2.2b)

τ b 0 ≈ τ oz τ wτ g τ r τ a − 0.013 ,(2.3a)

τ d 0 ≈ τ oz τ g τ w (1 − τ aτ r ) + 0.013(2.3b)

τ oz = exp[−0.0365(ml )0.7136 ] ,(2.3c)

τ w = min[1.0,0.909 − 0.036 ln(mw)] ,(2.3d)

τ g = exp(−0.0117m'0.3139 ) ,(2.3e)

τ r = exp(−0.008735m' λ r −4.08 ) ,(2.3f)

τ a = exp(− βmλ a −1.3 ) ,(2.3g)

λ r = 0.547 + 0.014m'−0.00038m'2 +4.6 × 10−6 m'3 ,(2.3h)

λ a = 0.6777 + 0.1464(mβ t ) − 0.00626(mβ t ) 2 ,(2.3i)

wherem = 1 /[sin h + 0.15(57.296h + 3.885) −1.253 ](2.3j)
m' = m p / p0
(2.3k)
According to the preceding equations, Rb 0 and Rd 0 can be calculated given surfacepressure p , Ångström turbidity β , precipitable water w , and ozone thickness l .
This model was evaluated as one of the best models in calculating surface solar radiation
under clear skies (Gueymard, 2003a, 2003b).
For practical application, the precipitable water is calculated from relative humidity( rh , %) and air temperature ( Ta , K):−1−1

w = 0.493 rh Ta exp[26.23 − 5416Ta ] .

28

3. Global distribution of Angstrom turbidity coefficient and ozone
The aerosol scattering is one of the most important radiation-damping processes. The
Ångström turbidity coefficient, a key parameter for the scattering, generally decreases withrespect to elevation and also decreases toward to high latitude. Ångström gives a formula toestimates it, but the formula can be questionable in some areas such as India and China
because of its high variability in both space and time.
In the new model, we introduce two data sets to calculate the turbidity. First, we follow
Hess et al. (1998) and calculate optical depth at wavelength 0.5 µm . The optical length isthen converted to Ångström turbidity β . This method can provide a global cover, but its

spatial resolution ( 50 × 50 ) and temporal resolution (summer and winter) are low, so we use it
as a background of turbidity; that is, it is used if no other data sources are available. The
second data set is a monthly mean data provided by AVHRR. It has a higher spatial resolution( 10 × 10 ) and temporal resolution (monthly), but no data is available beyond 70 degrees
latitude in both hemispheres.
The thickness of ozone is provided by a NASA global dataset with a 10 × 10 spatial
resolution and monthly temporal resolution.

4. Framework to develop a new general model
The traditional Ångström–Prescott model assumes that globe solar radiation can be scaledby the solar radiation at the top of the atmosphere. That is

R = (a + b S + c S 2 ) R0

(4.1)

However, the new model assumes that the globe solar radiation can be scaled by its value
under clear skies.
R = (a + b S + (1 − a − b) S 2 )( Rb + Rd )(4.2)

The new model has four features: (1) it has a simple form as Ångström–Prescott model;(2) model inputs are surface meteorological data (sunshine duration, air temperature, andrelative humidity) so that the model can be applied easily; (2) under clear skies, the estimatedsolar radiation is equal to the value calculated by sophisticated spectral models. (3) At the topof atmosphere, the estimated solar radiation is exactly equal to the theoretical value of solar
radiation. Hereafter, it is called hybrid model.

5. Model calibration
We calibrated the two models using the data at 53 stations in Japan. The maximum
surface elevation is less than 300 m, and the humidity is relative high. The models arecalibrated for hourly, daily and monthly solar radiation estimation

29

6. Model validation
We compare the two models at a number of stations by three indices: mean bias error(MBE), root mean square errors (RMSE), and correlation coefficient (R2). The following onlyshows the major index RMSE in USA and Saudi Arabia.
(1) In USA.
140

RMSE (W m-2)120(b) Hourly radiation

AM3

HM3

100
80
60
40
20
0
allsite

AU

CL

CN

ED

EP

ST

EC

ST

EC

ST

EC

Site

3.0

AM3

RMSE (MJ m-2)

2.5

HM3

2.0
1.5
1.0
0.5
0.0
allsite

AU

CL

CN

ED

EP

Site
2.5

RMSE (MJ m-2)

AM3

Monthly

2.0

HM3

1.5
1.0
0.5
0.0
allsite

AU

CL

CN

ED

EP

Site

Figure 1 Validation in USA. AM: Ångström–Prescott model; HM: Hybrid model. Upper panel,hourly data; Middle panel: daily data; Lower panel: monthly-mean daily data30

(2) In Saudi Arabia.
80(a) Hourly radiation

70

AM3

HM3

RMSE (W m-2)

60
50
40
30
20
10
0
allsite

AB

AH

SK

MA

QA

GN
Site

JN

GS

SH

SV

TB

WD

2.5

(b) daily radiation

AM3

RMSE (MJ m-2)

2.0

HM3

1.5
1.0
0.5
0.0

RMSE (MJ m-2)

allsite

2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

AB

AH

SK

MA

QA

GN
Site

(c) monthly-mean daily radiaiton

allsite

AB

AH

SK

MA

QA

GN
Site

JN

GS

AM3

HM3

JN

GS

SH

SV

TB

WD

SH

SV

TB

WD

Figure 2 Validation in Saudi Arabia. AM: Ångström–Prescott model; HM: Hybrid model.
Upper panel, hourly data; Middle panel: daily data; Lower panel: monthly-mean daily data
7. Conclusion
We develop a new model. The new model much improved the estimation for solar
radiation from surface meteorological data compared to the traditional Ångström–Prescottmodel.

31

1-5

The Characteristics of the Cloud Properties
Retrieved from Global Imager Aboard the
ADEOS-II (Midori-II) Earth Observation Satellite
Takashi Y. Nakajima1, Teruyuki Nakajima2
Shuichiro Katagiri3
1

JAXA and CEReS/Chiba University, 2CCSR/University of Tokyo, 3RIHN

Abstract : The characteristics of the cloud optical and microphysical properties obtained from
GLI aboard the ADEOS-II satellite were shown in the presentation. The GLI-derived results
were compared with the MODIS-derived results and got a different feature in the effective radiusbetween GLI and MODIS. The investigation of the difference has just started.

INTRODUCTION
It is pointed out by the Intergovernmental panel on Climate Change (IPCC) 2001, thatthe aerosol-cloud interaction process is one of the largest uncertainties in the climate change
studies (IPCC 2001). It emerged that aerosol in the atmosphere can influence the Earthradiation budget through changes in the cloud optical and microphysical properties by the twokinds of indirect effect of aerosol (e.g., Twomey et al. 1984; Coakley et al. 1987; Radke et al.
1989). The first kind effect is a change in the cloud radiative properties due to cloud particle
radius reduction by aerosols, and the second kind effect is a change in the cloud lifetime andprecipitation efficiency. The observations and the progress in cloud and aerosol microphysicalmodeling in the climate model will contribute to improving the accuracy of future climate
predictions.

DATA ANALYSISThe optical and microphysical properties of the warm water and cold ice clouds wereretrieved from the ADEOS-II (Midori-II) GLI data by use of the GLI data analysis system

Fig. 1 Cloud effective particle radii (water cloud) in April 2003, obtained from GLI data analysis.

32

Fig.2 Histogram of the global analysis obtained from GLI (red curve) and MODIS (blue curve). Cloud optical
thickness (Left), and cloud effective particle radius (Right) .

based on Nakajima and Nakajima (1995) algorithm with an enhancement of the water vapor
correction for the global scale analysis by Kawamoto et al (2001). The GLI data were analyzed
every four days and merged and averaged for the one-month period. The one-month mean
results of warm water clouds in April 2003 has interesting features. The very thick cloud optical
thickness appeared in the east coast of the North America, in the East Europe, and in theSouth-East Asia. The Atlantic Ocean and West to Middle Pacific oceans were covered by themoderately optically thick water clouds. The cloud effective particle radii were smaller on thecontinents and coastal areas than ocean area. The South-East Asia to the middle pacific area
was the region of the existence of the smaller cloud effective particles.

GLI AND MODIS
The comparison between GLI products and MODIS products of warm cloud
microphysical properties showed different features. The one-month mean of the GLI-derived
cloud effective radii showed a single mode with a mode radius of 12 micrometers whereas the
MODIS-derived results had bi-modes at 10 and 14 micrometers. Basically, the cloud effective
radii are larger in MODIS than GLI especially over the open ocean area. The investigations of
the difference are now undergoing. The possible reason for the differences can be due to thedifference of the cloud phase algorithm and/or different wavelength of the short wave infrared
channel, GLI (3.7 micron), MODIS (2.1 micron), that is used for the droplet size retrievals, andso on. On the other hand, the cloud optical thickness of GLI and MODIS showed almost thesame values.

SUMMARY
The seven-month global observing data obtained from GLI aboard ADEOS-II satellite
has analyzed by using data analysis system installed in JAXA. The global datasets of the cloudproperties were released to the users since December 2003. The characteristics of the
GLI-derived cloud properties qualitatively consist with the present scientific knowledge of the33

cloud statistics. However, the effective particle radius obtained from GLI and MODIS products
showed a different feature. The investigation of the difference has just started.

REFERENCES
Coakley, J. A., R. L. Bernstein, and P. A. Durkee, 1987: Effect of ship-stack effluents on cloudreflectivity. Science, 237, 1020-1022.
IPCC (the Intergovernmental Panel on Climate Change), 2001: Climate Change 2001: The
Scientific Basis, J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der
Linden,D. Xiaosu, K. Maskell, and C. A. Johnson (Eds.), 896 pp., Cambridge Univ.
Press, New York
Kawamoto K., T. Nakajima, and T. Y. Nakajima, “A Global Determination of Cloud
Microphysics with AVHRR Remote Sensing.,” J. Climate , 14, 2054-2068. 2001.
Katagiri S. and T. Nakajima, “Radiative characteristics of cirrus clouds as retrieved from
AVHRR.,”, J. Met. Soc. of Japan, 82, 81-99, 2004.
Nakajima T. Y. and T. Nakajima, “Wide-area determination of cloud microphysical properties
from NOAA AVHRR measurement for FIRE and ASTEX regions.,” J. Atmos. Sci. ,52, 4043-4059. 1995.
Radke, L. F., J. A. Coakley Jr., and M. D. King, 1989: Direct and remote sensing observationsof the effect of ships on clouds. Science, 246, 1146-1149.
Twomey, S., M. Piepgrass, and T. L. Wolfe, 1984: An assessment of the impact of pollution on
global cloud albedo. Tellus, 36, 356-366.

34

2-1

Measurements of clouds, and radiation from space forclimate studies
Ehrhard Raschke

University of Hamburg, Germany
Visiting Professor at NIPR, Tokyo, Japanand

Makoto Wada and Takashi Yamanouchi
National Institute for Polar Research, Tokyo, JapanThe technological advancements achieved during the past two to three decades enabledconsiderable progress in establishing space-borne observational systems for clouds andassociated radiation fields. Measurements are now available for worldwide studies of climate
variations. However they are still lacking in reliable accuracies and stability for global trend
identification. This paper concentrates on recent results obtained on clouds and their effects
on the radiation budget.
1. Introduction:
The early climatologists, as early astronomers for planets, were with highest priority
interested to obtain quantitative information on the radiation budget fields at the top andbottom of the atmosphere, knowing that any spatial gradients of it force redistributions ofenergy. The equator to pole gradient forces steadily related heat transports in the atmosphereand in the oceans. Furthermore clouds in the troposphere are not only indicators of various
dynamical processes. They modify the transfer of radiative energy and they are source of theever needed precipitation. Therefore already the first meteorological satellites, which weresuccessfully launched in the period between the years 1958 to 1970, carried (in comparison to
the present situation) relatively simple sensors to measure clouds and also the components of
the radiation budget at the top of the atmosphere. These early measurements formed alreadythe presently accepted general picture on the radiation budget of our planet (Raschke, 1972),
which however needs now details on its variations in space and time and on their uncertainty
limits.
Since then the drastic advances in instrumental and computer technologies enabled theconstruction of more complicated instruments with enormous high data rates to be send down
to earth and also rather detailed numerical models for the global climate system. The latterproduce results on clouds and radiation fields for the present and even for future climate states,
which however still do not agree with measurements of the same time period (see project
CMIP: Bader (ed.), 2004; Potter and Cess, 2003). Future demands for improvement of climate
and weather prediction and of the identification of regional or even (weaker) global climate
trends call for much improved models and for more accurate and stable measured information(see NISTIR 2004; and recommendations of various projects of the WCRP).
This paper describes briefly the present status of space-borne cloud and radiation
measurements and provides also a possible outline for the future.
2. Cloud observations from space
Tropospheric clouds, due to their composition of particles, modify the short-wave solar andlonger-wave terrestrial (infrared) radiation in such ways, that multi-spectral imaging
measurements contrast them against the mostly darker (solar) and warmer (infrared) ground.
When simultaneous multi-spectral observations are available as with the presently used multispectral imager MODIS, this contrast allows not only to estimate the total cloud cover within

35

given areas and often also at 2 to three different altitude ranges. They also allow to estimate
the effective optical thickness and mean effective particle size and phase (ice or water). There
are now many attempts reported to estimate also some aerosol characteristics from satellitedata, since aerosols affect directly and indirectly (e.g. Lohmann and Feister, 2004) theradiative transfer properties and also precipitation efficiency of cloud fields.
The very recent operational network of polar and geostationary satellites provides – inconjunction with all other meteorological and climatological observations – a solid basis toestablish longer time series which are superior to the earlier ones, which had to be based onrelatively simple ones (one or two spectral intervals, or multi-spectral sounders with relatively
course spatial resolution). Numerous new algorithm developments are now being discussed.
Some allow for identification and even some characterisation of aerosols within cloud freeareas. This information is required to identify quantitatively direct and indirect effects ofaerosols on clouds and also on the planetary radiation budget.
Two such time series, covering already time periods of about 20 years are now available. The
data series of the International Satellite Cloud Climatology Project (ISCCP: Rossow andDuenas, 2004; Zhang et al., 2004, or their website: http://isccp.giss.nasa.gov) begins during
the year 1983. Besides of this satellite-borne information on cloud fields it also contains all
supporting data (see Table 1 below), which are used in this retrieval algorithm, and all
original and well calibrated radiance measurements. Thus re-analyses on the basis of more
recent knowledge might always be possible.
Variables

Data set of variables

Cloud Cover, Optical Thickness, Top Temperature byType
Cloud Particle Size
Cloud Vertical Structure
Atmospheric Temperature and Tropospheric Humidity
Atmospheric Humidity (Upper Troposphere, Stratosphere)
Atmospheric Composition
Stratospheric Total Ozone
Stratospheric Ozone Profiles
Stratospheric Aerosols
Tropospheric Aerosols
Snow cover
Sea Ice cover
Diurnal Cycle of Air Temperature over Land

ISCCP satellite radiances

Surface Skin Temperature and Visible Reflectance
Surface Spectral Albedo and Emissivity by TypeISCCP-based Climatology
Combined ISCCP-Rawinsonde Climatology
TOVS, Oort Climatology for filling
SAGE Climatology
Actual record from Various Sources
TOMS, TOVS for filling
SAGE Climatology
SAGE
Baseline Current-day Climatology
NOAA product
NSIDC productClimatology based on surface weather reports and
NCEP reanalysis
From ISCCP retrievals
GISS GCM reconstruction by surface type andseasonTable 1: Variables to be used in the cloud retrieval from the ISCCP radiance data
Quantity

global

ocean

land

Cloud amount [%]

67

73

71 75

57 69

Cloud-top temperature [K]261 261266 264

251 256

Cloud-top pressure [hPa]587 607631 631489 547

Cloud optical thickness/
Effective emittance
Cloud water path [gm-2]

4.2
0.57
62.0

4.2
0.57
58.6

4.1
0.55
69.6

Atmospheric water vapour [cm w.e.]

2.51

2.60

2.23

Table 2: Global cloud properties from 5 years of ISCCP / TOVS Path-B (italic)

Table 2 summarizes the major results and compares them with those derived from other –often concurrent measurements with operational sounders (TOVS Path-B – see e.g. Scott et36

al., 1999; Stubenrauch et al., 1999). This sounding technique, which is performed
primarily to obtain operationally information on the vertical temperature structure of thelower and middle atmosphere, allows by proper combination of concurrent measurements indifferent spectral ranges (called “colour slicing”) a sharper contrasting of higher and medium
level clouds against the background. This time series, mostly obtained from a single satellite,
but in no case from geostationary orbit, covers a period from 1983 to 2001.
3. Radiation fields and cloud effects
3.1 Measured at TOA
Fields of the radiation budget components at TOA can be derived either from measurementswith wide-field-of-view radiometers, which “see” the entire disc of the earth under thesatellite, or from narrow-field-of-view radiometers. The latter enable budget analyses with aspatial resolution compatible with those of best global models (50 to 100 km grid length).
Early measurements began already in 1959 onboard of always single satellites of the TIROS-,ESSA-, and later Nimbus- and NOAA-series (e.g. Raschke, 1972). The first multi-satellite
system (ERBE, Earth Radiation Budget System; Barkstrom 1984) has been flown during theearly 80th, with scanning and non-scanning radiometers. Further instrumental achievements
enabled in particular various scanning techniques and more spectral information within thescope of the multi-satellite experiment CERES, where almost identical instruments are flown
onboard of different operational and experimental satellites (Wielicki et al., 1996). A
European development, ScaRaB (Scanner for Radiation Budget; Kandel et al., 1998),
complemented these latter observations during shorter time periods.
These latter measurements from ERBE and CERES allowed first estimates of the cloud effecton the net solar and net terrestrial radiation budget at TOA by proper selection of
measurements over cloud free areas. Global annual averages of the budget components are
summarized in Table 3. These annual global averages show, that clouds reduce the absorptionof solar radiation but keep also more thermal longwave radiation within the climate system.
Quantities at top of the atmosphere Mean values(TOA)
Outgoing terrestrial radiation
Absorbed solar radiation
Net radiation
Albedo-234 Wm-2
239 Wm-2+5 Wm-230%

For cloudless earth Effect of clouds-266 Wm-2
288 Wm-2+22 Wm-2
15%+32 Wm-2-49 Wm-2-17 Wm-2+15%

Table 3: Global averages of the radiation budget components at TOA and the effects of clouds (last column) on theoutgoing terrestrial radiation, computed from earlier ERBE data. The small positive value of the net radiation isconsidered within the error range of this analysis and should not interpreted as a heating of our planet.

3.2 Computed at TOA
The ISCCP data sets – see Table 1 for all complementary data – allows for direct calculationsof up- and downward radiation fluxes between TOA and the surface for both cloudy (as givenby the ISCCP clouds) and cloud free atmosphere. The radiation fields at TOA are not
identical with those observed simultaneously, showing regional differences between 10 to 20
Wm-2 for annual averages. But the general spatial patterns are quite similar showing also thealready with early Nimbus data computed annual radiation deficits over the major terrestrial
desert regions.

37

As an example we show here a global map of the cloud effect on the total radiation budget at
TOA, whose numbers are differences between the net radiation at cloudy and at clear skies.
Thus positive (negative) values mean, that the net radiation at cloudy skies is larger than atclear skies. The generally negative values mean, that cloudy generally reduce the net radiationbudget, dominantly due to the reflective power for solar radiation (see Table 3).

Fig. 1: Annual average of the effect of clouds on the net radiation budget (in Wm-2) during the period 1991 1995,
computed as the difference between the net radiation at cloudy and clear skies. Over most areas clouds tend to reduce
the net gain of radiative energy by the climate system (negative values), while over some small areas with high andpersistent convection they tend to enhance it. Over both Polar Regions this effect is relatively more inaccurate due tolarge uncertainties in cloud identifications. Highest and lowest values: +13 and -80 Wm-2; global average: -24 Wm-2.

3.3 Fields at surface
Radiation fields at the surface cannot be measured directly from space, but they can beinferred from such measurements with inclusion of various complementary data on the stateof the atmosphere and of the ground (see e.g. Table 1). Numerous techniques have now beendeveloped. Most recently Stackhouse et al. (November 2004, GEWEX NEWS) summarized
their work within the CERES project. An analysis of the ISCCP results obtained for the years
1991 to 1995 has been provided by Raschke et al., (2005, in print). Most of these and otherresults are compared to simultaneous measurements with ground-bases radiometers for solar
and terrestrial radiation, which are performed within different networks. These comparisons
signal an uncertainty range for annual regional averages of the order of 10 to 20 Wm-2 where
errors are possibly largest over both polar regions.

38

Fig. 2: Annual average of the total net radiation (or of the radiation budget) at the surface during the period 1991 to
1995, in Wm-2. All values, with a few exceptions over Greenland and Antarctica, are positive indicating, that the
Earth’s surface is everywhere heated by radiation. The small negative white areas are very close to zero. They are
possibly caused by errors in both components over those regions, due to inaccurate cloud identification over thoseregions. Highest and lowest values: + 211 and –16 Wm-2; global average: +116 Wm-2.

3.4 : The total radiation budget of the atmosphere

Annual Radiation Budget of the Globe [Wm-2]
Red: ISCCP-FD; Blue: Ohmura & Wild; Black: Kiehl & Trenberth

+342

TOA

-106, -102, -107

-233, -240, -235

Net: +3, 0, 0

Terrestrial Radiation

Solar Radiation

Atmosphere:+71, +96, +67-183, -200, -169

-112, -104, -102

Atmosphere net:

~-20

~-85

Compensating
upward fluxesof sensible andlatent heat

+189, +169, +198

-24, -25, -30+343, +345, +324

-393, -385, -390

Solar
Downward

Solar
UpwardTerrestrial
Downward

Terrestrial
UpwardBudget at ground:+115, +104, +102

Fig. 3: Annual radiation budget of the climate system(in Wm-2) calculated from the ISCCP data set (red numbers)
and compared with results from Ohmura and Wild (blue numbers) and Kiehl and Trenberth (black numbers).
Respective values for the total flux divergence in the atmosphere are –112, -104, -102 Wm-2, respectively.

39

For global annual averages of the total radiation budget at TOA and at the surface quite
similar values are found by these and other methods, however systematic differences occur inthe two components. Particular high discrepancies occur the vertical divergences of bothcomponents urging for more intensive data validations. As an example, in Fig 3 aresummarized the results of the ISCCP analysis by Raschke et al. (2005) and compared withothers (Kiehl and Trenberth 1997; Ohmura and Wild, 2004, priv.comm.).
Fig. 4 summarizes the zonal averages of annual and seasonal values of the total radiation flux
divergence within the atmosphere as computed from the ISCCP data set. All values arenegative indicating, that within the mechanisms of the global heat engine the atmosphere is
generally emitting radiation back to space, thus it must be heated from below by upward
fluxes of latent and sensible heat. Note the large discrepancies between values for the solar
and terrestrial vertical divergence, which were derived by those author teams. There are alsosignificant regional differences in this budget.
Clouds, see Fig. 5, generally diminish this deficit at near-equatorial regions between about 30
degrees latitude, and enhance it elsewhere. The absorption of solar radiation in the clouds
appears to be very low in this figure due to two causes: clouds reflect often more than 50% of
the incident solar radiation back to space and they reduce considerably the amount of solar
radiation into the lower tropospheric layers where most of the atmospheric water vapor rests.
Various maps of these quantities will be shown during the presentation.

Fig. 4: Zonal averages of the total radiant flux divergence in the atmosphere during all seasons. These graphs are
obtained for all seasons and for the annual average.

40

Fig. 5: Mean annual effect of clouds on the total radiant energy flux divergence in the atmosphere. Positive
values indicate that clouds contribute to the heating of the atmosphere. Clouds reduce the absorption of solar
radiation below their bottoms and may slightly increase the absorption of radiant energy in the stratosphericozone layer. The cloud effect in the infrared is due to partly compensating effects of emission from cloud tops andcloud bottoms at different altitudes and also the emission of lower atmospheric water vapor to ground.

4. Some discussion
This paper can provide only a short overview on existing and hopefully future data sets,
which are required to identify climate changes and to verify respective model results.
There is a need establish between different data sets more transparency about their
weaknesses and strengths. In particular uncertainty range must be justified (e.g. Zhang et al.,2004) which should not exceed about 1 to 3 W m-2 for regional monthly averages and about
one tenth of this limit for the annual stability to identify “trends”, as they occur in theradiation budget quantities. As at present already the satellite ICE-Sat and other radar
satellites, in future there will be dedicated active measurements with lidar and cloud radar,which will provide more information on he cloud geometry and internal structure, enabling
then higher accuracies in calculating their radiative transfer properties and identifying eveninteractions between clouds and aerosols (e.g. Stephens et al., 2002). These new observational
capabilities are a challenge for the research community.
5. References
Barkstrom, B.R., 1984: The Earth Radiation Budget Experiment (ERBE). Bulletin of the American MeteorologicalSociety 65: 1170-1186.
Bader, D. (ed.), 2004: An Appraisal of Coupled Climate Model Simulations. Report UCRL-TR-202550, Lawrence
Livermore National Laboratory, 73pp CMIP
Kandel, R., M. Viollier, P. Raberanto, J. Ph. Duvel, L. A. Pakhomov, V. A. Golovko, A. P. Trishchenko, J.
Mueller, E. Raschke, R. Stuhlmann, and the International ScaRaB Scientific Working Group, The ScaRaB earth
radiation budget dataset., Bulletin of the American Meteorological Society, 79, 765-783, 1998.
Kiehl J., and K.E. Trenberth, 1997: Earth’s Annual Global Mean Energy Budget. Bulletin of the American
Meteorological Society., 78, 197-208.

41

Lohmann, U., J. Feichter, 2004: Global indirect aerosol effects: a review. Atm. Chem. Phys. Discuss.,4, 7561-7614
NISTIR 7047: Ohring G., B. Wielicki, R. Spencer, B. Emery, R. Datla (ed.), 2004: Satellite Instrument Calibration
for Measuring Global Climate Change. US Department of Commerce, National Institute for Standards andTechnology, 100pp
Potter, G.L., R.D. Cess, 2004: Testing the impact of clouds on the radiation budgets of 19 atmospheric generalcirculation models. J. Geophys. Res., 109, D02106, doi:10.1029/2003JD004018
Raschke E., 1972: Die Strahlungsbilanz der Systems Erde-Atmosphäre – neuere Ergebnisse vonSatellitenmessungen. Zeitschrift f r Geophysik, 38: 967-1000.
Raschke, E., A. Ohmura, W. B. Rossow, B. E. Carlson, Y-C. Zhang, C. Stubenrauch, M. Kottek, M. Wild, 2005:
Cloud effects on the radiation budget based on ISCCP data ( 1991 to 1995). Int. Journ. Climatology, in press
Rossow, W.B. and E.N. Duenas, 2004 : The International Satellite Cloud Climatology Project (ISCCP) Web Site.
Bull. Am. Meteor. Soc., 85, 167-172
Scott, N. A., Chédin, A., Armante, R., Francis, J., Stubenrauch, C. J., Chaboureau, J.-P., Chevallier, F., Claud, C.,
Chéruy, F., 1999. Characteristics of the TOVS Pathfinder Path-B Dataset. Bulletin of the American MeteorologicalSociety, 80: 2679-2701.
Stephens, G. L., D. G. Vane, R. J. Boain, G. G. Mace, K. Sassen, Z. Wang, A. J. Illingworth, E. J. O’connor, W. B.
Rossow, S. L. Durden, S. D. Miller, R. T. Austin, A. Benedetti, C. Mitrescu, and The Cloudsat Science Team,
2002: The Cloudsat Mission and the A-Train - A New Dimension of Space-Based Observations of Clouds andPrecipitation. Bulletin of the American Meteorological Society, 83: 1771-1790.
Stubenrauch, C.J., W.B. Rossow, F. Cheruy, A. Chedin and N.A Scott, 1999: Clouds as seen by satellite sounders(3I) and imagers (ISCCP). Part I: Evaluation of cloud parameters. Journal of Climate, 12: 2189-2213.
Wielicki, B.A., B.R. Barkstrom, E.F. Harrison, R.B. Lee, G.L. Smith and J.E. Cooper, 1996: Clouds and theEarth's Radiant Energy System (CERES): An Earth Observing System experiment. Bulletin of the American
Meteorological Society, 77: 853-868.
Zhang, Y-C., W.B. Rossow, A.A. Lacis, V. Oinas and M.I. Mishchenko, 2004: Calculation of Radiative Flux
Profiles from the surface to top-of-atmosphere based on ISCCP and other global datasets: Refinements of theradiative transfer model and the input data. Journal of Geophysical Research, 109, D19105, doi:
101029/2003JD004457.

42

2-2

Reflectance Band Ratios in Japan Using Satellite
and Sky Observation Data
Y. Kawata, T. Umeki and K. Takemata
Earth and Social Information Core, Dept. of Computer Sciences
Kanazawa Institute of Technology,
Ogigaoka 7-1, Nonoichi, Ishikawa 921-8501, Japan
Abstract: We observed the aerosol optical properties, such as the optical thickness (τA ) and Ångström exponent
α over Kanazawa area, Japan for the period of seven months from Oct. 2003 to April 2004. using the sky
radiometer. The reflectance ratios between the visible and short wave infrared bands were computed for theclasses of the vegetation, urban, and the others using several data sets of Terra/MODIS and the sky observation
data. The computed band reflectance ratios were compared with those by Kaufman et al. (1997)1) and us (2003)2).
Key Words: Aerosol, Optical Thickness, Atmospheric Correction

1. INTRODUCTION
In the atmospheric correction of the remotely sensed earth image data we need theinformation on the aerosol optical parameters, such as the optical thickness τa, Ångström
exponent α, and the type of size distribution. A method for estimating them from thesatellite-measured data itself is highly desirable. It was found by Kaufman et al.(1977)1) that
there existed an empirical reflectance band ratio for a few land categories between the visible
reflectance and middle IR reflectance as follows:
rB1/ rB7 = C1 v=0.50
rB3/ rB7 = C3 v=0.25
rB1 / rB7 = C1u=0.69
rB3 / rB7 = C3u=0.42(vegetation)(vegetation)(urban, and the other)(urban, the other)

(1),
(2)
(3),
(4),

where rB7, rB1 and rB3 are the reflectance values in the band 7(2.15µm), band 1(0.645µm) andband 3(0.469µm) of Terra/MODIS. They obtained the reflectance band ratios using the
ground and airborne measurement data in USA. Since the aerosol scattering effects are
negligible in the band 7, we can obtain the surface reflectance in Band 7 by removing onlymolecular attenuation and water vapor absorption effects. The surface reflectance in band 1
and 3 can be easily found from the above reflectance band ratio. For given surface reflectancevalues for those classes, we can retrieve distributions of aerosol optical thickness in Bands 1
and 3 by using LUT(Look Up Tables) in which the theoretical radiances at the top of theatmosphere(TOA) are tabulated as a function of the surface reflectance and the aerosol optical
43

thickness for given bands and angles of the incident and reflection.
In our previous study2), these reflectance ratios in Japan were found using the simultaneoussky measurement data and several image data sets of Terra/MODIS over Japan (May 06, 2002,
April 07, April 16, May 09, and June 03 in 2003). as follows:
C1v=0.554, C3v=0.547
C1u=0.580, C3u=0.489
C1o=0.503, C3o=0.417

for vegetation
for urban
for others

(5),
(6),
(7),

We should note that there is a large discrepancy in the value of C3v between USA and Japan.
Seasonal variations in the reflectance ratio were studied in this paper.

2. COMPUTATION OF REFLECTANCE RATIO
We made sky observations using the sky radiometer (Prede: POM-01) at our study sitewhich is localted on the campus of Kanazawa Institute of Technology for a period of 7 months
from Oct. 2003 to April 2004. The aerosol optical parameters, namely, the optical thickness
τa(500) at 0.5µm and Ångström exponent α. are deduced by Sky-radiation Pack Code Ver.4.
For example, the scatter diagram of them is shown in the case of Oct. 2004 in Fig.1. From
Fig.1 it is said that we have aerosol particles with small size (0.8<α<1.7) when the aerosol
optical thickness is small (0.2<τa(500)<0.4). Whereas, we have those with large size (α=0.5)
when τa(500 is larger than 0.4. During the observation period we found 5 data sets of MODIS
in which the study site is included and is seen, namely, on Oct.09, Oct. 20, Oct.25, Oct 27,
and Nov. 01, 2003. We computed the surface reflectance values for the area near the study sitefrom the MODIS band 1 and 3 images by removing the atmospheric scattering effects using
the deduced aerosol optical thickness and . Ångström exponent values. In the atmosphericcorrection we assumed Haze M model(coastal aerosol) with refracted index, m=1.33 and the
mid-lattitude winter atmosphere model. Then, we classified the sub-image of MODIS near the
study site into 5 classes, namely, vegetation, urban, cloud & snow, sea, and the others, byusing the maximum likelihood method. The reflectance band ratio values for three classes ofvegetation, urban and the others were computed using corresponding surface reflectancevalues in band 1, 3 and band7. The scatter diagrams for the vegetation were shown in Fig.2.
The reflectance band ratios for three classes were by this study as follows:
C1v=0.561, C3v=0.475
C1u=0.671, C3u=0.525
C1o=0.616, C3o=0.487

for vegetation
for urban
for others

(8),
(9),
(10),

These reflectance ratios are different from those in the previous studies1), 2). In this study theacquired dates of used data sets were in Oct. and Nov. 2003 and we assumed the mid-lattitude
44

winter model, whereas they were in April to June 2003 and we assumed the mid-lattitude
summer model in our previous study. It is difficult to say that the differences in reflectanceband ratio between the current and previous studies is due to the seasonal changes without
making sensitivity test on this matter in detail. We have not done it yet.
We retrieved the aerosol optical thickness values over land in Band 1 and 3 of MODIS datausing pre-computed LUT of the theoretical radiance at TOP as a function of the reflectancefor given angular conditions. The validation results of the retrieved aerosol optical thickness
τa are shown in Fig.3, based on the new surface reflectance ratios found in this study.

3. CONCLUSIONS
We can conclude by this study as follows:(1) We presented the surface reflectance ratios between the visible and infrared bands using
both MODIS data sets and simultaneously observed sky data in the autumn.
(2) We found that the reflectance ratios for the autumn data are different from those for thespring-summer data. It is too early to say these differences due to the seasonal changes and wewill continue to make further efforts on this subject.

ACKNOWLEDGEMENTS
This research was supported partially by CEReS, Chiba Uni., Joint Project Research 16-18.
The MODIS data sets were provided by Yasuoka Lab., ISS, Univ. of Tokyo and Frontier
Research Center, Tokyo Univ. of Infor. Sciences. ASTER data set was provided by ERSDAC,
Japan. We appreciate very much for their kindness.

REFERENCES
1. Kaufman et al., 1997. The MODIS 2.1mm Channel-Correction with Visible Reflectance for
Use in Remote Sensing of Aerosol, IEEE Trans. GRS, vol.35, no.5, pp.1286-1298.
2. Y. Kawata, H. Fukui, and K. Takemata, 2003. Retrieval of Aerosol Optical Parameters from
Satellite Image Data over Lands, Intl. CEReS Symp. on Remote Sensing., CEReS, Chiba
Univ., pp. 165-168.

45

October, 2003

2

α

1.5

1

0.5

0
0

0.2

0.4

0.6
τa500

0.8

1

Fig.1. Scatter diagram of aerosol optical thickness τa(500nm) and Ångström exponent α.

Reflectance 0.645μm

0.2

0.15

0.1
y = 0.561x
0.05

0
0

0.05
0.1
0.15
Reflectance 2.13μm(a) C1v=0.561

46

0.2

Reflectance 0.469μm

0.2

0.15

0.1
y = 0.475x
0.05

0
0

0.05
0.1
0.15
Reflectance 2.13μm

0.2(b) C3v=0.475
Fig.2. Scatter diagrams and Reflectance band ratio. for vegetationy = 1.1798x - 0.011

1Retrived τ

0.8
0.6
0.4
0.2
0
0

0.2

0.4

0.6

0.8

1

Observed τ

Fig.3. Observed and retrieved τa(500) using the autumn reflectance band.

47

2-3

SeaWiFS and MODIS-derived Product Verification
using Normalized Water-leaving Radiance Modelin the Western Equatorial Pacific Ocean
Katsutoshi Kozai1, Anna Sasaki2
1 Kobe University Faculty of Maritime Sciences, 2 Kobe University Graduate School of Science and Technology
Abstract：The purpose of the study is to verify the SeaWiFS and MODIS-derived normalized water-leaving radiancesand chlorophyll-a concentrations based on in situ sea surface spectral radiance observation. Utilizing ship’s shadow for
estimating sky radiance in the normalized water-leaving radiance model, we estimated normalized water-leaving
radiance and compared with SeaWiFS and MODIS-derived corresponding products. It was found that SeaWiFS and
MODIS-derived normalized water-leaving radiances and chlorophyll-a concentrations show good agreements withcorresponding parameters derived from in situ spectral radiance observation.
Keywords：Western equatorial Pacific Ocean , Normalized water-leaving radiance , Chlorophyll-a concentration ,Sky
radiance ratio

follows1).

1. INTRODUCTION
In order to accurately extract chlorophyll-a asimportant indices of water quality and primary
production, it is necessary to estimate upward
radiances below the sea surface without an
influence of surface reflectance. However theconventional in situ measurement of sea surface
reflectance is not accurately enough to estimate
chlorophyll-a concentration based on the spectralreflectance of the sea surface. The purpose of thestudy is to verify the SeaWiFS and MODIS-derived
normalized
water-leaving
radiancesandchlorophyll-a concentrations based on in situ sea
surface spectral radiance observation during thecooperative air-sea interaction research cruise of
R/V MIRAI belonging to JAMSTEC.

Fig．1． R/V MR04-01 Stationary point within thecircle. (Feb.22-Mar.22,2004)
nLw(λ ) = RL (λ ) F0 (λ )RL (λ ) =２．DATA AND METHOD

S w (λ ) − ρ (θ ) S sky (λ )π SG (λ ) ρ G (θ , λ )S sky (λ ) = S w ( λ ) * SR (λ )R/V MIRAI stationed at 138.5 degrees East and 2
degrees North from March 3 to 15, 2004 in thewestern equatorial Pacific Ocean. (Fig.1) During the
research cruise we carried out in situ measurementsof upward spectral radiance from sea surface using aspectral radiometer GER1500 with 512 bands
ranging from 350 to 1050 nm and chlorophyll-aconcentration synchronized with SeaWiFS and
MODIS overpasses. Measurement times are 4 times
a day (10, 11, 13, 14 hours (local time)). In order toestimate normalized water-leaving radiance (nLw)
normalized water-leaving radiance model is used as
48

（１）

（２）

（３）

F0(λ) is defined as an mean spectral solar irradiance,
Sw (λ) , Ssky(λ), Sg(λ) are in situ spectral radiancesfrom sea surface, sky and standard white board
respectively. ρ(θ) and ρG（λ）are Fresnel reflection
coefficient and reflection coefficient of standard
white board. SR( λ ) is the ratio between sky
radiance and solar radiance. This ratio is defined
based on upward spectral radiance from sea surface
and the same radiance observed under shadowed
sea surface, which is solely illuminated by sky
radiance. Spectral sky radiance ratio is shown in
Fig.2.

Fig.2 Spectral sky radiance ratio．

３．RESULTS AND DISCUSSION
Fig.3 shows the results of estimated nLw(λ)
based on the nLw model above. Magnitude of nLw
reaches maximum around 410 nm and shows
minimum at the wavelength longer than 750 nm.
Comparing with the nLw(λ) products derived from
SeaWiFS and MODIS, the result of comparison isshown in Fig.4. Solid points are based onmodel-derived nLw and the ones derived from
SeaWiFS and MODIS. White points are based on
upward spectral radiance without using the nLw
model2). Model-derived nLw shows good
agreement with SeaWiFS and MODIS-derived
nLw.

Fig. 4 Comparison between estimated normalized
water-leaving radiance by GER-derived upwellingradiance and SeaWiFS and MODIS-derived
normalized water-leaving radiance. (Solid points
are based on model-derived nLw and the ones
derived from SeaWiFS and MODIS. White points
are based on upward spectral radiance without
using the nLw model2).)
As far as the comparison of chlorophyll-a isconcerned, the result of comparison between
nLw-model-derived
chlorophyll-aandchlorophyll-a without the nLw model is shown in
Fig.5. Both concentrations are calculated based on
OC4v4 algorithm3) which is the operationalalgorithm of SeaWiFS. OC4v4 algorithm isexpressed as follows.
C=10.0^(0.366-3.067R4S+1.930R24S+0.649R34S-1.5
32R44S)(4)
R4S=log10(R443555＞R490555＞510555)

Fig. 3
Estimated normalized water-leaving
radiance at the stationary point (Mar-7-14,2004).

(5)

where C is chlorophyll-a concentration (mg/m3),
R4S is defined as the logarithm of maximum ratio
between radiances from two bands of SeaWiFS.

49

Chlorophyll-a derived from nLw model shows
better agreement with the ones from SeaWiFS and
MODIS (solid triangles and squares in Fig.5.)
compared with the ones without using nLw model(cross marks).

REFERENCES
1) James L. Mueller, Roswell W .Austin : Ocean
Optics Protocols for SeaWiFS Validation,
Revision1, NASA Technical memorandum 104566,Vol. 25, pp45-46, pp53-54,1995.
2) Anna Sasaki and Katsutoshi Kozai : Validation of
SeaWiFS data based on in situ spectral reflectancemeasurement of sea surface in the westernequatorial Pacific Ocean, Proceedings of 36th
Annual meeting of the Remote Sensing Society ofJapan, pp.19-20, 2004.
3) O’Reilly, J.E., S. Maritorena, B.G.., Mitchell,
D.A., Siegel, K.L. Carder, S.A. Garver, M. Kahnu
and C. McClain : Ocean color chlorophyll
algorithms for SeaWiFS, J. Geophys. Res., vol. 103,
No. C11, pp.24937-24953, 1998.

Fig. 5 Comparison between GER estimatedchlorophyll-a concentration (solid triangles andsquares: nLw model-derived chlorophyll-a, crossmarks (x, *): chlorophyll-a without using nLw
model) and SeaWiFS and MODIS-derived
chlorophyll-a concentration

４．SUMMARY
By combining the normalized water-leaving
radiance model with in situ spectral radianceobservation utilizing the shadowed sea surface, we
verified the normalized water-leaving radiance andchlorophyll-a products derived from SeaWiFS and
MODIS.
It was found that SeaWiFS and MODIS-derived
normalized
water-leaving
radiancesandchlorophyll-aconcentrations
show
good
agreements with corresponding parameters derived
from the normalized water-leaving radiance modelwith in situ spectral radiance observation.

ACKNOWLEDGEMENTS
We acknowledge the captain and crews of R/V
MIRAI during the research cruise of MR04-01(Feb.22 to Mar.22, 2004). We also acknowledge
Marine Work Japan Inc. for their analytical skill ofchlorophyll-a measurement.

50

2-4

Uncertainty in cloud optical thickness estimation by
GMS-5 S-VISSR algorithm and influence on theestimated radiative budget
H. Takenaka1), T. Takamura2), I. Okada2)3), T. Y. Nakajima2)4), J. R. Dim2)
1) Graduate School of Science and Technology, Chiba University
2) Center for Environmental Remote Sensing, Chiba University
3) Japan Science and Technology Agency4) Japan Aerospace Exploration Agency

Abstract
APEX (Asian Atmospheric Particle Environment Change Experiment) project aimed at performing
simultaneous analysis of cloud, aerosol and atmospheric radiation by various observation methods(satellite,
airplane, ground base) in East China sea. Comparison of the optical thickness from GMS-5 S-VISSR with thoseretrieved from Terra-MODIS shows some remarkable differences. Causes of such errors were investigated. Oneof them was a scientific problem deriving from the difference in cloud droplet effective radius. However this can
not explain by itself all the error encountered in the estimation of the cloud optical thickness. The other cause oferror checked was the quantization noise. And, this happens to be an engineering problem. In the error
evaluation of the algorithm used, we realized that the quantization noise induced much of the optical thickness
difference. Using the radiative transfer code "RSTAR5b" we examined the influence of this error in theestimation of the radiative budget.

1.Introduction
GMS-5 is a geostationary satellite enabling observations every hour over the equative at alatitude of 140°E. By using these observation data, high temporal resolution is attained in
the estimation of Earth's radiative budget. Based on the GMS-5 S-VISSR (Stretched- Visible
and Infrared Spin-Scan Radiometer) visible channel data and assuming plane parallel cloud,the radiative transfer code "RSTAR4b" [Nakajima T. and M. Tanaka, 1986,1988] was used

Fig. 1 : Cloud optical thickness estimation by GMS-5 S-VISSR (Left) and MODIS (Right) on
April 9, 2001 in the study area (25°N—35°N and 125°E—135°E).
51

(a)

(b)
Cloud optical thickness at 30N in April 9, 2001

Water cloud { TH0=25°,TH1=37°,FI=174°}70

1.00E+00

7.00E-01
6.50E-01
5.50E-01
4.50E-01
3.50E-01

Cloud optical thickness

8.00E-01

Reflectance

60

Re=2
Re=4
Re=6
Re=8
Re=10
Re=15
Re=20
Re=25
Re=30
Re=35

9.00E-01

50
40
30
20
10
0

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
Cloud optical thickness

124

(c)

126

130

132

134

(d)
Distribution of effective radius [µm]
6.4%
7.7%

11.9%

7.3%

7.2%
16.1%
8.5%
7.8%

1.2%2.2%

8.2%

7.3%

4000

4000

0<re<=6
6<re<=88<re<=10
10<re<=1212<re<=14
14<re<=16
16<re<=18
18<re<=20
20<re<=22
22<re<=24
24<re<=26
26<re<=28
28<re<=30

3500
3000

2954

2500
Pixels

8.4%

2000

2087

1944

1923

21062048
1821

18121797

1592

1500
1000

538

500
0

(e)

253
16

29

1

3

5

7 9 11 13 15 17 19 21 23 25 27 29
Distribution of effective radius(f)
3*3pixels Average IR cloud top temperature

3*3pixels Average cloud optical thickness.
300

70

290

60

GMS = 0.8141*Modis

280

2

R = 0.8302

50

IR GMS (K)GMS cloud optical thicknes

128
Longitude

40
30
20

270

GMS = 0.9942*Modis2

R = 0.8549

260
250
240
230
220

10

210
200
200

0
0

10

20

30

40

50

60

70

210

220

230

240

250

260

270

280

290

300

IR MODIS (K)

MODIS cloud optical thickness

Fig. 2 : (a) Estimation curve of the cloud optical thickness with reflectance at GMS-5 SVISSR ch1. The deep purple solid line shows the estimation from the algorithm used, with thewater cloud droplet effective radius of 10µm. Assuming a solar zenith angle TH0=25[degrees] , satellite zenith angle TH1=37 [degrees] and azimuthal angle FI=174 [degrees].
These angle values were determined from average data of April 9, 2001 (25°N-35°N and
125°E-135°E). (b) Cloud optical thickness at 30°N latitude of Fig. 1 (with intervals of
0.05°). Blue points (diamond) represent the cloud optical thickness from MODIS (0.05-degree
data averaged from original 0.01-degree data), and red points (box) are those from GMS-5 SVISSR. (c) Cloud droplet effective radius distribution (in %) from MODIS on April 9, 2001.
The picked on the left of figure contains among other the10 [µm] effective radius. (d) Sameas (c) but with total number of pixels per range. (e) Comparison of 3x3 pixels-average cloudoptical thickness between MODIS and GMS-5 S-VISSR. (f) Comparison of corresponding tobrightness temperature to Fig. 2e .

52for retrieval of cloud optical thickness by the "solar reflection method" [T. Y. Nakajima,
2002] and the estimation of the radiative budget per hour. Analysis was conducted as in the
APEX project where comparison of various observation data are made. Comparison of theretrieved optical thickness from GMS-5 S-VISSR with those from Terra-MODIS (Moderate
Resolution Imaging Spectroradiometer) shows some remarkable differences. It seems that
GMS-5 S-VISSR algorithm underestimates cloud optical thickness in certain conditions.
Figure 1 is a comparison of the optical thickness data from MODIS and those from GMS-5 in
the East Asia (25°N—35°N and 125°E—135°E) on April 9, 2001. Although distinction ofcloud and the whole pattern look alike, the difference in absolute value can be noticed in thelayers where optical thickness is high, while small optical thickness data show better
correlation. MODIS data have a higher distribution of high optical thickness areas than GMS5 S-VISSR data. The absolute value is fatally different.
2.Model analysis
There seems to be several reasons of difference of the cloud optical thickness between bothsatellite sensors. One of them was a scientific problem originating in the difference of aneffective radius of cloud. Since GMS-5 S-VISSR does not have a water-absorbing channel(3.7micron), no effective radius can be retrieved from observation. For this reason, the cloud
droplet effective radius has been assumed as 10µm (based on ISCCP : International Satellite
Cloud Climatology Project) [Rossow et al., 1989]. Figure 2a is the estimation curve of cloudoptical thickness with observed reflectance. The solid line represents a curve for GMS-5 SVISSR estimation. Various studies have shown the dependence of reflectance on the cloud
droplet effective radius; reflectance is smaller when the effective radius becomes larger, and it
increases when the effective radius become smaller. This shows that even for the samereflectance, the optical thickness may have a different. Figure 2b presents the optical
thickness from MODIS and GMS-5 S-VISSR at 30°N latitude in Fig. 1. The difference
between MODIS and GMS-5 S-VISSR appears remarkably well. Although a clear judgment
is difficult around of 126°E and in the area from 133°E to 134°E (GMS-5 S-VISSR shows
exceeding value), it appears generally that GMS-5 S-VISSR data are underestimated incomparison to the MODIS data around of 129°E and 131°E. Figure 2c is the effective radius
distribution from MODIS data (25°N-35°N and 125°E-135°E) and Figure 2d is the number ofpixels on April 9, 2001. Since the effective radius can not be retrieved from S-VISSR (for thereason given earlier), we analyzed only MODIS data. This analysis shows that the effective
radius mainly varies from 7 to 28 [µm], with the peak is at 8<re<=10 [µm] (frequency ofoccurrence around 16.1%). The second highest frequency 11.9% corresponds to the range of10<re<=12 [µm]. The remaining frequencies are mostly around 7~8%. Though the near 10µm
range has the highest absolute value, and looking at the whole distribution, the contribution isrelatively small. There are indeed variations in the cloud droplet effective radius and, the
10µm fixed effective radius used for the S-VISSR retrieval of optical thickness can give someerrors. The fixed value, may result in an uncertainty in the estimation of the cloudoptical thickness from the GMS-5 S-VISSR algorithm. Figure 2e shows 3x3pixels-average
cloud optical thickness comparison of MODIS and GMS-5 S-VISSR. The regression line isinclined toward MODIS side, which means that GMS-5 S-VISSR values are lower than
MODIS data. Considering that the different effective radius values noticed on Fig. 2c, theoptical thickness differences may be related to the variation of the effective radius. Figure 2f
is a comparison of IR data 3x3pixels-average between GMS-5 S-VISSR and MODIS: theregression line is well in agreement; this means that the same clouds are observed.
Furthermore and although the difference of the reflectance of Ice cloud and Water cloud was
also considered without the effect of the ice non-spherical shape, the influence was smallenough.

53

3.Consideration of Quantization
We found a problem in work to examine the look-up table carefully. It has the possibility oflosing important information by quantization of the sensor installed in satellite. Furthermore,test have been performed to check the consistency of the algorithm. Chart 1 is the flow chartof “Reversible conversion test”. The problem is solved by assuming actual clouds andconducting comparison verifications with the output of the algorithm. This includes
quantization. Figure 3a is the result of this “Reversible conversion test”. Red points (diamond)
represents the 35µm effective radius and, the blue points (box) is the 2µm effective radius.
The influence of quantization is shown by the stair-like shapes on the figure and, the domain,which cannot be distinguished due to large errors occurrences is visible as well. Figure 3b isthe quantized reflectance (blue box points) and the relative error % (red diamond points).
Although the quantization made so that the changes shows a smooth trend, the red line on thisFigure shows that, the error is big. Figure 3c is the absolute difference between the assumed
true cloud optical thickness and the algorithm output cloud optical thickness. It appears thatthe error due to assumption of an effective radius is increased by the quantization noise insome cases. Based on Fig. 2d, it is thought that the weighting towards the red points(diamond) side, caused by the drift of the effective radius towards larger values than 10µm,
and this actually appears in regression line of Fig. 2e. These two error-elements overlap andcause the error in the optical thickness estimation of clouds. This error nonlinearly exceeds
30% in Fig. 3d. Although the important factor of error of the effective radius is essentially aphysical matter, quantization noise is not consistent with physics. If this type of error appears
nonlinear and shows a non-essentially physical trend, they will become a serious obstacle forthe estimation algorithm of physical quantities.

START

Tau , reAssume the true cloud optical thickness
and effective radius.

RSTAR4b

Calculate cloud reflectanceby discrete ordinate radiative transfer
code.

Tau ( assumed )Reflectance

Quantization

Quantization bit rate 6 bit.

Comparison

DN

GMS-5 S-VISSR
estimation algorithm

Tau

Estimation of cloud optical thickness.

Tau ( estimated )Estimated cloud optical thickness,
it is algorithm output.

STOP

Chart.1 : The flow chart of the “Reversible conversion test”.

54

100%

1.00

90%

0.90

80%

0.80

60

70%

0.70

50

60%

0.60

50%

0.50

40%

0.40

30%

0.30

20%

0.20

10%

0.10

GMS-5 algorithm output

70

2µm

40

35µm

30
20
10

0%
16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79
True cloud optical thickness

(b)

(c)

(d)

Absolute difference of cloud optical thickness (Quantization included)

Absolute relative error of cloud optical thickness (Quantization included)
40%

2µm

Absolute relative difference %True and algorithm output absolute defference

45
35
30
25

0.00
1 3 5 7 9 11 14 17 20 23 26 29 32 35 38 41 44 47 50 53 56 59 62
6 bit

(a)

40

Reflectance

Quantization of GMS-5 S-VISSR

Result of reversible conversion test (Quantization included)
80

35µm

20
15
10
5
0

35%

35µm

30%
25%
20%
15%
10%
16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79

16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79
True optical thickness

True optical thickness

Fig. 3 : (a) Result of reversible conversion test on GMS-5 S-VISSR algorithm, including
quantization noise. (b)Quantization of GMS-5 S-VISSR ch1: the Blue points (box) is thequantized reflectance, the red points (diamond) is the relative error of the reflectance. (c)Absolute difference of optical thickness between 10µm and 2µm than 35µm. (d) Absolute
relative difference of cloud optical thickness at cloud droplet effective radius of 35µm
including quantization noise on the worst case.
4.Influence on radiation budget
The cause of the differences became clear from the above-mentioned results. In this section,
we investigate how much influence this error would have on the radiative budget. The
radiative transfer code “RSTAR5b” was modified and used for calculation of radiative budget.
Figure 4a is the estimated value of the downward shortwave flux at the surface, which
includes each errors. When the cloud droplet effective radius is more than 10µm, radiativeflux is overestimated, and vice versa. This is because, cloud optical thickness is overestimatedwhen the effective radius is less than 10µm and underestimated when the radius is more than
10µm. The relative error on these conditions is set as on Fig. 4c. The proportion of the error
increases with the cloud optical thickness, and this error may reach an inverse proportion for
shortwave radiation at the surface. This error is nonlinear according to the quantization noise.
The quantization noise making the error increase is well understandable. Fig. 4b is theestimated value of the upward shortwave flux at the TOA. If an effective radius of 35µm is
assumed, the flux will be underestimated, and it is overestimated if a 2µm effective radius isassumed. Fig. 4d is the corresponding relative error. The error will decrease if the cloudoptical thickness increases, this is because the error is relatively small compared to theincrease of the reflectance of cloud. Fig. 4e is the albedo at the TOA, and Fig. 4f is the55relative error. Since the downward shortwave flux at the TOA is fixed, this graph resembles
the one of Fig.4d. And, the relative error of the energy absorbed by the Earth system is shownin Fig.5. The cloud optical thickness of 20 to 30 shows an error of 9% (overestimation).
(a)(b)
Dow nw ard shortw ave flux at the surface

Upw ard shortw ave flux at the TOA

500
450

SW Flux [W/m^ 2]

SW Flux [W/m^ 2]

400
350
300

35µm

250
200

2µm

150
100

15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78

900
875
850
825
800
775
750
725
700
675
650
625
600
575
550

2µm
35µm

15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78

True cloud optical thickness

True cloud optical thickness

(c)

(d)
55.00%
50.00%
45.00%
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%

Absolute relative error of upw ard shortw ave flux at the TOA
25.00%2µm

SW Flux absolute relative difference%

SW Flux absolute relative difference%

Absolute relative error of dow nw ard shortw ave flux at the surface

35µm

22.50%
20.00%
17.50%
12.50%

35µm

10.00%
7.50%
5.00%
2.50%
0.00%

1518 2124 273033 3639 4245 485154 5760 636669 7275 78

1518 2124 273033 3639 4245 485154 5760 636669 7275 78

True optical thickness

True optical thickness

(e)

(f)
albedo
0.75
0.73
0.70
0.68
0.65
0.63
0.60
0.58
0.55
0.53
0.50
0.48
0.45

Absolute relative error of albedo
25.00%

2µm

22.50%

Absolute relative difference %albedo2µm

15.00%

35µm

20.00%
17.50%

2µm

15.00%
12.50%

35µm

10.00%
7.50%
5.00%
2.50%
0.00%

15 18 21 24 27 3033 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78

1518 2124 273033 3639 424548 5154 576063 6669 727578

True optical thickness

True cloud optical thickness

Fig. 4 : Error influence on the radiative budget. X axis is the assumed true optical thickness,
Y axis corresponds to the radiative flux and albedo. The purple line is the estimation of theradiative budget by true cloud optical thickness. The red line includes errors by the 35µm
effective radius, the blue line by the 2µm effective radius. These values were calculated by theradiative transfer code RSTAR5b, modified for short wave flux calculation. Calculation
conditions are solar zenith angle = 25 degrees, an atmospheric model using US standard,surface albedo = 0.1 (assumed Lambert surface), and all clouds are assumed water cloud and56

plane parallel. (a) The downward shortwave flux at the surface. (b) The upward shortwave
flux at the TOA. (c) The downward shortwave flux absolute relative difference (%) at thesurface. (d) The upward shortwave flux absolute relative difference (%) at the TOA. (e) The
albedo from downward and upward shortwave flux at the TOA. (f) The albedo absolute
relative difference (%) at the TOA.
5.Conclusion
When estimation and comparison of the cloud optical thickness are performed, theremarkable differences are noticed. The causes were investigated; one is an error byassumption of a fixed effective radius, the other is quantization noise. There is a possibility
that a large difference is caused by them. These errors are nonlinear and exceed 30%
according to the conditions of estimation of cloud optical thickness. The influence of theseerrors on the radiation budget was estimated. The both errors have some influence on theestimation of the radiation budget of the Earth.
Relative error of Earth system absorption energy
10.00%
9.00%
8.00%

35µm

Δα/(1-α)

7.00%
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%

15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78

True cloud optical thickness

Fig. 5 : Earth system absorption energy relative error, including GMS-5 S-VISSR error due todifferences in cloud droplet effective radius and quantization noise.
References
Nakajima, T. and M. Tanaka, 1986: Matrix formulations for the transfer of solar radiation in aplane-parallel scattering atmosphere, J. Quant. Spectrosc. Radiat. Transfer, 35.
Nakajima, T. and M. Tanaka, 1988: Algorithms for radiative intensity calculations inmoderately thick atmospheres using a truncation approximation, J. Quant. Spectrosc. Radiat.
Transfer, 40.
T. Y. Nakajima, 2002: Development of a comprehensive analysis system for satellite
measurement of the cloud microphysical properties. EORC Bulletin Technical Report No.10.
Rossow, W.B., L. C. Garder, and A. A. Lacis, 1989: Global, seasonal cloud variations fromsatellite radiance measurements. Part I: sensitivity of analysis. J. Climate, 2.

57

Retrieval of aerosol optical properties over Chiba landarea from Landsat/TM imagery
Part I: Determination of spatial distribution of aerosol optical thickness
Mitsuo Minomura, Yoshiyasu Todate, Hiroaki Kuze, Nobuo Takeuchi
Center for environmental remote sensing (CEReS), Chiba University,
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

Abstract:

Satellite remote sensing data provide information on two-dimensional distributions of both thesurface albedo and optical thickness of the atmosphere. In this work, we report the method ofseparating the contributions of the surface albedo and aerosol optical thickness from Landsat-5 TM
visible-band data taken over the Chiba urban area. We focus on the determination of surface albedo
from the data observed on a relatively clear day. It is shown that when the aerosol optical thickness
is small, the atmospheric correction is not greatly affected by the aerosol model employed. The
resulting surface albedo distribution from the clear day, in turn, is used to determine the aerosol
distribution from the turbid image in the same season.
keywords: Landsat TM, Atmospheric correction, Albedo, Aerosol model

1. Introduction
In the analysis of satellite remote sensing data, the atmospheric correction technique is useful toderive the accurate distribution of surface albedo values that are free from the “contamination” dueto the atmospheric radiances [1-3]. From the viewpoint of the atmospheric studies, the precise
knowledge of the surface albedo leads to the possibility of deriving the aerosol distribution from thesatellite data. Over the ocean surfaces, the method has been applied to derive aerosol distributions[1]. Over the land surfaces, however, the application of this technique has been rather limited [2]because of the high variability of the surface albedo, in addition to the spatial variability in theaerosol properties.
In the present paper, we demonstrate that the separation of the surface and atmospheric (aerosol)
contributions can be achieved by suitably choosing satellite datasets within a season. The test areais the Chiba area, inside the Tokyo metropolitan area along the east coast of the Tokyo Bay. The
visible band images of Landsat-5/Thematic Mapper (TM) are analyzed by means of a fast
atmospheric correction algorithm reported by Minomura et al. [3].
2. Theory2-1. Relationship between surface albedo and optical thickness
The radiance observed by a satellite sensor is determined mainly by the surface albedo andatmospheric extinction. Both atmospheric molecules (Rayleigh scattering) and aerosol particles(Mie scattering) contribute to the extinction through the atmosphere. It is relatively easy to estimate
the molecular contribution, and the temporal and spatial variability of the atmospheric extinction
originates mostly from the aerosol effect. The aerosol optical thickness τa is one of the mosteffective parameters in the radiative transfer process.
The radiance Lobs observed by a satellite sensor is given as a digital number (DN), e.g. a value
between 0 and 255 in the case of an 8-bit sensor. We can define Lobs as a function of the DN, which,in turn, is related to the target albedo ρ and the optical thickness τa:(1)
Lobs = f (DN), DN=g(ρ , τa).
Here the function f is determined from the sensor calibration, and the function g is given once thegeometry of the satellite observation and the aerosol model are specified.
Figure 1 shows the relationship between ρ and τa calculated for Landsat-5/TM band 1. The
geometry is for the image of Kanto area observed on January 14, 1999. The ρ-τ relationship isplotted for DN values of 50 and 60, the dominant range for band 1. This relationship has been
calculated by the 6S code [4], employing three aerosol models (continental, maritime and urban)
58

2-5

that are built in the code. From Fig. 1, it is clear that the result for the urban aerosol model isdifferent from other two models. This is ascribable to the larger absorption coefficient incorporated
in the urban model than in other models. For example, when the relative humidity is less than 70%,
the single scattering albedo is 0.64 at 550 nm for the urban model, whereas it is about 0.9 for thecontinental and maritime models. Another important aspect found in Fig. 1 is that the difference
among aerosol models appears in the region of large optical thickness. When τa is small, on thecontrary, it is possible to retrieve the value of target albedo ρ that is more ore less independent of
the aerosol model.
For the clear-sky case (no cloud), the main contribution to the aerosol optical thickness comesfrom the lower troposphere, especially from the boundary layer. Under such a condition, theassumption that a value of the optical thickness measured by a sun photometer (τsp) at a certain
location within a satellite image represents the area is considered to be reasonable. This assumption
is used here to derive the distribution of the surface albedo ρ. Hereafter this albedo distribution will
be referred to as the “ρ map”. In this manner, the process of the atmospheric correction isaccomplished.
In the next step, this ρ map is used to evaluate the aerosol distribution from the satellite image
observed on a relatively turbid day in the same season as the clear day. The functional relationshipshown in Fig. 1 is used to obtain the value of τa from the known values of DN and ρ in this case.
The resulting distribution of the aerosol optical thickness will be referred to as the “τ map”.
2-2. Atmospheric correction
In the atmospheric correction of the clear-day image, the optical thickness is derived from thesun-photometer data (τsp) at the time of the Landsat-5 overpass. Figure 2 shows the DN-ρ transformfunction calculated by assuming three aerosol models (Continental, Maritime, and Urban) for TMband 1. The data were taken on January 14, 1999, when the aerosol optical thickness τsp was
relatively small (τsp =0.1 at 550 nm). The histograms of the raw DN and surface albedo ρ are also
shown in Fig.2. It is seen that the difference in the aerosol model does not yield noticeable change
in the DN-ρ relationship. Thus, the surface albedo ρ derived from this method is virtuallyindependent of the assumed aerosol model.
The precision of the albedo ρ as determined by the satellite sensors is around 10-3 for the 8-bit
Landsat-5/TM sensor. The resolution is different for other bands, due to the inherent gain of thesensor. In general, the DN-ρ relationship changes with the wavelength. At shorter wavelength, aquadratic relationship holds between DN and ρ when ρ is small. At longer wavelength, however, alinear relationship is seen between these parameters. Since the resolution of band 2 is insufficient toretrieve the optical thickness accurately, here the result is shown only for band 1. The ρ map isobtained from the image on a relatively clear day (January 14, 1999). This ρ map is used as areference to retrieve the optical thickness distribution in the same season (see below).
2-3. Aerosol optical thickness (τa map)
The distribution of aerosol optical thickness (τa map) is obtained as follows. For each band andaerosol model, the (ρ,τa) pair is calculated for each DN and stored in a form of a lookup table (LUT).
The optical thickness τa can be specified by the optical thickness at 550 nm (τ 550), since the aerosol
model dictates the dependence on the wavelength. This simulation is conducted by the 6S code, therange of τa being varied from 0 to 1 with a step of 0.01. In the application of the LUT, aninterpolation with a resolution of 0.001 is undertaken for τa. Figure 3 shows the functional
relationship in the LUT for the continental aerosol model calculated for the band 1 data on
December 13, 1998. This image was chosen as an example with a relatively large value of theaerosol optical thickness (τsp= 0.3). For each pixel, we assume the albedo value given in the ρ map
obtained from the clear-day image: thus, the aerosol optical thickness for that pixel can be
determined from the pixel DN value using the LUT for each aerosol model. As also inferred from
Fig. 3, the accuracy of τa can be estimated from the accuracy of ρ.
59

Fig. 1. The ρ-τa relationship for three aerosol models calculated for Landsat-5/TM band 1 image on January 14,
1999. Solid line is for Continental, dashed line for Maritime, and dotted line for Urban aerosol model. The curves
are calculated for DN=50 (thin lines) and 60 (thick lines) which define the dominant DN range in the band 1
image. Urban aerosol model is characterized by a small single scattering albedo (strong absorption). When τa issmall, the dependence on the aerosol model is insignificant.

Fig. 2. The DN-ρ transformation curve for each aerosol model for a particular value of τa as measured by the sun
photometer (τa=tsp). Histograms of the band-1 image are shown for both DN and ρ. When the value of τa is small,a unique value of the surface albedo ρ can be determined regardless of the choice of the aerosol model.

60Fig. 3. The functional relationship among DN, ρ, and τa as calculated in a form of the lookup table (LUT). The
curve is for the Continental aerosol model for the band-1 image observed on December 13, 1998.

3. Results and discussion
3.1 TM data and optical thickness
A target area chosen for the present study is a sub-scene of the Landsat-5/TM image (Path: 107,
Row: 35), an image with 2001x2001 pixels centered at Chiba University (about 30 km southeast ofTokyo). The atmospheric correction is applied to a pair of datasets in the same season. First dataset
is the one observed on January 14, 1999 (clear case). The correction of this dataset is used to obtainthe ρ map (reference albedo map) of the winter season in the area. The second dataset obtained on
December 13, 1998 (relatively turbid case) is used to derive the distribution of the optical thickness
τa (τa map). For both cases, the values of aerosol optical thickness observed by the sun-photometerτsp are shown in Table 1 for band 1-4.
Table 1. Aerosol optical thickness τsp observed by the sun-photometer at 9:50 JST on each day. The values of τsp
on January 14, 1999 (clear day) are smaller than those on December 13, 1998 (turbid day).

Date

Band 1

Band 2

Band 3

Band 4

Dec.13, 1998

0.294

0.241

0.192

0.140

Jan. 14, 1999

0.114

0.099

0.085

0.068

61

3.2 Albedo distribution (ρ map)
The ρ map (reference albedo) is derived from the data on January 14,1999. It is assumed that thevalues of τsp listed in Table 1 represent typical values in the TM scene. As seen from Fig. 2, more orless similar values of the surface albedo ρ can be obtained regardless of the type of the aerosol
model assumed in the atmospheric correction process.
The ρ map obtained for band 1 is shows in Fig. 4 (the corresponding histogram of ρ has alreadybeen illustrated in Fig. 2). In this band, the dominant range of ρ is between 0.02 and 0.12, and thesesmall values are mainly located in both the sea area in Tokyo Bay and inland. Pixels with highρ values surround the Tokyo Bay, including the artificial island (named Sea Firefly) in the east sideof the bay. A region with high ρ is also found in the sea area, presumably indicating the presence ofhaze. In order to avoid such an effect of local weather conditions, it is desirable to employ a numberof images obtained on different days to construct the ρ map.

Fig. 4. Surface albedo map (ρ map) obtained from the atmospheric correction of the TM band-1 image observedon January 14,1999.

3.3 Retrieval of τa map
The aerosol distribution map (τa map) is constructed from the image observed on December 13,
1998. The aerosol models employed for this study are the continental and maritime models. Since
the relationship among the parameters (Fig. 3) varies according to the model, the value of τachanges even if the same pair of the input parameter (DN, ρ) is used. The result is shown in Fig. 5,
in which the value of τa is mapped in a range of 0 and 1: the region with τa>1.0 is included in theregion with τa =1.

62

Fig. 5. Aerosol optical-thickness map (τa map) from the TM band-1 image on December 13, 1998.

The optical thickness over the most part of the sea area in the Tokyo Bay is found to be 0.3 orsmaller. The result over the sea area is of course not free from the effect of the wind, since thealbedo is dependent on the wind speed. The optical thickness is somewhat larger along the seashore.
The value of τa is under-estimated due to the presence of haze in the lower left part of Fig. 5. On theartificial island, we obtain τa >1 because of large ρ. High ρ values are also found in the urban area
(near the center in Fig. 5) for both aerosol models. The distribution, however, is not necessarily
correlated with the ρ map. In the vegetation areas inland, aerosol loading is relatively small, though
the albedo values are similar to those in the sea region. It is possible that the aerosol model may
change locally. During the winter season, the northwest wind prevails due to the pressure pattern(high in west ad low in east), leading to the relative dominance of the inland (polluted) aerosols
rather than the maritime aerosols.
Figure 6 shows the histograms of the τa distribution for band 1 and band 2. Aerosol models arecontinental and maritime, and the resolution of τa is 0.01 in the simulation. The distribution turns
out to be more compact for the continental model than for the maritime model, and sparser for band2 than for band 1. This latter feature results from the higher gain in band 2, indicating the sensor
gain is also important for the retrieval of τa.

Fig. 6. Histograms of the τa distribution for TM band 1 and band 2.

63

4. Conclusions
We have discussed the derivation of the ground albedo map and the aerosol optical-thickness
map from Landsat/TM data. In the determination of the ρ map (i.e. atmospheric correction), thedependence on the aerosol model is small when the optical thickness is small. This indicates that areasonable “reference albedo map” can be retrieved using the satellite image observed on arelatively clear day. In the study of the τa map, on the other hand, the results are different fordifferent aerosol models. Generally, the value of τa is smaller for the continental model than for the
maritime model. High τa values are observed for the region with high albedo values. Finally, theresult of this paper is developed in the Part II of this series paper [5] to further incorporate the studyof aerosol size distribution from satellite data.
References
1) L. Stowe, A. Ignatov, R. Shing, Development, validation, and potential enhancements to thesecond-generation operational aerosol product at the National Environmental Satellite, Data andInformation Service of the NOAA, J. Geophys. Res. 102, 16923-16934 (1997).
2) L.A. Remer, D. Tanre, Y. J. Kaufman, C. Ichoku, S. Mattoo, R. Levy, D.A. Chu, B. Holben, O.
Dubovik, A. Smirnov, J.V. Martins, R.-R. Li, Z. Ahmad, Validation of MODIS aerosol retrieval
over ocean, Geophys. Res. Lett.29(12), 1618 MOD3 1-4(2002)
3) M. Minomura, H. Kuze, N. Takeuchi, Atmospheric correction of visible and near-infrared
satellite data using radiance components: an improved treatment of adjacency effect, J. Rem. Sen.
Soc. Jpn., 21(3) 260-271 (2001)
4) E.F. Vermote, D. Tanre, J. L. Deuze, M. Herman, J. J. Mocrette, Second simulation of thesatellite signal in the solar spectrum, 6S: An overview, IEEE Trans. Geosci. Remote Sensing 35 (3),657-686 (1997).
5) K. Asakuma, M. Minomura, H. Kuze, N. Takeuchi, Retrieval of aerosol optical properties over
Chiba land area from Landsat/TM imagery- Part II: Determination of aerosol size distribution (this volume)

64

3-1

Characters of marine atmospheric boundary layer structure
and aerosol profile observed by HSRL
Zhi-shen Liu Zhao-ai Yan

Bing-yi Liu

Zhao-bin Sun

Ocean Remote Sensing Laboratory of Ministry of Education,
Ocean University of China, Qingdao, China
Abstract: This paper briefly discusses a YAG high spectral resolution lidar (HSRL) systemdeveloped by the Ocean Remote Sensing Laboratory of the Ministry of Education of China, Ocean
University of China (OUC). In this system, iodine vapor filter was used to reject the returning
signal of aerosol. We got backscattering ratio using HSRL and the result indicated that thecorrelation index of backscattering ratio of aerosol and the total returning signal is about 0.8. We
also developed a new method to measure backscattering ratio using ordinary aerosol lidar. We
compared two different methods to measure the altitude of boundary layer, and the validation
experiments showed that the deviation is about 0.13km.
Key words: atmospheric aerosol; High Spectral Resolution Lidar (HSRL); iodine filter;backscattering ratio

1、 Introduction
Research on atmospheric aerosols have attracted extensive attention due to its role in humanactivities, climate change and biochemical circulation. Aerosol originated from atmosphericpollution is one of the most important factors in atmospheric pollution monitoring. Atmosphericaerosol has a great influence on the performance of visible remote sensing sensors andelectro-optical system. Few data are available about the aerosol characteristics over the Eastern
China Seas, where a large amount of dusts, which originate from the loess plateau exist in theatmosphere, thus making its distribution very complicated.
Atmospheric aerosols vary with time, location and altitude frequently. The components ofaerosols, which make it difficult to measure their refractive index and extinction properties, arealso very complicated.
The progress in modern lidar technology provides an effective means of measuring aerosols.
The first attempt at detecting aerosols with lidar was initiated by Fiocco et al., and many othermeasurements have been carried out since then. These measurements include Mie scattering lidar,
Rayleigh-Mie lidar, differential absorption lidar (DIAL) and multiple-wavelength lidar. Sasanoused DIAL technique to determine aerosol and molecular distribution, while She et al proposed tomeasure the vertical profiles of aerosol and atmospheric properties using the high spectral
resolution Rayleigh-Mie lidar. They used two barium atomic filters to measure atmospherictemperature, density and aerosol scattering parameters in their system. Fischer used two

65

Fabry-Parot interferometers to detect the vertical distribution of winds and aerosols. Another new
lidar technique for measuring aerosols is described in the following passages. This techniquemeasures the Rayleigh scattering component of atmospheric molecules directly, where iodine filter
was used to block the scattering component of aerosols to extract the contribution of Rayleighcomponent. Then scattering ratio measured by lidar was used to compute aerosol contribution.
This kind of high spectral resolution method has potential application in the detection of aerosols
in both troposphere and stratosphere, where aerosols are rare. We have built a YAG high spectral
resolution lidar (HSRL) and detected atmospheric aerosols profile successfully.
2、Experimental setup and methodology
It is well known that Rayleigh scattering of air molecules, mainly due to Doppler broadening,yields a spectrum in the visible wavelength spreading over about 2.7GHz width at temperaturesnear 300K.The width of aerosol spectrum is confined to a region below 100MHz.The backscattering signals measured with lidar are composed of the contributions from both aerosol andmolecules. At the marine boundary layer, the aerosol scattering signal is usually several orders ofmagnitude higher than molecular scattering. H Shimizu et al gave a typical sketch of atmosphericscattering spectrum and the typical variation of aerosol and molecular scattering with altitude wasshown by McCormic.
To detect aerosol, it is necessary to separate the aerosol scattering and molecular scattering.
Previously, measurement of atmospheric parameters (such as atmospheric density for different
altitudes) and Rayleigh scattering theory were used for directly computing the contribution ofmolecular scattering, however, these theoretically calculated values have significant error due tothe influence of many other parameters.
At present, high spectral resolution Fabry-Perot interferometers are used in HSRL lidar systems.
But F-P etalon is very expensive, and it has such disadvantages as narrow field of view and low
receiving efficiency, etc. At the beginning of the 1990s, a novel atomic or molecular absorption
filter with high spectral resolution was developed. There are a number of advantages of molecularfilters, including ultra-high Q value (105~106), narrow bandwidth (0.001nm), more-stabletransmission characteristics, a much wider field of view (180°), and an ease of optical alignment.
In addition, perhaps most important for the molecular filter method is its excellent ability to rejectaerosol scattering. When operated at a frequency at the center of the absorption notch, the aerosol
signal may be attenuated by better than 70 dB, allowing the aerosol scattering ratio to be measured.
The excellent aerosol-rejection capability and the ease of optical alignment of molecular filter had
been field tested, and the Wisconsin group replaced Fabry–Perot etalons with an iodine vapor
filter in their lidar for the measurement of the aerosol-scattering ratio. Iodine molecular filter,
which can work at low temperature and has several absorption lines near the output wavelength of
double-frequency Nd:YAG laser (532nm), can serve as a high spectral resolution filter to take theplace of Fabry-Perot interferometer . Iodine filters are also used in our experimental system asshown in Fig.1.
The schematic diagram for lidar transmitter, receiver, and frequency control is shown in Fig.
1.The master of the system is the two-wavelength diode pumped cw single-mode tunable Nd:
YAG seed laser. The fundamental output at 1064 nm is used to seed a Continuum Powerlite 7000
Nd: YAG pulsed laser, as shown in the upper part of Fig. 1.The 532-nm output of the seed laser issent to iodine filter cell 1,shown in the lower part of Fig.1, to control and lock the seed laser
frequency.

66

Fig. 1. Optical Diagram for injection-seeded Q-switched pulsed Nd:YAG laser transmitteron the left panels. The lidar receiver is shown on the right, where the iodine cell 2 is the frequency
discriminator.
The received backscattered light is collected by a Cassegrain telescope with 300-mm aperture, andthen passes through a temperature-controlled narrowband Daystar Corporation interference filter,bandwidth 0.15 nm, peak transmission 35.06%. As shown in the right of Fig. 1, the collected
light is split into two channels; approximately 30% of it is detected directly by a photomultiplier inthe reference channel, and the remaining light is passed through iodine filter cell 2 used forremoving the aerosol component from the total backscattering signals and then detected by asecond photomultiplier in the measurement channel. CR105 photomultipliers with gating control
are used, and the signals from the two channels are digitalized by a dual channel 60-MHz, 14-bit
analog-to-digital converter. Absorption line No. 1109 of the iodine is selected for our lidar system.
Under the operating conditions, its normalized transmission function is shown in Fig. 2.
Iodine Filter Transmission, Line 1109
1.2

no
ssi
i
m
s
na
rt
de
zi
al
m
or
N

1

Fig. 2. Normalized transmission of absorption line 1109

0.8

of the iodine filter near 532 nm. The wave numbers at
reference point A and operation point are 18787.796 cm-1

0.6

E

and 18787.830 cm-1 (1.02 GHz apart).

0.4

0.2

A
0
-3

-2

-1

0

1

2

3

4

Relative Frequency (GHz)67

The detection iodine cell, which is stabilized through control of separately, has been constructed
and found to have long-term stability. Since the vapor density is more sensitive to change in iodine
vapor pressure, the temperature of the cell finger is controlled to near 65 °C within 0.01 °C rms,
while its cell body is near 70 °C within 0.1 °C. The resulting transmission curve for the vapor
filter near 532 nm is shown in Fig. 2, where the absorption line center at 18787.796 cm is markedA.
We can measure the aerosol backscattering ratio, Rb =(βa/βm), using the system by locking thefrequency of the injection seed laser to the point A at the 1109 line center of the I2 filter where theaerosol backscattering will be mostly absorbed by the filter (measured to be 70dB). It is
straightforward to show that the detected photo counts at a range r in the total scatteringreference channel, NR, and that in the measurement channel, NM, are given in Eq. (1) and Eq. (2),adopting from Hair et al., below:()⎛ ∆r ⎞
N R = k R ⎜ 2 ⎟[β a + β m ]exp − 2∫ dr [α a (r ′) + α m (r ′)]
⎝r ⎠

(

Eq.1

)

⎛ ∆r ⎞N M = k M ⎜ 2 ⎟[ f a β a + f m β m ]exp − 2∫ dr [α a (r ′) + α m (r ′)]
⎝r ⎠

Eq.2

Where:f a (ν ) = F (ν )f m (T , P,ν ) = ∫ ℜ(ν '−ν , T , P) F (ν ' )dν '∫ ℜ(ν '−ν , T , P)dν ' = 1
Which are, respectively, the aerosol attenuation factor due to iodine filter at the receiving
frequency ν=ν0, the attenuation factor for Cabannes-Brillouin scattering by the iodine filter, andthe expression showing that the normalized Cabannes-Brillouin scattering function, ℜ(ν, T, P) isused here. F (v) is the transmission function of the iodine filter for the return signal; it iscorrelation of the measured (cw) iodine filter function and laser line shape function. In Eq.1 andEq.2, kR and kM are system constants, respectively, for reference and measurement channel,
depending on total laser emission energy, optical efficiency and range factor; and ∆r is the range
resolution. βa and βm (αa and αm) are aerosol and molecular volume backscatter (extinction)coefficients, respectively.
Setting fa =0 in Eq.1, the atmospheric aerosol scattering ratio Rb can be expressed as:

Rb (r ) =β a (r ) + β m (r )k f N (r )−1 = M m R−1
β m (r )k R N M (r )In this manner, the aerosol scattering ratio Rb can be measured with accuracy better than 10%.
3、Experimental results, comparisons and discussion
We measured backscattering ratio using our HSRL in October and December 2004, the weather
conditions including fine, fog and cloudy day, approximately 1Km off Qingdao seashore. The
traditional method to extract the backscattering ratio using the aerosol lidar is to gain the ratio of68

real and standard atmosphere backscattering signal. As stated above, the signal NM we received
using HSRL is a signal without any influence of aerosol because the returning signal of aerosol
has been rejected by iodine filter; in fact, it can basically represent the return signal profile of clear
atmosphere. Therefore if there are similar conditions such as location, weather conditions, etc, we
can get the backscattering ratio by our new method. (Shown in Fig.3 in cloudy day, Fig.4 in fineday, and Fig.5 in foggy day.)Fig .3 Backscattering Ratio βa/ βm(weather: cloudy)

Fig.4 Backscattering Ratio βa/ βm(weather: fine)Fig. 5 Backscatter Ratio βa/ βm (weather: fog)
βm should be stable considering similar location and weather conditions, thus, what we need ismeasuring Nm just one time passing through iodine vapor filter by which we can get βm ofdifferent time. Then we can get different Rb with different returning signal profile. (Shown in Fig.6,
Fig.7, Fig.8)

69

Fig.6 Backscattering ratio βa/ βm , Na and Nm at the same time

Fig.7 Backscattering ratio βa/ βm
Na 23:35 October21, 2004;
Nm 00:10 October 22,2004

Fig.8 Backscattering ratio βa/ βm
Na 00:20 October22, 2004;
Nm 00:10 October 22,2004

Fig.9 Correlation:0. 8881

70

Fig.10 Correlation: 0.8093

Fig. 11 Correlation: 0.8737

Fig.12 Correlation: 0.8611
We can also get the altitude of boundary layer ( H ) by the differential of backscattering signal.
To validate feasibility of this method, we can compare a series of altitude of boundary layer using
the profile of Rb. with differential method (shown in table 1)

H determinated by theDate

H determinated by Rb.（km）differential of backscattering
signal（km）

2004.10.15

1.25

1.45

2004.10.16

2.00

2.01

71

2004.10.19

0.85

0.73

2004.10. 21

1.44

1.26

2004.10.22

1.33

1.32

2004.10.23

1.87

2.05

2004.10.24

1.12

1.19

Table1: altitude of boundary layer with different method (Deviation:

0.13)

From table 1 above, we can get that the deviation is about 0.13km. Comparing the two methods,
we can conclude that the differential method is feasible in determining the altitude of boundary
layer.
References
1、ZhiShen Liu, Dong Wu, Jin-Tao Liu, et al, Low-Altitude atmospheric wind measurement fromthe combined Mie and Rayleigh backscattering by Doppler lidar with iodine filter,20 November
2002/vol.41,No.33/APPLIED OPTICS p7079-7086.
2、H. S. Shimizu, A. Lee, and C. Y. She, High spectral resolution lidar system with atomic
blocking filters for measuring atmospheric parameters, Appl. Opt., 22, 1373--1391, 1983.
3、Z. S. Liu, W. B. Chen, T. L. Zhang, J. W. Hair, C.Y. She, “ Proposed ground-based incoherent
Doppler lidar with iodine filter discriminator for atmospheric wind profiling,” in Application of
Lidar to current Atmospheric Topics, Denver .A.J.Sedlacek, ed., Proc. SPIE. Vol.2833, 128-135(1996).
4、R. J. Alvarez II, L. M. Caldwell, Y. H. Li, D. A. Krueger, and C. Y. She, "High Spectral
Resolution Lidar Measurement of Tropospheric Backscatter-Ratio Using Barium Atomic Blocking
Filter," J. Atm. Oceanic Tech. 7, 876-881 (1991).
5、E. W. Eloranta,, P. Piironen, "An I2-absorption filter based High Spectral Resolution lidar formeasurements of the optical properties of aerosols and clouds", Atmospheric Propagation andRemote Sensing III, SPIE, Orlando, Florida, April 5-8, 1994.
6、P. Piironen, E. W. Eloranta, Demonstration of a high-spectral- resolution lidar based on an
iodine absorption filter, Opt. Letter, 1994, Vol.19, No.3, 234-236.
7、S. Gerstenkorn and P. Luc, Atlas du spectre d’absorption de la molecule d’iode, Centre Nationalde la Recherché Scientifique, Paris. 1978.
8、J. W. Hair, L. M. Caldwell, D. A. Krueger, Chiao-Yao She, High-spectral-resolution lidar withiodine-vapor filters: measurement of atmospheric-state and aerosol profiles, Appl. Opt. 2001,
Vol.40, No.30, 5280-5294.

72

3-2

Characterization of Asian Tropospheric Aerosols
with Multi-wavelength Mie-Raman Lidar andSkyradiometer
Toshiyuki Murayama∗a, Miho Sekiguchia, Detlef Müllerb, Katsuya Wadaa,
and Yasuharu Saitoa
abFaculty of Marine Technology, 2-1-6 Etchujima, Koto, Tokyo 135-8533, Japan
Institute for Tropospheric Research, Permoserstr. 15, 04318 Leipzig, Germany

ABSTRACT

We have extensively observed tropospheric aerosols with lidar and Sun/sky radiometer inTokyo. Recently, we have extended the lidar system to dual-wavelength Raman lidar (Theprimary laser wavelengths are 355 and 532 nm.). We found that the intensive opticalproperties such as Angstrom exponent and lidar ratios derived from the Raman lidar
measurements are quite useful to characterize the aerosols resolving with height. As
highlighted examples, we will show the case studies of Asian dust and Siberian smoke. For
the smoke case (as spherical aerosol case), we retrieved the microphysical properties byapplying an inversion with regularization. The results show an accumulation-mode dominate
size-distribution and a low absorption; the effective radius and the single scattering albedo are~0.22µm and ~0.95, respectively. The column-averaged microphysical properties derived
from the skyradiometer are also consistent with the results. We also estimated the aerosol
direct radiative forcing using the microphysical properties. Further systematic analyses andimprovement of lidar system are now in progress.
Keywords: Raman lidar, Asian dust, Siberian forest-fire smoke, aerosol optical properties,
aerosol radiative forcing

1. INTRODUCTION
Aerosols play an important role in the Earth’s radiation budget through the scattering andabsorption of light, and present a key uncertainty in the assessment of radiative forcing.1 They
also serve as cloud condensation nuclei and modulate the cloud properties. East Asia isconsidered as a region where emission of anthropogenic aerosol is rapidly increasing due to
growing economy. Recently, the Asia-Pacific Regional Aerosol Characterization Experiment(ACE-Asia) showed that the spatial variability of aerosol composition and properties is rather
high in this area.2, 3 It is also shown that mineral dust has an indirect effect on clouds byserving as effective ice nuclei.4-8 Therefore, it is highly important to study not only columnaveraged but also height-resolved aerosol optical properties regularly, in order to assess theirradiative impact. However, it is rather difficult to perform aircraft measurements frequently.
Nowadays, sophisticated multi-wavelength Raman lidars enable us to characterize
tropospheric aerosols by the observable quantities themselves, and in further to retrieve
microphysical properties from them.9-11 From the point of view of feasibility, we installed a
UV-Raman lidar system that emits the third harmonic frequency (355 nm) on the basis of asecond Nd:YAG laser in addition to our existed lidar system at the Etchujima campus (35.66°
N, 139.80° E)of Tokyo University of Marine Science and Technology (TUMSAT).12 Thus∗

mrayama@e.kaiyodai.ac.jp; phone +81-3-5345-7464; fax +81-3-5245-7339
73our lidar system is capable of measuring backscatter coefficients at 355, 532, 1064 nm,
extinction coefficients at 355 and 532 nm, particle depolarization ratio at 532 nm, and water–
vapor mixing ratio, simultaneously. To our knowledge, this is the first dual-wavelength
Raman lidar installed in East Asia. In this paper, we demonstrate how the dual-wavelength
Raman lidar is useful for the characterization of aerosol optical properties depending on
regional aerosol events. We outline the Raman lidar system and the analysis method in section
2. In section 3, we presents results of observations for the case of Asian dust and Siberian
forest-fire smoke events in the spring of 2003 with emphasis on the aerosol characterization
by observed aerosol optical properties. In section 4, we present the microphysical parametersof the smoke derived from the inversion code, comparison with the results from theskyradiometer analysis, and an application for estimating the aerosol radiative forcing using
these results. Most of the results presented here are described in the reference 13.

2. LIDAR SYSTEM AND ANALYTICAL PROCEDURE
We installed a UV-Raman lidar system next to the existing Mie-Polarization-Raman lidar,
which uses the laser beams at 532 (VIS) and 1064 nm (IR)3,12 The schematic view of the full
lidar system is shown in Fig. 1. The UV-Raman lidar system is a stand-alone system, which
employs another Q-switched Nd:YAG laser with a third harmonics generator. The repetition
frequency of the laser pulse is 10 Hz, which is synchronized with the VIS/IR system. The
receiving telescope is of Schmid-Cassegranian type with 35.5-cm diameter and the field ofview is 2 mrad. The optical axes of the laser beams and the telescopes were well co-aligned
each other in the vertically direction. These conditions allow us to observe nearly the same
volume in time and space with both lidar systems. Dichroic mirrors and following narrowinterference filters, which bandwidths are 1-3 nm, are used to separate the backscattered lightfrom Mie-Rayleigh scattering (355 nm) and from Raman scattering from nitrogen (387 nm)
and water-vapor molecules (408 nm). Similarly the Raman signals at 607 nm and theMie/Rayleigh signals at 1064 nm were separated from the signals at 532 nm in the VIS/IR
lidar system. Photo-multiplier tubes are used for the detection. The data were stored every
4094 shots (~7 minutes) using transient recorders (TR20-160, Licel). Typical laser powers
during operation are approximately 100, 100, 200 mJ per pulse for the 355, 532 and 1064 nm
laser beams. Lower height data evaluation were limited by the incomplete overlapping
between the telescope field of view and the laser beams below ~1-1.5 km. Only thebackscatter ratio at 355 nm and the water-vapor mixing ratio are evaluated almost down to thesurface because we can regard the geometrical form factor as identical for both elastic andinelastic Raman channels. It is not the case for the backscatter ratio at 532 nm presented here
because we used different receivers for the 532 nm and 607 nm signals. At this moment, theoperation of Raman channels is limited in nighttime.
We have analyzed averaged or integrated analog and photon-counting data for observations
lasting a few hours when the vertical structure did not vary significantly. The extinction
coefficient and the scattering ratio of the aerosols at 355 and 532 nm, and the mixing ratio arederived by the methods given the references 14-16. The atmospheric density profile wascalculated from routine radiosonde observations at Tateno (36.05° N, 140.12° E) at 12 UTC.
Normalization of the scattering ratio is made at an almost aerosol-free height, typically over
10 km. The backscatter coefficients are derived from the backscatter ratios. The backscatter
coefficient at 1064 nm is obtained from the Mie-Rayleigh signal by using the method
proposed by Fernald17 and assuming a lidar ratio of 40 sr. The lidar-derived water-vapor
mixing ratio is normalized so that the lowest value (~75 m above the lidar) matches to thesurface observation at TUMSAT with assuming the error of ±10 %. The mixing ratio profile
was converted to the relative humidity one using the temperature and pressure profiles

74

obtained from the radiosonde data. Deviations of the mixing ratio and relative humidity
obtained from the lidar at TUMSAT to the respective quantities derived with the radiosonde
can be attributed to the spatial distance of the two sites and different measurement times, andthe unknown error of the calibration factor of the lidar.

3. EXAMPLES OF OBSERVATION
3.1.Case of Asian dust
Fig. 2 presents the time-height cross-sections of the backscatter coefficient and total (particle+ molecular) depolarization ratio at 532 nm on 12 March 2003. The depolarization ratio is anindicator of irregularly shaped particles. Fig. 3 shows the mean profiles for a measurement
period from 1056 to 1306 UTC. We also indicated the Ångström exponent (AE) and thebackscatter-related Ångström exponent (BAE) derived from the profiles of the extinction andbackscatter coefficient at 355 and 532 nm, respectively. The error bars are based on statisticaland estimated systematic errors. The aerosol layer between 3 and 5 km seems to be composedof two layers as indicated by the horizontal dashed lines A and B in Figure 2: layer A is rather
narrow with the peak at 4.6 km, layer B is broader with the peak at 4.0 km. The lower layer B
carries features of mineral dust as suggested from the high particle depolarization ratio(PDR>20 %), while the PDR in layer A is as small as 6 %. The mean lidar ratios at 355 (S355)
and 532 nm (S532) were nearly same within the standard deviations: 48.6(±8.5) and 43.1(±7.0)
sr, respectively in the dust-like layer (3.5-4.3 km). The value of PDR and S532 is similar to ourprevious observations.3,18 The value of S355 is close to the lidar ratio observed for Saharan dust
at 351 nm in southern Italy (~50 sr)19, but smaller than what was observed in Leipzig,
Germany (50-80 sr).10 Mattis et al. also found that S355 is 10-30 % higher than S532.10 The
difference in the lidar ratio values might be attributed to differences in shape, size or
absorption properties of mineral particles.
In addition, we can see interesting differences of aerosol optical properties besides the large
difference of the PDR in the layers A and B; a higher BAE in layer A (~1.2) than in layer B(~0.3) in Fig. 3, which suggests that layer A might be composed of finer aerosols. Since therelative humidity in the layers A and B are nearly identical (~40 %) as shown in Fig. 3, we
can reject the assumption that hygroscopic growth of mineral dust reduces the depolarization
ratio.
3.2.Case of Siberian forest-fire smoke
In the spring and summer of 2003, an unusually high number of forest fires occurred in
Siberia.11 Smoke plumes originating from these fires were transported over Japan withwesterly wind. Fig. 4 shows a time-height plot of the backscatter coefficient and the total
depolarization ratio at 532 nm on 21 May 2003. A high aerosol optical depth (τa) at the
wavelength (λ) of 500 nm of more than 2.0 was observed in the daytime by collocated Sun
photometer. The back trajectory analyses show that the airmass from 2 to 4 km trace back toregions of intense forest-fire from the west of Lake Baykal to the east of the border of China,
Russia and Mongolia. The smoke layer sharply dropped off at ~4 km. The upper part (3-4 km)of the smoke layer showed a depolarization ratio of about 6 %. A similar feature was also
observed in Suwon (37.14° N, 127.04° E), Korea.20
Fig. 5 shows the mean profiles of optical properties for a measurement from 1040 to 1349
UTC. A prominent layer between 2.5 and 4 km shows different optical properties separated at
the peak (~3.2 km) from the intensive parameters, i.e., S532, PDR, and AE. The upper part
except the rim had the following features: i) BAE is high (~1.9) while AE is small (~0.7), ii)
S532 (~65 sr) is apparently higher than S355 (~40 sr), iii) PDR (~6 %) is higher than that in the75lower part. On the other hand, in the lower part, S532 and PDR drop to ~40 sr and ~3 %,respectively, while AE increases to be ~1.2.
The relationship between S355 and S532, i.e., S532>S355, is consistent with the observation overcentral Europe of aged smoke from Canada during the Lindenberg Aerosol Characterization
Experiment LACE98.21 A part of this Siberian smoke was also detected over central Europe
and confirmed the same relationship between S355 and S532.11 The non-zero depolarization
ratio might be caused by soil material that was uplifted into the forest fire plume22, or thenonsphericity of the particles due to coagulation of smoke particles.23 The latter explanation
sounds more presumable because no signature of mineral dust was found by a chemical
analysis of aerosols sampled at the summit of Mt. Fuji (3,776 m above sea level and ~100 km
west from Tokyo) in the same period.24
The relatively small AE (~0.7) in the wavelength range from 355-532 nm for the smoke layer
is supported by the evidence observed by Sun/sky photometer for cases of heavy smoke
events in South America and South Africa25: a significant positive curvature in the log(τa)
versus log(λ) relationship, and in fact the analysis of the collocated TUMSAT skyradiometer(POM-01, Prede) also shows such relationship in the daytime.

4. DISCUSSIONS
4.1. Microphysical properties of the smoke aerosol retrieved from inversion
We applied the method of inversion with regularization only for the case of smoke to retrieve
the microphysical properties using the backscatter coefficients at 355, 532, 1064 nm and theextinction coefficients at 355 and 532 nm.9,21,26 We cannot apply currently the inversion code
for Asian dust case which poses a high PDR, i.e., nonsphericity, because the inversion
strongly relay on the assumption of spherical (Mie) particles. We found 0.22(±0.04) µm forthe effective (surface-area mean) radius and 0.95(±0.06) for the single scattering albedo(SSA) at 532 nm around the peak of the smoke layer. Fig. 6 shows effective radius and single
scattering albedo for selected height ranges. Comparison between a typical column-integrated
volume size distribution derived from the skyradiometer27 and that from the present inversion
is indicated in Fig. 7. The results indicate that particles in the accumulation mode aredominant, which is a typical feature for this kind of aerosols. The relatively high SSA
indicates low absorbing particles. It should be worthwhile to mention that the larger effective
radius of 0.35(±0.07) µm with similar SSA of 0.97(±0.04) was retrieved with the same
inversion code for the hemispheric transported smoke over Leipzig, Germany on May 29,
2003, which suggests the evolvement of the smoke aerosols.28
In comparison, we mention that the typical mean radius and the SSA of Asian dust plumes
obtained by intensive airborne in situ measurements during the ACE-Asia are 2-3 µm and0.96(±0.01) at 550 nm, respectively.3,29
4.2.Estimation of aerosol direct radiative forcing for the smoke case
There are relatively few literatures which presents the radiative forcing calculations using
lidar data.22,30,31 Here we attempted to estimate the aerosol direct radiative forcing (ADRF) for
the smoke case on 21 May 2003 base on the extinction coefficient profile from the lidar
measurement at 532 nm and microphysical properties derived from the inversion. We used theradaitive transfer code FSTAR5C.33 We estimated the ADRF in the shortwave radiation (0.24.0 µm) by assuming the flowing constant atmospheric condition; the temperature, pressure,
relative humidity profiles from the radiosonde data at Tateno, 12 UTC. Daily mean ADRF isthus evaluated as –39 and –51 W/m2 at the top of the atmosphere and the surface,respectively. Since we used wavelength independent refractive indices of particles from theinversion, we also calculated the ADRF using the column-averaged size distribution and76refractive indices (wavelength dependent) retrieved from the skyradiometer analysis in thedaytime. We found that both values agree each other if the mean refractive index is similar.
The daily mean heating rate due to the aerosols is also obtained, which maximum reaches~0.7 K per day just above the peak of the aerosol extinction profile.

5. CONCLUSIONS AND SUMMARY
The distinct optical signatures for mineral dust and aged smoke presented here welldemonstrate how multi-wavelength Raman lidar including a depolarization channel is usefulto characterize tropospheric aerosols resolving with height; for the Asian dust case we found ahigh PDR at 532 nm over 20 % and a small wavelength dependence of the lidar ratio between
355 and 532 nm (43-49 sr), for the smoke case we found that a small PDR of 5-8 % or lessand a high wavelength dependence of the lidar ratio: S355 is ~40 sr, while S532 is ~65 sr. We
successfully derived the climate-relevant microphysical parameters, e.g. effective radius andsingle scattering albedo, for the smoke case using the inversion code. We also attempted toevaluate the aerosol radiative forcing based on the height-resolved parameters using theradiative transfer code. Such an advanced Raman lidar is especially useful for thecharacterization of the complex aerosols found over East Asia and has a potential for
application in global aerosol lidar networking in future.

ACKNOWLEDGEMENTS
This work is supported by Grand-in-Aid for Scientific Research on Priority Areas under Grant
No. 14048232 from the Ministry of Education, Culture, Sports, Science and Technology, andGlobal Environment Research Fund for the project “Study of the dynamic transportmechanism and environmental effect of Kosa aerosol originated from the northern Chineseareas”, from the Ministry of the Environment. We also acknowledge partial supports from the
projects “Variability of Marine Aerosol Properties (VMAP)” and “Asian Atmospheric particle
Environment Change Studies (APEX)” of CREST of Japan Science and Technology Agency,
and the joint research programs of CEReS, Chiba University (14-5), (15-7) and (16-4).

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doi:10.1029/2004GL021105, 2004.
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18. T. Murayama, “Optical properties of Asian dust aerosol lofted over Tokyo observed by
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ESA SP-561, pp.535-538, 2004.
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28. I. Mattis et al., “Siberian forest-fire smoke observed over central Europe in spring/summer
2003 in the framework of EARLINET”, in Reviewed and Revised Papers presented at the22nd International Laser Radar Conference, G. Papparado and A. Amodeo Editors, ESA
SP-561, pp.857-860, 2004.
29. T. L. Anderson et al., “Variability of aerosol optical properties derived from in situ
aircraft measurements during ACE-Asia”, J. Gephys. Res., 108(D23), 8647,
doi:10.1029/2002JD003247.
30. J. Redemann et al., “Case studies of the vertical structure of the direct shortwave aerosol
radiative forcing during TARFOX”, J. Geophys. Res., 105(D8), pp.9971-9979, 2000.
31. J. G. Won et al., “Estimation of direct radiative forcing of Asian dust aerosols with
Sun/sky radiometer and lidar measurements at Gosan, Korea”, J. Metorol. Soc. Jpn., 82, 1,
pp.115-130, 2004.
32. T. Y. Nakajima, T. Nakajima, T. Aoki, A. Higurashi, A. Tanaka, M. M. Verstraete, Y.
Hashibe, GSS Reference Hand book (Rstar Reference Handbook), JAXA EORC Bulletin,
15, 2004.

Fig. 1. Schematic view of the TUMSAT multi-wavelength Raman lidar system.

79

Fig. 2. Time-height cross section of aerosol backscatter coefficients and total depolarization
ratio at 532 nm over TUMSAT in Tokyo, Japan on 12 March 2003.13

05Backscatter-related AE
0.5
1
1.5

0

Angstrom Exponent
12

3

PDR (%)
10
20

0

30

0

Relative Humidity (%)
20 40 60 80 100

Mar.12, 2003
1056-1306UTC

A
B

Altitude (km)

4

3

355nm
532nm
1064nmBAE

MR(Lidar)MR(Sonde)
RH(Lidar)RH(Sonde)

355nm
532nm
PDR

355nm
532nm
AE

2

1
0
1
2
3
4
-1 -1
Backscatter Coefficient (Mm sr )00.05 0.1 0.15 0.2 0.25
-1Extinction Coeffcient (km )020

40 60 80
Lidar Ratio (sr)

100

0

1
2
Mixing Ratio (g/kg)

Fig. 3. Mean profiles of aerosol optical properties and water-vapor mixing ratio for themeasurement from 1056 to 1306 UTC on 12 March 2003.13 BAE and AE denote Backscatterrelated Ångström Exponent and Ångström Exponent for the extinction coefficient between
355 and 532 nm, respectively. The mixing ratio and relative humidity obtained fromradiosonde at Tateno (12 UTC) are shown for reference.

803

Fig. 4. As in Fig. 2, except for 21 May 2003.13

50Backscatter-related AE
1
2

3

0

May 21, 2003
1040-1349UTC

3

0

5

PDR (%)
10
15

20

0

Relative Humidity (%)
20 40 60 80 100
MR(Lidar)MR(Sonde)
RH(Lidar)RH(Sonde)

355nm
532nm
AE

4
Altitude (km)Angstrom Exponent
1
2

3

2

355nm
532nm
PDR

355nm
532nm
1064nm
BAE

1
0
5
10
15
20
-1 -1
Backscatter Coefficient (Mm sr )

0

0.2
0.4
0.6
0.8
-1
Extinction Coefficient (km )020

40 60 80
Lidar Ratio (sr)

100

0

4
8
Mixing Ratio (g/kg)

Fig. 5. As in Fig. 3, except for the period from 1040 to 1349 UTC on 21 May 2003.13

81

12

Single Scattering Albedo
0.5

0.6

0.7

0.8

0.9

1.0
reff
ssa
β532

4

Altitude (km)

1.1

3

2

0

0.1

0.2

0.3

0

2

4

6

0.4

0.5

Effective Radius (µm)
810Backsactter Coeff. (Mm sr )-1

-1

Fig. 6. Vertical variation of the effective radius and single scattering albedo at 532 nm
retrieved from the Raman lidar observation shown in Fig. 5.13

Fig. 7. Comparison between the volume size distributions retrieved from the Raman-lidar andskyradiometer inversions on 21 May 2003.

82

3-3

A Study of Optical Properties of Urban Aerosols in China
Jinhuan Qiu
Institute of Atmospheric Physics, Chinese Academy o Sciences, Beijing 100029, China
ABSTRACT
Some broadband radiation methods are developed to retrieve aerosol optical depth, imaginary partof its refractive index, aerosol single scatter albedo and its meteorological extinction coefficient profile.
These methods include:
1. Broadband extinction method (Qiu: J. Atmos. Sci., 1998; Qiu: Tellus, 2001; Qiu: J. Appl. Meteo.,2003) to retrieve aerosol optical depth from pyrheliometer data;
2. Broadband diffuse solar radiation method (Qiu et al.: Tellus, 2004) to retrieve the aerosol
imaginary part and single scatter albedo. In the method, the solar diffuse radiation is determinedfrom joint pyrheliometer and pyranometer data in order to avoid paranometer shading ring effect.
3. Method to derive so-called scaling height of tropospheric aerosol and its meteorological(yearly/seasonally/monthly mean) extinction efficient profile, using joint aerosol optical depth andsurface visibility (Qiu et al.: Journal of Aerosol Science, in press). The scaling height is definedusing an assumption of exponent-type aerosol extinction coefficient profile. The scaling heightand exponent-type extinction coefficient profile are determined from ground-measured aerosol
optical depth and surface visibility data.
These methods are used to retrieve aerosol optical depths, aerosol imaginary part (AIP) and its
single scatter albedo (SSA), and aerosol extinction coefficient profiles over 14 sites in China. Thesesites locate over 14 cities of Urumqi, Geermu, Harbin, Shengyang, Beijing, Zhengzhou, Kunming,
Shanghai, Wuhan, Guangzhou, Lanzou, Ejinaqi, Lhasa and Sanya, which cover most areas in China.
Yearly/seasonally/monthly mean characteristics of these optical parameters are emphatically analyzed.
Some main results and conclusions are as follows:(1) Variation Trend of aerosol optical depths
During 1980-1993, aerosol optical depths have increasing trends for all sites. But after 1994 andfor such sites as Beijing, Harbin and Lanzhou, the depths have decreasing trends.
(2) Asian sand-dust event effects
Over most areas in China, sand-dust events mainly originated from Inner Mongolia and Xinjiang
deserts are an important aerosol pollution source. It is found from the monthly-averaged AODs that
the AODs in the spring are larger for most cities, especially in northern China.
(3) Characteristics of aerosol imaginary part and its single scatter albedo
The total yearly-mean AIPs during 1993-2001 change from 0.0207 to 0.0301 for 6 cities inNorthern China, and the corresponding single scatter albedo (SSA) from 0.851 to 0.803 with a mean

83

value of 0.832. The AIP is larger during winter (warming period in northern China), especially for
Shenyang site, owing to coal-burning increase. The AIP during summer is usually smaller. There aredifferent variation trends in AIP and SSA. Over Harbin site the AIP (respectively, SSA) has anevidently increasing (respectively, decreasing) trend during 1993-2001. Over Zhengzhou site, the AIP
has an increasing trend during 1993-1995 but evidently decreasing after 1997.
(4) Characteristics of scaling height of tropospheric aerosol and its mean extinction coefficientprofileCharacteristics of seasonally/yearly mean scaling heights and aerosol extinction profiles over 11
sites in China during 1994-2001 are analyzed.
It is found that the aerosol scaling heights over different sites are very variable. The yearly meanscaling heights changes between 1.30 (Shenyang) and 2.67km (Wuhan). The aerosol scaling heights
for different seasons are also very variable. The heights during spring are larger for all sites, especiallyin northern China, due to Asian dust storm events. The 11-site-mean yearly-mean aerosol scalingheights change between 1.85 and 2.11km during 1994-2001, being close to MODTRAN rural aerosol
scaling height (2.04km), and they have an increasing trend with the yearly increasing rate of 0.031km,
implying a shifting-up trend of aerosol particles in the troposphere.
The yearly-mean tropospheric aerosol extinction coefficient profiles are very variable for different
sites. Shenyang is the capital of Liaoning province, a heavy-industry center in China. Here the aerosol
pollution in the near to surface atmosphere is relatively more serious, resulting in the larger surface
aerosol extinction coefficient and then the lower surface visibility. Another characteristic of aerosol
extinction coefficient profile over the Shenyang site is its stronger decrease with an increase of theheight. The Geermu site locates over Qinghai-Tibet Plateau, having the sea level of 2808m. Here, thesurface visibility is usually larger than 30km; the surface aerosol extinction coefficient is relatively
smaller, there are relatively more aerosol particles in the upper troposphere, especially during spring.

84

3-4

Optical, Physical and Chemical Properties of
Aerosols around Japan Based on the R/V Shirase
Shipboard Measurements
M. Shiobara1), M. Yabuki1), K. Hara1), and H. Kobayashi2)
1)

National Institute of Polar Research, Tokyo, Japan
2)
University of Yamanashi, Kofu, Japan

Abstract
In order to investigate the optical, physical and chemical properties of marine boundar layer aerosols and its
relation to the origin of aerosols for estimating the direct radiative effect of aerosols in East Asia, shipboard
aerosol measurements were carried out around Japan. The instrumented Research Vessel Shirase madecounterclockwise coastal cruises from Yokosuka via the Western-North Pacific, the Sea of Japan, and the East
China Sea in early autumns of 2002, 2003, and 2004. This paper includes results not only from opticalmeasurements using an Optical Particle Counter, an Integrating Nephelometer, and a Particle Soot/Absorption
Photometer but also chemical analysis of aerosols collected by an impactor-filter system. Water-insoluble
aerosols were measured with a Coulter Counter. It was found that different features of optical, physical andchemical properties of aerosols in the marine boundary layer around Japan were strongly related to the origin andhistory of air mass.

1. Introduction

Aerosols play an important role in the global climate system due to their radiative effect tothe Earth’s radiation energy balance. Though it is well known that aerosols have two-foldsignificant effects characterized by “direct” and “indirect”, the quantity of both effects is still
uncertain. The direct effect is for aerosols to affect directly on the radiation budget byscattering and absorbing solar radiation in the atmosphere. Therefore the aerosol opticalproperties are essential for quantitative estimation of the direct effect of aerosols.
The aim of this study is to investigate the spatial variation of aerosol optical, physical andchemical properties and its relation to the origin of aerosols for estimating the direct effect ofaerosols in East Asia. For this purpose, we carried out the aerosol measurement on board the
instrumented R/V Shirase during the Japanese Antarctic Research Expedition (JARE)
Training Cruise with counterclockwise coastal round cruises from Yokosuka via theWestern-North Pacific, the Sea of Japan, and the East China Sea in early autumns of 2002,
2003, and 2004 (Shiobara et al., 2004a and 2004b; Yabuki et al, 2004). In this paper, resultsfrom the shipboard aerosol measurement in 2002 and 2003 are shown and compared for
discussion on the optical, physical and chemical properties of aerosols in the marine boundary
layer around Japan.

2. Measurements

Shipboard aerosol measurements were carried out on board the Antarctic R/V Shirase, anicebreaker operated by Japan Maritime Self-Defence Force, cruising around Japan prior to thevoyage for the Antarctic research activity. The R/V Shirase made coastal round cruises
counterclockwise from Yokosuka for the period from 28 August to 29 September in 2002, 4September to 4 October in 2003, and 1 September to 5 October in 2004.
Aerosol optical properties were measured with an optical particle counter (OPC; RION
KC01D), an Integrating Nephelometer (IN; Radiance Research M903), and a Particle
Soot/Absorption Photometer (PSAP; Radiance Research). The OPC measured the number
density of particles for five radius ranges of r > 0.15, 0.25, 0.5, 1.0, and 2.5 µm: the data werecorrected for the coincidence loss. The IN measured the scattering coefficient at the85wavelength of λ = 530 nm. The PSAP measured the absorption coefficient at λ = 565 nm. The
extinction coefficient and the single scattering albedo at 565 nm were calculated by assumingthat the scattering coefficient was inversely proportional to the wavelength (i.e., Angstrom
Parameter = 1). Considering the contamination by the exhaust from the ship engine, the data
were excluded when the wind blew from the sector of 60- 240° relative to the ship heading.
Aerosol particles and acidic gases were collected using a 3-stage mid-volume impactor
with back-up filter in series of 2-stage alkaline impregnated filters during the cruise. Air
sampler was set on the central front of the upper deck. The collected samples were kept infreezer until analysis in laboratory. After extraction by ultra pure water (18.3 MΩ; Milli-Q),
each water-soluble constituent was analyzed with an ion chromatograph.
Aerosol particles were also collected on Nuclepore filters with the 0.4-µm pore size tomeasure the size distribution of water-insoluble particles. The size distribution was measured
with a Coulter Counter (Beckman-Coulter Inc. Multisizer III) after collected samples weredispersed into electrolyte with an ultrasonic cleaner.
Figure 1 shows the cruise tracks for 2002 (left panel) and 2003 (right panel). The region(A) in the left panel and the regions (B) and (C) in the right panel are selected for extracting
the different feature of aerosol properties to be referred to in the following discussion.

Hakodate(B)

SakataKure
SaseboYokkaichi(A)

Karatsu

Himeji Kinuura(C)

Yokosuka
Naha

Hachinohe
Kanazawa(a) 2002

Yokosuka(b) 2003

Figure 1. Cruise tracks of the R/V Shirase JARE Training Cruise for (a) 2002 and (b) 2003.

3. Results and discussion

3.1. Weather condition and air trajectories
The weather conditions during the 2002 cruise are briefly described below. Although atyphoon was moving north in the western part of Japan in the end of August 2002, the weather
was rather fine under a high pressure system centered in the North-Western Pacific Ocean,that is, the meteorological situation was typical for Japan’s summer until 2 September. Then ahigh pressure system located in the north of China was coming strong, and yielding stationary
fronts over Japan by competing with another high pressure system over the ocean for 7 – 12
September when R/V Shirase was cruising in the Sea of Japan and the East China Sea. Since
14 September, during the latter half of the cruise from Naha, cyclones accompanied by fronts
were alternately passing through the southern part of the mainland. That was a normal
weather pattern of the summer – autumn transition season in Japan.
The weather conditions for September and early October of 2003 were rather normal withalternately traveling cyclone and high pressure systems. Another significant feature of
meteorological situation during the 2003 cruise is that two typhoons, Typhoon 14 andTyphoon 15, have passed in the vicinity of Japan Islands. Passing of these typhoons caused
atmospheric disturbance and air mass exchange along with strong wind and precipitation.
Thus the atmospheric situation for the 2003 cruise was quite different from the 2002 cruise
while the summer of 2002 had remained in early September.
86

Figure 2 shows the results from the 5-day backward trajectory analysis calculated from1000 m above sea level for some selected cases along the ship track of the 2002 cruise (leftpanel) and the 2003 cruise (right panel). The NOAA HYSPLIT4 Model (Draxler and Rolpf,
2003) with the vertical motion mode was employed for this analysis. During the cruise in
2002, the trajectories indicated that the air mass was transported from the various areas; e.g.,
from remote Pacific Ocean during the Pacific Ocean leg (Region (A) in Fig. 1), from ChineseContinent during the Sea of Japan leg, and sometimes across the Mainland Japan. On theother hand, in the case of 2003, the air mass mostly passed over the continent (and theMainland) by the northwesterly wind.

(a) 2002(b) 2003

Figure 2. Five-day backward trajectories with the vertical motion mode for(a) the 2002 cruise and (b) the 2003 cruise.

3.2. Chemical properties of water-soluble aerosol particles
Selected chemical compositions for anthropogenic aerosols and natural sources are shownin Fig. 3. Describing in general on the size and composition of aerosols for both cruises in2002 and 2003, sea-salt (Na+, top panels), and NO3- (second top panels) were mostly
distributed in coarse modes (D = 2-6 µm and >6 µm). In contrast, nss- (non-sea-salt) SO42(second bottom panels) was present in fine (D = 0.2 – 2 µm) and Aitken (D<0.2 µm) modes.
Particulate oxalate (bottom panels) was mostly distributed in both coarse (D = 2-6 µm) andfine (D = 0.2-2 µm) modes.
Aerosol Constituents
JARE45 Shirase Coastal Cruise
400

275

Yokosuka

KinuuraYokosuka

270

-3

300
200
100

0
5

0
10

4

8

Oxalate-3nmol m-3

100nmol m20

Himeji200

nmol mnss-SO42-

nmol mOxalate

2-

40

400

300

-3

4

60

0

0
400

nss-SO80

Hachinohe

-3

NO3-

nmol mYokkaichi

Kure

Kure

Naha

SaseboSakataOhminato

Hakodate

Yokosuka

nmol m

NO

3

-

-3

60

20

100
0
100

0
80

40

200

Karatsu

+

100

>6 µm
2 - 6 µm
0.2 - 2 µm
<0.2 µm

300

Ohminato

200

Na
-3
nmol m-3+nmol mNa>6 µm
2 - 6 µm
0 .2 - 2 µ m< 0 .2 µ m

300

Kanazawa
Sasebo

400

3
2
1

6
4
2
0

0
240

245

250

255

260

265

270

D ay of year 2002

Figure 3.

245

250

255

260

265

Day of Year 2003

Variations of aerosol constituents (Na+, NO3-, nss-SO42-, and oxalate) during
the 2002 (left panel) and 2003 (right panel) cruises.

87

280

During the cruise of the region (A) in 2002, sea-salt particles were dominant and theconcentrations of anthropogenic species such as particulate NO3-, non-sea-salt (nss-) SO42and oxalate were quite low. According to the trajectory analysis described earlier, the air massin the region (A) is identified as clean maritime air from the remote Pacific Ocean.
On the other hand, the most significant chemical feature of anthropogenic aerosols is foundin the results from cruises off shore the west of Kyushu Island both in 2002 and 2003. Fromthe 2002 cruise, the concentrations of anthropogenic species such as NO3-, nss-SO42-, andoxalate in the region around Sasebo and Naha were evidently higher than those in the region(A). The backward trajectory suggested that human activities in Chinese continent, Korean
Plateau and Japan Islands might affect to the quality of air masses in the marine boundary
layer during the cruise off shore Kyushu Island. Also from the 2003 cruise, the highest
concentrations of anthropogenic species were observed in the region (B) corresponding tohigh number concentration of aerosol particles, especially in fine particles. Despite of clear sky
on 17 September, the atmospheric visibility at Sasebo was lower than 10 km due to heavy
haze. The backward trajectory exhibited that air mass loading anthropogenic species had
passed in boundary layer – lower free troposphere over Korea, Yellow Sea, the northeast areaof China for the previous five days.

dV/dlnr (µm3/cm3)

3.3. Size distribution of water-insoluble aerosol particles
Figure 4 shows the volume-size distribution of water-insoluble aerosol particles to see
difference in the results from the Pacific Ocean cruise (28-29 August 2002) and the East
China Sea cruise (12-13 September 2002). The error bars mean standard deviations of blanksamples. During the 28-29 August cruise, corresponding to the region (A), water-insoluble
particles were not significantly detected. As shown in earlier, during the cruise of the region(A), the air was transported mainly from the central Pacific Ocean, so that water-insoluble
aerosols such as soil and solid dust were not expected in the air mass.
On the other hand, the result from the 12-13
September cruise shows evidently a different
0.1
size distribution. As soil particles generally
show a log-normal size distribution, themeasured size distribution can be decomposed
to a log-normal distribution for soil particles
and a power-law distribution. From the curve
fitting for the 12-13 September measurement,
0.05
the existence of the log-normal size
distribution with the geometrical mean radiusof 1.35 µm and the geometrical standard
deviation of 1.55 was found. During the 12-13
September cruise, the air was transported
0
mainly from Korea and the northeast of China,
0.1
1
10
and possibly included soil particles originatedRadius (µm)
in these areas. However, it should be notedthat uncertainty in the absolute values of the
Figure 4. Volume-size distribution of
volume-size distribution still remains due towater-insoluble particles measured by a Coulter
uncertainty of the extraction efficiency ofMultisizer for the Pacific Ocean cruise (8/28-8/29,
particles deposited on a filter into electrodecircles) and the East China Sea cruise (9/12-9/13,
and due to smash of extracted particles bysquares). The curve shows a log-normal size
distribution fit to the 9/12-9/13 result.
ultrasonic cleaning.

88

ω0

0.3 <
0.1-0.2

0.9-0.95

< 0.1

(b)

(a)

<5

5-10

10 <

(c)

Single scattering albedo(a)

N 0.15－0.25µm / N 0.25－0.5µm(a) The extinction coefficient, (b) the single scattering albedo, and (c) the number density ratio of
the 0.15 – 0.25 µm radius bin to the 0.25 – 0.5 µm radius bin for the 2002 cruise.

Extinction coefficient (km-1 )Figure 5.

0.95 << 0.9

N 0.15－0.25µm / N 0.25－0.5µm

σ ext (km-1)

3.4. Optical and physical properties
Figures 5 and 6 depict spatial variations of (a) the extinction coefficient at 565 nm, (b) the
single scattering albedo at 565 nm, and (c) the ratio of the number density of the 0.15-0.25 µm
radius bin to that of the 0.25-0.5 µm radius bin, for the 2002 and 2003 cruises, respectively.
The present study focuses on the three regions (A, B and C) as shown in Fig. 1. In the region(A), the air mass was transported from central Pacific Ocean without passing over land
surfaces. In addition, large and non-absorbing particles were dominant as indicated in Fig. 5(b) and (c). The main source in this region might be identified as maritime aerosols. In theregion (B), a strong haze event was observed. The absolute value of the extinction coefficientindicated the highest in the whole cruise of 2003. It was correlated with the increase of fineparticles. As described in the previous section, simultaneous measurements of chemical
constituents of aerosol particles revealed that the concentrations of anthropogenic species
such as nss-SO42-, oxalate, and NO3- gradually increased in the region (B). Also, the backward
trajectory showed the outflow from Korea and Chinese continent. In the region (C), the single
scattering albedo was relatively low due to the increase of absorbing particles. Based on the
trajectory analysis, it is suggested that the air in the region (C) was polluted by human activity
in the Mainland.

Figure 6.

(b)

(c)

Same as Figure 4 but for the 2003 cruise.

The complex refractive index was retrieved by a method proposed in Yabuki et al.(2003).
The procedure was applied for simultaneous measurements of the aerosol size distribution,
scattering coefficient and absorption coefficient with OPC, IN and PSAP. Generally speaking
for the result, the averaged imaginary part of the refractive index during the 2003 cruise wasabout 0.005. This value was twice as large as that for the 2002 cruise. With regard to theselected regions in Fig. 1, the values of the imaginary part in the regions (A, B and C) wereranged in 0-0.001, 0.001-0.004, and 0.004-0.015, respectively. The values of the real part
ranged in 1.38-1.40 for the region (A) and 1.51-1.57 for the region (C). Existence of sea-salt

89rich aerosol particles is reflected on such low values of the imaginary index in the region (A)as supposed from the chemical analysis described earlier. The complex refractive index forthe region (A) was nearly consistent with that for maritime aerosols. Slightly high but rather
low imaginary values in the region (B) under the haze condition might be affected by reduced
absorption of aerosol particles due to the increase of the scattering matters with water-soluble
particles such as sulfate. High values of both real and imaginary indices in the region (C) werepossibly caused by aerosol particles mixed with soil dust and anthropogenic pollution.

4. Summary

We described optical, physical and chemical properties of aerosols in the marine boundary
layer around Japan Those aerosol properties were quantitatively obtained from optical andchemical measurements on board the R/V Shirase in 2002 and 2003. The result shows somecharacteristic features of aerosol properties and their difference in spatial distribution.
Summarizing the result, we can understand that the marine boundary layer aerosol mainly
consists of maritime sea-salt particles from remote ocean, soil dust and sulfate aerosol from
Chinese Continent, and the anthropogenic aerosol produced in Japan and neighboring
countries including Korea and China. The mixing state of these aerosols is strongly related tothe air mass origin and history. The trajectory analysis must be helpful for understanding thefraction of mixing of each aerosol. The practical values of the aerosol optical parameters
obtained in this study are expected to improve the accuracy in estimating the aerosol direct
radiative forcing in the East Asia.

Acknowledgments

The present study was done with the Grant-in-Aid for Scientific Research in Priority Areas,
Gr. No. 14048228, supported by the Ministry of Education, Culture, Sports, Science andTechnology. Aerosol measurements on board the R/V Shirase were carried out as part of the
JARE-44, 45 and 46 Training Cruise Program operated by Japan Defense Agency. This work
was partly arranged for a collaborative research program of CEReS, under Project III led by
Prof. Kuze of Chiba University.

References

Draxler, R.R. and G.D. Rolph, 2003: HYSPLIT (HYbrid Single-Particle Lagrangian Integrated
Trajectory) Model access via NOAA ARL READY Website (http://www.arl.noaa.gov/
ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD.
Shiobara, M., K. Hara, M. Yabuki, and H. Kobayashi, 2004a: Optical and chemical properties of themarine boundary layer aerosol around Japan from the R/V Shirase 2002 shipboard measurement.
Submitted to J. Meteorol. Soc. Japan.
Shiobara, M., N. Kaneyasu, K. Hara, M. Yabuki, H. Kobayashi, K. Osada and T. Murayama, 2004b:
Field experiments for the direct effect of aerosols: Results from R/V Shirase and Chichi-jima.
Atmospheric Environmental Impacts of Aerosols in East Asia (AIE) Research Report 2003, March
2004, Ed. M. Kasahara, 115-122.
Yabuki, M., M. Shiobara, H. Kobayashi, M. Hayashi, K. Hara, K. Osada, H. Kuze, and N. Takeuchi,
2003: Optical properties of aerosols in the marine boundary layer during a cruise from Tokyo,
Japan to Fremantle, Australia. J. Meteorol. Soc. Japan, 81, 151-162.
Yabuki, M., M. Shiobara, K. Hara, and H. Kobayashi, 2004: Optical properties of aerosols in themarine boundary layer around Japan. Nucleation and Atmospheric Aerosols 2004: 16th Int l Conf.,
Ed. M. Kasahara and M. Kulmala, Kyoto Univ. Press, 772-775.

90

3-5

Observation on Radiative Properties of Aerosol
Particles Over the Urban Area of Nagoya
Pradeep Khatri1, Yutaka Ishizaka2, and Tamio Takamura3
1

Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601,
Japan
2
Hydrosphoric Atmospheric Research Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601,
Japan
3
Center for Environmental Remote Sensing, Chiba University, Yayoi-cho, Inage-ku, Chiba shi, Chiba, 263-8522,
Japan

ABSTRACT

In order to understand the effects of anthropogenic air pollutants on atmospheric heat budget, the radiative
properties of aerosols were investigated over the urban area of Nagoya. Measurements included size distributionsof aerosols greater than 0.3 μm in diameter, scattering coefficients and absorption coefficients. The meanvalues with standard deviations for hourly averages of scattering coefficients, absorption coefficients and single
scattering abledos were reported to be 52.3±39.3Mm-1, 24.6±18.3Mm-1 and 0.66±0.13 respectively. Such low
values of single scattering albedos suggest that aerosols of the urban area of Nagoya are highly absorptive innature, which have a potential to warm our climate. Comparison of our results with other polluted cities of US
and China were also performed. Mass scattering efficiency of dry aerosols and mass absorption efficiency of
black carbon over the urban area of Nagoya were found to be around 4.7m2g-1, and 14.6m2g-1 respectively.
Before and after the rain events, diurnal variations of radiative properties were observed. Such diurnal variations
were suggested to be driven by local pollutants rather than meteorological changes throughout the day. The
prominent peaks of black carbon concentrations and absorption coefficients during morning were suggested tobe the effects of local sources such as burning and vehicles whereas peaks of scattering coefficients observed
during afternoon were found to be the effects of photochemically driven aerosol production. Two different
effects of rain events on scattering coefficients were also observed. It was estimated by the observation datathat the rain events reduced scattering coefficients by washing out coarse and accumulation mode aerosols,
whereas the continuous rain events increased scattering coefficients by condensing water vapor to non-washedaerosols of the atmosphere.
Keywords: Air pollutants, scattering coefficients, absorption coefficients, black carbon, single scattering albedo

1. INTRODUCTION
An estimate of the total global radiative forcing due to the direct and indirect effects ofatmospheric aerosols has become a subject of debate in recent years. Unlike well-mixed
greenhouse gases, the climatic impact of anthropogenic aerosols is still subject to large
uncertainties [Penner et al., 2001]. Spatial and temporal variations of aerosols with short life
time in the atmosphere, seasonal nature of aerosol emissions, insufficient information
regarding the mixing state of aerosols in the atmosphere and nonuniform relative humidity
dependency of physical and optical properties of different aerosol species have arisendifficulties to accurately model the radiative forcing of aerosols in a global scale. For thisreason, it is very necessary to measure and study the aerosol radiative properties at various
locations around the Earth.
Several field campaigns such as ACE1, ACE2, INDOEX, TARFOX, LACE98 and APEX
were designed to investigate the aerosol properties at different parts of the Earth to reduce91

existed scientific uncertainties. However the role of aerosols over polluted urban areas that
contain highly absorbing boundary layer particles is still not very well investigated. To ourknowledge, there have not been measurements of aerosol radiative properties reported for theurban area of Nagoya (3507’N, 136058’E). The existence of costal industrial zones at around
Nagoya port with heavy industries such as iron and steel, an oil refinery, petrochemicals, gas
production, power generation, and so on emit a large amount of anthropogenic aerosols.
Further, air pollution by automobile exhaust is also high in Nagoya. The major components of
Nagoya urban aerosols are reported to be organic aerosols and elemental carbon [Kadowaki,
1990]. Kasahara et al. [2000; Personal communication] also reported of around 51% ofelemental carbon in this region. Aerosols with high fraction of light absorbing black carbon
have a potential to warm our climate. In recognition of this situation, it is of interest to study
optical properties of aerosols over the urban area of Nagoya.

2. INSTRUMENTATION AND DATA
Aerosol measurements were made at the 5th floor of Hydrospheric-AtmosphericResearch Center (HyARC) of Nagoya University located in the east section of the city. The
experimental place is unique in that traffic is for the most part prohibited on campus andtherefore the measurements don’t include short-term peaks from very nearby mobile
sources, but instead reflect the broad average conditions over the eastern Nagoya. A
nephelometer (Model M903, Radiance Research) was used to measure the light scattering
coefficient(σsp) at 530nm. The instrument was calibrated prior to the experiment using
clean (particle-free) air as well as CO2 for the span gas. The light absorption coefficient (σab) was measured at 565nm using a Particle Soot Absorption Photometer(PSAP) (Radiance
Research). At the same time mass concentration of black carbon was measured using a
7-wavelength “Spectrum” aethalometer (Model AE 30, Magee Scientific). Number
concentrations of aerosol particles larger than 0.3μm in diameter were measured using
Laser Particle Counter (LPC) (Model TF-500,Kanomax). Meteorological data wereobtained from Nagoya Regional Observatory, JMA.

3. RESULTS AND DISCUSSION
3.1. TIME SERIES OF AEROSOL RADIATIVE PROPERTIES
Figure1 shows statistical analysis of daily scattering coefficient and black carbon
concentration(μg/m3) of aerosols from July 23, 2004 to September 5, 2004. The measured
scattering coefficients ranged from 5 Mm-1 to 222 Mm-1, resulting in a mean value withstandard deviation of 52.3±39.3Mm-1. Similarly the measured black carbon (measurement
made by aethalometer at 880nm wavelength) concentration ranged from 0.1μg/m3 to 8.0μg/m3 , resulting in a mean value with standard deviation of 1.7±1.25μg/m3. The measured
scattering coefficients showed excellent correlation with total number of particles measured

92

by LPC (R2=0.98). There was not a clear relationship between total particle number
concentration, scattering coefficient and black carbon concentration with wind speed. On theother hand, particle number concentration and scattering coefficients showed relationship withwind direction. Statistical analysis results showed that westerly winds reaching theobservation site were characterized by relatively high particle number concentrations and highscattering coefficients whereas south south easterly and south easterly winds reaching theobservation site were characterized by comparatively low particle number concentrations andlow scattering coefficients. Not surprisingly, the coastal industrial zones which is the main
source of anthropogenic aerosols are located at the west side of observation site whereas
pacific ocean is located at the south south east and south east direction of the observation site.

31.5mm136.0mm

3.5mm
34.0mm
6.5mm
0.5mm
20.5mm

1.0mm
9.5mm

25.0mm

8.5mm

21.0mm

1.0mm

0.5mm

2.5mm
0.5mm

50

4.5mm
2.5mm

100

19.5mm

150

15.5mm

200

0

8
7
6
5
4
3
2
1
0
7/23
7/24
7/25
7/26
7/27
7/28
7/29
7/30
7/31
8/1
8/2
8/3
8/4
8/5
8/6
8/7
8/8
8/9
8/10
8/11
8/12
8/13
8/14
8/15
8/16
8/17
8/18
8/19
8/20
8/21
8/22
8/23
8/24
8/25
8/26
8/27
8/28
8/29
8/30
8/31
9/1
9/2
9/3
9/4
9/5

Black Carbon(μg/m3)

Scattering Coefficient(Mm -1 )250

Figure 1. Time series of the scattering coefficients (Upper) at 530nm and mass concentrations of black carbon(Lower) at urban area of Nagoya, between 23 July and 5 September. Boxes are determined by the 25th and 75th
percentiles, and whiskers are determined by the 5th and 95th percentiles. Close circles with the solid line and open
circles show the daily mean and median of hourly averaged data respectively.

Two effects of rain events were observed during the entire study period. The first effectshowed the decrease of scattering coefficients and the second effect showed the increase of
scattering coefficients. Sharp decrease of scattering coefficient on 26 July after a light rain
event on 25 July, the lowest scattering coefficient detected on 2 August followed by a93

continuous decrease of scattering coefficients from 28 July due to frequent rain events
observed on 28 July, 29 July, 30 July, 31 July, and 1 August and decrease in scattering
coefficients from 22 August till 25 August followed by frequent rain events on 22 August, 23
August, 24 August, and 25 August were suggested due to washout of atmospheric aerosols byrain events. The second effect showed the increase of scattering coefficient during rain events.
Most probably rain events occurred on 29 August and 30 August had greatly washed out theaerosols of the atmosphere and the increase of scattering coefficients from 30 August to 2
September were due to the effects of relative humidity. It should be noted that nephelometer
was not completely drying the aerosol, and therefore a significant amount of measured lightscattering was due to condensed water. More or less similar effects of rain events wereobserved on mass concentrations of black carbons. Chen et al. [2001] suggested thatelemental carbon from atmosphere is removed primarily through precipitation scavenging
after it is internally mixed with salts (such as sulfate) and becomes soluble. The precipitation
scavenging of black carbon suggested that black carbon might have internally mixed withsulfate in the urban area of Nagoya.
With the estimated absorption efficiency of black carbon as 14.6 m2/g in the urban areaof Nagoya (see section 3.3), absorption coefficients were calculated from mass concentrationof black carbon to estimate single scattering albedos of aerosols. The mean value withstandard deviation of the overall experimental period was found to be 0.66±0.13. Note thatthe average single scattering albedo may increase by certain percentage after correcting
absorption coefficient data measured by PSAP instrument for both light scattering andinstrument overestimation as described by Bond et al. [1999] and after correcting angulartruncation of nephelometer. This part of work is on progress and will be reported during thepresentation. The previous study reported the single scattering albedo of around 0.55 from theanalysis of physical and chemical properties of urban aerosols of Nagoya [ Kasahara et al.,
2000; Personal communication]. Overall, it was found that aerosols of Nagoya are highly
absorptive in nature, which have a potential to warm the climate.

3.2.

DIURNAL VARIATION OF AEROSOL RADIATIVE PROPERTIES

Before and after the rain events all the aerosol properties exhibit a characteristic diurnal
cycle. Figure 2 shows the statistical analysis of scattering coefficients and black carbon
concentrations grouped by hour of a day. Statistical analyses were carried out only for the nonrainy days. The peaks in mean scattering coefficients were observed at 1100-1500 JST withthe highest peak at 1300 JST. The cycle is even more pronounced when looking at the 75th
and 95th percentile populations.
It was suggested that number concentration of aerosol particles were reduced duringmorning and night time as a result of coagulation and highly reduced sources and OH

94

Scattering Coefficient(Mm-1)

productions when there was little or no solar insolation [Iziomon and Lohmann, 2003]. During
afternoon, sulfate formation from SO2 emissions from local power plants or refineries,
photochemically driven productions of organic or N-containing particles were possible. The
highest values of total particle number concentrations and scattering coefficients were noticed
under clear skies relative to cloud sky and all sky conditions in increasing difference in theafternoon. It was observed that the frequency of westerly winds reaching the observation site
was higher in afternoon that morning and night This suggested that photochemically driven
aerosol production was the main reason for high peaks of scattering coefficients observed atafternoon in our study location. Further mass scattering efficiency of aerosols below 4.7m2g-1
during afternoon (see section 3.3) suggested that sulfate aerosols were dominant.
The pronounced morning peaks of black carbon concentrations observed at
0500-0800JST might be the effects of local sources such as burning and vehicles.
180
160
140
120
100
80
60
40
20
0

3

Black Carbon(ug/m )

7
6
5
4
3
2
1
0
0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hours of Day (JST)Figure 2. Statistical analysis showing hourly averages of scattering coefficients (Upper) at 530nm and massconcentrations of black carbon (Lower) over the non rainy days of field campaign from 23 July to 5 September.
Boxes are determined by the 25th and 75th percentiles, and whiskers are determined by the 5th and 95th percentiles.
Close circles with the solid line and open circles show the daily mean and median of hourly averaged data
respectively.

95

The calculated daily cycles of hourly averaged single scattering albedos from theinformation of daily cycles of hourly averaged scattering coefficients and hourly averaged
absorption coefficients showed quite similarity with hourly averaged absorption coefficients
but inversely related. This suggested that the single scattering cycle was being driven more by
the variability in absorption coefficient than the variability in scattering coefficients.

3.3.

MASS SCATTERING EFFICIENCY OF DRY AEROSOLS AND
MASS ABSORPTION EFFICIENCY OF BLACK CARBON

From the hourly averaged size distributions of relatively dry aerosols with ambient RH
less than 60%, we calculated the mass concentrations of aerosols considering the density ofdry aerosols as 1.7gcm-3. Calculated mass concentrations were plotted against the scattering
coefficients as shown in figure 3(left). An excellent correlation between mass concentration ofaerosols and scattering coefficients with R2=0.93 was found. The estimated mass scattering
efficiency was 4.7m2g-1. This value could be considered as the upper limit mass scattering
efficiency of urban aerosols in Nagoya because the minimum detection size range of LPC
instrument was 0.3μm and masses of aerosols below 0.3μm weren’t included. Since
aerosols of the afternoon hours were comparatively dry and ambient relative humidity
100

250
y = 4.7x - 11.27
R 2 = 0.93

R 2 = 0.95
Y = 15.05X 5.9μm + 0.5680-1σap (Mm )σsp (Mm -1 )200

Y = 17.7X 5.71μm + 0.53

150
100

R 2 = 0.95
Y = 14.6X 8.8μm + 0.29
R 2 = 0.96

60

40

50

20

0

0

571nm
590nm
880nm
Transmittance(Tr>0.5)

0

20
40
Dry Aerosol mass(μg/m 3 )

0

60

2

4

6

Aethalometer BC(μg/m3)

Figure 3. Mass scattering efficiency of dry aerosols with relative humidity less than 60% (Left) and mass
absorption efficiency of black carbon (Right).

generally dropped below 60%. Thus the reported mass scattering efficiency could beconsidered as the representative value of aerosols of afternoon. The value falls within therange of sulfate scattering efficiency values of 2.4 to 4.7 m2g-1 reported in previous
publications [Andrae et al., 2002]. Based upon this finding, it could be suggested that sulfate

96

formation from SO2 emissions from local power plants or refineries was the main
phenomenon to increase particle number concentration and scattering coefficients during theafternoon in urban location of Nagoya.
Absorption coefficients measured by PSAP were correlated with mass concentrations ofabsorbing aerosols measured by aethalometer at 571nm, 590nm, and 880nm. Excellent
correlations were observed between two types of measurements and the best correlation wasobserved between PSAP absorption coefficient and aethalometer black carbon (measured at
880nm) and value of absorption efficiency of black carbon was found to be 14.6 m2g-1 with
R2=0.96. The estimated mass absorption efficiency of black carbon in this study falls within
the range of reported mass absorption efficiency of 5-20 m2g-1 [Liousse et al., 1993]. Further,
this value is comparable to the values of mass absorption efficiencies of other urban locations
reported in literatures.

4. CONCLUSION
The measurement of aerosol radiative properties was performed over the urban area of
Nagoya from 23 July 2004 to 5 September 2004. The measured scattering coefficients ranged
from 5 Mm-1 to 222 Mm-1, resulting in a mean value with standard deviation of 52.3±
39.3Mm-1. Similarly the measured black carbon (measurement made by aethalometer at
880nm wavelength) concentration ranged from 0.1μg/m3 to 8.0μg/m3 , resulting in a meanvalue with standard deviation of 1.7±1.25μg/m3. Mass absorption efficiency of black
carbon was found to be 14.6 m2g-1 . Since PSAP data with Transmission (Tr>0.5) were notavailable for the entire field experiment, hourly averaged absorption coefficients of the entire
field campaign were estimated from the information of black carbon concentrations and mass
absorption efficiency of black carbon. The absorption coefficients ranged from 1.5 Mm-1 to116.6 Mm-1 , resulting in a mean value with standard deviation of 24.6±18.3 Mm-1.
Light scattering coefficients of our study location was found to be comparable to themeasurements of scattering coefficients observed in several urban areas of the United Statesover varying time periods during the 1970s which ranged from 30 to 210 Mm-1 [Waggoner etal., 1981]. On the other hand, the mean light scattering coefficients of the urban area of
Nagoya was roughly 2 times lower than the mean value of 120 Mm-1 measured in Atlanta (arelatively polluted city in the USA) during August 1999 as a part of Atlanta SuperSite
Experiment [Carrico et al., 2003], roughly 6 times lower than the mean value of 353Mm-1
measured in Linan, China (Agriculture Yangtze delta region of China) during November 1999
[Xu et al., 2002], and roughly 9 times lower than the mean value of 488 Mm-1 measured in
Beijing, China during June 1999 [ Bergin et al., 2001].
Light absorption coefficients of our study location was found to be comparable to theabsorption coefficients made at several urban locations of US over varying time periods

97during the 1970s which ranged from 27 to 118 Mm-1[Waggoner et al., 1981]. The meanvalue of absorption coefficients of our study location was higher than the mean value of 16
Mm-1 in Atlanta, US reported by Carrico et al. [2003], comparable to the mean value of 23
Mm-1 in Linan, China reported by Xu et al. [2002], and roughly 4 times lower than the meanvalue of 88 Mm-1 in Beijing, China reported by Bergin et al. [2001].
The diurnal pattern of scattering coefficients in the urban area of Nagoya wascharacterized by peak values during afternoon, which was suggested due to photochemicaly
driven aerosol productions. The pronounced morning peaks of black carbon concentrations
and absorption coefficients observed during morning were suggested due to the effects oflocal sources such as burning and vehicles.
Overall, aerosols of Nagoya are characterized by high absorbing aerosols, which result tovery low single scattering albedo and have a potential to warm our climate by absorbing solar
radiation.

5. ACKNOWLEDGEMENT
The authors would like to express sincere thanks to Mr. Tomokazu Takahashi, Ms.
Mandira Adhikari and Mr. Kazuo Yamamoto of Hydrospheric Atmospheric Research Center,
Nagoya University for their sincere help during field observation.
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Bond T. C., T. L. Anderson, and D. Campbell, Calibration intercomparison of filter-based
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Carrico, C. M., M. H. Bergin, J. Xu, K. Baumann, and H. Maring, Urban aerosol radiative
properties: Measurements during the 1999 Atlanta Supersite Experiment, J. Geophys. Res.,108(D7),8422, doi:10.1029/2001JD001222, 2003.
Chen, L. -W. A., B. G. Doddridge, R. R. Dickerson, J. C. Chow, P. K. Mueller, J. Quinn, andW. A. Butler, Seasonal variations in elemental carbon aerosol, carbon monoxide and sulfur
dioxide: Implications for sources, Geophys. Res. Lett., 28(9), 1711-1714, 2001.
Iziomon, M. G., and U. Lohmann, Characteristics and direct radiative effect of mid-latitudecontinental aerosols: the ARM case, Atoms. Chem. Phys., 3, 1903-1917, 2003.

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Kadowaki, S., Characterization of carbonaceous aerosols in the Nagoya urban aera. 1.
Elemental carbon and organic carbon concentration and the origin of organic aerosols,
Environmental science and technology, pp. 741-744, 1990.
Liousse, C., H. Cachier, and S. G. Jennings, Optical and thermal measurements of black
carbon aerosols content in different environments: variation of the specific attenuation
cross-section, sigma (σ), Atmos. Environ.,27(8), 1203-1211, 1993.
Penner, J., R. Leaitch, D. Murphy, J. Nganga, G. Pitari, Aerosols, their direct and indirect
effects, in Climate Change: The Scientific Basis, edited by J.Houghton, Y. Ding, D. J.
Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, C. A. Johnson, pp. 289-348,
Cambridge Univ. Press, New York, 2001.
Waggoner, A. P., R. E. Weiss, N. C. Ahlquist, D. S. Covert, S. Will, and R. J. Charlson,
Optical characteristics of atmospheric aerosols, Atmos. Environ., 15, 1891-1909, 1981.
Xu, J., M. H. Bergin, X. Yu, G. Liu, J. Zhao, C. M. Carrico, and K. Baumann, Measurement ofaerosol chemical, physical and radiative properties in the Yangtze delta region of China,
Atmos. Environ., 36(2), 161-173, 2002.

99

3-6

Dust storm observations in Mongolia in spring 2004
Jugder DULAM (Ph.D) and Erdenetsetseg BAASANDAI
Institute of Meteorology and Hydrology
ABSTRACT
A study on dust storms observed over the territory of Mongolia from January to May
in 2004 was carried out. Asian regional and local weather maps at surface and 500-hPa
level, and NOAA satellite images were used for the study.

The study focused onformation, intensity and movement of dust events originated in Mongolian Gobi Desert
area. Atmospheric circulation factors causing formation of dust events are reviewed.
During the spring, there were 10 cases of strong dust events and 6 moderate or weak
dust events in Mongolia. About 6 of them arrived at and Japan via NE China and the
Korean Peninsular. Favourable and unfavourable conditions including winter and springsnowfall for the formation of intense dust storms were studied. Snowfall amount was 112 mm over the Gobi Desert area from January to February, and there was no snowfall
over the some areas of the Gobi Desert in those months. Monthly precipitation amount inJanuary and February fluctuated around the climate normal between 1961 and 1990, andless than that. March and April of 2004 were the driest months in Mongolia including theGobi Desert areas. Monthly precipitation amounts were about 0-5 mm over the mostareas of Mongolia in those months. Monthly precipitation amounts were less than the
climate normal over the Gobi Desert area in Mongolia in the period from March to May.
Soil wet was small in winter and spring, and it was favourable for the formation of dust
storms in those periods.

NOAA satellite images discover dust clouds over NE Asia inspring.

1. Introduction
The Gobi Deserts of Mongolia is a source area for the formation of dust storm in East
Asia (Natsagdorj et al., 2003). Occurrences of dust storms are different every year
especially in spring season. Some spring is relatively fine for dust events depending on
atmospheric circulation pattern, amount of precipitation, soil condition etc. The spring100

of 2003 was comparatively wetter than other years and formation of dust storms was withless frequent both in Mongolia and Inner Mongolia of China (Chung et al., 2004).
The study aim was to investigate dust storms in spring 2004 in relation with
precipitation amount, soil condition and atmospheric front. In addition, transportation of
dusty air and indication of dust aerosols on the satellite images were compared withground visual observations on dust storms.
2. Data used
Dust storm observations, snow cover, monthly amount of precipitation and its anomaly
over the territory of Mongolia from January to May 2004 were used in the study. In
addition, some examples of NOAA satellite images were included in the study.
3. Basic conditions for the formation of dust storms in Mongolia from January to
May 2004
Snowfall and precipitation in January to May in 2004
Monthly amount of precipitation and its anomaly are shown in the Fig.1 and Fig.2.
Monthly precipitation anomaly was less than normal over the 40-50% of the territory ofMongolia from January to March and 80-90 % from April to May. Monthly amount ofprecipitation was 0.1-12 mm in January to February and 0.1-37 mm from March to May.
According to the precipitation data, the territory of Mongolia had received the small
amount of precipitation in winter and spring 2004.
Small amount of snowfall and precipitation over the Gobi Desert areas was positiveeffect for the formation of dust storms.
Snow cover in January to March
Snow cover maps from January to March are shown in Fig.3. Snow depth was 0-5 cm
over the Gobi Desert areas in January and February 2004. The Gobi Desert areas were
no snow cover from March to May 2004. This situation is contribution for the soil
dryness and formation of dust storms.

101

Soil wet

Soil wet in the Gobi Desert area is not measured in Mongolia. However,precipitation and snow cover data show that soil wet was not enough and soil was dry
in the Gobi Desert area in Mongolia in spring 2004. In addition, the Gobi Desert area
is with scarce vegetation.

Fig.1. Monthly amount ofprecipitation in Mongolia fromJanuary to May 2004.
From the top, January (left),
February (right), March (left), April(right), May (left)

102

Fig.2. Precipitation anomaly in
Mongolia from January to May
2004.
From the top, January (left),
February (right), March (left),
April (right), May (left)

103Fig.3. Snow cover in Mongolia
from January to March 2004.
From the top, January (left),
February (right), March (left)

4. Dust storm observations in Mongolia from January to May 2004
Dust storm observations
However, soil condition in the Gobi Desert areas was drier due to the small amount ofprecipitation and snowfall, frequency of dust storms observed in Mongolia in spring 2004
was not many. Dust storm observations are shown in Table 1. Severe dust storms, which
covered large areas, were included in Table 1. Strong winds that were 16-28 m/c could
produce severe dust storms in the Gobi Desert areas with dry and loose dusty/sandy soil.

104

Table 1. Dusty days in Mongolia in spring 2004
2004/
Month

Day

February 23-24
February 27
March

8-9

March

27-28

April 13-14
April 16-17
April 20
April 27-28
May
May

1-2
4

May

6-8

May

11

May

18

Synoptic situation
Cold front over Mongolia
on 23
Cold front over Mongolia
on 27
Cold front over Mongolia
on 8-9Cold front over Mongolia
on 27-28
Cold front over Mongolia
on 14
Zonal Cold front over
Mongolia on 17
Cold front over Mongolia
on 20
Cold front over Mongolia
on 27-28
Cyclone and cold front
Cold front over the eastof Mongolia on 4
Cyclone sector on 6 andcold front on 7-8
Cyclone and cold front
on 11
Cold front over Mongolia
on 18

Number ofstations withdust storms
19

Gusty
wind
m/s
12-28

14

14-22

38

12-28

14

10-28

36

12-24

25

12-34

22

12-19

23

10-24

39
16

9-24
12-20

21

10-20

12

12-20

16

12-34

Commentary
Arrived at Japanon 26 Feb.
Arrived at Japanon 11 March.
Arrived at Japanon 17 April.
Arrived at Japanon 22 April

Arrived at Japanon 7 May.
Arrived at Japanon 11 May.

NOAA images
Detection of dust storms using satellite images is one of useful methodologies forobservations because ground network is sparse in Mongolia. Also it is important for themonitoring transportation and movement of dust storms over a region included borderingand nearby countries. Dust aerosols appeared on the NOAA Satellite images are shown in
the Fig. 4.

105

Fig.4. Dust aerosols appeared on the NOAA satellite images on 9, 12, 27, 29 March
2004.

5. Atmospheric situations causing formation of dust storms
In association with atmospheric cold fronts and upper air troughs arriving at the
territory of Mongolia all dust storm events occurred in Mongolia in spring 2004 (Table 1).
Air pressure differences in the cold frontal zone generate gusty winds and dust storms
over the Gobi Desert area in Mongolia.

106

6. Conclusions
Passages of cold fronts, small amount of snowfall or precipitation and soil dryness
were basic cause for formation of dust storms in dusty and sandy areas in the world.
Snowfall, precipitation amount, soil condition and atmospheric situations are dissimilar
every year and they differently influence on the formation of dust storms in Mongolia.
For the winter and spring 2004, precipitation was small and soil condition was dry in theGobi Desert areas in Mongolia. These situations were favorable for the formation of dust
storms in the Gobi Desert areas.
In addition, it is necessary to measure dust concentrations and vertical distributions ofaerosols in the source areas. For this purpose, NE Asian countries need to establish a
joint-project and researchers need to implement a joint-study.

References
Agro-meteorological and environmental bulletin, 2004: Institute of Meteorology andHydrology, Mongolia, No.1-15.
Chung Y.S., Kim H.S., Park K.H., Jugder Dulam and Tao Gao, 2004: Dust-storm
observations in China, Mongolia and Korea from March to May 2003, Journal of
the Korean Meteorological Society, 40, 3, 2004, p.241-257.
Natsagdorj L., D. Jugder and Y.S. Chung, 2003: Analysis of dust storms observed in
Mongolia during 1937-1999, Journal of the Atmospheric Environment, Volume
37, Issues 9-10, (March 2003) Pages 1401-1411, Elsevier Science Ltd., Printed in
the England.

107

4-1

The Radiation Monitoring Network of Chinese
Ecosystem Research: (CERN)
Yuesi Wang
Institute of Atmospheric Physics, Chinese Academy o Sciences, Beijing 100029, China

Abstract:
Solar radiation is the original dynamical system of the earth. It is an essential parameter ofEarth-climate physical process, and controls all courses of life-form on the earth. Energy fromthe sun sustains all life on the earth, and sustains all climate dynamic system. The differencesof climate in the world are caused by different receivers of radiation energy on the surface ofthe earth. Ground-based radiation measurements are essential in forecasting the climaticchange trends, evaluating the solar radiation transport theory in the atmosphere, assessing
climate model computations, estimating retrial results by satellite, and surface radiation
balance research. Radiations are also absolutely necessary parameters in environmental study.
Chinese ecosystem research network (CERN) was established in 1988. It consisted of 29
ecological research stations at beginning and expended to 36 stations in 2003, including 14
stations for agriculture, 9 for forest, 2 for grassland, 5 for desert, 1 for marsh, 2 for lake and 3
for marine ecosystems, representing diverse ecosystems. CERN has been placed as a base for
ecological monitoring, research and demonstration in china. The CERN is engaged onmonitoring the water, soil, atmosphere and biological elements of major ecosystems, theimportant ecological processes such as the energy and matter flow, and land use and land
cover changes in the surrounding areas of the stations, by the standardized methods andorganization. The goal of radiation monitoring is to obtain high quality dataset of radiation
and ancillary measurements to develop climate model, adjust satellite retrieval, research on
climate change and environment. The station map is showed in Fig 1. The routine
observations of solar spectral radiation were carried out at 28 stations by the Chinese
Academy of Sciences from 1 January 1989. The observational instruments are model
TBQ-4-1 (Jinzhou ,china) and as follows. (1) There are three sensors with sensing wavelength
ranges of 270–3200nm, 400–3200 nm, and 700–3200 nm, respectively. The main technicalcharacteristics of the sensors are: sensitivity is 5–10 mV kW-1 m2, response time is ≤1s(1/e),and stability is ≤2%.
The data on global irradiation on a horizontal surface in 28 stations of CERN during the
6-year period 1998–2003 are analyzed. The object of this study is obtained the global
radiation time series and spatial variation characteristic in China.
In this topic, we want discuss distribution properties of global radiation in CERN. The maincontents as fellow:
1, distribution properties of yearly global radiation
2, distribution properties of monthly global radiation
3, the global radiation extremum value
We rebuild radiation measurement system of CERN from August 2004.In the new observe109system Global radiation was measured using a Kipp&Zonen model CM-21, reflect radiation
using CM6B, ultraviolet radiation using CUV3, Net radiation using QMN101, sunshine
duration using CSD2, and soil heat flux using HFP01SC (Delft, The Netherlands). The
Photosynthetically active radiation was measured with a Licro quantum sensor LI-190Sz (Lin-coln,
Nebraska, USA). This station was completed with a data acquisition system (Vaisala M520
data-logger, and a storage module, Finland). The signals were conditioned with the respective
instrument factors into units of W/m2 and µEm-2s-1(quantum mol-1s-1 or µmolm-2s-1). Global
radiation measurements have an estimated experimental error of ± 2-3%, while the
Photosynthetically active radiation sensor has a relative error of lees then 5%. All radiation values
were measured by one-minute interval, and the hourly values were driven from minute value
by integrating them. The global radiometers were calibrated against a reference pyranometer,
which is calibrated a standard pyrheliomter, while the LI-190SA pyranometer is calibrated
against calibration lamps at factory. All this calibrated work was done at the beginning and theend of data collected. In this observe system HMP45D used for temperature and humidity,
WAA151 used for wind speed, WAV151 used for wind direction, DPA501 used foratmospheric pressure, 8 pieces of QMT110 used for profile of soil temperature (0cm, 5cm,
10cm, 15cm, 20cm, 40cm, 60cm, 100cm)(Vaisala, Finland), and RG13 used for precipitation(Casella, USA).

Fig.1 Distributions map of ecosystem research stations of CERN
thrd
1-14 Stations are Research stations for Agricultural Ecosystems; 15-23 stations are ResearchththStations for Forest Ecosystems; 24-25 stations are Research Stations for Grassland Ecosystems; 26
ststation is Research Station for Marsh Ecosystem; 27-31 stations are Research Stations for Desertrdth
Ecosystems; 32-33 stations are Research Stations for Lake Ecosystems; 34-36 stations are Research
Stations for Marine Ecosystems

110Introduction
CERN has been operating as a network of surface radiation monitoring observatories forover 10 years. The determination of a global climatology of the radiation budget at the surfaceof the Earth is fundamental to understanding the Earth’s climate system, climate variability
and climate change resulting from human influence. Estimation of the surface radiationbudget from satellite observation can’t be obtained without high accuracy surface-based
measurements at various sites in contrasting climatic region for calibrating and validation.
Long-term observations are essential for the study on climate variation trend and radiationstransform theory.
The original motivation for the CERN is provide data for climatic change, ecology systemstudy and the relationship between these two fields. The goals and objectives were prescribedas fellow:
Monitor regional radiation fluxes at the surface
Get the radiation variation trend
Provide data for ecology system study
Provide data for calibrating satellite estimates of the surface radiation budget.
Instruments and database
The routine observations of solar spectral radiation were carried out at 28 stations by the
Chinese Academy of Sciences from 1 January 1989(Donghu station no data). The
observational instruments are model TBQ-4-1 (Jinzhou, china) and as follows. (1) There arethree sensors with sensing wavelength ranges of 270–3200nm, 400–3200 nm, and 700–3200
nm, respectively. The main technical characteristics of the sensors are: sensitivity is 5–10 mV
kW-1 m2, response time is ≤1s(1/e), and stability is ≤2%.These instruments are fixed in theold observation system.
The instrument fixed in new observation system is showed as fellow.
Global radiation was measured using a Kipp&Zonen model CM-11 (wavelength ranges of
300–3200nm) (Delft, The Netherlands). The PAR photon flux was measured with a Licro
quantum
sensorLI-190Sz(wavelength
rangesof
400–700nm)(Lin-coln,Nebraska,USA).Reflect radiation is measured by a Kipp&Zonen model
CM-6B.Ultraviolet radiation is measured by a Kipp&Zonen model CUV3(wavelength rangesof 290–400nm).Net radiation is measured by a Kipp&Zonen model QMN101(wavelength
ranges of 300–10000nm).This station complete with data acquisition system(Vaisala M520
data-logger, and a storage module, Finland). Global radiation measurements have an
estimated experimental error of 2-3%, while the PAR sensor has a relative error of lees then
5%. All radiation values were measured by one-minute interval, and the hourly values were
driven from minute value by integrating them.
A metrology observation system is also fixed in this system. (Vaisala automatic weather
station with wind speed, wind direction, temperature, humiditity, rainfall, atmosphericpressure, and soil temperature)
The data used in this topical is get from the old observation system in 28 station from 2001 to
2003.

111

Results and discussion

50
45
40
35
30
25
20

80

90

100

110

120

130

Fig.2 The distribution of annual global radiation in 2003(CERN)

The figure of global radiation annual variation characteristic show that there are four low
center, namely, Yanting of Sichuang,Huitong of Hunan,Changshu of Jiangsu ,and Beijing.
There is a high central of global radiation in Inner Mongolia. The global values of inner land
are higher than that of inshore province, and dry area values of global radiation is higher thanglobal radiation in humidification circumscription. There is a lower values appears in
Sichuang,this phenomena is caused by there are many rainy and more cloudy day in this area,at the same time pollution concentration is higher in this area.

112

50
45
40
35
30
25
20

80

90

100

110

120

130

Fig.3 The distribution global radiation in January 2003
In winter the distribution characteristic of global radiation is similitude as that in annual
global variation characters. In the eastern area, the value of south area is bigger than the northarea. The distribute properties is depend on the law of sun revolution. The lower values in
Sichuang is caused by cloudy and pollution concentration.

50
45
40
35
30
25
20

80

90

100

110

120

Fig.4 The distribution of global radiation in July 2003

113

130

The isoclines of global radiation, which stretch from northeast to southwest, are higher innorthwest hinterland than that in coastland except Sichuan province.
Conclusions
The global radiation on ground of Inner Mongolia and Xinjiang is higher than other area ofChina. Contrariwise, it is very low in Sichuan province, which is decided by the climate of
local area. The global radiation measured by CERN radiation observation system is consistent
with the observation results from ninety-eight radiation-observing stations distributed over
our country. Thus, the observation stations we have established are so representative that theresults can reflect the characteristics of global radiation on the mainland of China.

114

4-2

EARLINET: The First Continental-Scale Lidar
Network for Vertical Aerosol Profiling
Ulla Wandinger
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Abstract. An overview on the establishment and the objectives of the European Aerosol Research Lidar Networkis given. Major results of the intensive observational period from May 2000 to November 2002 are presented.
The air-flow-related aerosol modification across Europe and the observation of special events are discussed.
These results describing the European haze plume in three dimensions and the regional to continental aerosol
transport are based on the network observations in combination with extensive backward trajectory analysis.
Key words: lidar, networking, aerosol measurements, vertical profiling

INTRODUCTION
The European Aerosol Research Lidar Network (EARLINET) is the first continent-scale lidar
network for monitoring of anthropogenic haze [Bösenberg et al., 2003]. The main objectives
of EARLINET are the establishment of a comprehensive and quantitative statistical data baseof the horizontal and vertical distribution of aerosols on the European scale, and the use of
these data for studies related to the impact of aerosols on a variety of environmental andclimate-relevant problems.

METHODOLOGY AND QUALITY ASSURANCE
During the intensive observational period from May 2000 to November 2002, coordinatedlidar measurements (three observations per week, Monday afternoon and evening, Thursday
evening) were performed at 20 EARLINET stations distributed over most of Europe. In
addition to the regular schedule, individual observations addressed special situations such as
Saharan dust outbreaks, long-range transport of forest-fire aerosols, smog episodes, anddiurnal cycles in the planetary boundary layer. Typical measurement wavelengths are 355,
532 and/or 1064 nm; most of the systems operate in the UV. Thirteen stations have
implemented the Raman technique for an independent retrieval of aerosol extinction andbackscatter coefficient profiles.
Because the network consists of individual lidar systems designed at research institutes fordifferent purposes, an extended quality-assurance program based on the intercomparison ofindividual lidar systems [Matthias et al., 2004a] as well as retrieval algorithms [Böckmann etal., 2004; Pappalardo et al., 2004] was conducted to ensure a homogeneous data set.
Evaluated data in terms of profiles of aerosol backscatter and extinction coefficients are stored
in a platform-independent common data base. Backward trajectories for each station for two
daily arrival times and six pressure levels provided by the German Weather Service
complement the data set.

STATISTICAL ANALYSISEARLINET provided statistical findings on the temporal and spatial distribution of boundarylayer heights and of aerosol extinction and backscatter coefficients in the planetary boundary
layer (PBL) and in the free troposphere as well as on the wavelength dependence of the115

optical properties [Mattis et al., 2004; Matthias et al., 2004b]. From Raman lidar observations
at 10 stations a data base of extinction-to-backscatter ratios has been established. For
moderately polluted sites in central Europe mean PBL extinction coefficients of the order of
200 and 100 Mm-1 at 355 and 532 nm, respectively, are found. The values are a factor of 2
lower at maritime sites at the western rim of the continent and up to a factor of 2–3 higher at
polluted south-eastern European stations. Mean PBL heights range from 1200 to 2000 m, withlowest values at the coast and highest at continental sites, and show a strong seasonal
dependence especially over the continent. In northern Europe, the free troposphere contributes
10–20% to the total aerosol optical depth. In southern Europe 25–40% of the aerosols arefound above 2 km height. This high value is mainly attributed to the transport of Saharan dust
across the Mediterranean (see below). Extinction-to-backscatter, or lidar, ratios vary strongly
depending on the observed aerosol types. Values of 50–60 sr are found for industrial-pollution
aerosols. Lidar ratios are of the order of 30–50 sr at stations which are mainly influenced bymaritime air masses. In Saharan dust, values of 40–80 sr are observed.

AIR-FLOW-RELATED MODIFICATION OF AEROSOL PROPERTIES
Changes in the aerosol optical properties of the European haze plume related to anthropogenic
activities can be observed when clean air crosses the densely populated, highly industrialized
continent with the prevailing westerly winds [Wandinger et al., 2004]. Air flows across the
predominantly flat terrain in the northern part of Europe show an increase of the optical depth
at 355 nm from 0.05 at the most western station at Aberystwyth, Wales, to values of 0.3–0.4
in central Europe (Hamburg, Leipzig) and 0.6 at Belsk, Poland. During easterly flows opticaldepths of 0.5–0.6 are measured at the central European sites, and the values are as high as 0.9
when air from the Black Sea region arrives with south-easterly flows at the most eastern
stations of Belsk, Poland, and Minsk, Belarus.
The Alps represent a major orographic barrier between the northern and southern part ofEARLINET. In southern Europe, the diverse orography which can lead to a complex aerosol
layering due to sea breezes and mountain-induced winds [Perez et al., 2004] complicates theinvestigation of air-flow-related changes of the aerosol properties. However, even in this partof Europe the influence of anthropogenic activities, e.g., when air masses cross Italy from
northwest to southeast, can be documented.

SPECIAL OBSERVATIONS
A variety of special observation programs have been performed within EARLINET. One of
the major projects was the characterization of Saharan dust outbreaks towards Europe. In
southern Europe as many as 50 dust events per year have been monitored. Up to 10 dust
plumes per year reach central Europe. In these situations the dust, which is mainly found in
the free troposphere, often dominates the aerosol properties of the whole troposphere. Dust
optical depths of up to 0.8 at 532 nm have been found in central Europe [Ansmann et al.,2003]. Because dust particles are relatively large and non-spherical their optical properties
differ significantly from other aerosol types [Mattis et al., 2002; De Tomasi et al., 2003; Baliset al., 2004; Murayama et al., 2004].
Wild fires have been identified as another import source of aerosols in the free troposphere.
Smoke from forest fires can be transported in the free troposphere over very long distancesand remain in the atmosphere for several weeks. In May–July 2003 aerosol extinction in thefree troposphere was a factor of 3–4 higher than in the respective months of the years 2000–
2002 because of severe forest fires in Siberia and Canada [Mattis et al., 2003].

116

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Pelon, L. Sauvage, P. Sobolewsky, A. Stohl, O. Torres, G. Vaughan, U. Wandinger, and M. Wiegner: Longrange transport of Saharan dust to northern Europe: The 11-16 October 2001 outbreak observed withEARLINET. J. Geophys. Res. 108, 10.1029/2003JD003757, 2003.
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Frioud, I.V. Grigorov, A. Hågård, M. Horvat, M. Iarlori, L. Komguem, S. Kreipl, G. Larchevêque, V. Matthias,
A. Papayannis, G. Pappalardo, F. Rocadenbosch, J.A. Rodrigues, J. Schneider, V. Shcherbakov, and M.
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algorithms. Appl. Opt. 43, 977-989, 2004.
J. Bösenberg et al.: EARLINET: A European Aerosol Research Lidar Network to establish an aerosol
climatology. MPI Report No. 348, Max Planck Institute for Meteorology Hamburg, Germany, 2003.
F. De Tomasi, A. Blanco, M.R. Perrone: Raman lidar monitoring of extinction and backscattering of African
dust layers and dust characterization. Appl. Opt. 42, 1699-1709, 2003.
V. Matthias, V. Freudenthaler, A. Amodeo, J. Balin, D. Balis, J. Bösenberg, A.P. Chaikovsky, G. Chourdakis, A.
Comerón, A. Delaval, F. De Tomasi, R. Eixmann, A. Hågård, L. Komguem, S. Kreipl, R. Matthey, V. Rizi, J.A.
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V. Matthias, D. Balis, J. Bösenberg, R. Eixmann, M. Iarlori, L. Komguem, I. Mattis, A. Papayannis, G.
Pappalardo, M.R. Perrone, and X. Wang: Vertical aerosol distribution over Europe: Statistical analysis of Raman
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4-3

Ultraviolet Radiation Measurement in the South of
Sinkiang Using a Compact Zenith-sky Spectrometer
Pinhua Xie, Yihuai Lu, Yujun Zhang, Ang Li, Jianguo Liu and Wenqing Liu
Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, Anhui, 230031

Abstract
Terrestrial levels of solar UV radiation determine the impact on human health. Ozone depletion and associatedincreases in solar UV radiation reaching the earth’s surface are therefore major environmental issues. The quality(spectrum) and quantity (intensity) of terrestrial UV radiation vary with factors including the elevation of the sun
above the horizon and absorption and scattering by molecules in the atmosphere, notably ozone, and by clouds.
Here we report the measurement of UV radiation in the south of Sinkiang, the northwest part of China, using acompact zenith-sky spectrometer in summer 2004. The measurement site, as an area of 1090 meters altitude near
the planetary boundary layer, has more extreme climatic conditions and air with less anthropogenic pollution.
The spectral power distribution and the diurnal variation of ambient UV radiation from 290nm to 380nm were
measured during days of clear sky. The zenith-sky spectrometer consists of a small telescope pointing zenith
direction with a field of view of 13 mrad, a fibre-coupled compact spectrometer with 0.6 spectral resolution anda CCD detector. A filter ( ZWB3 ) was used to block the light above 400nm to reduce stray light. Due to the highspectral resolution, the stratospheric O3 slant columns were analyzed from the UV spectra by Differential Optical
Absorption Spectroscopy methodology at the same time. The compact zenith-sky spectrometer gains in terms offield-operability, mobility and flexibility as well as being a cost-effective alternative to the normal zenith-sky
system with expensive spectrometer and CCD detector. Additionally, the vertical profile of temperature,humidity and pressure were measured under 30km height by sounding radio.
Key Words: solar UV radiation, slant column density of O3, zenith-sky spectrometer

1. INTRODUCTION
Ultraviolet (UV) radiation is part of the electromagnetic spectrum. Although solar UV
radiation constitutes only about 5% of terrestrial sunlight, it holds the greatest interest in the
biological effects which strongly impact on human health and plant life. Ozone layer acts as
the protector of the earth by its strong absorption of UV radiation. Therefore ozone depletion
and associated increases in UV radiation are major environmental issues and attract theattention of scientific community and governments [1][2][3][4].
This paper reports a measurement of UV radiation in the south of Sinkiang in China with acompact zenith-sky spectrometer. The preliminary results are discussed, including relative
UV spectral power distribution, diurnal variation of UV radiation, slant column density of O3
as well as the vertical profile of temperature, humidity and pressure.
118

2. MEASUREMENT
The measurement site locates in the south of Sinkiang with 1090 meters altitude near theplanetary boundary layer (northwest part of China). It represents the area with more extreme
climatic conditions and air with less anthropogenic pollution. The measurement was
performed from June 29th to July 2nd and on July 7th, 2004. During most time period, it was
sunny with clear sky.
A schematic diagram of the zenith-sky spectrometer is shown in Figure 1. The system is basedon a single holographic grating spectrograph, fiber-coupled to a telescope which collects thezenith-sky sunlight and a CCD detector. A portable computer was used to collect data fromthe CCD detector with 12 bit A/D. The spectral resolution is 0.68 nm over a wavelength rangeof 290~380 nm. The telescope consisting of one quartz lens and a filter collect the ultraviolet
light scattered from zenith sky. The field-of view of the telescope is about 13 mrad. A filter(ZWB3) is used to blocking visible light above 400nm to reduce the stray light.
Normally the devices used to measure terrestrial UV radiation consist of complex
spectrometer (e.g. double monochromator) and diffuser input optics with sufficient accuracy,
resolution and reproducibility. Our system is very compact and at low cost. Still it can give
useful information about the variation of UV radiation during daytime as well as measuring
the stratospheric O3.
Incoming Sunlight

CCD

Telescope
UV

Filterspectrograph

GratingFig.1 Schematic diagram of the zenith-sky spectrometer

3. RESULTS
3.1 UV radiation
Figure 1 shows the spectral power distributions of UV radiation from 290 nm to 380nm at
different daytime. The UV radiation reaches the peak level around noon time 13:00 (21º SZA).
This can also be clear seen from Figure 2, which shows the diurnal variation of UV radiation(integration radiation of 290-380nm) on July 2, 2004. The UV radiation changes very
119

smoothly except for some small undulations over the smooth trend. This could be caused byclouds in the sky. Around 13:00, few clouds appeared in the sky.

July 2, 2004
13:00
10:30
16:50

Relative Unit

4000

3000

2000

1000

0
300

320

340

360

380

400

Wavelength(nm)Figure 2. Spectral power distribution of clear sky, terrestrial UV radiation
measured in July 2, 2004

6

2.0x106

1.8x106

Relative Unit

1.6x106

1.4x106

1.2x106

1.0x105

8.0x105

6.0x10

10:00

12:00

14:00

16:00

18:00

20:00

Local Time

Figure 3. Variation of ambient UV radiation from 290-380nm on July 7, 2004 in Sinkiang.

3.2 O3 Slant column density
The measurement technique, which is called differential absorption spectroscopy, is used forhighly structured NO2 and O3 absorption in UV/Vis spectral range[5][6][7]. During sunrise andsunset, the observed scattered sunlight passes through a long slant path, which enhances theabsorption. The observation was carried out from the morning to the evening (SZA from 21.2
º to 67.6º). The spectra at 21.2 º SZA (noon time) are used as reference spectra. The ratio of
zenith sky spectra of noontime and other time is taken to eliminate the Fraunhofer line
structure and this ratio spectrum gives the effect of absorption of O3 column relative to thereference spectrum. The ration is used to determine the SCDs of O3. Figure 4 shows themeasured spectrum with O3 absorption structures and the O3 differential cross section after120

same DOAS data processing procedure (e.g. high pass and low pass filter). We found good
agreement between them. The SCDs of O3 are calculated by DOAS method. Figure 5 shows
the SCD of O3 measured on July 7, 2004.
1.05

Measurement
O3 cross section

1.04
1.03
1.02

OD

1.01
1.00
0.99
0.98
0.97
0.96
315

320

325

330

335

Wavelength (nm)Figure 4. Comparison of observed and calculated OD of O3.

19

1.0x1019

1.0x1018

8.0x1018

8.0x10mol/cm2

18

18

mol/cm2

6.0x106.0x1018

4.0x1018

4.0x1018

2.0x1018

2.0x100.0

0.0

2004-7-7 09:00

2004-7-7 12:00

2004-7-7 15:00

15

2004-7-7 18:00

25

35

45

55

65

75

SZA

Beijing Time

Figure 5. Measurement of O3 on July 7, 2004.

10000

30000
30000

9000
25000

8000

25000

7000

15000

Altitude (m)Altitude (m)Altitude(m)

20000
20000

15000

10000

10000

5000

5000

0

0

6000
5000
4000
3000
2000
1000

-60

-40

-20

0
o

Temperautre( C)

20

40

0

200

400

600

Pressure

800

1000

0
0

10

20

30

Humidity (%)

Figure 6. Vertical profile of temperature, pressure and humidity on July 7, 2004.

121

40

50

60

3.3 Vertical profile of temperature, pressure and humidity
During the measurement time, vertical profile of temperature, air pressure and humidity was
obtained by sounding radio, as shown in Figure 6. The top of troposphere is about 17km
altitude. The humidity reached zero at about 6km altitude.

4. CONCLUSIONS
A compact zenith-sky spectrometer was used to measure UV radiation and O3 SCDs, both ofwhich are very important atmospheric parameters needed long term observation. The
measurement in Singkiang in summer of 2004 gives relative spectral power distribution anddiurnal profile of UV radiation as well as the SCDs of O3. The compact device demonstrates
its gains in speed, cost and portability.
For the future work, the compact device will be further temperature controlled and calibrated
for absolute measurement of UV radiation. SCDs of O3 will be combined with air mass factor(AMF) calculated from radiative transfer model to obtain the total O3 amount. The accuracyof the measurement will be further compared with other instruments (e.g. spectroradiometerand Dobson spectrophotometer).

5. REFERENCES[1] B. L. Diffey, Sources and measurement of ultraviolet radiation, Methods 28 (2002) 4–13

[2] M. Steinmetz, Continuous solar UV monitoring in Germany, Journal of Photochemistry andPhotobiology B: Biology 41(1997)181-187[3] J.M. Sabburg, A.V. Parisi, M.G. Kimlin, Enhanced spectral UV irradiance: a 1 year preliminary study,
Atmospheric Research 66 (2003) 261–272[4] P.C. Wang, B.Y. Wu and W.X. Zhang, Analysis on the factors affecting surface UV radiation, Chinese
Journal of Atmospheric Science, Vol.23, No.1(1999) 1-8[5] J. F. Noxon, Nitrogen dioxide in the stratosphere and troposphere measured by ground based spectroscopy.
Science, 1975, 189, 547-549[6] U. Platt, “Differential Optical Absorption Spectroscopy (DOAS)”. In M. W. Sigrist M.W. (Ed.), Monitoringby Spectroscopic Techniques. John Wiley & Sons, New York. 1994
[7] G..S. Meena, D.B. Jadhav and C.S. Bhosale, Total column density variations of NO2 and O3 by automatic
visible spectrometry over Pune, India, Current Science, Vol. 85, No.2, 25, 2003

122

5-1

Progress and Outstanding Challenges in
Estimating Surface Radiation Budgets by
Methods of Remote Sensing
R. T. Pinker
Department of Meteorology, University of Maryland, College Park, MD 20742

ABSTRACT
Attempts to obtain information on shortwave surface radiative fluxes from satellite observations
have been made since the very early stages of satellite explorations. During the last two decades themethodologies to obtain such fluxes have advanced to a level where results have been found to be of
sufficient quality to address a wide range of climate issues. The current inference schemes havecapabilities to derive not only the total down-welling shortwave fluxes, but also fluxes absorbed in theatmosphere and at the surface both under clear and cloudy conditions, and diffuse and spectral fluxes. The
framework for advancing the development of such capabilities was provided by the Global Energy andWater Cycle Experiment (GEWEX) program aimed at understanding and modelling the globalhydrological cycle and programs such as the Coordinated Enhanced observing Period (CEOP) activity.
The satellite based flux estimates have proven to be very valuable for evaluating large scale models that
require information on radiative fluxes at global and regional scales. Yet, many outstanding issues of thesedata sets as related to spatial sampling, temporal sampling, adequate representation of the diurnal cycle,
spectral resolution of the observations and the quality of the auxiliary information used to drive theinference schemes (such as information on aerosols), need to be addressed in a systematic manner. Sincesatellite estimates are now available for periods of at least twenty years, there is also interest in assessing
the temporal variability of the radiative balance and implications for climate change studies. In thispresentation, reviewed will be the current status of available information on shortwave radiative fluxes,
discussed will be current issues related to outstanding problems, and presented will be examples ofcollaborative activities in progress.
Key Words: surface radiation budgets; satellite inference of radiation budgets

INTRODUCTION
Since the early seventies, attempts to derive radiative fluxes from satellite
observations have been in progress (VonderHaar et al., 1973; Raschke and Preuss, 1979).
A comprehensive effort to obtain such information at global scale was initiated by the
Global Energy and Water Cycle Experiment (GEWEX) program of the World Climate
Research Program (WCRP) (Chahine, 1992; Sorooshian, 2003) aimed at: improvement of
the understanding and modeling of the hydrological cycle on time scales from diurnal tointerannual; evaluation of land surface parameterizations; and testing their implications
for global climate and weather forecast models. Such information is also required at theboundary of the upper ocean since radiative fluxes influence the sea surface temperatureand precipitation.
Capabilities to derive radiative fluxes at global and regional scales for extended
time periods from geostationary and polar orbiting satellites have been demonstrated(Stuhlman et al. (1990); Pinker and Laszlo (1992); Brison et al. (1994); Chou (1994);
Whitlock et al. (1995); Pinker et al. (1995); Rossow and Zhang (1995); Gupta et al. (1999,
2001); Ceballos et al. (2004); Pinker et al. (2003); Zhang et al. (2004)). The global scale
effort was facilitated by the joint NOAA/NASA PATHFINDER activity (Dodge and123

Ohring, 1992) under which uniform and long -term observations taken from numerous
satellites have been processed into reduced resolution, global coverage homogeneous
information. The various versions of the International Cloud Climatology Project(ISCCP) data (Schiffer and Rossow, 1985) that are a sub-set of the PATHFINDER data,
have been extensively used for inferring global scale distributions of clouds, cloudproperties, and surface radiative fluxes (Zhang et al., 1995; Pinker and Laszlo, 1992;
Stackhouse et al., 2002). The WCRP GEWEX Surface Radiation Budget (SRB) project
was specially tasked to produce, validate, and assess long-term surface and atmosphericradiative
budgets on a global scale(Stackhouse et al., 2002).
The satellite estimates
have
been
evaluated
extensively
bytheproducers of the data as
well as by independent
investigators. The groundtruth as available from the
Swiss Federal Institute ofTechnology,
GlobalEnergy Balance Archive(GEBA) (Ohmura et al.,1998), and various othersources as compiled at theNASA Langley Data
Center have been used.
Manyground-based
Figure 1.
Comparison of University of Maryland SRB model(Version 2.1) retrieved monthly mean surface
observational networks are
downward shortwave fluxes against ground truth fromnow available (BSRN;
GEBA for the entire year of 1986, using satellite
SURFRAD;ARM;
observations from ISSCP D1.
SKYNET; AERONET)that provide information needed for evaluation. Extensive evaluations are also conductedagainst fluxes at the top of the atmosphere using observations from ERBE (Barkstrom etal., 1984), SCARAB (Hollmann et al., 1999) and CERES (Wielicki, 1999).
The satellite based estimates of radiative fluxes at global scale have already beenused in a wide range of applications (Hahmann et al., 1995; Bony et al., 1997; Machado etal., 1998; Sui et al., 2001). New types of experiments for improving weather prediction atregional scale are in progress. Improving weather and seasonal climate prediction requires
information on initial states of the atmosphere, oceans and land. Sparse observations need
to be assimilated from various observing platforms into atmospheric initial states via 4dimensional data assimilation (4DDA) to blend observations with the background fields ofan LSM. Yet, errors remain in soil moisture/temperature and surface energy/water fluxes,
due to biases in the surface forcing. Under the North American Land Data Assimilation
System (NLDAS) project (Mitchell et al., 2004) quality controlled, spatially and temporally
consistent, real-time and retrospective forcing data of rainfall and radiation as obtained
from observations and satellite based estimates of radiative fluxes (Pinker et al., 2003) have124

been used to support LSM activities. This activity is a first real-time operational prototype
of a continental-scale uncoupled land 4DDA assimilation executed daily at the National
Centers for Environmental Pedictions (NCEP) that utilizes real-time streams of hourly todaily precipitation and insolation fields (from satellites and/or ground observations). It is
hoped that such an approach will reduce the errors in the storage of soil moisture andenergy which are often present in NWP models and which degrade the accuracy of
forecasts.
In parallel to the NLDAS activity, a Global Land Data Assimilation System(GLDAS) is being developed jointly by scientists at NASA’s Goddard Space Flight
Center (GSFC) and NOAA’s National Centers for Environmental Prediction (NCEP)(Rodell et al., 2003). Its purpose is similar to that of NLDAS but runs globally andproduces results in near-real time (typically within 48 hours of the present). The global
land surface fields provided by GLDAS will be used to initialize weather and climate
prediction models and will promote various hydrometeorological studies and applications.
As yet, radiation fields are not produced globally in real time based on satellites but
rather use clouds and parameterization of their effects. Similar activities are in progressin the European LDAS Community (ELDAS) (Meetschen et al., 2004).
Other programs of relevance that promoted the use of satellite estimates ofradiative fluxes are the various GEWEX Continental Scale studies such as LBA (Nobreet al., 2001), BALTEX (Raschke, 1998) or GAME (Nakajima, 2001; Takamura et al.,
2001; Sekiguchi et al., 2003). The Coordinated Enhanced Observing Period (CEOP)
program (Grassl, 2002) supports research objectives in climate prediction and monsoon
system studies, and surface radiation is an important element of the program. CEOP has
been endorsed as the first element of the new Integrated Global Water Cycle Observation
Theme approved by the Integrated Global Observing System Partners (IGOS-P)(http://www.ceop.net/).
The goal of research on satellite based inference schemes is to determine theatmospheric and surface radiative fluxes at a level of precision needed to predict transient
climate variations and climate trends, improve capabilities to predict changes in water
resources and soil moisture and aid in the development of global models to a level thatwill allow them to determine if the Earth's energy budget and water cycle are changing.
In this presentation addressed will be issues that still require attention before such goals
can be achieved.

SELECTED INFORMATION ON SURFACE RADIATIVE FLUXES
•

•
•
•

First WCRP Surface Radiation Budget Global Data Sets, Short-wave Radiation
Parameters March 1985-December 1988, NASA Earth Observing System
Distributed Active Archive Center, NASA Langley Research Center, Hampton,
VA (Whitlock et al., 1995), at monthly time scale.
Global Data Sets for Land-Atmosphere Models, ISLSCP Initiative 1: 1987-1988,
Volume 1-5, NASA Goddard DAAC Science Data Series, at three hourlyintervals (Sellers et al., 1996).
ISLSCP Initiative II consists of a 10-year core global data collection spanning
the years 1987 to 1995 at spatial resolutions of one-quarter to one degree.
http://islscp2.sesda.com/ISLSCP2_1/html_pages/islscp2_home.html
Global Ecosystem Database, Disk B, National Environmental Satellite, Data, and125

Information Center National Geophysical Data Center, Boulder, Colorado
November 1997. Monthly averaged on Photosynthetically Active Radiation, forthe period July 1983-July 1988.
The most recent global scale and long term data sets on satellite surface radiative fluxes
can be found at: http://www.gewex.org/srb.html; http://atmos.umd.edu/~srb/pathfinder;and http://isccp.giss.nasa.gov/

ADDRESSING SELECTED OUTSTANDING PROBLEMS
1.

Spatial Inhomogeneity

Satellite observations are incomplete in space or/and in time. In order to use suchobservations to monitor climate, in climate modeling, or to drive general circulation models,there is a need to subject them first to some type of optimal interpolation, to obtain
homogeneous information. The magnitude of errors in available estimates of surface
radiative fluxes due to this deficiency is not well established and requires attention.
An approach was developed to obtain homogeneous data sets, and the methodology
was applied to the widely used International Cloud Climatology Project (ISCCP) DX and
D1 data. These provide observations independently for each geostationary and polar
orbiting satellite, sampled at 30 km. Data gaps, degraded spatial resolution near boundaries
of geostationary satellites, and different viewing geometries in areas of satellite overlap,
could result in inhomogeneous estimates of radiative fluxes. An Empirical Orthogonal
Function (EOF) iteration scheme was introduced for homogenizing the radiative fluxes(Zhang, Pinker and Stackhouse, 2004). When compared against ground truth over Europe,
Africa and United States for a period of about four years, the rms error was reduced byabout 2 W/m2 when compared to unsmoothed results.
2.

Aerosol effects

Aerosols scatter and absorb solar radiation and their effect on the hydrologicalcycle has been of concern (Ramanathan et al., 2002). Information on aerosol opticalproperties is therefore important for accurate modeling of the surface radiation budget. In
recent years, progress has been made to derive aerosol optical depth (AOD) from avariety of sources. To estimate uncertainties in SRB due to aerosols, a methodology wasdeveloped to merge satellite observations, chemical models, as well as groundobservations. The method keeps the large-scale spatial and temporal variation patterns asderived from satellites and models, and simultaneously regulates the magnitude using
ground measurements (Liu, Pinker and Holben, 2004). Specifically, the following data
were used:•
GOCART model simulation: A three-dimensional chemical transport model(Chin et al., 2000, 2002; Ginoux et al., 2001) capable of reproducing prominentspatial and temporal variations, especially in areas with strong signals (biomassburning and dust).
•
MODIS satellite retrieval: The MODerate resolution Imaging Spectroradiometer(MODIS) provides information on AOD (King et al., 2002). Used are the
MODIS Level-3 monthly mean AOD data.
•
AERONET ground measurement: The AERONET is a globally distributed
federated network of ground-based observations representing a wide variety of
atmospheric conditions using state-of-the-art sunphotometers (Holben et al., 1998;

126

Eck et al., 1999). Used are AOD monthly mean data from 159 sites in operation
during the analysis period.
The derived AOD distribution used is presented in Figure 1. The impact
was tested in a region frequented by dust. A “control run’ was made with “nominal
values of AOD, assigned according to land use. A second run was performed with therealistic AOD information. Results are presented in Figure 2. As evident, over the ocean
adjacent to the coast of Africa, aerosols can reduce surface fluxes by as much as
12 W/m2.

Figure 2.

3.

Monthly mean values of aerosol optical depths as derived from the combined estimatesfrom MODIS, GOCART and AERONET for February 2001 (left) and the impact on SRB
when compared to nominal AOD (right)

Calibration uncertainties

Parameters derived from satellite observations depend on the quality ofcalibration of the satellite measurements. Sensitivity of surface shortwave downward
fluxes to different calibration methods for the visible sensor aboard the GOES-8
geostationary satellite were investigated (Wonsick and Pinker, 2004). One calibration
method was developed at the National Oceanographic and Atmospheric Administration(NOAA) National Environmental Satellite Data and Information Service (NESDIS)(Weinreb et al., 1997). The second evolved from a study conducted at the NASA
Langley Research Center, in which data from research satellites carrying onboard
calibration systems were used as reference sources to develop new calibration
coefficients for GOES-8 (Minnis et al., 2002). Evaluated were differences in absolutereflectance, derived cloud cover, and estimated surface shortwave fluxes as compared to
ground measurements. Differences in visible reflectance ranged from -0.5 to 3%. The
average difference in monthly mean cloud amount was ~3%, and the average difference
in monthly mean shortwave downward flux was 5 W/m2. Differences in bias and RMS in
evaluation against ground station measurements were less than 2 W/m2. Neither
calibration method was shown to consistently outperform the other. This evaluation
yields an estimate of the errors in fluxes that can be attributed to calibration. In the caseof the ISCCP data, homogeneity of the satellite observations is achieved by normalizing
all the geostationary satellites used to polar orbiting satellites, which enjoy a long historyof calibration know-how (Desormeaux et al., 1993).

127

A MULTI-SATELLITE APPROACH
In the past, emphasis has been on the use of geostationary satellites to capture thediurnal variability in cloud distributions that determines the amount of energy received at
the surface during the course of a day. Many of these satellites are limited in theircapability to accurately detect cloud or aerosol optical properties that are importantelements of the radiation budget. The MODIS instrument onboard the Terra and Aqua
satellites and the Midori II GLI observed with 36 spectral bands with the capability formeasuring atmospheric and surface properties at higher accuracy and consistency. It is
important to utilize such observations to evaluate the performance of algorithms applied
to satellites that do not provide direct information on cloud and aerosol opticalcharacteristics. The “Atsk 13” SRB algorithm that was developed for use with GLI
observations was implemented with the MODIS prototype data at 10 resolution as well aswith the pixel level MODIS swath data as illustrated in Figure 4. Work is in progress on
the evaluation of surface radiative fluxes from such multiple-source observations andvarious algorithms in the domain of the GMS satellite, using ground truth as available
from the SKYNET.

Figure 3.

Monthly mean shortwave surface downward flux (W/m2) with MODIS V004 data at 10
resolution for September 2001 (left) and pixel level MODIS data for July 4, 2001 overJapan (right). Cloud fraction, cloud optical thickness, aerosol optical thickness,precipitable water, total column ozone amount, spectral surface albedo and solar zenith
angle are from MODIS data.

SUMMARY
Global data sets on surface radiative fluxes have been developed by several
groups and are being used extensively in a wide range of climate applications. In the
United States, Europe and Brazil, work has started on providing such information in an
operational mode for use as forcing functions in numerical weather prediction models.
Yet, uncertainties in available estimates of SRB are not fully known and require further
attention. Many new satellite missions have been launched that can help to address
specific issues related to uncertainties. For instance, MODIS on Terra and Aqua
satellites (King et al., 2002) and the GLI instrument on ADEOS-II observe the earth in an
unprecedented number of spectral intervals and at high spatial resolution. Such128

observations can be utilized for assessing limitations of the operational satellites withlimited spectral capabilities. New observations from a suite of sensors on the
METEOSAT Second Generation (MSG) mission, to be made with the Spinning
Enhanced Visible and Infra-Red Imager (SEVIRI) and with the Geostationary Earth
Radiation Budget (GERB), will facilitate improvements in existing methodologies toderive surface radiative fluxes. Level 1.5 data from SEVIRI and GERB have half hourly
observations in all 12 SEVIRI channels and two GERB channels. The two instruments
are collocated and therefore, it would be possible to test narrow-to-broadband
transformations that are needed when narrow-band observations are used to inferbroadband fluxes. New angular models based on CERES should be also helpful in
reducing uncertainties in the derived fluxes. Application of high quality satellite based
radiative fluxes to process studies in models can help to improve: extended range
forecasts; water resource variability; simulations of climate response to anthropogenic
forcing and provide input to other climate related programs such as CLIVAR(http://www.clivar.org/).
Acknowledgements: Thanks are due to H. Wang, H. Liu, and M. Wonsick, Graduate
Students, University of Maryland for their contributions. Support under NASA grants
EOD/IDS NAG59634 and NAG56464 to the University of Maryland and JAXA grant
AEO1208001 are greatly appreciated. The ISCCP D1 and the GEBA data were obtained
from the NASA Langley Research Center Atmospheric Sciences Data Center.
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5-2

Seasonal and Long-term Variations of Shortwave Radiation in China
Tadahiro Hayasaka1, Kazuaki Kawamoto1, Jianqing Xu2 and Guangyu Shi3
1

Research Institute for Humanity and Nature, Kyoto 602-0878, Japan
Frontier Research System for Global Change, Yokohama 236-0001, Japan
3
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2

1. Introduction
Surface radiation budget is one of the most important factors in the earth’s climate system.
The changes in surface radiation as well as radiation at top of the atmosphere due toanthropogenic emissions of aerosols and greenhouse gases have been recently studied by using
ground-based measurements, satellite measurements, and climate model simulations (IPCC,
2001). Downward shortwave radiation at the surface is complicatedly related to atmosphereincluding clouds, aerosols, water vapor and other constituents, and thus surface radiation
measurements are important although the number of station with high quality operation islimited. Calculation based on satellite cloud data can evaluate surface SW surface radiation ina wide area for the past two decades. However, those calculation also has some limits such asplane-parallel atmosphere assumption and spatial and temporal resolution of cloud data. It istherefore worthwhile to compare and comprehensively evaluate surface SW radiation among thevarious data set.
In the present study, we compare the monthly averaged surface SW radiation data between
two satellite derived data set and pyranometer measurements in China for 11 years. In addition
to those data, SW radiation data calculated by using parameterization with meteorological data
other than pyranometer measurements are also used to check the quality of pyranometer data.
The pyranometer measurements need careful operation such as keeping level and clean glassdome as well as precise calibration. Therefore it is quite important to compare the dataobtained by independent methods. After checking the quality of the pyranometer data, we will
discuss the seasonal and long-term variations of surface shortwave radiation in China.
2. Comparison of surface shortwave radiation data.
Ground based measurement data, i.e., pyranometer measurements and parameterizationfrom meteorological data are compared with each other, and also with the 1km grid data derived
from satellite observations. All these data were compared month by month. A simple lenear
regression analysis was carried out for the comparison.
Chinese Meteorological Administration (CMA) has been measuring downward SW radiativeflux at more than 120 stations in which more than 60 stations continue the measurements sincelate 1950’s or early 1960’s. All of the data were originally acquired every hour and daily data
were reported. In this study monthly average data based on daily average data were used forthe analysis. Calibration before early 1990s was carried out every 5 year by using the Eppley
blackbody cavity radiometer PMO-6 which is calibrated in Meteorological Research Institute,Japan Meteorological Agency. Recently CMA has been directly attending the intercomparison
with the international calibration standard of World Radiation Center in Davos, Switzerland.
There are several products of surface SW radiation data set derived from satellite cloud data.
We used NASA/GEWEX SRB product provided by NASA Langley Research Center, for which132the algorithm is originally based on Pinker andLaszlo (1992). The SRB data used here are
daily average and 1 degree by 1 degree inspatial resolution, which are calculated from
ISCCP DX cloud data and objective analysis
meteorological data.
Monthly average data of surface SW
radiation are also obtained from operational
meteorological data by using parameterization
method.
The surface SW radiation isbasically estimated with water vapor amount,tubidity coefficient, and sunshine duration as aproxy of cloud amount (Xu et al., 2005).
Figure 1 shows examples of thecomparison among the three data set. SSAT
and SXU correspond to satellite derived dataand parametrization data, respectively. The
period for the comparison is 1984-1994. It isshown that satellite derived data have anegative bias against pyranometer data in thearid area Golmud (upper panel) and a positive
bias in the large city area Shanghai (lower
panel). On the other hand, parametarization
data are rather consistent with pyranometerdata at both stations.
Fig. 1. comparison among the three data set. SPYR,
We compared pyranometer data with
SSAT and SXU correspond to pyranometer data,
satellite derived data at 65 stations, and withsatellite derived data and parametrization data,
parameterization data at 15 stations. It is
respectively.
found from the comparison that SRB data
overestimate SW radiation in large city areas,
while underestimates it in desert areas. These discrepancies are ascribed to aerosol assumption
in the calculation of SW radiation from satellite cloud data. Absorbing aerosols in the
sub-cloud layer, which is not observed from space, is important for SW radiation on the surface.
On the other hand, incorrect assumption on the aerosol loading seems to be the reason of thenegative bias in Golmud. Other factors for the discrepancy is effect of cloud inhomogeneity.
However, this effect seems to be small if the cloud properties are averaged for the results
retrieved from various viewing angle radiance data (Iwabuchi and Hayasaka, 2002).
Golmud350

y = 1.0264x - 4.2125
R2 = 0.9761

300

250

200

150

y = 0.8682x - 5.7129
2

R = 0.9602

100

SSAT
SXU

50

線形 (SSAT)
線形 (SXU)

0

0

50

100

150

200

250

300

350

SPYR (W/m2)

Shanghai300

y = 0.9901x + 26.695
R2 = 0.9204

250

200

150

y = 1.0114x + 8.8704
R2 = 0.9255

100

SSAT

SXU

50

線形 (SSAT)
線形 (SXU)

0

0

50

100

150

200

250

300

SPYR (W/m2)

3. Seasonal and long-term variations of shortwave radiation
Figure 2 shows a typical distribution of shortwave radiation obtained from SRB data. It isfound that shortwave radiation over China has seasonal variations as follows. In spring andsummer, large values of SW radiative flux extend over desert and semi-desert area in the northpart of China, whereas those in the southern part are small. These properties are associatedwith front system. It is inferred from ISCCP cloud analysis that low-level cloud amount ismore important in spring rather than optical thickness, while optical thickness as well as cloudamount is also important in summer. In autumn and winter, spatial variation of SW radiative

133

Jan.

Apr.

Jul.

Oct.

(W/m2)
0

50

100

150

200

250

300

Fig. 2. Shortwave sueface radiative flux in 1995 over China estimated from SRB data

flux is small except for the minimum value around Sichuan basin where heavy aerosol loadingis always observed.
It is inferred from ISCCP cloud analyses that low-level cloud amount is more important inspring rather than optical thickness, while optical thickness as well as cloud amount is also
important in summer. It is also found that spatial variations of these cloud properties change
year to year.
We retrieved cloud amount, optical thickness and effective particle radius of low-level clouds
from NOAA/AVHRR GAC data, based on the reflection method (Kawamoto et al., 2000).
This method basiclly uses visible and near infrared channels, by comparing the data andtheoretical calculations. In order to avoid the errors in the retrieval due to cloudinhomogeneity, data with scanning angle less than 25 degree were used (Iwabuchi and
Hayasaka, 2002). Results of AVHRR analysis suggest that aerosols strongly affect not only
cloud amount but also effective particle radius and optical thickness, for example, small cloudparticles and large optical thickness are observed around Sichuan basin corresponding to aerosol
optical thickness distribution. In autumn and winter, effective particle radius is smaller thansummer. Low-level cloud system is affected by aerosols in boundary layer and thus aerosol
indirect effects become more important. Therefore the seasonal variation as shown in Fig. 2
may reflect the indirect effect of aerosol.
Long-term variation of SW radiation was analyzed for 1971-2000 by using mainly
pyranometer measurement data after chacking the quality of the data as dissucused above. It is134found from preliminary analysis that SW radiation almost all over China has a tendency todecrease for 1971-2000, but looks to increase in south region for 1991-2000.
4. Summary
Downward shortwave radiative flux on the surface in China were evaluated by using
pyranometer data, calculated results by parameterization with ground-based meteorological datasuch as sunshine duration and water vapor.
The results are also compared with
NASA/GEWEX SRB data set for 1984-1994. These radiation data are in general consistent
with each other for monthly average values although SRB has positive bias for large city areas
whereas negative bias for the desert area in the west part of China. One of the reasons forthese biases is ascribed to inappropriate assumption of aerosols in the SRB.
The amplitude of seasonal variation of shortwave radiation is large in the northeast region,
while it is small and complicated, i.e., it is not a simple seasonal variation . As for long-term
variation of SW radiation, the decrease tendency was observed in almost all stations. The
synthetic analysis by using various data set should be discussed because the satellite data arelimited after 1980s and pyranometer measurements have some difficulties in keeping quality ofdata due to the calibration and operation.
Acknowledgments.
We would like to thank Drs. P. W. Stackhouse Jr. and S. Gupta for providing GEWEX/SRB
data set calculated from ISCCP data.
References
IPCC, 2001: Climate Change 2001, The Scientific Basis, edited by J. T. Houghton et al.,Cambridge Univ. Press, New York.
Iwabuchi, H. and T. Hayasaka, 2002: Effects of cloud horizontal inhomogeneity on the optical
thickness retrieved from moderate-resolution satellite data. J. Atmos. Sci., 59, 2227-2242.
Kawamoto, K., T. Nakajima and T. Y. Nakajima, 2001: Aglobal determination of cloud
microphysics with AVHRR remote sensing. J. Climate, 14, 2054-2068.
Pinker, R. T. and I. Laszlo, 1992: Modeling surface solar irradiance for satellite applications on
a global scale. J. Appl. Meteor., 31, 194-211.
Xu, J., T. Hayasaka, K. Kawamoto and S. Haginoya, 2005: An estimation of downward
surface radiation over China. J. Meteor. Soc. Japan (in press).

135

5-3

Properties of long-time digital camera records
in Changchun and Ulaanbaatar
Kisei Kinoshita (1), Hiroyuki Kikukawa (2), Naoko Iino (3),Wang Ning (4), Zhang Gang (4), Jugder Dulam (5),Tsatsaral Batmunkh (5) and Satoshi Hamada (1)(1) Faculty of Education, Kagoshima University, Kagoshima, 890-0065, Japan(2) Faculty of Fisheries, Kagoshima University, 890-0056, Japan(3) Department of Mechanical Engineering. Kagoshima University, 890-0065, Japan(4) Environmental Science Department, Northeast Normal University, Changchun, China(5) Institute of Meteorology and Hydrology, Ulaanbaatar 210646, Mongolia
Abstract : Digital cameras to record fixed wide views with one-hour interval are operating in Changchun,
China since the middle of March 2003, and in Ulaanbaatar, Mongolia since the middle of March 2004, to study
visibility and air turbidity affected by Asian dust. Digital video cameras with ten-minute interval are also in
operation there. We discuss the results of the observation in the spring and summer of 2004, and the opticalproperties of the digital photo records.
Key Words: Asian dust, digital image ,interval recording, turbidity, visibility

1. Introduction
Long-time visual recordings by using digital cameras are very effective for thestudies of atmospheric phenomena as reviewed in [1] concerning volcanic cloud observation.
In order to observe the Asian dust phenomena from the ground in Northern Asia, digital photoand video cameras have been set at Northeast Normal University in Changchun, Jilin
Province, China since 18 March 2003, and also at the Institute of Meteorology and Hydrology,
Ulaanbaatar, Mongolia since 16 March 2004. Digital photo cameras were also set at two
stations, Bulgan and Dalanzadgad in southern Gobi, Mongolia in the spring of 2004. The
ground observation at these stations, shown in Fig. 1, are important to study the rise and thetransport of the dusty air in the northern roots toward Northern Pacific, as often observed in
the satellite imagery [2]. On the other hand, the web-camera recording in Kagoshima,southwest Japan, started in the end of 2000, preceded by video recording since 1987 andextended to southern islands including Mayon volcano, Philippines, as summarized in [3].
The results of ground observations and satellite imagery of dust events in 2003 werealready reported in [4]. In this report, we discuss the ground observation results in Changchun,
Ulaanbartar and two stations in southern Gobi in 2004, with special attention to the opticalproperties and the performance of digital cameras. Preliminary results concerning the dust
events supplemented with the satellite imagery are reported in [5, 6].
136

Fig. 1. Location map ofobservation stations.

2. Observation in Changchun and digital RGB analysis
Interval recordings by digital photo and video cameras in Changchun in 2004 started
in the evening on March 9. On the next day, very dusty air was observed almost all the day.
The recordings continued until August 21, by changing medias on May 24. The cameras wereset at the window toward the north on the fifth floor in a building of Environmental Science
Department, College of Urban and Environmental Sciences, Northeast Normal University.
Fig. 2 (a), (b) and (c) are typical images by photo camera Casio QV-R4 of clear sky, cloudy
and dusty scenes in the daytime in the middle of March. The differences in color and thebrightness change according to the vertical angle are obvious in the images.

(a) clear sky: 13 March at 11 CST(b) cloudy: 16 March at 12 CST(d)(c) dusty: 10 March at 14 CST(e)Fig. 2. The photo scenes (a-c) at the Changchun station, RGB profiles (d) along a common vertical line shownin (c), and the scatter diagram (e) of R/G vs. B/G at the upper part of the line indicated in (a) for three scenes.

137

These images are taken by the format 1280 pixels* 960 lines in the normal mode. Quantitative study
can be done by taking the RGB (Red, Green andBlue) profiles in 8 bit, such as shown in Fig.2(d),along a common vertical line in the sky shown in
Fig. 2(c). In the case of clear sky, the relationshipamong three color-components is B > G > R andthe difference of each is significantly large
especially in the sky region far away from thehorizon. For cloudy sky, the differences become
small, especially between G and R, but neverchange the relation. For dusty sky, the above
relationship is reversed as B < G < R, and thedifferences increase with dusty levels. A scatter
diagram of the values B/G and R/G normalized by
the Green component in the upper part of thevertical line, shown in Fig. 2(a), are exhibited inFig. 2(e), where three clusters are clearly separated
corresponding to the air turbidity.
We may also study the decrease of thevisibility in dusty air in the photo data. Fig. 3(a) is
a comparison of the views near the horizon in Fig.
2 (a, b and c). The RGB values along a line there
are displayed in Fig. 3 (b, c and d). The decreaseof the contrast between the buildings far away
and the background sky is obvious for cloudy anddusty scenes.
In order to see the air turbidity from thecolor information of the sky, the white-balance ofthe color was set to the daylight (outdoor) mode.
The zooming of the camera was set as wide as
possible.
All of the photo records were edited byhtml to see a few days from dawn to dusk at a
glance, keeping original jpeg images withoutchange, after renaming to indicate the place andthe date-time and separating nighttime scenes.

Fig. 3. (a) Parts of photo scenes near thehorizon in Fig. 2 (a-c). (b-d) RGB values
along the line indicated in (a).

Fig. 4. A list of photos on June 29 in Changchun, as indicated by ch040629hh with CST (=UTC+8) for hh.

138Thus, we may have quick look images of seasonal change of daytime hours as well as
weather changes in day and time. Fig. 4 is a sample of a photo list in a fine and longest day inthe end of June. We see that the development and decrease of cumulus clouds around the
mid-day.
In Changchun, a video camera Sony DCR-TRV40E has been operating with
ten-minute interval, with somewhat close-up because of the limitation of the window frame
for wider view. In Fig. 5, we compare the photo and video records in dusty and clear days in
March. We see that the difference of the air turbidity is remarkable, though the color contents
somewhat depend on the cameras.

Fig. 5. Comparison of photo (line 1 and 2) and video (line 3 and 4) records on 10 and 13 March,corresponding to dusty and clear days respectively in Changchun.

3. Observation in Ulaanbaatar
In Ulaanbaatar, a photo camera
with wide view, Ricoh Caplio G4wide, anda video camera Sony DCR-TRV900 withsemi-fish eye converter lens Kenko x0.45
have been set at a window toward the west
on the third floor of Institute ofMeteorology and Hydrology (IMH). A tallbank building in front of the window
somewhat disturbs the view as shown in
Fig. 6, which is a semi-fish eye view of theFig. 6. A semi-fish eye video scene at IMH,
video camera. The photo camera has been
Ulaanbaatar..
tilted by 90 degrees, so as to take widevertical view. In 2004, photo records were obtained during 16 March and 20 June with
one-hour interval, and video records between 16 March and 2 June with ten minutes interval
as in Changchun. The video records were converted into mpeg files for each day separatelyfrom dawn to dusk. Fig. 7 is a quick look list of photos for seven days in the middle of139

April from the sunrise to dusk for a day in a line. The filename of each photo indicates theplace (UB) and the date-time, where Mongolian standard time (MST= UTC+8) is used. In
these photos, we see the mirror images of rising sun around 6 MST, and the direct images offalling sun in the afternoon partly shielded by the bank building. In Fig. 7, light dusts wereoccasionally seen on 13-14 and 17-19, while very fine sky is seen in many other days andtimes.

Fig. 7. A sample of quick look scenes at IMH, Ulaanbaatar, for 13-19 April, 2004, 6-19 MST.

4. Observation in southern Gobi
At Bulgan and Dalanzadgad in southern Gobi, photo cameras Casio QV-R4 andSharp MD-PS1 were set at the windows toward the south and the west respectively on the
ground floors in the meteorological stations there, with the zoomings of the cameras as wide
as possible. At the Bulgan station isolated in Gobi desert, the camera was powered by abattery-pack outside. We got the records there for 26 days since 18 March. (At the otherstations in general, the cameras have been powered by AC converters connected to AC powersources through uninterrupted power supplies.) At the Dalanzadgad station, restart of thecamera was necessary for many times, and the photos were obtained during 27 March and 17
June, lacking the records of March 31, April 1-7 and 14-19, May 6, 12-13 and 20-31, andJune 8-10. The white-balance of MD-PS1 had to run by automatic mode only.
Fig. 8 shows quick look images for two days in the afternoon in the end of March attwo stations. We may see dusty air to the south of these stations in the afternoon on 27 March.
140

Fig. 8. Quick look images at Bulgan (BL) and Dalanzadgad (DZ), in the afternoon on 27-28 March 2004.

5. Remarks
Interval digital records of photo and video cameras are useful for the studies of dusty
air and many other aspects of weather changes. Studies of Asian dust in conjunction withsatellite imagery and other related data are in progress, as partly reported in [5, 6]. Threephotos per day at local standard time 9, 12 and 15 hours at Changchun and Ulaanbaatar are
archived in http://volceye.edu.kagoshima-u.ac.jp/webcam/archive/ .
References[1] K. Kinoshita, C. Kanagaki, A. Minaka, S. Tsuchida, T. Matsui, A. Tupper, H. Yakiwara and N. Iino: Groundand Satellite Monitoring of Volcanic Aerosols in Visible and Infrared Bands, Proc. CEReS Int. Symp. Remote
Sensing, Chiba, Japan, pp.187-196, 2003.
[2] Kagoshima Kosa Analysis Group: Satellite Imagery of Asian Dust Events, Kagoshima Univ., 159 pp. 2001;
N. Iino, K. Kinoshita, R. Iwasaki, T. Masumizu, T. Yano: NOAA and GMS observations of Asian dust events
during 2000-2002, Proc. SPIE Vol. 4895 Applications with Weather Satellites, SPIE, Bellingham, WA, USA, pp.
18-27, 2003.
[3] Kagoshima Univ. Volcanic Cloud Research Group: Volcanic Eruption Clouds in the Western Pacific Ground and satellite based observations and analyses -, Kagoshima Univ., 142pp., 2004.
[4] K. Kinoshita, N. Wang, G. Zhang, S. Hamada, S. Tsuchida, A. Tupper and N. Iino: Continual observation of
Asian dust in Changchun and Kagoshima, 2nd International Workshop on Sandstorms and Associated Dustfall,
Nagoya, 2003.
[5] K. Kinoshita,

N. Iino, S. Hamada, H. Kikukawa, T. Batmunkh, D. Jugder, N. Wang,G. Zhang and A.

Tupper: Automatic image recording network of sand storm and dusty airs in northern Asia, International
Radiation Symposium: Current Problems in Atmospheric Radiation, August 23-28, Busan, Korea, 2004.
[6] K. Kinoshita, S. Hamada, N. Iino, H. Kikukawa, J. Dulam, T. Batmunkh, N. Wang and G. Zhang: Interval
Camera Recordings of 2004 Asian Dusts in Mongolia, Northeast China and Southwest Japan, 4th ADEC
Workshop, Jan. 26-28, 2005, Nagasaki, Japan.

141

5-4

Analysis And Impact Study Of Global Positioning
System Radio Occultation Precipitable Water
Vapor Over East Asia Region
D.Rajan and GR.Iyengar
Department of Science & Technology, New Delhi, India

Abstract.
The Asian summer monsoon governs the very pulse of life on the whole continent. Understanding
and predicting the variability of this monsoon is, therefore, extremely challenging for the wellbeing of billions of people and the diverse flora and fauna inhabiting the Asian region. This Asian
land mass is unique in the sense of its geographical distribution, where the observation by remotesensing techniques are essential. The lack of accurate measurements of atmospheric parameters
over the oceans and other remote regions contributes greatly to the uncertainties in the initial stateof weather over this region. Meeting the ever-increasing demand of the atmospheric parameters
over this landmass is a challenging task for researchers in the field of environmental remotesensing. The science and applications that develop from space observing systems will have farreaching impact on mankind’s ability to sustain a vigorous and fruitful life in the Asian region.
The Global Positioning System (GPS) is developing into a powerful source of information in thisfield of science with the application of the remote sensing techniques. In this study, a detailed
analysis of the GPS METeorological parameters (GPS/MET) is carried out for the Asian summer
monsoon period 2001 and March 2002. The results of the GPS/MET quality are described in detailin comparison to other source of meteorological parameters.
Key words: (Radio occultation, monsoon, Remote sensing, Spectral Statistical Interpolation, Dataassimilation, Precipitable Water Vapor)

INTRODUCTION
The Asian land mass is unique in the sense of its geographical distribution ofoceans, forests, mountains, valleys, deserts, etc. The advent of remote sensing
technique has added new dimensions to the study this distribution in depth. In
recent years, there has been a substantial increase in the understanding of thisunique landmass by the use of remote sensing techniques.
The implementation of the GPS network of satellites with the development ofsmall, high-performance instruments to receive GPS signals have created theopportunities for active remote sounding of the earth’s atmosphere by radio
occultation at comparatively low cost. With the development of the GPS satellite
network, it becomes possible to measure precipitable water vapor, accurate
temperature, pressure, etc using radio occultation. Since the first launch of
GPS/MET instrument package, namely receiver, a major goal has been to
demonstrate the potential value of these GPS radio occultation measurements innumerical weather analysis and prediction. Many studies have revealed that thequality of the GPS/MET retrieved temperature/moisture measurements ascompared to other types of data, such as operational global analysis, radio-sondes
and satellite data (Ware et al., 1996; Kursinski et al., 1995, 1996; Rocken et al,
1997; Kuo et al., 1998) is reasonably good. These detailed studies suggest that142

GPS/MET occultation measurements are accurate and have the potential to be
useful in numerical weather prediction and climate studies.

BACKGROUND OF THE GPS OBSERVATION

Detailed numerical weather prediction requires observational data of good quality
and of high spatial/temporal resolution. Due to various limitations, importantvariables such as tropospheric water vapor are often inadequately covered byconventional observations from radio-sondes or by meteorological satellites
systems. The GPS is developing into a powerful tool for the moisture content for
use in fine-scale regional models and the global models. Recent years have seen
great advances in GPS meteorology and related fields. The GPS was developedby the United States Department of Defense primarily for military uses. It consistsof 24 GPS satellites that are evenly distributed in six orbit planes around the earth.
The altitude of each GPS satellite is 20200 km. Almost all the Low Earth Orbiting(LEO) satellites are at about 750 km altitude. Each GPS satellite is equipped witha transmitter with highly stable frequency. The GPS receiver can be located (i) on
the LEO satellite (ii) at the earth stations to transmit data ranging from 1.2 to 1.6
GHz (L-band signals) to ground-based or space-based receivers. Basically the
GPS system measures time delays between the transmission and reception of theelectromagnetic signals. During an occultation, the satellites move in such a waythat the electromagnetic ray connecting them traverses the atmosphere.
The receivers are also located on the ground. Geographical Survey Institute (GSI),
Japan, established GPS Observation NETwork called GEONET with about 1000
GPS ground stations spread over its territory. Back in 1997, Japan launched a
GPS project, an application of precipitable water from a nationwide GPS array(GEONET) for data assimilation in Numerical Weather Prediction (NWP), and an
improvement of geodetic accuracy of GPS based on NWP database. Today theJapanese GEONET, which consists of more than 1200 GPS permanent stations, is
one of the largest GPS networks of the world. The details of the GPS project overthis East Asian region, is given in Table-1. In addition, there are a few on-goingassessments of prototype missions available for us-namely GPS/MET, theGerman Challenging Minisatellite Payload (CHAMP), and Argentine SAC-C.
Some of the developed operational missions are Constellation of Observing
System for Meteorology, Ionosphere, and Climate (COSMIC), METOP/GRAS,
NPOESS/GPSOS. GPS ground measurements are the only means to obtain
integrated precipitable water vapor values under all weather conditions.
The GEONET database offers datasets with high spatial resolution and a very
dense time scale. The data from June to September, 2001 & March 2002 are
obtained from the web page of the Chiba University for this study. This database
contains the following meteorological parameters: (i) Zenith Total Delay (ZTD),(ii) Precipitable Water Vapor (PWV), (iii) water vapor mixing ratio (q), (iv)
Relative Humidity (RH), (v) precipitation, (vi)air pressure, (vii) air temperature,(viii) zonal component of wind, and (ix) vertical component of wind. The
parameters are analyzed and the accuracy of these GPS/MET parameters is143

statistically examined in this study. For this study a 3 dimensional Spectral
Statistical Interpolation (SSI) weather analysis scheme is used. The detailed
explanation of this spectral statistical analysis technique is given below.

UNIQUE FEATURES OF GPS RADIO OCCULTATION

Some important features of GPS RO are (i) Highly accurate averaged temperature
profiles with an accuracy of up to 0.1° K (ii) assured long-term stability (ii) allweather operations (iii) global 3-dimensional coverage (iv) high verticalresolution (less than 100 m) in the lower troposphere (v) independent of height,
pressure, and temperature (vi) the GPS RO instrument is very compact (not morethan size of shoe box), (vii) low-power, low-cost sensors required.

SPECTRAL STATISTICAL INTERPOLATION ANALYSIS

In spectral analysis the actual state of the atmosphere is represented by an
expansion of a series of space dependent functions with time dependent
coefficients.
The analysis procedure consists of determination of thesecoefficients, which make series expansion a best fit with the observed data. The
meteorological data from various observing platforms from all over the globe isreceived at the regional telecommunication hub, New Delhi through the Global
Telecommunication System (GTS). The Spectral Statistical Interpolation (SSI) isa three-dimensional variational analysis scheme, here after called as SSI. The
observation residuals are analyzed in spectral space on sigma surfaces, In the SSI
scheme the objective function to be minimized is defined in terms of thedeviations of the desired analysis from the first guess field (6hr forecast) and theobservations, weighted by the inverse of the forecast and the observational error
variances respectively. The objective function is based on the Parish (1992)
concept of minimizing a cost function consisting of a cost function of mainly twoparts as follows J= J ges + J obs, the two terms in J on the right hand side of thisequation deal with the best fit of the first guess field and the observations with theanalysis respectively. The analysis variables are chosen to simplify backgroundterm, for speedy convergence and to include balance. The analysis variables canbe transformed to model variables. The analysis variables in this scheme arevorticity (ζ), fast or unbalanced part of divergence (D), temperature (T) andlogarithm of surface pressure (ln Ps) and mixing ratio (ω).
For this experiment the year 2001 data of various types viz SYNOP/SHIP, BUOY,
TEMP, PILOT, AIREP, SATOB, INSAT, GMS, GOES, and METEOSAT are
assimilated. These observations falling within ± 3 hours of the respective hour ofassimilation are being used in the corresponding hours of assimilation. Thus fourtimes (00, 06, 12, 18 UTC) assimilation cycle is made. Once the initial condition
is prepared, the prediction equations are integrated and weather predictions are
obtained. The impact of (i) PWV obtained from NOAA/TOVS, (ii) moistureobtained from INSAT, (iii) specific humidity from GMS with this SSI technique
has been documented, in Rajan et al (2000, 2001, 2002a, 2002b).

144

ANALYSIS OF GPS OBSERVATION

GPS/MET is one of the most cost-effective remote sensing systems tested
recently at the National Oceanic and Atmospheric Administration (NOAA). Herethe GPS/MET data has been obtained from the Chiba university website
dbx.cr.chiba-u.jp. The primary results have been listed here.
There are more than 980 reports available for each of the 3 hourly (00, 03, 06, 09,
12, 15, 18, 21) observations in a day. The reports are spread from 24. 06° N to
45.4° N in the north south direction, and 122.94° E to 145.8° E in the east west
direction. Each report has GPS meteorological parameters with a particular
latitude, longitude, altitude and pressure level. This information is available in thepressure level from 1000 hPa to 800 hPa only. The air temperature is attaining
minimum value at 21 hrs rather than 00 hrs. The PWV variations are too large inthis sample database. The mean monthly PWV average is 35 mm, with maximumof 84 mm, and minimum of 1.5 mm. The corresponding maximum Relative
Humidity (RH) of 84 % is observed in the summer monsoon period. The RH
reaches the maximum of 99.14 % with an average value of 76 % in the month of
September. It seems that for the year 2001, September is wetter than earlier
monsoon months. The earlier study indicates that GPS PWV estimates areaccurate to a millimeter level and look very promising for use in NWP models.
The statistical study about the diurnal variations of the PWV at the location 43°N,
142°E shows in Fig-1. The PWV attains its maximum value at 15-18 hrs of atypical day 01 June 2001. The mean monthly moisture variable, specific humidity(q) is computed as 22 g/kg with standard variations of 6 g/kg for the month ofAugust 2001. In August, the vertical distribution of GPS-PWV and SSI-PWV
over Fuji Mountain in Japan is shown in the Fig –2. It is clearly seen that the GPS
PWV has best fit with the SSI-PWV. The monthly distribution (June, July,
August and September) of PWV over ‘Chiba’ is shown in the Fig-3. The
geographical distribution of mean August month SSI-PWV is plotted in the Fig-4.
From this, it is noted that the gradient over the latitude 40° to 45° N is much morethan that over Japan’s land areas.
This GEONET GPS station measures the delay that the ratio signal from GPS
satellite suffers as it propagates through the atmosphere. When several satellites
are seen from the stations, the delays are combined or ‘mapped’ into a zenith total
delay (ZTD) (Niell, 1996). The largest part of ZTD is the ‘dry’ delay (90 %) due
to the dry air gases; the remaining delay or ‘wet’ delay depends on the moisture
content of atmosphere and is therefore highly variable. The average value of the
ZTD is 2.65. The PWV can be derived from ZTD using an approximate formulathat requires information on the pressure and temperature at the GPS station(Bevis et al., 1994). They can be derived for high frequency and from a dense
network, and are not affected by rain or cloud. In this sense any type of weather
does not affect all GPS measurements at the time of observation. The
disadvantage of having only one piece of information along the vertical can be145alleviated with the aid of the data assimilation technique used in NWP. It is seenthat there is a small wet-bias in GPS data with respect to radio-sonde, SSI, andnumerical model predictions. The azimuthal variations in slant-wet delay contain
information on relative variations in water vapor.

SUMMARY AND OUTLOOKIndividual GEONET observation soundings when compared with radio-sondes,
SSI analysis and prediction from numerical models, provide additional insight on
the PWV component of monsoon studies over this East Asian region. The present
preliminary study shows better agreement of GEONET PWV over many locations.
The location of the typhoon (# 11) on 20-22 August 2001 is very clearly brought
out from GEONET-PWV observation associated with heavy rainfall observation
over this region. The combination of GEONET, SSI’s PWV offers the possibility
to understand new concepts on water vapor cycle in the atmosphere. In the current
scenario, an altimetry space mission (TOPEX) is one of the first satellites to havea GPS receiver on-board. This study reported here will be extended to a
GPS/MET long-term study for the period 1996 to 2002.

ACKNOWLEDGEMENTS

Acknowledgements are due to GEONET database of Chiba University provided
by the GPS Meteorology project of Ministry of Education, Sports, Science andTechnology (MEXT), Japan. Thanks are due to Dr.Kondoh, Akihiko, CEReS,
Japan. The author acknowledges the support and encouragement given by Dr
B.K.Basu and AK. Bohra.

REFERENCES

Bevis. M., S.Businger, S.Chiswell, T.A.Herring, R.A Anthes, C. Rocken and R.H.
ware, 1994: GPS meteorology: mapping zenith wet delay onto precipitable water,
Journal of Applied Meteorology., 33, 379-386.
Kuo, Y.H., X.Zou, S.J.Chen, Y.R.Guo, W.Huang, R.Anthes, D.Hunt, M.Exner,
C.Rocken and S.Sokolovskiy 1998: A GPS/MET sounding through an intense
upper-level front. Bulletin of American Meteorological Society., 79, 617-626.
Kursinski, E. R., G.A.Hajj, W.I. Bertiger, S.S.Leroy, T.K. Meehan, L.J.Romans,
J.T.Schofield, D.J.McCleese, W.G.Melbourne, C.L.Thornton, T.P. Yunck, J.R.
Eyre and R.N.Nagatani 1996: Initial results of radio occultation observations ofearth’s atmosphere using the Global Positioning System. Science., 271, 11071110.
Kursinski, E.R., G.A. Hajj, K.R.Hardy, L.J.Romans and J.T.Schofield, 1995:
Observing tropospheric water vapor by radio occultation using the Global
Positioning System. Geophysical Research Letter., 22, 2365-2368.
Niell, A., 1996: Global mapping function for the atmosphere delay at ratio
wavelengths. Journal of Geophysical Research., 101, 3227-3246.

146

Parrish, D.F. and J.C. Derber, 1992: The National Meteorological Center’s
spectral statistical interpolation analysis system. Monthly Weather Review., 120,
1747-1763.
Rajan. D., A.K. Bohra, A.K. Mitra, R.K. Paliwal, V.S. Prasad and V.B. Bhatia
2002(a): Moisture profiles from satellite data over Indian Ocean area.
International Journal of Remote Sensing., 23 (15), 2951-2969.
Rajan. D., A.K. Mitra, S.R.H. Rizvi, R.K. Paliwal, A.K. Bohra and V.B. Bhatia,
2001: Impact of Satellite Derived Moisture in a Global Numerical Weather
Prediction Model, Atmosfera., 14, 203-209.
Rajan. D., B. Simon, P.C. Joshi, V.B. Bhatia, A.K. Mitra and R.K. Paliwal
2002(b): Impact of NOAA/TOVS derived moisture profile over ocean on global
data assimilation and medium range weather forecasting, Atmosfera., 15, 223-236.
Rajan. D., S.R.H. Rizvi, V.B. Bhatia and H.V. Gupta 2000: Intercomparison ofsatellite derived OLR and total precipitable water content over India and theirimpact on the analysis-forecast system. Indian Journal of Radio & Space Physics,
29, 341-348.
Rocken, C., R.Anthes, M.Exner, D.Hunt, S.Sokolovskiy, R.Ware, M.Gorbunov,
W.Schreiner, D.Feng, B.Herman, Y.Kuo and X.Zou 1997: Analysis andvalidation of GPS/MET data in the neutral atmosphere. Journal of GeophysicalResearch., 102, 29849-29866.
Ware, R., M. Exner, D.Feng, M. Gorbunov, K.Hardy, B. Herman, Y.Kuo,
T.Meehan, W.Melbourne, C.Rocken, W. Schreiner, S.Sokolovskiy, F.Solheim,
X.Zou, R.Anthes, S.Businger and K. Trenberth 1996: GPS soundings of theatmosphere from low earth orbit: Preliminary results. Bulletin of American
Meteorological Society., 77, 19-40.

Amount in mm

Diurnal variation of PWV 01 June

30
20
10
0
0

3

6

9

12

Hours of the day

Figure-1

147

15

18

21

Figure-2
Monthly GPS PWV over Chiba
80
70
PWV in mm

60
50
40
30
20
10
0
1

6

11

16

21

26

Days of the month
Jun

Jul

Figure-3

148

Aug

Sep

31

Figure-4
Table-1
Year
1988
1993
1994
1994 -october
1995-1997
1996-1997
1997-2000
1999
2000-2003
2003
2004

Event
The first workshop on GPS tropospheric delay
COntinuous Strain MOnitoring System (COSMOS) withGPS and GSI- 110 sites
GEONET –partial service started
GPS Regional Array for Precise Surveying/physical earth service
GRAPES –210 sites
GPS tomography experiment method
Feasibility study of GPS/MET Japan
GPS/MET Japan (First period)
GEONET-610 sites
GPS/MET Japan (Second period)
International workshop on GPS meteorology
Today more than 1400 sites.

149

5-5

Optical and Microphysical Properties of the 2003 Yamase
Clouds Estimated from Satellite Remote Sensing andShipboard Observation
Shoji ASANO1), Masaya KOJIMA1), and Tamio TAKAMURA2)
1) Center for Atmospheric and Oceanic Studies, Tohoku University(E-mail: asano@caos-a.geophys.tohoku.ac.jp, kojima@caos-a.geophys.tohoku.ac.jp)
2) Center for Environmental Remote Sensing, Chiba University(E-mail: takamura@ceres.cr.chiba-u.ac.jp)
Abstract: ‘Yamase’ clouds are one of typical marine boundary-layer clouds, for which the present
state-of-the-art performance of weather prediction models and general circulation models is not good enough tosimulate properly the clouds, mainly because of coarse spatial-resolution of those models. Yamase clouds
frequently appear over the North-Western Pacific Ocean east off the Sanriku district in summer under easterly
cool winds, called Yamase, blown out from Okhotsk anti-cyclones. In order to validate numerical simulation andsatellite remote sensing of Yamase clouds, we have carried out cloud observations on board the Koufu-maru of
the Hakodate Marine Observatory in June of recent years. Here we discuss the optical and microphysicalproperties of the Yamase clouds, estimated from the shipboard observation and satellite remote sensing during
the 2003 Koufu-maru cruise, in which for the first time we observed the evolution features of Yamase
clouds from their formation to decade. Remote sensing using the contemporary AVHRR data fromNOAA-17 in the morning orbit revealed that the Yamase clouds were rather thin, stratiform low-level clouds
with an area-averaged optical thickness and effective particle radius of about 12 and 13 µm, respectively, andwith a mean liquid-water-path of about 110 gm-2.
(Key Words: Yamase clouds, marine boundary-layer clouds, cloud microphysical properties, satellite remotesensing, NOAA-17/AVHRR data, shipboard observation, 2003 Yamase event)

1. INTRODUCTION
Marine stratiform clouds appearing in the maritime atmospheric-boundary-layer (ABL) play asignificant roll in the Earth’s radiation budget due to their large horizontal extent, long lifetime, andhigh reflectivity for solar radiation. The state-of-the-art performance of such models as numerical
weather prediction and general circulation models is not good enough to simulate properly the
low-level stratiform clouds, mainly because of coarse spatial-resolution of these models. Generally,
the marine stratiform clouds occur in a wide regional scale under some characteristic synoptic weather
condition, but within the vertically thin ABL. Recently, many studies have tried to simulate theboundary-layer clouds by using various cloud resolving models. However, even high-resolution
models still have difficulties to reproduce ‘correct’ features of cloud structure and physical properties;
the simulated cloud structure and properties tend to differ for different models and/or resolutions [1].
Moreover, there are very few observational data available to validate the model performance. The
cloud physical properties are generally different for different cloud types and different stages of thecloud lifetime, so they are highly variable with time and space. Satellite remote sensing is an efficienttechnique to observe wide-area distributions of cloud properties. However, it is critically important tovalidate the performance of satellite remote sensing through comparison with in-situ measurements.
‘Yamase’ clouds are one of typical marine boundary-layer clouds, that appear over the ocean east
off the Sanriku area (the east of the Northern District of the Main Island of Japan) in early summer
season under easterly cool winds, i.e., the so-called Yamase [2], blown out from Okhotsk anti-cyclones.
We are studying the formation processes of Yamase clouds through numerical simulations by using a
non-hydrostatic cloud-resolving model [1, 3]. The preliminary results suggest that the model-produced
clouds strongly depend on the used spatial resolution as well as parameterizations of such physicalprocesses as turbulence, cloud and radiation processes. Further, we are going to retrieve the cloudproperties such as optical thickness and effective particle radius of Yamase clouds from the AVHRR
data of NOAA satellites. To validate the results from the numerical simulation and satellite remotesensing of Yamase clouds, we have carried out a few times of shipboard observations in June of thelatest years. Here we present the cloud optical and microphysical properties estimated from satellite

150

remote sensing and the shipboard observation for the Yamase event in June 2003. The 2003 summer in
the northern Japan is remembered as an unusually cool summer with severe shortages of insolationand crop damage due to the Yamase weather.
2. SHIPBOARD YAMASE OBSERVATIONThe cloud observations on board a ship have been carried out within the Yamase Intensive
Experiment (YIE) conducted by the Sendai District Meteorological Observatory and the Hakodate
Marine Observatory (HMO) of the Japan Meteorological Agency, in collaboration with the Center for
Atmospheric and Oceanic Studies (CAOS) of Tohoku University. The marine observation vessel
Koufu-maru of the HMO operated the shipboard YIEs in an area east off the Sanriku [4] in June of therecent years. In addition to the routine marine weather observations and intensive GPS-sondelaunchings, the CAOS-group conducted cloud observations for measuring cloud parameters by using
various radiometric instruments as well as an aerosol particle-counter aboard the Koufu-maru. The
measured parameters can be used in validation of the products from satellite remote sensing andnumerical simulations. Among these parameters, cloud liquid-water-path (LWP) and cloud-base heightare particularly useful parameters for the validation; they were measured by a dual-frequency
microwave-radiometer (Radiometric Co., WVR-1100) and a laser ceilometer (ImpulsePhysik, LD-25),
respectively. Further, the temperature, humidity and wind profiles measured by GPS-sondes can becompared with the model-simulated profiles.
In the 2003 YIE, we fortunately encountered a Yamase event, in which, for the first time, we
observed a series of the formation and evolution processes of Yamase clouds from the evening of 22
June through the evening of 24 June 2003. The Koufu-maru stayed near the point (39ºN, 143ºE) for
operating the YIE from the afternoon of 22 June to the morning of 25 June. Figure 1 shows time
variation of the temperature and humidity profiles in the lower troposphere during the Yamase event.
In the figure, the cloud-base heights measured by the ceilometer and the wind profiles measured by theGPS-sondes are also plotted. Over the location of Koufu-maru, the lower part of ABL became cool andhumid with the inflow of Yamase wind in the evening of 22 June, and very low Yamase clouds (might
be fogs) appeared with the cloud-base heights of a few tens meters in the night of 22 June through themorning of 23 June. During the daytime of 23 June, the cloud-base was lifted up, at highest, to 300 mwith the development of mixing in the ABL, although the cloud layer became rather thin and patchy;
the cloud-base height gradually decreased during the night down to about 100 m in the morning of 24
June. In the daytime of 24 June, the cloud-base was again lifted up, and finally it reached the heights
higher than 1 km in the night, when the ABL was well mixed with an almost constant equivalenttemperature up to about 1.2 km. In the morning of 25 June, the low-level Yamase clouds disappeared
over the Koufu-maru site. The mean cloud-base height averaged over the duration was about 270 m,
and the corresponding mean LWP was about 60 gm-2 for the Yamase cloud measured on board theKoufu-maru. The observational data of time variations of the atmospheric profiles and associatedcloud fields will be useful for validation of simulations of the Yamase event.

Fig. 1. Time variation in UTC of the temperature (left) and relative humidity (right) profiles,
interpolated from those measured by the GPS-sondes launched from the Koufu-maru, in themarine lower atmosphere from 21 June to 25 June 2003. In the figure, the cloud-baseheights (black dots) measure by the ceilometer and the wind profiles (red arrows) measured
by the GPS-sondes are also superimposed.

151

3. SATELLITE REMOTE SENSING
The cloud optical thickness (τc) and effective particle radius (reff) of the Yamase clouds have been
retrieved from the contemporary NOAA/AVHRR data in a wide area in the Western North Pacific region. The
AVHRR data used in the present study were processed from the High Resolution Picture Transmission data of
NOAA-17 satellite in the local morning orbit; the data are being archived at Tohoku University, Sendai.

Simultaneous data of the visible and near-infrared reflected radiances in the AVHRR channels 1 (0.58 0.68 µm) and 3A (1.57 - 1.78 µm), respectively, were used to retrieve τc and reff. The infrared channel
data in channels 4 (10.3 – 11.3 µm) and 5 (11.5 – 12.4 µm) were used to discriminate low-level water
clouds from higher-level clouds by estimating the cloud-top temperatures. In the satellite data analysis,
we employed the atmospheric profiles from the NCEP/NCAR reanalysis data, and the sea surface
temperatures from the NGSST (new generation sea surface temperature) products released from theCAOS [5]. From the retrieved τc and reff, the liquid-water-path LWP can be estimated by using theapproximate relation, LWP=2ρτcreff /3, where ρ is the density of liquid water. The performance ofsatellite remote sensing was validated by comparing the satellite-derived LWP and the shipborne
WVR-measured LWP for the collocated scenes. During the 2003 YIE cruise, we had two scenes forwhich the Koufu-maru site was completely covered by low-level clouds and the NOAA-17 satellite
simultaneously observed the clouds over the site. For the two cases, the satellite-derived LWP and the
WVR-measured LWP agreed with each other within an uncertainty of 20 gm-2. Therefore, the satellite
remote sensing of the low-level clouds can be regarded as reasonable and reliable.
Figure 2 shows an example of the retrieved τc and reff for the low-level clouds in the morning of 24
June 2003. The easterly surface winds are also depicted by the arrows superimposed on the panels.
The figure shows a wide distribution of optically thin and uniform, stratiform low-level clouds in theWestern North Pacific region. However, in some locations, the cloud distribution exhibits band-likefeatures and cellar structures, particularly, in the leeward places. The retrieved τc and reff are, in general,
positively correlated each other with larger reff for larger τc, and vice versa; this suggests that the cloud
was, as a whole, in a developing stage. However, the opposite correlations between τc and reff werealso noticed in some places, where there might be dominated by drizzle and precipitation formation or
ship-track clouds [6]. The cloud distribution features and the τc vs reff correlations changed in time andlocation during the Yamase event.
From the five days’ remote sensing during 23 June to 27 June, the occurrence probability of theretrieved τc, reff and LWP were analyzed for the low-level clouds in a wide area of about 400
km×400km, east off the Sanriku district. Figure 3 shows the frequency histograms of the retrieved τc,
reff and LWP. It is shown that most clouds were fairly thin with optical thicknesses between 4 and 20

Fig.2. Optical thickness (left) and effective radius (right) retrieved from the AVHRR dataof NOAA-17 for the low-level clouds on 24 June 2003. The black areas indicate the areasof no data and/or covered by higher clouds. The white arrows indicate wind velocity at1000 hPa from the NCEP/NCAR reanalysis data.

152

(mean nalue of 12) and with rather uniform particle radii between 8 and 16 µm (13 µm). The meanvalue of LWP averaged over the area was about 110 gm-2.

(B)

(A)

Fig. 3. Frequency histograms of theretrieved optical thickness (A),effective radius (B), and liquidwater-path (C) for the low-levelclouds observed by NOAA-17 during
23 June to 27 June 2003 in the Western
North Pacific region.

(C)

4. Concluding remarks
We have investigated the optical and microphysical properties of Yamase clouds in June 2003 bymeans of the shipboard observation on Koufu-maru and remote sensing using the contemporary
AVHRR data from NOAA-17 satellite. For the first time, we could observe the evolution features ofmaritime low-level clouds in Yamase event from their formation to decade. The present results can beused to validate numerical simulations of the 2003 Yamase event by using non-hydrostatic
cloud-resolving models. The shipboard experiment should be continued to accumulate more
observational data usable to validate and improve the performance of numerical models and satellite
remote sensing of low-level clouds for different weather conditions and different places.
Acknowledgements: The shipboard observations were carried out within the YIE under the cooperation with the
Sendai District Meteorological Observatory and the Hakodate Marine Observatory. We are grateful to Drs. F.
Sakaida and H. Iwabuchi for their kind assistance in the analysis of NOAA/AVHRR data. The study was partly
supported by the CEReS Joint Research Program and by the Research Revolution 2002 Project.

REFERENCES:[1] R. Nagasawa, T. Iwasaki, S. Asano, K. Saito, and H. Okamoto, 2004: A numerical study of low-levelcloud formation in ‘Yamase’ with a nonhydrostatic multi-nested regional climate model, J. Meteor. Soc.
Jpn (submitted).
[2] H. Kawamura (Ed.), 1995: YAMASE, Meteorol. Res. Note (Japan Met. Soc.), No. 183, 179pp (inJapanese).
[3] T. Iwasaki, S. Asano, H. Okamoto, and R. Nagasawa, 2002: A cloud study system using anonhydrostatic multi-nested regional climate model. Proc. EarthCARE Workshop (Harumi, Tokyo,
17-18 July 2002), 171-174.
[4] S. Asano, M. Kojima, Y. Yoshida, and T. Takamura, 2003: Validation experiment for satellite remotesensing and numerical models of low-level clouds: Shipboard observation of YAMASE clouds, Proc.
CEReS Int l Symp. Remote Sensing (Chiba Univ., 16-17 December 2003), 135-138.
[5] See, http://www.ocean.caos.tohoku.ac.jp/~merge/sstbinary/actvalbm.cgi.
[6] S. Asano, M. Shiobara, and A. Asano, 1995: Estimation of cloud physical parameters from airborne
solar spectral reflectance measurements for stratocumulus clouds, J. Atmos. Sci., 52, 3556-3576.

153

5-6

IMPROVING ALONG-BEAM SPATIALRESOLUTION OF RADAR MEASUREMENTS
Nick Schutgens, Hiroshi Kumagai
NICT
National Institute of Information and Communication Technology
4-2-1 Nukui-kitamachi, Koganei, Tokyo, 184-8795
Japan
We consider the possibility of improving the range or along-beam spatial resolution of a pulse radarthrough oversampled observations. Radar observations are related to the state of an atmospheric volume,
about half a pulse-length in size along the beam. The sampling distance of observations (gate separation)
may however be much shorter. As a consequence, such observations become strongly correlated. Still, they
will also contain independent information. We have constructed a deconvolution algorithm and study its
efficiency using simulated observations based on actual 95 GHz (SPIDER) observations. Initial results
show that the success rate of the algorithm depends on the number of independent samples Nindep on which
the observation is based, and the noise level of the receiver as well as the abundance of oversampledobservations. This work is relevant to the space missions CloudSat and EarthCARE.
Keywords: radar – CloudSat – EarthCARE – range weighting – deconvolution

INTRODUCTION

It is usually assumed that the range or along beam resolution of a radar is determined byits pulse-length. A careful analysis involves the range weighting function W , which
determines the relative contributions of different parts of the atmosphere to the voltage
measured at the receiver. This range weighting function depends on both the emitted
pulse-shape and the receiver filter characteristics. Total expected power is given by thewell-known expression (e.g. Doviak & Zrnic 1993):
2g 2 λ 2 Pt 2 πθ 12 ∞ W ( r0 − r )P ( r0 ) =η (r ) dr ,( 4π ) 3 8ln 2 ∫0
r4

(1)

where the pre-factors are related to calibration and the beam pattern, while r is the
distance and η (r ) the atmospheric backscatter profile along the beam . We have ignored
attenuation as it is not relevant to the present study. The range or along beam resolution isoften defined as the width of | W (r ) | 2 at the 3 dB or 6 dB level. For a radar with amatched receiver filter, the width at 6 dB corresponds to about half a pulse-length. This
would be the extent of atmosphere that significantly contributes to the power observed ina single radar range gate.
Consequently, many radars only sample the reflectivity profile at gate separations
comparable to ½ a pulse-length. Still, several radars have used or will use shorter gate154

separations, particularly when the pulse-length is fairly long (e.g. 1000 m). This is called
oversampling. Examples are windprofilers and the cloud profiling radars that will be
employed on the upcoming space mission CloudSat (NASA) and the proposed space
mission EarthCARE (ESA/JAXA). Typically, they sample at ¼ or ⅛ of the pulse-length.
Equation (1) is mathematically speaking a convolution, with | W | 2 / r 4 the convolution
kernel. Is it possible to perform a deconvolution, given enough observations P(r0) at
various distances, and derive a backscatter profile η (r ) that has a higher spatial
resolution than the original observations? If so, what restrictions are there to the signalto-noise ratio of the observations? What should be the gate separation of the oversampledobservations? We will attempt a preliminary answer to these questions in this note.

METHOD AND DATA

Equation (1) can be solved numerically through a matrix inversion after discretization.
Unfortunately matrix inversions are unstable operations, so we apply Tikhonov
regularisation (Tikhonov & Arsenin 1977). That is, we try to minimize the following
expression by finding a suitable η ,A • η − P + λ2 η(2)where P is a vector containing the observed profile and η is a vector of the backscatter
profile we want to retrieve. The matrix A contains information on W2/r4 . When theregularisation parameter λ = 0 , instability of the procedure often causes unphysicalspikes in the solution. For small λ ≠ 0 , the second term will act as a filter and suppress
these high frequency components in the numerical solution that are unphysical. The exact
choice of λ is a study in itself. Usually Expression (2) is minimized for many different
values of λ , after which the optimal value is chosen based on some criterion thatcompares the various solutions (Hansen 2001). In this note, we only present results forthe quasi-optimality criterion. Other criteria (i.e. L-curve, generalised cross-validation)
were examined as well, but were generally found to be inferior.

We will test this method using simulated radar observations based on actual groundbased 95 GHz SPIDER observations, sampled to 1 km horizontal resolution based onprevailing wind-speeds. The simulated observations include correct Rayleigh fading
statistics of receiver power, as well as correct correlations between range gates. For theradar, we have used EarthCARE design parameters, which include a pulse-length of 1000m, a gate separation of 100 m (5 times oversampling) and a noise level of -20 dBz.
The number of independent samples Nindep is either 104 or 105. The case Nindep=104 agrees
with along-track integration over 10 km for the space-borne EarthCARE radar (satellite
speed ~ 7 km/s). The case Nindep=105 is included to show the effect of the number of
independent samples. For satellite observations, this would imply along-track integration
over the ridiculous length of 100 km. But note that ground-based radars can easily
achieve Nindep=105 for 1 km horizontal resolutions (wind-speed ~ 30 m/s).

155

DECONVOLUTION OF OBSERVED POWER PROFILES

We will look in detail at the results for a single profile observed on January 10, 2003 at
1:08 AM. We have looked at other profiles as well, and have also conducted less detailed
studies over larger datasets but always found similar results.
In Fig 1, we show the original SPIDER profile (gate separation of 82.5 m, butinterpolated to 10 m) as a solid line. Based on this, a thousand observations for
EarthCARE parameters were generated. The blocky dotted line represents the averageobservation, corrected for average noise level. Each observed profile was thendeconvolved. The average reflectivity of the deconvolved profiles is shown by black dots.
The horizontal lines extending from these dots represent the 1 σ variation over those 1000
samples.
In the figure, we see that the deconvolution is very reliable for high reflectivities, but notfor low reflectivities. Since Eq. 1 and 2 are linear in η , high and low should beinterpreted relative to typical signal and noise power levels in the profile. When the
Number of Independent samples increases, the reliability of the deconvolution increases
as well.
Note that reliability is not the same as accuracy. The averages and standard deviations in
Fig. 1 and 2 were computed without any attempt to remove obviously unphysicalsolutions from among the 1000 samples.
4

5

N=10
3500

3000

3000

2500

2500

Height [m]Height [m]

N=10
3500

2000

2000

1500

1500

1000

1000

500

500

-35

-30

-25
-20
Reflectivity [dBz]

-15

-10

-35

-30

-25
-20
Reflectivity [dBz]

-15

-10

Figure 1 For a gate separation of 100 m (5 times oversampling), the effect of Nindep is shown.

156

Figure 2 shows that the gate separation is also an important factor, as accuracy improves
when the oversampling is increased. Even though these oversampled observations arestrongly correlated, they still contain useful independent information.
4

5

N=10
3500

3000

3000

2500

2500

Height [m]Height [m]

N=10
3500

2000

2000

1500

1500

1000

1000

500

500

-35

-30

-25
-20
Reflectivity [dBz]

-15

-10

-35

-30

-25
-20
Reflectivity [dBz]

-15

-10

Figure 2 For a gate separation of 25 m (20 times oversampling), the effect of Nindep is shown.

SUMMARY

Signal-to-noise ratios increase with increasing pulse-lengths, but at the cost of reduced
range resolution. Oversampled observations can improve the range resolution, but thisrequires a deconvolution. Our results so-far show that such a deconvolution is possible,
but not always reliable. Further research will concentrate on more reliable deconvolution
techniques (better criteria for the regularisation parameter) and estimates of the reliabilityof a particular deconvolved profile without additional knowledge.
REFERENCES

Doviak RJ, Zrnić DS, “Doppler radar and weather observations”, Academic Press inc.
Hansen PC, “Regularization tools a mathlab package for analysis and solution of discrete ill posed
problems” , version 3.1, downloadable from http://ww.imm.dtu.dk/~pck .
Tikhonov AN, Arsenin VY, “Solutions of ill-posed problems”, Winston & sons, 1977.

157

5-7

Aerosol Properties over Asiawith ADEOS-1 & -2/POLDER
Itaru Sano (1), Sonoyo Mukai (1),Yasuhiko Okada (2), and Masayoshi Yasumoto (3)1. Faculty of Science and Technology, Kinki University, Japan
2. Faculty of Science, Kobe University, Japan
3. RIST, Kinki University, JapanThe major properties of atmospheric aerosols are the optical thickness (AOT: τλ) and theÅngström exponent (α) which is calculated from the spectral tendency of optical thickness ofaerosols as below:
α = -ln(τλ1/τλ2) / ln(λ1/λ2),(1)where wavelengths λ1 and λ2 take values of the central wavelength of observing channels,
respectively. The values of α are closely related to the aerosol size distribution. For example,
the small values of α indicate the large particles, and the large values represent small particles
such as artificial aerosols. In general, the values of Ångström exponent (α) from ~0 to 1
shows coarse particles (such as sea salt solution, and soil dusts), on the contrary, 1< α < ~2.5
indicates small particles (such as sulfate, biomass burning etc.).
Figure 1 and 2 show the aerosol optical thickness and Ångström exponent on 13 April
2003. These results are derived from ADEOS-2/POLDER polarization as well as radiancedata. Although the details of retrieval procedure have been interpreted in the previous paper [1],the basic idea of our aerosol retrieval is that the scattering behavior of aerosols plays asufficient role in the polarization field of the Earth atmosphere-surface system in the nearinfrared wavelengths. The area loading lots of Asian dust flown from China is circled by thesolid curve in Figure 1. It is clear that Asian dusts distribute over from the Yellow sea toJapan. Note that there are some uncertainties to distinguish the heavy aerosol loading fromthe thin cloud.
It is found that aerosol optical thickness in the dust event is more than double of clear
value, and Ångström exponent takes small values in dust event.
These space-based aerosol properties are validated from the ground based AERONET
data in a global scale. It is of interest to mention that the annual change of aerosol
characteristics is found from comparison of POLDER products from ADEOS-1 with thosefrom ADEOS-2.

158

Figure.1.

Distribution of aerosol optical thickness at a wavelength of 0.55 µm over East
Asia on 13 April 2003 derived from ADEOS-2 / POLDER.

Figure.2.

Distribution of Ångström exponent on 13 April 2003 derived from ADEOS-2 /POLDER

The ADEOS-2/POLDER data were provided by CNES / France.
REFERENCES[1] I. Sano, “Optical properties and Angstrom exponent of aerosols over the land and ocean
from space-borne polarimetric data”, Adv. Space Res., 34(4), pp 833-837,
doi:10.1016/j.asr.2003.06.039, (2004).

159

5-8

Observations of Cloud Properties Using the Developed
Millimeter-Wave FM-CW Radar at 95 GHz
Toshiaki TAKANO (1,2,3), Ken-ichi AKITA(1), Hiroshi KUBO(1),Youhei KAWAMURA(2), Hiroshi KUMAGAI (4),
Tamio TAKAMURA (5), Yuji NAKANISHI(6), and Teruyuki NAKAJIMA(7)(1)

Graduate School of Science and Technology, Chiba University,
Inage, Chiba 263-8522, Japan, takano@faculty.chiba-u.jp,(2)
Faculty of Engineering, Chiba University,(3)
Center for Frontier Electronics and Photonics, Chiba University,(4)
Communications Research Laboratory,(5)
Center for Environmental Remote Sensing, Chiba University,(6)
SciTech,(7)
Center for Climate System Research, The University of Tokyo
Abstract. We developed a low-power and high-sensitivity cloud profiling radar transmitting
frequency modulated continuous wave (FM-CW) at 95 GHz for ground-based observations.
Millimeter wave at 95 GHz is used to realize much higher sensitivity than lower frequencies tosmall cloud particles. An FM-CW type radar realizes similar sensitivity with much smaller output
power to a pulse type radar. Two 1m-diameter parabolic antennas separated by 1.4m each otherare used for transmitting and receiving the wave. The direction of the antennas is fixed at thezenith. The radar is designed to observe clouds between 0.3 and 20 km in height with a resolutionof 15 m. Using the facility, test observations have been done. Results of observations show thatthe system can observe clouds of –30dBZ at the distance around 5km, which is sensitive enough toobserve various kinds of clouds.
Using the developed millimeter-wave FM-CW radar at 95 GHz, we observed clouds in a
campaign observation in Amami Island in March 2003, and on a sail on Mirai, a Japanese scientificresearch vessel, in September 2004 in the Arctic Ocean. The radar provided good and sensitive datain these long-term observations.
Keywords: Cloud Profiling Radar, Millimeter Wave Radar, FM-CW Radar, Cloud Properties,
Active Measurements of Clouds

1. INTRODUCTION
It is getting more important to know the global environment and the global change of climate forthe human beings. It is necessary to know balance of solar energy coming to the Earth and cycleof water for the comprehension and to solve severe problems such as the greenhouse warming, thedrying, the ozone holes and so on. One of the most significant features to know them is cloud,which reflects and absorbs incoming solar radiation, traps the radiation from the ground, transfers
the energy in it, and radiates it outside. Information on 3-dimensional structures of clouds, sizes
and distribution of cloud particles, dependence on size of optical characteristics of cloud particles,
motions of particles in clouds, and so on are all desirable to solve role of clouds. .
Characteristics of clouds described above, however, have not been well known yet because oflack of enough observational data to present confidential results. Observations of clouds withradars would be most powerful method to derive the information because of following advantages:a) radio waves do not suffer from heavy extinction such as visible light, and consequently caninvestigate interior of clouds, b) the radar technique, which is an active sensing method, has great
advantage of investigating interior structures of clouds to passive methods such as total power
observations of irradiance of clouds, c) Doppler measurements of clouds which have low
velocities around 1 m/sec is applicable only to radio frequency waves.
Conventional radars operated at 5GHz can detect precipitation particles but are not able to detect
particles in clouds because their sizes, less than a few tens microns, are much smaller that the160wavelengths and, therefore, their cross sections are quite small. The cross sections of particles
increase rapidly with frequency in Rayleigh scattering region. Rader observations of cloudparticles at millimeter waves, which have been realized recently, have much more sensitive [1].
Several groups have reported the development and preliminary observational results that
demonstrate powerful performances to investigate cloud particles [2],[3],[4].
We have designed and developed a cloud profiling radar at 94GHz. The purposes of thedevelopment of the FM-CW radar are a) evaluation and verification of an FM-CW radar at 95 GHz
in Japan comparing to a pulse radar, b) obtaining millimeter wave FM-CW radar techniques andalgorism of data reduction, and c) contribution to scientific research on cloud physics. In thispaper, we present first observational results with the newly developed cloud profiling FM-CW radaras well as design concepts and specifications of the radar.

2. DESIGN CONCEPTS AND CONASTRUCTION
Whole view of the developed radar is shown in Fig.1 [5].

Diameter of each antenna is 1m.

2.1 FM-CW Radar
We adopt a frequency-modulated continuous wave (FM-CW) radar rather than a pulse radar
because the former can achieve more sensitive system than the latter if comparing with same
instantaneous output power of transmitted millimeter wave. The principle of an FM-CW radar isshown in Fig.2. The signal frequency is modulated in the range of f0±F. Transmitted signal
from one of the antennas is reflected by cloud particles, returns, and is received by the other antenna
with a delay time of t relative to the original transmitted signal. Mixing the transmitted andreceived frequencies, beat frequencies fb are observed in the spectra, which are caused by ensembleof clouds particles:fb= 4 F r / (c Tm)(1),where r is the height of the clouds, Tm is the modulation interval, and c is the light velocity.
When the objects move in the line of sight, the frequencies of reflected signals change by fd :fd

= -2 ( f0 / c ) ( dr / dt )(2).

Fig.2. Principle of an FM-CW radar.

Fig.1. The developed cloud profiling FM-CW
radar at 95 GHz.

161

2.2 Design Concept and Requirements
Because one of the purposes of the facility is evaluation and verification of an FM-CW radar at
95 GHz, we design it to be a simple system so as to develop with commercially available
components and to make maintenance and upgrade by ourselves.
We designed the facility to observe clouds between 0.3 and 15 km in height with a resolution of
15 m. The velocities measured as Doppler shift should be less than 1 m/sec. The facility should be
mobile for measurement at variety of places.
2.3 Antennas and Mounting
According with the requirements described above, we decided parameters of antenna listed inTable 1 [6].
2.4 Transmitter and Receiver Section
The block diagram of the transmitter and receiver section is shown in Fig.3 and parameters are
summarized in Fig. 2. All signals including the transmitted FM-CW signal at 95GHz and local
frequencies are generated from and/or referred to two signal generators in 140 MHz range, which
are synchronized each other.
The integrated transmitter section is shown in Fig. 4. The integrated system has been measured
on its stabilities and sensitivities in laboratories [7]. Facilities for radar measurements, highstabilities of transmitted signals are necessary to obtain useful data. We measured stability of thesystem. In order to stabilize the output of the power of the transmitting wave, the final power
amplifier for transmitting signal at 95GHz is cooled with a Peltier cooler to be around 50 OC
otherwise it goes as hot as 90 OC.
The noise figure of the pre-amplifier at 95GHz was measured to be around 5.5 dB. Long term
stabilities and sensitivities were measured and are good enough for our purpose.

Fig.3. Block diagram of the transmitter and receiver section.

162

Table 1. Designed parameters of antennas.
Antenna Diameterf/D ration of Antenna
Antenna Optics
Gain of Antennas
Beam Width
Antenna Separation
Direction of Antennas
Polarization

1m
0.35
Cassegrain
57 dBi0.18 degree
1.4 m
Zenith1 Linear

Table 2. Comparison between the developed
FM-CW radar and SPIDER.

Fig.4. The transmitter section of the developed
FM-CW radar.

3. Simultaneous Observations with SPIDER
After measurements of long-term stabilities, we made simultaneous observations with a pulse
radar named SPIDER shown in Fig.5 of the National Institute of Information and Communications
Technology (NICT), Japan. Comparison between the developed FM-CW radar and SPIDER issummarized in Table 2. We have to stress that the output power at 95 GHz of SPIDER is
3000ntimes higher than that of our FM-CW radar. An example of results is shown in Fig.6. We
can recognize that same clouds were detected in both data. There results show that the radar has
good performance to detect thin clouds.

Fig.6 First results of test measurements of clouds.
Measurement started 11:30 JST on 2002 June 26th andcontinued for 7 hours.

Fig.5. SPIDER: a pulse radar of the National Instituteof Information and Communications Technology
NICT) Japan.

163

4. Observations of Cloud Properties
Using the developed millimeter-wave FM-CW radar at 95 GHz, we observed clouds in a
campaign observation in Amami Island in March 2003, and on a sail on Mirai, a Japanese scientificresearch vessel, in September 2004 in the Arctic Ocean. Fig.7 shows an example of observed
profiles with the lidar of the National Institute for Environmental Studies by Nobuo Sugimoto andhis co-workers and our FM-CW radar at 95 GHz. FM-CW radar observed fine shower around 11:20
and structure of interior of clouds on March 20th.
The developed millimeter-wave FM-CW radar was installed on the Japanese scientific research
vessel, Mirai, in August 2004 as shown in Figs.8 and 9. Mirai made observations in the Arctic
Ocean in September 2004. Fig.10 shows example of observed data using the developed FM-CW
cloud profiling radar during the project lead by Yasushi Fujiyoshi of Hokkaido Univ.

Fig.7. Observations in Amami Island in March 2003. FM-CW radar observed fine shower

around 11:20 and structure of interior of clouds on March 20th.

Acknowledgments
The authors thank Nobuo Sugimoto of NICT and Yasushi Fujiyoshi of Hokkaido Univ providing
their data in campaign observations. This work is supported with the project APEX (Asian
Atmospheric Particle Environment Change Studies) in CREST (Core Research for Evolutional
Science and Technology) of Japan Science and Technology Corporation and is in part supportedwith the Grant-in- Aid for Scientific Research by the Japanese Ministry of Education, Culture,
Sports Science and Technology (14380241), and an open use program in the Center for
Environmental Remote Sensing, Chiba University (#14-12, #15-12, #16-31).
164

Fig.8. Container of the millimeter-wave FM-CW radar
installed on Mirai.

Fig.9.

Japanese scientific research vessel, Mirai.

20

15
10
5
0
00:00

00:00

00:00

00:00

24:00

Tim e[UT]Fig.10. Example of data obtained in the Arctic Ocean using the millimeter-wave FM-CW radar.

References[1] Clothiaux, E.E., M.A.Miller, B.A.Albrecht, T.P. Ackerman, J.Verlinde, D.M.Babb, R.M.Peters, and W.J.Syrett, “Anevaluation of a 94-GHz radar for remote sensing of cloud properties”, Journal of Atmospheric and Oceanic
Technology, Vol.12, No.2, p.201, 1995.
[2] Horie, H., T.Iguchi, H.Hanado, H.Kuroiwa, H.Okamoto, and H.Kumagai, “Development of a 95-GHz cloudprofiling radar (SPIDER) – Technical aspects”, The Transactions of the Institute of Electronics, Information andCommunication Engineers, Vol.E83-B, No.9, p.2010, 2000.
[3] Kumagai, H., H.Okamoto, H.Horie, H.Kuroiwa, and S.Iwasaki, ”Vertical profiling of liquid cloud properties
retrieved from 95-GHz cloud radar and microwave radiometer”, 2001 Asia-Pacific Radio Science Conference(Tokyo), PF-27 (p.333), 2001.
[4] Matkin, N., J.Nash, T.Oakley, B.Ellison, M.Oldfield, and J.Bradford, “A trial of the Rutherford Appelleton
Laboratory (RAL) 78.2GHz cloud radar”, Fifth International Symposium on Tropospheric Profiling, Adelaide,
Australia, Dec.4-8, p.113, 2000.
[5] Suga, Y., S.Hoshi T.Takano, S.Shimakura, H.Kumagai, T.Takamura, and T.Nakajima, “Design and development of
an FM-CW radar at 94GHz for observations of cloud particles”, 2001 Asia-Pacific Radio Science Conference(Tokyo), PF-28 (p.333), 2001.
[6] Hoshi, S., Y.Suga, Y.Kawamura, T.Takano, and S.Shimakura, “Development of an FM-CW radar at 94GHz for
observations of cloud particles – Antenna section”, Proceedings of the Society of Atmospheric Electricity of Japan,
No.58, p.116, 2001 (in japanese).
[7] Takano, T., Y.Suga, K.Takei, Y.Kawamura, T.Takamura, H.Kumagai, and T.Nakajima, “Development of a Cloud
Profiling FM-CW Radar at 94GHz”, Proceedings of the 27nd General Assembly of the International Union of Radio
Science (URSI) (Maastricht, The Netherlands), FP-11, p1789, 2002.

165

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Retrieval of precipitable water using ADEOS-II /GLI near infrared data
Makoto Kuji*a, Nobuyuki Kikuchib, and Akihiro UchiyamacabDept. of Information and Computer Sciences, Nara Women’s Univ., Japan
Earth Observation Research and application Center, Japan Aerospace Exploration Agency, Japanc
Meteorological Research Institute, Japan Meteorological Agency, Japan

ABSTRACT

Retrieval of vertically integrated water vapor amount (precipitable water) is proposed using near infrared
channels of Global Imager onboard Advanced Earth Observing Satellite-II (GLI / ADEOS-II). The principle ofretrieval algorithm is based upon that adopted with Moderate Resolution Imaging Spectroradiometer (MODIS)
onboard Earth Observing System (EOS) satellite series. Simulations were carried out with GLI Signal
Simulator (GSS) to calculate the radiance ratio between water vapor absorbing bands and non-absorbing bands.
As a result, it is found that for the case of high spectral reflectance background (a bright target) such as the land
surface, the calibration curves are sensitive to the precipitable water variation. It turns out that aerosol loading
has little influence on the retrieval over a bright target for the aerosol optical thickness less than about 1.0 at 500
nm wavelength. A preliminary analysis of GLI data was also carried out and the retrieved result is compared toradiosonde observations. In spite of a lag of several hours between the ADEOS-II / GLI and the radiosonde
observations, the retrieved precipitable water values were coincident to those of in situ observations within 2.0mm at a few radiosonde sites. Currently, we are trying to make fully simultaneous comparisons of the retrieved
precipitable water values to those derived from continuous observations with skyradiometers or microwave
radiometers. As a result, we will validate the accuracy of the retrieval algorithm for the purpose of its global
application. It is also anticipated that simultaneous retrieval of the water vapor amount using GLI data alongwith other channels will lead to improved accuracy of the determination of surface geophysical properties, suchas vegetation, ocean color, and snow and ice, through the better atmospheric correction.
Key Words: water vapor, near infrared, ADEOS-II / GLI.
1)

1. INTRODUCTION. Although TOVS is a splendid verticalsounder with a number of channels sensitive
to water vapor absorption, their footprints
are about several tens kilometers, rather
larger than those of environmental sensors
onboard earth-observing satellites. Better
spatial resolutions of several hundred meters
to several kilometers are available with
MODIS / EOS sensors, wavelengths andbandwidths of which are very similar to
those of TOVS.
Combining the nearinfrared data of water vapor absorbing andnon-absorbing channels with the thermal
infrared data, precipitable water is derived
from the MODIS mission with a relatively
high spatial resolution along withinformation of clouds and aerosols 2).
In contrast to TOVS and MODIS, GLI
onboard ADEOS-II is designed to obtaindata of both the surface properties(vegetation, ocean color, and snow and ice,etc.) and atmospheric properties (cloud,aerosol, and radiation budget, etc.). In

Water vapor is one of typical gas species
governingthegreenhouseeffect.
Investigation of water vapor distribution is aclue to understand the radiation budget ofearth atmosphere system as well as theglobal energy and hydrological circulation.
Although water vapor is mostly distributedin the lower atmosphere (planetary boundary
layer from surface to a few kilometers), thewater vapor amount often increases in the
middleandupper
troposphereaccompanying horizontal advection of
humid air mass. Thus, precipitable water,(i.e. the vertically integrated water vapor
amount) is considered to be the mostrepresentative quantity of water vapor
amount in the atmosphere.
Until now, TIROS-N Operational Vertical
Sounder (TOVS) data have been often used
to estimate water vapor amount at lower,
middle, and upper regions of the troposphere166

(spectral regions with smaller transmittance)
are located at around 810 nm, 940 nm, 1135
nm, and 1380 nm, whereas non-absorbing
bands (regions with larger transmittance; i.e.,
atmospheric window region) are found at
865 nm, 1050 nm, and 1240 nm. The six
atmospheric models and their values for
precipitable water are summarized in Table
1. The retrieval algorithm utilizes thesecharacteristics as follows: in the water vapor
absorbing bands, transmittance (i.e.,radiance to be observed) varies with thewater vapor amount (precipitable water)
assumed in each atmospheric model, whilein the non-absorbing bands, changes aremuch smaller.

general,atmosphericcorrectionis
indispensable when surface geophysicalproperties are retrieved from satellite remotesensing data. Correction of atmosphericozone, aerosol, and in particular, water
vapor is important for precise retrieval ofvegetation conditions and ocean color. In
the GLI mission, it is currently planned thatthe water vapor information is incorporated
from the quasi-real-time objective analysis
data. Nevertheless, it is desirable to use thewater vapor information concurrently
obtained with the same spatial resolution asother channels. In this context, we study
feasibility of using near infrared channels of
GLI in the retrieval of precipitable water andmake a preliminary analysis of the GLI dataand its validation. The principle of theretrieval algorithm is described in Sec. 2
together with assumptions made in thepresent simulation. In Sec. 3, retrieved
precipitable water is analyzed and validatedwith radiosonde observation. Section 4
presents the summary and related future
works.

Table 1 Atmospheric models and their precipitablewater.

2.
RETRIEVAL ALGORITHM
AND SIMULATION
2.1 Principle
The retrieval algorithm is based upon theradiance ratio method, which utilizes theradiance ratio between water vapor
absorbing and non-absorbing bands toretrieve the precipitable water. A similar
method was already adopted with MODIS /EOS series 3, 4). In the MODIS case, 940
nm and 865 nm bands were utilized as the
water
vapor
absorbingband
andnon-absorbing band 3), respectively, and theretrieved results on a global scale werealready reported 4).
Principle of the retrieval algorithm is
explained concisely here.
Figure 1
illustrates transmittance curves of water
vapor in the near infrared region, calculatedusing the LOWTRAN 7 code 5). As seenfrom Fig. 1, water vapor absorbing bands

ModelPrecipitable Water

Atmosphere(mm)

Tropical

40.0

Midlatitude Summer

28.5

Subarctic Summer

20.4

US Standard 1976

13.9

Midlatitude Winter8.38

Subarctic Winter4.10

For the GLI application, here we propose a
retrieval method in which calibration curves
are determined between the radiance ratio ofwater vapor absorbing band (e.g., Ch. 25;1135 nm) to non-absorbing band (e.g., Ch.
26; 1240 nm) and water vapor amount(precipitable water) in a form of nonlinear
regression. The GLI channel specifications
related to this feasibility study aresummarized in Table 2 6).

167

Table 2 Related Channel Specifications of GLI.

ChannelCentralBand

IFOV aMaximum

NumberWavelength

Width

(km)

Radiance b

(nm)

(nm)

19

865

10

1

228 c

97.5 cWindow Region

24

1050

20

1

203

300

Window Region

25

1135

70

1

200

350

Water Vapor

26

1240

20

1

138

70

Window Region

27

1380

40

1

94

120

Water VaporaInstantaneous Field of View.

b

In units of W m-2 sr-1 m-1.

c

These values were attenuated by 25 % from the original ones 3).

d

Signal to Noise Ratio.

SNR dComment

Ch. 24 (20 nm).
The magnitude ofradiance of Ch. 19 (865 nm), however, ismuch larger than those of Ch. 25 (1135 nm),which leads to the smaller dynamic range ofthe radiance ratio (defined later). As aresult, Ch. 26 (1240 nm) is selected as
non-absorbing channels.
Simulations were carried out with the GLI
Signal Simulator (GSS) 7) so as to calculate
radiances to be observed at the GLI /ADEOS-II.
The code enables us tocalculate the radiance to be observed with
GLI, assuming several atmospheric models
including aerosol loading as well as water
vapor. In the previous feasibility study,
two cases of a bright target (land) and a darktarget (ocean) are considered as groundsurfaces with the GSS simulations 9) and we
present the bright target analyses in thisstudy.
The land model is assumed as a Lambertiansurface whose spectral reflectance is 50 %,Based upon the principle of the retrieval
algorithm, the channel which is mostsensitive to variation of water vapor amountis chosen as a water vapor absorption
channel and, on the other hand, the one
which is least sensitive to it as a
non-absorbing channel. As seen from Fig.
1, there are two water vapor absorbing bands
in the near infrared region, Ch. 25 (1135nm) and Ch. 27 (1380 nm). Between two
channels, Fig. 1 also indicates that Ch. 25(with a band width of 70 nm) is moresensitive to the variation of water vapor
amount than Ch. 27 (40 nm). As a result,Ch. 25 (1135 nm) is selected as a watervapor
absorbingchannel.
For
non-absorbing bands, there are threechannels, Ch. 19 (865 nm), Ch. 24 (1050nm), and Ch. 26 (1240 nm). From Fig. 1, it
is expected that Ch. 26 (with a band width of
20 nm) is more sensitive to the variation
than other two channels: Ch. 19 (10 nm) and

168

The upward radiances at the top of theatmosphere were simulated for the following
case: over a bright target (i.e., land). Twobands, that is, one water vapor absorbing
channel (1135 nm) and one non-absorbing
channels (1240 nm), was used to carry out
simulations, that is, the combination of 1135
nm and 1240 nm was examined to calculate
the radiance ratio.
In addition, thefollowing case is assumed for scangeometry: solar zenith angle 60˚, satellite
zenith angle 60˚, and relative azimuthal
angle 90˚.

chosen as a representative case of a brighttarget. This assumption is based upon thegrass model in the ASTER spectral library 8),
which shows surface reflectance of 50.7 %,and 48.8 % for 1135 nm, and 1240 nm
spectral region, respectively. For simplicity,
the effect of bidirectional reflectance
distribution function is not considered in thisstudy. Simulations were carried out underclear sky conditions. Moreover, the effectof aerosol loading in the model atmosphere
was also examined. For the aerosol model,
a rural one incorporated in the GSS was
utilized over land 7).

1.4

GLI Ch.

24

25

TropicalMidlat. Summer
Midlat. WinterSubarctic Summer
Subarctic Winter
US Standard 1976

26

1.2
1.0
0.8
0.6
0.4
0.2
0.0
800

900

1000

1100

1200

1300

1400

1500

1600

Wavelength (nm)Fig. 1. Atmospheric transmittance related to water vapor in the near infrared spectral region between 800 nm to1600 nm. Curves are calculated with the LOWTRAN 7 code for vertical one way path, as products between
water vapor absorption lines and continuum absorption. The six transmittance lines correspond to the six
model atmospheres listed in Table 1. Closed and open rectangles are the spectral ranges of the GLI for water
vapor absorbing and window channels, respectively.

2.2 Retrieval algorithm
In this study, the radiance ratio ( Tw ) isdefined as
Tw (ch1,ch2) ≡

Rch1
,
Rch 2

radiation undergoes strong absorption due towater vapor, ch2 is a non-absorbing
channel (i.e., 1240 nm), and R is theradiance simulated with the GSS at the topof the atmosphere for the specified channel.
This definition is essentially equivalent tothat of Kaufman and Gao 3), except for a
factor originating from the ratio of the(1)

where ch1 indicates a water vapor
absorbing channel (i.e., 1135 nm), at which169

on the calibration curve, the precipitablewater (W ) is converted to the water vapor
path ( W * ). The relationship between thesetwo parameters is given as

extraterrestrial solar incident irradiances
between the specified channels.
Based upon the above definition, a
relationship (a calibration curve) is proposed
between the radiance ratio ( Tw ) and theprecipitable water ( W ):()

Tw (ch1,ch2) = a + bexp −c W ,⎛ 1
1 ⎞W * =W⎜+⎟,⎝ cosθ cos θ 0 ⎠

(2)

(3)

where θ and θ 0 are the satellite and solar
zenith angles, respectively.
Using W *instead of W , Eq. (2) is modified aswhere a , b and c are calibration
coefficients. If the coefficient a is set tozero, this relationship is equivalent to that ofKaufman and Gao 3), since the effect of thesolar terrestrial incident irradiance was
implicitly included in the coefficient b .
Here the coefficient a is added explicitly
to consider a bias that arises from themolecular scattering and aerosol loading
effects 9).
In order to study the effect of scan geometry()

Tw (ch1, ch 2) = a* + b* exp −c * W * . (4)In the following analysis, radiance ratios( Tw ) are calculated with Eq. (1) from theradiances simulated with the GSS, andleast-squares fitting procedures are carriedout with Eq. (4)1.4
τa=0.0τa=0.11.2

τa=1.0

1.0
0.8
0.6
0.4
0.2
0.0

0

50

100

150

200

Water Vapor Path (mm)Fig. 2. Calibration curves between the radiance ratio and scaled water vapor path under rural aerosol loading( τ a =0.0, 0.1, and 1.0) conditions over a bright target (land). All circles denote the radiance ratio between the1135 nm and 1240 nm channels and are fitted with the calibration curves (Eq. (4)). All the cases are for a scangeometry of solar zenith angle 60˚, satellite zenith angle 60˚, and relative azimuthal angle 90˚.

2.3 Simulations over a bright target withaerosol loading
Figure 2 shows relationships between
radiance ratio and water vapor pathsimulated with the six atmospheric models
in Table 1. This is the case with the solar

zenith angle 60˚, satellite zenith angle 60˚,and relative azimuthal angle 90˚. In orderto minimize the effect of scan geometry,scaled water vapor path in Eq. (3) is usedinstead of the precipitable water. Here we
investigated the effect of aerosol loading on

170

the retrieval sensitivity under clear
atmospheric conditions, too. Simulations
with the GSS were carried out with the six
model atmospheres over a bright target
while changing the aerosol optical thickness
at 500 nm ( τ a ) as 0.0, 0.1, and 1.0 over abright target with rural aerosol loading.
Apparently, all the three cases are fitted wellwith the calibration curves in Eq. (4) and thecurves are almost identical to each other.
This indicates that the use of water vapor
path enables us to retrieve the precipitablewater from the observed radiance ratio overbright targets (e.g., land surfaces), evenunder moderate aerosol loading. In thissimulation, the land surface reflectanceswere assumed to be 50 % for all the twochannels. Consequently, the reflectanceratios of land surface are unity. But, evenusing the land surface reflectances in the
ASTER spectral library 8), they are actually
1.04 for R1135/R1240. It turns out that thisassumption is not influential to the resultsvery much in the simulations.

The algorithm was applied to the GLI data
set as a preliminary analysis. The imagesof the relevant GLI data are illustrated in Fig.
3. There extends huge cloud system over
the western part of the North American
continent around California. Cloudy pixels
are not retrieved, since the algorithm isapplicable only over land and snow/ice
surfaces, currently. Comparing these twopanels, an apparent difference exists near the
cloud edge over the California peninsula:
there are darker pixels in the water vapor
absorbing channel (Fig. 3a) compared to the
non-absorbing one (Fig. 3b).
The retrieval procedure is as follows: At
first, cloud screening based upon GLI
processing system is carried out. Once theavailable pixels are determined, the radianceratio ( Tw ) is calculated with the two-channel
data as describe in Eq. (1). And then, thescaled water vapor path ( W * ) was estimatedwith Eq. (4). Using the solar and satellite
zenith angles of the pixels concerned,
precipitable water ( W ) is estimated with Eq.
(3) from the water vapor path. A map ofprecipitable water retrieved above procedure
is illustrated in Fig. 4.

3. APPLICATION TO GLI DATA
AND ITS VALIDATION
(a)

(b)

Fig. 3. An imagery of GLI near infrared data in April 10, 2003 around California, the United States ofAmerica: (a) ch25 (1135nm), (b) ch26 (1240nm). The channels 25 and 26 are a water vapor absorbing and ano-absorbing channel, respectively. There extended cloud system from the western part of a North American
continent to the eastern Pacific Ocean.

171

Fig. 4. A retrieved precipitable water distribution in unit of mm in April 10, 2003. The radiance ratio between
two channels (1135 nm and 1240 nm) was utilized in the retrieval. The retrieval was performed only over landand snow/ice regions, that is, it was not carried out over cloud and water (ocean). The distribution is mappedon the iso-latitude and iso-longitude coordinate. The numbers around the left and bottom frames denotelatitude (˚N) and loingitude (˚E), respectively. Here, a minus sign corresponds to a western longitude.

Table 3 Comparison of precipitable water. Observation time is 1200 UTC for every radiosonde
event.

Location

Precipitable water (mm)Site # *Latitude (˚W)

Longitude (˚W)

GLIRadiosonde

72293

32.83

117.12

7.3

6.9

72274

32.12

110.93

1.5

4.9

76256

27.95

110.77

11.6

10.3* WMO site number (five digits).

172

compared to the radiosonde observationsites and the retrieved precipitable water iscoincident within 2 mm at a few sites. We
have a plan to compare the retrieved resultswith the continuous observations such asskyradiometer and microwave radiometersites for the purpose of its global
application.

As is expected with comparison of these twochannels, much precipitable water wasobserved at the southern part of the
California peninsula next to the cloudy
pixels.
At the northern part of thepeninsula, on the other hand, there existed
much smaller water vapor amountcorresponding to the mountain features.
The retrieved precipitable water wascompared to the radiosonde observations.
Table 3 summarizes the validation. The
radiosonde observation suggests that thedifferences between retrieved and in situ
observation is about 1-2 mm for a few
radiosonde sites. There was a case that had
a severe discrepancy (up to factor 3)
between satellite and in situ observations,however.
The comparison showed thefurther validation study was necessary sinceradiosonde observation (00 and 12 UTC) is
not fully coincident to the ADEOS-II / GLI
operation (around 10:30 AM at local time).
We have a plan to compare the retrieved
values to skyradiometer and microwave
radiometer observations.

ACKNOWLEDGMENTS
The authors thank GLI Algorithm
Integration Team (GAIT) / EORC / JAXA
for simulations with GSS and the GLI data
handling. This study was supported by
Japan Aerospace Exploration Agency(A2-RA-G-0030).

REFERENCES
1. B. J. Soden and F. P. Bretherton:Interpretation of TOVS water vapor
radiances in terms of layer-averagerelative humidities: Method andclimatology for the upper, middle, andlower troposphere. J. Geophys. Res.,
101, pp. 9333-9343, 1996.
2. M. D. King, Y. J. Kaufman, W. P.
Menzel, and D. Tanre: Remote sensingof cloud, aerosol, and water vapor
properties from the moderate resolution
imaging spectrometer (MODIS). IEEE
Trans. Geoscience and Remote Sensing,30, pp. 2-27, 1992.
3. Y. J. Kaufman and B.-C. Gao: Remotesensing of water vapor in the near IRfrom EOS/MODIS. IEEE Trans.
Geoscience and Remote Sensing, 30, pp.
871-884, 1992.
4. B.-C. Gao and Y. J. Kaufman: Remotesensing of water vapor and thin cirrus
clouds using MODIS near-IR channels.
Proc. of SPIE, 4150, pp. 217-224, 2000.
5. F. X. Kneizys, E. P. Shettle, L. W.
Abreu, J. H. Chetwynd, G. P. Anderson,
W. O. Gallery, J. E. A. Selby, and S. A.
Clough: Users guide to LOWTRAN 7.
AFGL-TR-88-0177, 146 pp, 1988.
6. T. Y. Nakajima, T. Nakajima, M.
Nakajima, H. Fukushima, M. Kuji, A.
Uchiyama, and M. Kishino: The

4. SUMMARY AND CONCLUDING REMARKS
We have proposed the retrieval of water
vapor amount (precipitable water) using GLI
near infrared channels.
The retrieval
approach proposed in this study enables us
to retrieve water vapor amount using GLI
onboard ADEOS-II.
The results ofsimulations indicate that the radiance ratio
between 1135 nm and 1240 nm channels iswell sensitive to the precipitable water overa bright target (higher reflectance at groundlevel) with Lambertian reflectivity. Over abright target with spectral reflectance of
50 %, aerosol loading with optical thickness(at 500 nm) of less than 1.0 is not influential
to the calibration curves of the retrieval.
The retrieval algorithm was applied to the
GLI data around western part of the UnitedStates of America. In this preliminary
analysis, the retrieval procedure worked wellin the GLI data analysis system in JAXA /EORC.
The retrieved results were173

for earth observation. Proc. of SPIE,
3870, pp. 314-322, 1999.
8. http://speclib.jpl.nasa.gov/archive/jhu/be
cknic/vegetation/txt/grass.txt
9. M. Kuji and A. Uchiyama: Retrieval ofprecipitable water using near infrared
channels of Global Imager / Advanced
Earth Observing Satellite-II (GLI /ADEOS-II). J. Remote Sens. Soc. Japan,
22, pp. 149-162, 2002.

optimization of the Advanced Earth
Observing Satellite II Global Imager
channels by use of radiative transfer
calculations. Appl. Opt., 37, pp.
3149-3163, 1998.
7. T. Nakajima, T. Y. Nakajima, M.
Nakajima,
and
GLI
Algorithm
Integration Team (GAIT): Developmentof ADEOS-II/GLI operational algorithm

174

P-1

Laboratory Test of Atmospheric Turbulence and Its
Implication in the Satellite Observations

Hiroshi Okayama
Center for Environmental Remote Sensing, Chiba University
1-33 Yayoicho Inage-ku Chiba 2638522 Japan

Abstract In connection with the quantity of each parameter obtained from satellite
data the atmospheric environmental information from the earth surface is measured
simultaneously, and the analytical algorisms of satellite data are verified to get highprecision. Major parameters to be measured are downward sun radiation, water vapor,the precipitation from clouds, the optical characteristic of aerosol, the column quantityof atmospheric molecular distribution which participates in the process of radiation, andthe degrees of atmospheric turbulence.
Atmospheric turbulence is one of the important correction factors to evaluate theearth’s surface using a sensor on a satellite. Aerosol and water vapor are selected asfactors of turbulence. The effects of turbulence caused by aerosol and water vapor on
the light reflected from the earth’s surface are estimated by measuring the degradationof spatial coherence of light in a chamber in which atmospheric turbulence is generated.
Particles contained in cigarette smoke are used as an example of aerosol.
Degradation of spatial coherence is measured in relation to the increase of aerosol.
Turbulence caused by water vapor is measured in the range of humidity from 49 ％ to96 ％ . The spatial coherence of light in the chamber degrades in relation to theincrease of humidity and as a result the turbulence increases. The relation between the
turbulence and the degree of spatial coherence is explained in formula.

I．INTRODUCTION
The quality of images obtained by an astronomical telescope set on the earth surface
is damaged by atmospheric turbulence, although the images obtained by Hubble spacetelescope launched into the space are free from the effects of turbulence. These havebeen many papers on the study of turbulence such as those by Gamo et al 1(1978), Hill

175

et al 2 (1978)，Richard et al.3 (1979), Okayama et al.4,5 (1999) and Ho 6 (1970). The
experiment using an atmospheric turbulence chamber was described by Gamo 1 , themodified spectrum of atmospheric temperature fluctuations was discussed by Hill 2 , andthe optical propagation of the light in laboratory turbulence was experimented by
Richard 3. We previously made measurements of spatial coherence of the light that has
passed through turbulent air at different temperatures (Okayama ４，5). In a remotesensing study, the effects of atmospheric turbulence on satellite data need to becorrected to make earth surface data as accurate as possible. Primary factors that areconsidered to cause turbulence in the atmosphere are temperature, aerosol, and carbon
dioxide etc. In an attempt to quantify the turbulence, we made a chamber to simulate
atmosphere and measured the spatial coherence of light with reference to the change oftemperature and the quantity of aerosol. The propagation of mutual coherence in arandom medium was studied by Beran 7 and Fied 8.
Correction of errors in satellite data caused by the turbulence of atmosphere is a very
important subject to solve in the remote sensing field. As factors causing turbulence
other than temperature, we considered water vapor and aerosol in the present
experiment. Water vapor is one of the gas components of atmosphere like nitrogenand oxygen. The turbulence of atmosphere is caused by the changes of temperature,
water vapor, aerosol, etc. We designed a chamber to simulate atmospheric turbulence,and measured the spatial coherence of the light passing through the chamber to estimate
its turbulence (Okayama et al. 1999) 3, 4 . The results obtained by this experiment haveverified theoretical ones by Ho 5 (1970) which are based on Kolmogorv’s theory todescribe atmospheric turbulence (Andrews et al.6 1994, Fante 7,1974, Lutomirski et al. 81971).

II. DEGREES OF COHERENCE
The degree of coherence g is given by the following formula : are the intensities

g ( x1 , x2 ) =G ( x1 , x2 ){ I ( x ) ﾗI ( x )}
1

1

2

2

1
2

,

where G ( x1 , x2 ) is a mutual coherence function, and I1 ( x1 ) and I 2 ( x2 ) are theintensities of the light. Papers by Richard et al.3 (1979), Andrews et al.4 (1994),
Fente10 (1974), and Lutmirski et al. (1971) are related to coherence turbulence.
In our experiment, the interference fringes caused by the coherence were176

photographed to obtain the degree of coherence.
fringes was calculated by the following formula :The visibility of the interference1

2 ( I1 ﾗI 2 ) 2
I -I
V = max min =g 12 ,
I max + I min
I1 + I 2

where Imax and Imin are the maximum and minimum values of effective exposuresobtained when the interference fringes were exposed. I 1 and I 2 represent the laser
intensities but in this experiment I 1 = I 2 = I . The degree of coherence g equals tothe visibility V.
V=

I max - I min=g
I max + I min.

The theoretical analysis to obtain the degree of coherence effected by turbulence wasmade by Ho5 (1970). His results show that the degree of coherence degrades with theincrease of turbulence.

III. FLUCTATIONSIn optical turbulence in the atmosphere, the functions for the velocity field thefluctuation of temperature are given by2

DRR ( R ) = CV2 R 3 ,
2DT ( R ) = CT2 R 3 .
Where CV2 is the velocity (wind) structure constant and R is the spatial position.

CT2 is the temperature structure constant. Similarly in the density of water vapor oraerosol, the structure will be given by2

DD ( R ) = CD2 R 3 ,where CD2 is the density structure constant.

For statistically homogeneous andisotropic turbulence, the related structure function exhibits the asymptotic behavior
2

Dn ( R ) = Cn2 R 3 ,where Cn2 is the index-of refraction structure parameter.

177Physically, the refractive-index structure parameter Cn2 is a measure of the strengthof the fluctuations in the refractive index.

IV. EXPERIMENTL APPARATUS
An atmospheric turbulence chamber as shown in Fig. 1 was made for this experiment.
The beam is multiply reflected in the chamber. The optical path is about 43m long.
An argon laser (Lexel Model 65) was used as an optical source. The light which hascome out from the chamber after multiple reflection goes through a pinhole and next
through double slits (slit width : 25μ m, space between slits : 50 μ m)and theinterference fringes are photographed by a digital camera. When the turbulence
increases, the degree of coherence decreases, and the turbulence change is evaluated bymeasuring the degree of coherence. The double slits are shown in Fig.2.
For the experiment of the coherence degree of water vapor, the same chamber as wasused for the aerosol experiment is used. The humidity in the chamber was raised byuse of a humidifier. Particles contained in cigarette smoke are used as an example ofaerosol.

EXPERIMENTAL RESULTS
The interference fringes obtained through the double slits were photographed bydigital camera as shown in Fig. 3. Degrees of coherence obtained theoretically are
shown in Fig.4. For the experiments of water vapor, one，two and no phase screens areused as illustrated Fig.5, 6, and 7, respectively. The contrast, namely coherence degree,of the interference fringes is calculated by a computer and the visibility V is obtained.
The experimental results are shown in Fig. 8 and 9. When the quantities of carbon
dioxide increase, the coherence degrees decrease and in the experiment of water vapor,when the humidity increases the coherence degrees decrease, and the turbulence
increases. These results coincide with the theoretical analysis by Ho 5 ; namely when
the degrees of turbulence increase, those of coherence decrease.
If he random medium exists along any part of the propagation path between thetransmitter and the receiver, under the Ryotov approximation (Tatarski 12 1961，Brown13
1967, and Yura et al.14, 1983), the optical field at the distance z = L from the transmitter
is

V.

178

U (r , L) = U 0 (r , L) exp[Ψ1 (r , L) + Ψ2 (r , L) LL],

where U o (r , L ) is the optical wave in the absence of the turbulence andΨ1 (r , L) and Ψ2 (r , L)

are first-order and second-order complex phase

perturbation caused by the random medium.

VI. CONCLUSION
In this paper, we took up aerosol and water vapor as factors which cause turbulenceof the atmosphere. If the quantity of water vapor is measured as precisely as those ofother factors such as aerosol, variance of temperature, and moisture in the atmosphere,
the degree of turbulence caused by aerosol will be estimated from the results of thisexperiment.
The results obtained by our experiments are considered to be correspondent with thetheoretical results of Ho5 . If the quantity of the objects causing turbulence in theatmosphere can be estimated in the future, these results will be applicable to thecorrection of satellite data.
The experimental errors are caused by those of the readings of the densities of theinterference fringes. When the contrast of interference fringes is digitally obtained, theerrors will become almost negligible.

REFERENCES[1] Hideya Gamo, and Arun K. Majumdar, “Atmospheric turbulence chamber for
optical transmission experiment : characterization by thermal method”, Appl. Opt.,
Vol.17, pp3755-3762 (1978).
[2] R. J. Hill and S. F. Clifford, “Modified Spectrum of Atmospheric Temperature
Fluctuations and Its Application to Optical Propagation”, J. Opt. Soc. Am. Vol. 68,
pp892-899 (1978).
[3] Richard A. Elliott, J. Rchard Kerr, and Philip A. Pncus, “Optical propagation inlaboratory-generated turbulence”, Appl. Opt., Vol. 18, pp3315-3323 (1979).
[4] H. Okayama and Li-Z. Wang, “Measurement of the spatial coherence of lightinfluenced by turbulence”，Appl., Opt., Vol. 38, pp2342-2345 1999).
[5] H. Okayama and Li-Z. Wang, Spatial Coherence Degradation of Light Influenced
by Temperature and Aerosol by Use of Atmospheric Turbulence Chamber”,

179Remote Sen. Environ., Vol. 69, pp189-193 (1999).
[6]

T. L. Ho，”Coherence degradation of Gaussian beams in a turbulent atmosphere”, J.
Opt. Soc. Am., Vol. 60, pp667-673 (1970).
[7] M. J. Beran, “Propagation of the mutual coherence function through random
media”, J. Opt. Soc. Am. Vol. 56, No. 11, pp1475-1480 (1966).
[8] D. I. Fied, “Diffusion analysis for the propagation of mutual coherence”, J. Opt.
Soc. Am., Vol.58, No. 7, pp961-969(1968).
[9] L. C. Andrews, W. B. Miller and J. C. Ricklin, “Spatial coherence ofGaussian-beam wave in weak and strong optical turbulence”, J. Opt. Soc. Am.,
Vol. 11, pp1653-1660 (1994).
[10] Ronald L. Ante, “Mutual Coherence Function and Frequency Spectrum of Laser
Beam Propagation Through Atmospheric Turbulence”, J. Opt. Soc. Am., Vol. 64,
pp592-598 (1974).
[11] R. F. Lutomirski and H. T. Yura, “Wave structure Function and Mutual Coherence
Function of an Optical Wave in a Turbulent Atmosphere”, J. Opt. Soc. Am., Vol.
61, pp482-487 (1971).
[12] V. I. Tatarski, “Wave Propagation in a Turbulent Medium”, (McGraw-Hill, NewYork 1961).
[13] W. P.Brown, Jr, “Validity of the Rytov Approximation”, J. Opt. Soc. Am., Vol. 57,
pp1539-1543 (1967).
[14] H. T. Yura, C. C. Sung, S. F. Clifford and R. J. Hill, “Second-order Rytov
approximation”, J. Opt. Soc. Am., Vol. 73, pp500-502 (1983).

180

Fig.1. Optical system of the multiple reflection simulator that was used to measureatmospheric turbulence.

Fig.2. Double slits.

Fig.3. Interference fringe obtained through double slits.

181

Fig.4. Degree of coherence of a collimated Gaussian beam.

Fig.5. Propagation geometry for a single phase screen.

182

Fig.6. Propagation geometry for two phase screens.
.

Fig.7. Propagation geometry for an extended random medium.

183

Fig.8. Experimental results of water vapor.

Fig.9. Experimental results of aerosol.

184

P-2
Comprehensive Study on Photosynthetically active radiation in Beijing
Hu Bo, Wang Yuesi, Liu Guangren,Ma Zhiqiang
Institute of atmospheric physics, Chinese academy of sciences, Beijing 100029
Abstract
The data, measured by using global photosynthetically active radiometer (LI-COR
quantum sensor) and global solar radiometer in Beijing, has been analyzed, forinvestigating the relationship of these two radiant fluxes in Beijing. The data is used
to determining timescale variability of the ratio of Photosynthetically active radiation(PAR) to global solar radiation (Rs). The seasonal variation of the ratio ranges from1.713 to 1.990μmol J-1,monthly from 1.626 to 2.039μmol J-1,and daily from 1.42 to
2.57μmol J-1, with annual mean value 1.841μmol J-1.
Keywords: Solar radiation, PAR, estimate
1 Introduction
Photosynthetically active radiation (PAR) is the visible part of the electromagnetic
spectrum of the solar radiation that received at the Earth’s surface. Apart from its
relevance to daily human activity, it contributes significantly in comprehensive studiesof radiation climate, remote sensing of vegetation, radiation regimes of plant canopy
and plays an important part in provides energy supports photosynthetic and primary
production by green plants through the processes of chlorophyll synthesis andphotosynthesis which result in the conversion of radiation energy into chemicalenergy and through photosensitive regulatory mechanisms such as phototropism andphotoperiodic activity(Udo and Aro,1999; Jacovides et al., 2004; Dye,2004).PAR
covering both photon and energy terms lies between 400nm and 700nm. PAR is a key
variable in process-based models of terrestrial photosynthesis andecosystem-atmosphere CO2 exchange (Dye, 2004). So extensive study on PAR isnecessarily and valuable. PAR is necessary input parameters in dealing with plant
physiology, biomass production, natural illumination in greenhouse, and carbon cycle.
At present, there are three methods were used to measure PAR radiation:(1) indirect
method, in this method PAR is often obtained as the difference between two
irradiance measurements;(2) spectrally integrating method, in this method PAR is
obtained by integrating spectral irradiance distribution measurements over the
waveband 400-700nm;(3) direct method, in this method PAR is measured quantum
sensor, the unit of PAR is µmol m-2s-1. This is base on the fact that photosynthetic
efficiency of green plants is proportional to the number of photons absorbed in the
spectral range 400-700nm,and not their energy. (Udo and Aro, 1999). Currently
Chinese scholars often use indirect method to obtain PAR value, i.e. use different
spectral solar radiometer to measure radiation, and then obtain PAR value through thedifference between this two different radiometer.
Most reports on experimental results use measured values of PAR and global radiation
185

(Rs) to determining the PAR fraction of the global radiation, and then use this PAR
fraction calculate PAR from the routinely measured global radiation. Nevertheless, therange of the PAR fraction is influenced by local climatic and geographic differences
like clearness and brightness (Udo and Aro, 1999), solar elevation, dewpointtemperature and fine mode aerosols. The accurate determination of PAR is important in many
applications, such as net primary productivity and carbon cycle modeling (Pinker and Laszlo,
1992 and Frouin and Pinker, 1995). Comprehensive study on PAR can provide accuracy radiation
for carbon cycle study, present evidence for carbon budget.Hence, approaches to determine PAR
from more widely measured variables have merit. In this paper, the measured PAR andglobal radiation values are used to analyzes the characteristic of PAR in Beijing andquantify the relationship between these two important solar radiation components.
Based on the ratio of PAR to Rs, an empirical model was developed to estimate PAR
from Rs.
2 Materials and Methods
2.1 Site descriptions:
Beijing, Capital of the People's Republic of China, and located in the northern part of
the North China Plain at 39°56' north latitude and 116°20' east longitude. Mountains
in the east, north and west surround it. Beijing covers an area of 16,807.8 sq. km andhas a population of 11 million.
The climate in Beijing is of the continental type, with cold and dry winters, due to theSiberian air masses that move southward across the Mongolian Plateau. The summers
are hot owing to warm and humid monsoon winds from the southeast bringing Beijing
most of its annual precipitation.
Sampling site is situated at the crossing of the forth ring road and the third ring road.
This area is part of downtown. There are inhabitant housing in the north and south，and a freeway near the eastern. Wang (2001) report measurements in this area canrepresentation the average condition of Beijing city.
The sampling instruments are installed on a flat platform at the top of the building
housing chemic laboratories, Institute of atmospheric Physics, Chinese Academy of
Sciences.
2.2 Instruments used
Global radiation was measured using a Kipp&Zonen model CM-21 (Delft, The
Netherlands). The PAR photon flux was measured with a Licro quantum sensor
LI-190Sz (Lin-coln,Nebraska,USA).This station complete with data acquisition
system(Vaisala M520 data-logger, and a storage module, Finland).The signals wereconditioned with the respective instrument factors into units of W/m2 andµEm-2s-1(quantum mol-1s-1 or µmolm-2s-1).Global radiation measurements have an
estimated experimental error of 2-3%,while the PAR sensor has a relative error of lees
then 5%.All radiation values was measured by one minute interval, and the hourly
values was driven from minute value by integrating them.
A sun tracker with a direct radiometer (TBS-2) and a global radiometer(TBQ-2)(Junzhou, China) was used to measure direct and global radiation. Direct
186radiation measurements have an estimated experimental error of 2-3%, while theglobal radiometer has a relative error of lees then 5%. A yuanhua data logger(Yuanhua,Taiwan,China) was used to collect these measured data. The measurement
interval is also one minute.
Daily checks were made to ensure that the radiometers were positioned horizontallyby monitoring and adjusting. The global radiometers were calibrated against areference pyranometer,which is calibrated a standard pyrheliomter,while theLI-190SA pyranometer is calibrated against calibration lamps at factory. All thiscalibrated work was done at the beginning and the end of data collected. The data
used is this paper from April 2004 to March, and 2005.
2.3 Methods
2.3.1 Analysis methods for PAR
The analysis is based on hourly radiometric data collected in Chinese terrestrial
ecological research stations, for a period of one year (Feb. 2004-Mar.2005). The
hourly data were checked for inconsistencies to eliminate problems associated withquestionable measurements. Due to cosine response problems this analysis is limited
to cases with solar elevation 15°< h<85° .
The well-known sky indices，sky clearnessεand brightness kt (Perez et al., 1990)
are used to classify the sky conditions. They are computed from radiation values anddefined as:ε=

Rd + Rb
（1）
Rd

∆=

Rd
（2）
R0 sinh

With Rd and Rb are diffuse and direct radiations on Earth’s surface horizontal
respectively, and R0 is the extraterrestrial radiation. Sky condition is divided into three
types. Overcast, clear and intermediate shies have been considered (Jacovides et al.,2004): (i) overcast skies (∆ <0.1, ε < 1.2); (ii) clear skies (∆< 0.1, ε > 5.2); and(iii) intermediate skies (0.2 <∆< 0.3 and 1.2 < ε <5.2).
2.3.2 Method for develop empirical model
A multiple linear regression equation used to develop the empirical model for estimate
PAR from global radiation. The regression equation including sky conditions
parameters, atmospheric water vapor, and concentration of PM10 and PM2.5. The
multilinear regression is used to analyze the data; in the regression equation intercept
is forced to be zero.
3 Results and discussion
3.1 Time series measurements of PAR and Rs
The hourly values of solar components radiation are grouped into daily, monthly and187

seasonal data sets. The daily ratio of PAR to Rs is derived from hourly values, then themonthly ratio values is from daily average values.Fig.1 shows the daily variability ofboth PAR and Rs for the period of the measurements. It is evident that PAR is highly
correlated with Rs.So the values of PAR can be obtained directly from Rs.It has
generally been the practice to express the measured hourly or daily relationshipbetween PAR and global radiation Rs(Alados et al.,1996;Papaioannou et al.,1996;Udo
and Aro,1999;Gonzalez and Callbo,2003;Jacovides et al.,2004).
global radiation(wm-2)
PAR(μmolm-2)

1800
1600
1400
1200
1000
800
600
400
200
0
17:00

8:00

9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

8:00

9:00

10:00

11:00

12:00

13:00

14:00

hour(LST)

Fig. 1 Diurnal variation of PAR and RS in Beijing
The variation characteristics of PAR and global radiation are distinct, and thevariation type is presented in Fig. 1.The variation trend of Par is similarly as that ofglobal radiation. The diurnal, seasonal variation model of PAR is the same as that ofglobal radiation in Beijing. In the diurnal variation rule, the tine of PAR and global
radiation appears at one time, the maximum value appears at noon. In the seasonalvariation trend, the high values appear in summer and the low values are in winter, thevalues of spring and autumn are in intermediate (the variation rule is lie on the Earthrotation and the sun revolution rule). There is good correlation ship between these twosolar radiation components, from this variation characteristic one of them is can be
estimated from another one.

188

average of PAR/Rs
PAR/Rs

PAR/Rs

2.5

2.0

1.5

1.0
8:00

9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

8:00

9:00

10:00

11:00

12:00

13:00

14:00

15:00

hour(LST)

Fig. 2 Daily ratio of PAR to Rs
Daily ratio of PAR to Rs is showed in Fig.2. The minimum and maximum daily ratio
value is 2.5 and 1.6 molMJ-1 respectively. The annual average of daily ratio was 1.954±0.086 molMJ-1,this agrees with the monthly result in Table 1.For overcast days theminimum and maximum daily ratio value are 2.6 molMJ-1, 1.9 molMJ-1respectively,
and in clear days the minimum and maximum daily ratio value are 2.5 molMJ-1, 1.8
molMJ-1 respectively. The cloudy days ratio was 4.3% higher than that of clear days.
These results show that cloudiness and vapor have effect on the ratio.
Table 1 Ratios of PAR to global radiation on monthly, and seasonalStandard deviation
PeriodNumber of days Ratio
R2
April
30
1.87
0.995
0.037
May
31
1.91
0.992
0.079
June
30
1.96
0.998
0.043
July
31
2.04
0.990
0.072
August
31
1.99
0.996
0.063
September
OctoberNovember

30
31
30

1.83
1.70
1.63

0.997
0.997
0.99

189

0.056
0.034
0.068

April
May

2.6
2.4
2.2
2.0
1.8
1.6

June

2.6
2.4
2.2
2.0
1.8
1.6

July

2.6
2.4
2.2
2.0
1.8
1.6

August

2.6
2.4
2.2
2.0
1.8
1.6

October

Septemberthe ratio of PAR to Rs

2.6
2.4
2.2
2.0
1.8
1.6

2.6
2.4
2.2
2.0
1.8
1.6
2.6
2.4
2.2
2.0
1.8
1.6
7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:00

hours(LST)

Fig. 3 Monthly mean diurnal variation of the ratio of PAR to Rs
The mean diurnal ratio on monthly was calculated from hourly ratio values. Monthly
characteristic of the ratio is showed in Fig. 3. The monthly variation scope issmoother than that of daily variation range. This phenomena show that the influenceof cloud on the monthly ratio is smaller than that on daily ratio. Fig.3 show that thevariation of monthly ratio has a significant trend, namely, higher ratio during sunrise
and sunset, and lower at noon (there is a different diurnal variation of the ratio inafternoon of October and September, this is caused by higher aerosol concentrationand different variation model of humidity. In October and December dewpointtemperature is the lower in the morning, humidity is increase gradual and after noon
humidity decrease.). From April the ratio is gradually increase, and the appears the
maximum ratio in July, then the ratio values is decrease gradually, and the lowest ratio
is appears in January. The climate variation characteristic of the control factor of theratio variation trend, East Asia season is the mainly climate characteristic of Beijing.
In this climate, there is significant rainy season, and a dry period. Monsoon is start inJuly (summer), and end in early September as the dry season sets in. There are many
active synoptic systems (depressions) in Beijing, in July. So the surface heating causes
increase moisture content. Water vapor through absorption processes markedly affects
the longer wavelengths, leaving the spectral PAR portion invariability, consequently
decreasing the global radiation greater than that of PAR, so the ratio increase withwater vapor increasing. On the contrary, there is stabilization system control and the
dry season strong, and there is almost clear day in Januarys. The concentration ofaerosol is higher in this month, because of steady boundary layer and aerosol
accumulative. The aerosol through scattering the PAR spectral, and have a little
190

effects on longer wavelength radiation, so the ratio is decreasing with theconcentration of aerosol (especially the fine particle in the atmospheric aerosol)increasing.

Fig. 4 Weather systems in East Asia
2.05
2.00
1.95

PAR/Rs

1.90
1.85
1.80
1.75
1.70
1.65
4

5

6

7

8

9

10

month

Fig. 5 Average ratio of PAR to Rs in monthly
Seasonally variation properties of the ratio is that, the highest value appeared insummer, the smallest values of ratio is appeared in winter, and the ratio values inautumn is smaller then that in spring. The mainly reason of these monthly variation
properties is caused by: Beijing is situating in the East Asian monsoon area, there area rainy season and a dry season in this region. There are many active synoptic systems(depressions) happen in this area, but concentration of the water vapor is limited andhas few rain in this region, in spring (March, April, May). The rainy season is settingin June, ending in august and dry season coming. The mainly rainfall is produced in

191

July. In autumn season (September, October, November), Beijing is often controlled
by anticyclone; the sky condition is always clearness. Subtropical anticyclone
frequent happens in winter season (December, January, February), rainless andcloudless is the mainly sky condition type in this region. Spring, and winter is called
dry season, because there are few rainfalls in these season. In rainy season, there areabundant water vapors in Beijing, so the ratio is highest. In the dry season, there islittle water vapor and high aerosol concentration, so the smallest ratio values
happened in this season.

2.1

1.8

2.0

PAR/Rs

PAr/Rs

1.9
1.7

1.8

1.7

1.6
0.10

( a)
0.15

0.20

0.25

0.30

( b)

1.6
1

0.35

2

3

4

5

6

ε

△

2.00
1.95

PAR/Rs

1.90
1.85
1.80
1.75
1.70
1.65
0.25

( c)
0.30

0.35

0.40

0.45

0.50

0.55

si n(β )Fig.6 The Ratio of PAR to Rs vs. sky clearnessε (a), brightness ∆ (b), solar elevationsin (h), and Kt. all parameters’ means values are calculated for 10% intervals.

192

2.2
2.1

PAR/Rs

2.0
1.9
1.8
1.7
1.6
1.5
0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Pricipitable water(cm)Fig. 7 The Ratio of PAR to Rs vs. percipitable water
Fig. 6-Fig. 8 show the ratio of PAR to Rs is mainly depend on the percipitable water.
The ratio is gradually decreased with sky clearnessε,and brightness increase ,thenthere is inflexion in the decrease trend, finally the ratio variation is steady.
3.2 Estimate model development
The sky condition index Ks＝Rs/ H0 is used in the estimate equation rather than sky
clearnessε,because there is enslaved to diffuse radiation observe generally. R0 is theextraterrestrial radiation is calculated by equation(Iqbal,1983):
H0 =

24

π

S 0 L0 [(π 180 )ws (sin δ sin φ ) + (cos δ cos φ sin ws )](3)

Where S0= the solar constant energy unit =1367Wm-2,L0 is the correction factor of theearth’s orbit,δ the solar declination,ws is sunrise hour angle and Φ is the geographicallatitude.
3.2.1 Estimating equation
Firstly, The global radiation and PAR dataset was used to developed the estimate
equation. In this step the sky conditions, water vapor and the solar elevation is omitted.
A linear regression equation from origin is used for estimating equation. From theanalysis results, the ratio of PAR to global radiation has a significant seasonalvariation characteristic, so all the data were used to developed the primary estimatedequation, then the dataset is divided into dry season and rainy season to developed theprimary estimating equation.

⎧1.7761 ∗ Rs , r = 0.979, clear sky⎪
PAR = ⎨1.985 ∗ Rs , r = 0.9869, int ermediate⎪2.09266 ∗ R , r = 0.9889, overcasts⎩(mod el 1)

Secondly, The mainly influence factor of the ratio of PAR to global radiation is thevapor, so in the secondly step the percepitable water and sky conditions were used for193the estimating equation developed. The percepitable water is calculated from groundhumidity and temperature (Yang, and Qiu, 2002).
W = exp(b0′ + b1′t d ),W

isprecepitible water

(4)

c×b
, b = 15.0 × a − 2.1 × T + 2711.5, a = 100ln( RH )c × (a 2) + b − 273.16
c = T + 273.16, RH
is humidity , T
is teperature

td =Because there is no scattered radiation and direct radiation in a great number ofradiation observation station in china solar radiation observation station, So A
estimating equation without sky conditions parameters(sky clearnessε, brightness ∆)is being developed. In this developed estimate equation progress, the means sky
condition parameter Ks is used for developing the estimate equation. The sky
condition index Ks is calculated by equation 3.

⎧1.576 − 0.177 ln(k s ) − 0.1w + 0.31sin(elevation), R = 0.746
PAR / Rs = ⎨(mod el 2)⎩1.534 − 0.121 ln(k s ) + 0.21sin(elevation), R = 0.66
where,w is precipitable water, elevation is solar elevation,ks is means sky conditon.
4 conclusions
The analyses of measured PAR and global radiation in Beijing are summarized as
fellow: The ratio of PAR to global radiation has a significant seasonal variation trend,high values in summer (1.98 μmol J-1) and low values in autumn μmol J-1).The
values of ratio in winter and spring are higher than that on autumn. The highest ratio
value is presented in July(2.04 μ mol J-1) and the lest ratio value presented in
December (1.626μmol J-1).
The estimating equation can be used estimate PAR from global radiation.
The diffuse PAR radiation has higher light use efficiency for plant canopy, has muchless tendency to cause canopy photosynthetic saturation (Lianhong Gu et al., 2001). Farther work
on PAR will setup a diffuse PAR measured systems and estimating equation for diffuse PAR will
be developed.

194

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activeradiation: measurements and modeling. Agric. For. Meteorol.78, 121-131.
Dennis G.Dye, 2004.Spectral composition and quanta-to-energy ratio of diffuse
photosynthetically active radiation under diverse cloud conditions. J. Geophys. Res.
109, dio: 10.1029/2003JD004251.
Frouin, R. and Pinker, R.T., 1995. Estimating photosynthetically active radiation(PAR) at the earth’s surface from satellite observations. Remote Sens. Environ. 51,
98–107.
Gonzalez,J. A., Callbo,J.,2002,Modelled and measured ratio of PAR to global
radiation under cloudless skies, Agric. For. Meteorol.110, 319-325.
Jacovides,C. P.,Timvios,F. S.,Papaioannou,G.,Asimakopoulos,D. N.,Theofilou,C.
M.,2004,Ratio of PAR to broadband solar radiation measured in Cyprus, Agric. For.
Meteorol.121, 135-140.
Lianhong Gu,Dennis Baldocchi,,Shashi B. Verma,T. A. Black,Timo Vesala,Eva M.
Falge,Pete R. Dowty, Advantages of diffuse radiation for terrestrial ecosystemproductivity. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D6,4050, doi:10.1029/2001JD001242, 2002
Pinker, R.T. and Laszlo, I., 1992. Global distribution of photosynthetically active
radiation as observed from satellites. J. Clim. 5, 56–65.
Perez,R.,Ineichen,P.,Seals,R.,Michalsky,J.,Stewart,R.,1990.Modelingdaylight
availability and irradiance components from direct and global irradiance. Solar
Energy, 44,271-289.
Papaioannou,G.,Nikolidakis,G.,Asimakopoulos,D.,Redailis,D.,1996,
Photosynthetically active radiation in Athens. Agric. For. Meteorol.81, 287-298.
Udo, S.O., Aro,T.O., 1999.Global PAR related to global solar radiation for central
Nigeria, Agric. For. Meteorol.97, 21-31.
Yang Jingmei,Qiu jinghuan,A methold for estimating precipitable water and effective
water vapor content from ground humidity parameters. Chinese Journal of
atmospheric sciences, 26,9-26. (in chinese)
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195

P-3
Environmental application of the all-sky survey high-resolution air-shower (ASHRA)
telescope ― aerosol distribution measurement using a bistatic, imaging lidar
Shunsuke Fukagawa a, Ikue Kouga a, c, Hiroaki Kuze a, Nobuo Takeuchi a,
Makoto Sasaki b, Yoichi Asaoka b, Satoru Ogawa c
a Center for Environmental Remote Sensing (CEReS), Chiba University

1-33 Yayoi-chi, Inage-ku, Chiba 263-8522, Japanb Institute for Cosmic Ray Research, University of Tokyo

5-1-5 Kashiwa-no-Ha, Kashiwa City, Chiba 277-8582, Japanc Faculty of Science, Toho University

2-2-1 Miyama, Funabashi City, Chiba 274-8510, Japan

Abstract: All-sky Survey High Resolution Air-shower (Ashra) telescope has been developed todetect cosmic-ray neutrinos with extremely high energy, larger than 1016 eV. The importantfeatures of the telescope system are the wide FOV (50×50 deg) with high angular resolution (1arcmin) and a high-speed, highly sensitive imaging system with an intelligent triggering capability.
In this paper we report an imaging lidar on the basis of this novel telescope system applied to thedetection of atmospheric aerosols. Because of the wide acceptance angel, the bistatic configuration
enables light detection in the forward-scattering mode, greatly enhancing the signal-to-noise ratio.
Eye-safe lidar operation is achieved with a laser wavelength in the operational wavelength range of
the Ashra telescope (300-420 nm). Simulation studies are undertaken to establish the retrieval
algorithm of aerosol distribution from the bistatic measurement.
conventional monostatic lidar, the telescope isplaced apart from the laser source. The lightscattered in the atmosphere is detected in the
scattering-angel range of 0<θ<180 deg.
Although bistatic measurements of aerosols
have so far been reported6,7), the scanning of
the laser beam was not attempted, leading
mainly to the detection near the backscattering
scheme. In contrast, forward scattering yields
high signal-to-noise (S/N) ratio owing to thegeneral property of the Mie scattering fromaerosols. In the case of Ashra system, anotheradvantage is the usage of the intelligent
triggering. This greatly contributes to reducethe background light and thereby to increase
the S/N ratio. These features make thethree-dimensional, wide area, and real-timemonitoring of atmospheric aerosols possible.
Moreover, using the laser wavelength at 351nm

1. Introduction
The All-sky Survey High Resolution
Air-shower (Ashra) telescope project is acombined effort in the different categories of
cosmic-ray physics, particle physics, andremote-sensing techniques of atmosphericsounding. In this project, the CEReS group is incharge of the application of the Ashra telescopeto the monitoring of the atmosphere. The novel
concept of Ashra telescope has been developedby the group of Institute for Cosmic Ray
Research (ICRR), University of Tokyo. The
remarkable features of the system are a wide
field-of-view (FOV) (50 × 50 deg) with highangular resolution (1 arcmin = 0.29 µrad) and ahigh-speed, highly sensitive imaging systemwith an intelligent triggering capability 1-4).
The imaging lidar observation5) is conductedin bistatic mode, in which, unlike the196Here, θFOV is the FOV for a single pixel, α theextinction coefficient, and f the phase function.
Subscript 1 is for aerosol and 2 for air molecule.
The transmittance along the combined laser
path and viewing path is expressed as

or 355 nm, which are in the design wavelengthof Ashra telescope (300-420 nm), ensureseye-safe lidar operation, since the level ofmaximum permissible exposure (MPE) is very
high in this wavelength region.
In this paper, we describe the scheme ofbistatic lidar measurement, with the results ofrelated simulations and our preliminary
experiment of the bistatic lidar measurement.

T = exp[− ( τ t + τ r ) ] .

Here, τt is the optical thickness of thetransmitter side and τr that of the receiver side.
These parameter are given as

2. Bistatic measurement of aerosol
The basic configuration of the bistatic
measurement is shown in Fig. 1. A laser and atelescope are located separately, with a baseline
distance of L. The laser is illuminating a
direction θlaser (elevation angle), while thetelescope is observing the beam path toward a
direction θview. In this bistatic measurement,
the lidar equation is written asA(1)
P = P0 K 2 ds β(θscat ) T ,rwhere P is the received signal intensity, P0 theemitted laser intensity, K the efficiency of thereceiving optics, A the area of telescope’s mirror,
r the distance between the target (aerosol
particle) and the telescope, and θscat the
scattering angle. The portion of the laser beam
path subtended by the FOV for a single pixel ofthe array detector is denoted as ds, which is
given asds =r θFOV .
sin (θscat )

(2)

Fig.1

z

∫ {α ( z ′) +α ( z ′)}dz ′ ,
τ =
t

0

1

2

sin θ laser(5)z∫ {α ( z ′) +α ( z ′)} dz ′ .
τ =
0

r

1

2

sin θ view

(6)

3.

Inversion of bistatic lidar data
In this section, we describe the inversion
procedure used to derive the spatial
distribution of the aerosol extinction coefficientfrom the bistatic lidar data. We assume thatthe laser beam is scanned as shown in Fig. 2.
The viewing direction is numbered with anindex i (= 0, 1, …) and the laser beam direction
with j (= 0, 1, …). The aerosol extinction
coefficient (i.e. the product of the aerosol
number density and the total cross section) at
the intersection defined by indices i and j isexpressed as α1(i, j). From Eqs. (1) and (2), theratio of the received signal intensities at (i, j)and (i, j + 1) is given as

P (i , j + 1)=
P (i , j )The generalized scattering coefficient, β (θscat),is defined asβ(θscat ) = α1 f1 (θscat ) + α2 f 2 (θscat ) .

(4)( i , j + 1)
(i , j )( scat) r (i , j ) sin( scat
) T (i , j + 1)
(i , j )( i , j + 1)
( scat) r ( i , j + 1) sin( scat
) T (i , j )(7)

If α1(i, j) is known, this equation can be used to
calculate α1(i, j +1) from the bistatic lidar data.
For this purpose, we define an error(3)

Fig.2 Schematic diagram of the inversion algorithm.

Schematic diagram of the bistatic measurement.

197

Delay I.I. before reaching a fine image sensor,
and the other path is connected to a
multi-anode photo multiplier tube (MAPMT,
128 × 128 pixels). If the signal intensity
detected by a certain pixel on MAPMT exceeds
a trigger level, only a pixel region on the fineimage sensor that corresponds to the pixel
position on MAPMT is gated. The temporal
width of the gate is determined by the time
period in which the MAPMT pixel detects thesignal (> a few ns). This operation minimizes
the accumulation of sky background light on
the fine image sensor, hence the S/N ratio is
expected to increase significantly.
The estimation of the S/N ratio is conducted
for the bistatic lidar system with and without
the intelligent trigger. An example of thecalculated S/N ratio is shown in Fig. 4. This
simulation is based on a spectrum of daytime
sky radiance reported by C.P. Jacovides et.al8)
and the aerosol extinction coefficient at the
ground level of 1.0 × 10-4 m-1. The verticalprofile is proportional to an extinction profile
model reported by Sasano9). Figure 4 showsthat the usage of the intelligent trigger
enhances the S/N ratio by 2-3 orders ofmagnitude. A related advantage of the bistatic
lidar measurement as compared with themonostatic case is the limited variation in the
dynamic range of the signal intensity, which
results in the ease in the detector design as awhole.

Fig.3 Schematic diagram of the imaging system of Ashra
telescope with the intelligent trigger.

parameter ε as the difference between thecalculated ratio and the observed ratio of thesignal intensities:ε =( i , j +1)
Pobs
P ( i , j +1).
−(i , j )Pobs
P (i , j )(8)Here, Pobs is the observed intensity and P istheoretically calculated using Eq.(1). The
bisection method is employed to search theminimum value of |ε| by changing α1(i, j +1).
Since the boundary condition, α1(i, 0), can beobtained from the ground-based scattering
measurement, the sequential application of thepresent procedure yields all the required valuesof α1(i, j). In practice, the application has to be
iterative, since the value of the transmission T
in Eq.(4) is improved successively: values of the
optical thickness in Eq.(4) are re-calculatedusing the equations of

α1( n +1, j ) + α1( n , j ) z ( n +1, j ) − z ( n , j )
, (9)
j
2
sin(θ laser
)
n=0
i −1

τ t( i , j ) = ∑α1(i , n +1) + α1(i , n ) z (i , n +1) − z (i , n ), (10)i2
sin(θ view
)
n=0
j −1

τ r( i , j ) = ∑where z is the height of the intersection point.
The iterative calculation is repeated until theextinction coefficient at each intersection point
converges.
3.

Simulation

3.1 Signal-to-Noise Ratio

The schematic diagram of the imaging
system of Ashra telescope is shown in Fig. 3.
The image signal that passes through anamplification image intensifier (Amp. I.I.) is
split into two paths. The first path is toward a

Fig.4 Two-dimensional distribution of the S/N ratio (a)without and (b) with gating (100 ns). The telescope islocated at the point of 0 m viewing the FOV of 50 deg
and the laser at that of 100 m. Following parameters are
assumed in this calculation; laser power: 100 µJ/pulse,
repetition rate: 3000 Hz, exposure time: 1 s.

198Acknowledgment
The financial support from the Frontier
Program of the Japan Promotion of Science andTechnology Fund is gratefully acknowledged.
References1.

Fig.5 (a) Phase function assumed in the simulation data
calculation, and difference between the one and

M. Sasaki, A. Kusaka, Y. Asaoka, Design of
UHECR telescope with 1 arcmin resolution andthatin the inversion. (b) Error distribution of the extinction

50°field of view, Nucl. Instr. Meth. Phys. Res.

coefficient.

A492, 49-56 (2002).
2.

M.

Sasaki,Y.

“Self-triggered

3.2 Error estimation of retrieved extinction
coefficient

Asaoka,
image

and

M.

intensifier

Jobashi,tube

forhigh-resolution UHECR imaging detector,” Nucl.

Instr. Meth. A501, 359-366 (2003).

In the inversion of lidar data, an assumptionof a phase function is indispensable. The error
arising from this assumption is evaluated using
a simulation study. The simulation data is
calculated as in the previous section. The phasefunction is obtained from the Mie scattering
calculation with the urban model size
distribution reported by Jaenicke10) and thecomplex refractive index of 1.50 – 0.001i . The
inversion is conducted assuming a different
phase function, i.e. the same size distribution
but for different values of the complex
refractive index, e.g. 1.55 – 0.001i. These
functions are shown in Fig.5(a). The
distribution of the inversion error is obtainedby comparing the assumed and the retrieved
distributions of the extinction coefficient.
Figure 5 (b) indicates that the resulting error iswithin a ±10% range.

3.

Y. Arai, Y. Aita, T. Aoki, Y. Asaoka, et al.
(co-authors: 33), “ASHRA Trigger and Readout
Pixel Sensors,” Proc. 28th International Cosmic
Ray Conference (Tsukuba), 961 (2003).

4.

Y. Aita, T. Aoki, Y. Arai, Y. Asaoka, et al.
(co-authors: 33), “The ASHRA Detector,” Proc.
28th

International

CosmicRay

Conference(Tsukuba), 1061 (2003).
5.

H. Kuze, S. Fukagawa, N. Takeuchi, Y. Asaoka,
and M. Sasaki, “Development of a wide-area
imaging lidar for atmospheric monitoring,” 29th
SICE Remote Sensing Symposium (Tsukuba),
61-64 (2003).

6.

K. Meki, K. Yamaguchi, X. Li, Y. Saito, T.D.
Kawahara, and A. Nomura, “Range- resolved
bistatic imaging lidar for the measurement of thelower atmosphere,” Opt. Lett. 21 (17), 1318-1320(1996).

7.

J.E. Barnes, S. Bronner, R. Beck, and N.C. Parikh,“Boundary layer scattering measurements with acharge-coupled device camera lidar,” Appl. Opt. 42

4. Bistatic lidar measurement
The preliminary experiment of the bistatic
lidar measurement has been conducted using acooled CCD camera and a pulsed Nd:YAG laser(532 nm). Since the acceptance angel and thebaseline distance are both insufficient, theexperimental situation is rather limited ascompared with the Ashra telescope case.
Nevertheless, the two-dimensional measurement reveals the variation of the phasefunction (mostly from the Rayleigh signal) as
expected from the theoretical consideration.
The details will be discussed in thepresentation at the conference.

(15), 2647-2652 (2003).
8.

C.P. Jacovides, F. Timbios, D.N. Asimakopoulos,
and M.D. Steven, “Urban aerosol and clear skies
spectra for global and diffuse photosynthetically
active radiation,” Agric. For. Meteorol. 87, 91-104(1997).

9.

Y.

Sasano,“Troposphericaerosol

extinction

coefficient profiles derived from scanning lidar
measurement over Tsukuba, Japan, from 1990 to
1993, Appl.Opt.,” 35 (24), 4941-4952 (1996).
10. R. Jaenicke, Tropospheric Aerosols, in “AerosolCloud-Climate Interactions” (P.V. Hobbs, ed.),Academic, 1-31 (1993).

199

P-4

Mie-scattering simulation and measurement of mass
extinction efficiency from portable automated lidar andsuspended particulate matter measurements
Gerry Bagtasa a,Nofel Lagrosas a, Hiroaki Kuze a, Nobuo Takeuchi a,
Shunsuke Fukagawa a, Yotsumi Yoshii a, Suekazu Naito b, Masanori Yabuki caCenter for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho,Inage-ku, Chiba 263-8522, Japanb
Chiba Prefectural Environmental Research Center, 1-8-8 Iwasaki-nishi,
Ichihara 290-0046, Japanc
National Institute of Polar Research, 9-10 Kaga 1-chome, Itabashi-ku, Tokyo
173-8515

Abstract
The continuous operation of the portable automated lidar (PAL) and measurement of suspended particulatematter (SPM) results in the observation of the diurnal changes of the mass extinction efficiency (MEE). MEE isthe ratio of the extinction coefficient to the SPM concentration which relates the amount of mass to the opticalextinction of aerosols. Our results show that in the height region of 300 – 500 m, the daytime MEE values are
smaller than the nighttime MEE values. This can be attributed to the aerosol growth (rapid growth) due to thehigher relative humidity during nighttime.
Using the Mie scattering theory, a simulation study of MEE is also undertaken by computing the extinction
coefficient from the 6-year averaged air sampling measurements in Chiba University. Hygroscopic effects on
aerosols are considered in the simulation and results show that MEE increases with RH, in agreement with theobservation stated above. When this type of direct effect of RH (rapid growth) is irrelevant, high and low MEE
values can be ascribed to fine and coarse particles, respectively, for both measurement and simulation.
In addition, MEE values are computed for individual aerosol components (elemental carbon (EC), organiccarbon, NH4SO4, NH4NO3, sea salt, soil, EC-NH4SO4 mixture), with EC having the highest computed MEE of
7.3 m2g-1 and NH4NO3 with the lowest MEE of 1.2 m2g-1.

Keywords: Mass Extinction Efficiency, Lidar, Tropospheric aerosol, Mie scattering theory200

I. INTRODUCTION
Aerosols and clouds play an importantrole in the radiation budget of theatmosphere. Atmospheric sensing using acompact, continuously operated lidar canlead to the real-time observation of the
dynamics of the aerosols and clouds. One of
the candidates of this type of remote sensing
is the portable automated lidar (PAL) system,
which is capable of real-time and unattended
monitoring of the atmosphere.1) In this paper,
we discuss the application of the PAL data to
the monitoring of tropospheric aerosols.2)
The inversion of the PAL data yields thevertical profile of the aerosol extinction
coefficient. In the ground sampling, theaerosol quantity is often measured by theto the presence of fine (roughly equivalent
to PM2.5) and coarse particles (PM10except the PM2.5 components). However,when the diameters of fine aerosols growwith relative humidity (RH), the efficiencyof the particles to scatter light increases andthis also leads to the increase in the MEE.
Figure 1 shows MEE values of different
aerosol types as a function of size parameterx taken from various literatures.2)

β-ray method as the suspended particulatematter (SPM) concentration. If theextinction coefficient values from the PAL
data are matched with the ground-based
SPM concentration measurements, the mass
extinction efficiency (MEE) of the aerosols
can be calculated.

Fig. 1 MEE values as a function of size parameterfrom literature

It can be seen that MEE values decrease
with increasing size parameter, hence theterminology of size effect. It has been
pointed out that the lens effect associatedwith the particles of internal mixture greatly
enhances the value of MEE.2) This isespecially important for the soot aerosols
surrounded by water (solution) droplet. On
the other hand, MEE of a particular aerosol
component may change due to a change in
RH, and this is called the rapid growth. Thislatter effect is related to the rapid increase of

II. Mass Extinction Efficiency
The MEE parameter links the massamount to the optical extinction of theaerosols. Mathematically, the MEE is given
by
r2

MEE =

π ∫ r 2 Qext (r , λ , m )n(r )drr1r2
4πρ ∫ r 3 n(r )dr
r1
3

(1)

where Qext(r,λ,m) is the extinction efficiency,r is the radius, λ is the wavelength, m is therefractive index, ρ is density, and n(r) is theparticle size distribution. The high and low
values of the MEE are generally attributed201

Qext(r,λ,m) in a particular range of the size
parameter (approximately 1<x<5).

III. Setup

University and is able to measure massconcentration of elemental carbon (EC),
organic carbon (OC), ammonium sulfate(NH4SO4), ammonium nitrate (NH4NO3),a) PAL System
The PAL system has been developed by
CEReS, Chiba University, and operated at
the Chiba Prefectural Environmental
Research Center at Ichihara city (35.52N,
140.07E, about 40 km southeast of Tokyo).
It is a compact Mie lidar system that canoperate continuously and unattended forlong periods of time. The system is equippedwith an automatic re-alignment system that
readjusts the laser direction every 15
minutes so that the proper system alignmentis always maintained. Table 1 shows amore detailed specification of the PAL
system.

sea salt, and soil with aerodynamic sizes ofless than 2.5 µm (PM2.5), 2.5 µm to 10 µm(PM10 excluding PM2.5) and greater than
10 µm. Only the measurements at PM2.5
and PM10 stages are used in the simulation.

IV. RESULTSa) PAL - SPM counter
Each data set is composed of 12 points(one point per hour): the daytime data set istaken from 0600H to 1800H and nighttime
data from 1800H to 0600H the following
day. Data from January of 2003 to Januaryof 2004 are analyzed. When there is agood correlation between the lidar-derived
extinction coefficient and the ground-based
SPM concentration measurements, the MEE
for that data set is computed.
Highcorrelation coefficient (R>0.7) is usuallyobtained when there is a relatively clear
atmosphere or high backscattered signal isdetected.

Table 1: PAL Specification
Transmitter
Laser

LD-pumped

Q-switch

Nd:YAG
Wavelength

532 nmRepetition Rate

1.4 kHz

Laser Pulse Energy

15 µJ

Laser Beam Divergence

50 µradReceiver
Telescope Diameter

20 cm

Telescope Type

Cassegrain

Field of View

0.2 mradDetector

PMT

Model

HPK-R1924P

Quantum Efficiency

10% - 25%b) Low volume impactor
A three-stage low volume impactor (LVI)
is used to characterize the local aerosol size
distribution used for the present simulation.
The air sampler is located at Chiba

Fig. 2 Clear atmosphere range corrected Lidar data
(13-14 Dec2003)

202

Figure 2 shows the range corrected PAL
data taken on 13-14 of December 2003: thisis considered clear data since no opticallythick clouds are present near the ground.
Fig. 3 shows the computed extinction
coefficient from the same data set plottedagainst the SPM concentration measurement.
The observed MEE value is 11.3 m2g-1.

(b)Fig. 4 (a) Range corrected PAL data, and (b)extinction coefficient vs. SPM concentration(1April 2003)

Table 2 shows the average MEE values
and average relative humidity for all theanalyzed data sets.
Table 2 Average (standard deviation) mass extinction
Fig. 3 Extinction coefficient vs. SPM concentration

efficiency (in m2g-1) and relative humidity (RH)

(13-14 Dec2003)

DaytimeNighttime

7.9 (5.2)

13.0 (6.2)

56%

74%

5.4 (4.0)

11.0 (7.6)

71%

81%

Relatively

Figure. 4(a) shows lidar data of 1 April
2003. This data represents a high backscattercase due to the high signal intensity detected
near the ground. Fig. 4(b) shows theextinction coefficient plotted with SPM
concentration giving an MEE value of 8.1
m2g-1.

Clear
Average RH
HighBackscatter
Average RH

Our results show that MEE values range
from approximately 4-13 m2g-1. Generally,nighttime data sets yield a higher MEE
values than the daytime data: this can be
attributed to the Rapid Growth, due to thehigher observed average relative humidity
during nighttime.
Figure 5 shows the variation of theaverage extinction coefficient with relative
humidity. Below around 70% RH, thelidar-derived extinction coefficient values
appear not to change very much, whereas
above 70% RH, it is seen that the extinction

(a)

203

result for each aerosol component. It isnoted that although the MEE of a specificaerosol component remains constantregardless of the size distribution, the total
MEE varies with the size distribution.

coefficient increases with RH. This is
attributed to the aerosol growth (rapid
growth).

Table 3: Simulated MEE for each aerosol type
Aerosol Type
EC

7.290

OC

2.092

NH4SO4

3.516

NH4NO3

1.194

Sea Salt

1.273

Soil

1.224

EC – NH4SO4

4.606

Fig. 5 Relative Humidity vs. Extinction Coefficient

Figure 6 shows the observed MEE plottedagainst the coarse particle concentration.
The MEE value decreases with increasing
amount of coarse particles, whichcorrespond to the size effect shown in fig. 1.

Computed MEE (m2g-1)

mixtureFigure 7 shows the total MEE of the
6-yearaveragemass
concentration
measurement by the LVI with varying
amounts of RH. There is a significant
increase in the MEE value when the RH
reaches 70%, which is the point whereaerosols start to be coated with water. MEE
value increases up to almost 300% when RH
reaches 90%.
This result verifies thehigher MEE values observed during
nighttime.

Fig. 6 MEE vs. Coarse SPM concentrationb) Simulation based on Mie scattering
theoryThe 6-year data of mass concentration
measurements from the LVI is used to model
the local aerosol size distribution in the
Chiba city area for each of the following
aerosol components: EC, OC, NH4SO4,
NH4NO3, sea salt, soil and EC-NH4SO4
mixture.
From the derived size distributions, MEE
value for each aerosol component can becomputed using Eq. (1). Table 3 shows the

7.0

MEE (m^2/g)

6.0
5.0
4.0
3.0
2.0
1.0
20

30

40

50
60
70
Relative Humidity RH %80

Fig. 7 MEE vs. RH Simulation

204

90

100

V. CONCLUSION

particles, New York Wiley5) I. Tang (1996) Chemical and size effectsof hygroscopic aerosols on light scattering
coefficients, Journal of GeophysicalResearch, 101, 19245-19250.

This study has shown that the continuous
operation of the portable automated lidar(PAL) system has the capability ofobserving the dynamics of atmosphericaerosols. The correlation study shows that
MEE observed during nighttime is generally
higher than MEE observed during daytime,suggesting the influence of higher averagerelative
humidity
duringnighttime.
Simulations using the Mie Scattering theory
support this observation: by varying theamount of relative humidity, it is shown that
MEE increases with RH.
Also, oursimulation reveals elemental carbon (EC) as
one having the highest MEE value of 7.3
m2g-1: this result is to be expected since EC
has one of the highest imaginary refractive
index, consequently the highest extinction.

REFERENCES
1) N. Lagrosas, Y. Yoshii, H. Kuze, N.
Takeuchi, S. Naito, A. Sone, H. Kan (2004)
Observation of boundary layer aerosols
using a continuously operated portable lidar
system, Atm. Environ. 38, 3885-3892.
2) N. Lagrosas, H. Kuze, N. Takeuchi, S.
Fukagawa, G. Bagtasa, Y. Yoshii, S. Naito,
M. Yabuki (2004) Correlation study between
suspended particulate matter and portable
automated lidar data, Aerosol Science., inpress.
3) Yabuki, M. (2003) Study on algorithms
for deriving optical parameters of
tropospheric aerosols (in Japanese). Ph.D.
thesis, Chiba University
4) Bohren, C.F., Huffman D.R. (1983)
Absorption and scattering of light by small
205

P-5

Net Radiation Estimation Using MODIS-TERRA
Data for Clear Sky Days Over Homogeneous Areasin Mongolia
J. Batbayar1, S.Tuya2, N. Tugjsuren3
1

Department of atmospheric physics, Mongolian University of Science and Technology
Center for Environmental Remote Sensing (CEReS), Chiba University, JAPAN3
Mongolian University of Science and Technology, School of Materials science2

Abstract
Satellite remote sensing to estimate net radiation is essential to obtain the global energy budget. The net
radiation is key role-played for the monitoring of surface energy budget. Much of heat comes from radiantenergy initially provided by the absorption of solar radiation. The energy is used to warm of climate system,
evaporate water and variation of the environmental processes.
The objective is to estimate instantaneous distribution of the net radiation over a large homogeneous areas in
Dundgovi province of Mongolia, from the Moderate Resolution Imaging Spectroradiometer (MODIS). For thisstudy, we used the parametrization scheme for downward short wave radiation have been developed by Nimelä
et al. (2001) and for downward long wave radiation developed by Prata (1996). In this paper presented theresults of estimation of the net radiation and satellite image analyses for homogeneous land surface in semi-arid
and arid environment of Mongolia. Estimated incoming short wave and long wave radiations using MODISTERRA and ground data compared with experimental station data in Dundgovi aimag of Mongolia.
Key words: solar radiation, net radiation, longwave radiation, shortwave radiation, emissivity, albedo

INTRODUCTION
Solar radiation is primary resource of heat of the earth and atmosphere and plays animportant role in air-land surface interactions; agriculture, pasture and energy from biomass,
because the growth of biomass directly depends on the photosynthesis and therefore on solar
radiation. The success of satellite remote sensing arises from that fact that it can provide an
unparalleled spatial and temporal coverage of the globe. To benefit from this tool, research is
being directed to develop better understanding of land-atmosphere interactions.
Last years several studies have attempted to estimate net radiation (or its components) by
combining remote sensing observations with ancillary surface and atmospheric data (Gautier
et al., 1980; Diak and Gautier, 1983; Jacobs et al., 2000; Ma et al., 2002). Recently, several
parameterization schemes have been developed to estimate short wave and long wave fluxes(Niemelä et al., 2001a; Niemelä et al., 2001b; Ellingson, 1995 and Pinker et al., 1995). The
climate of Mongolia has characterized by a high moisture deficit, low humidity and low
levels of incident energy. Despite 260 days (mo than 3000 hours) of sunshine, total heat units

206

above 100C rarely exceed 2000 and in some areas less than 1000. Snow cover is very light sosoils are completely frozen in the winter. As a consequence the effective vegetation growing
period is short, generally from 80 to 100, days although it can vary from 70 to 130 days
depending on latitude and location (Batjargal. Z, 2001). The pastureland and agriculturalareas occurs about 80 % of total territory of Mongolia and the main economic branch of ourcountry is the agriculture and pastoral husbandry. Mongolian agriculture is strongly
dominated by the geographical location and its climate. Spring and summer seasons have along daytime and sunshine, while winter has a short day time but lmost all clear days due tohigh pressure systems (Tugjsuren. N et al., 2002). Many agricultural and pastoral
management applications require the knowledge of surface radiation balance over a range of
temporal and spatial scales in Mongolia.
The research of solar radiation species incoming into land surfice and its variability features
is very important for agriculture for continental climate of Mongolia (Tugjsuren. N, 1991).
In this paper, we have attempted to estimate instantaneous distribution net radiation fromremotely sensed data over homogeneous areas for clear sky days in Mongolia.

STUDY SITE
The study site is Dundgovi aimag (province) of Mongolia. The coordinates are 44000′N and
46000′N, 103000′E and 109000′E, and the area is 78 thousand km2 an arid and semi-arid
region. Annual mean precipitation is about 150-250 mm and 85-90 percent of the annual
precipitation falls as rain during the summer of which 50-60% in July and August. Cloudy
skies are infrequent, and 4,467 hours of sunshine per year giving a total annual solar radiation
input of 7,793 MJ m-2.

a)
c)
b)

Fig.1. Study site. a) Mongolian territory, b) Study area of Dundgovi province, c) View ofhomogeneous area in Mandalgovi province

207

METHODOLOGY
Instantaneous net radiation (INR) estimates are obtained using various MODIS-Terra land
products (land surface temperature, land surface emissivities and land surface albedo).
Niemelä’s (2001) and Prata’s (1996) parameterization schemes are used to estimate
downward short wave and long wave flux and air emissivity respectively.
Instantaneous net radiation
Net radiation (Rn) is given by the surface radiation balance can be expressed in terms of its
components as:
Rn= R↓s - R↑s + R↓L - R↑L(1)

where R↓s and R↑s are short wave radiation fluxes downward (Wm-2) and upward (Wm-2)
respectively and R↓L and R↑L are the long wave radiation fluxes downward (Wm-2) andupward (Wm-2) respectively.
Short wave downward radiation
The short wave radiation can be expressed as:R↓s - R↑s = (1-α) R↓s(2)

where α is land surface albedo
In the calculation of downward short wave radiation (R↓s) we used the parameterization
scheme developed by Niemel et al., 2001. The downward short wave radiation can be
expressed as:R↓s =Soτsw cosθ(3)

where τsw is the atmospheric clear sky short wave transmission factor, So is the solar constantat the atmospheric top, which is about 1367 Wm-2 and θ is the solar zenith angle.
The long wave radiation can be expressed using the Steffan-Boltzmann equation as:
R↓L - R↑L = σεaT4a - σεsT4s(4)where εa is air emissivity, εs is surface emissivity, Ta is air temperature (Kelvin), Ts is land
surface temperature (Kelvin) and σ = 5.67x10-8 W m-2 K-4 is the Steffan-Boltzmann constant.
Long wave upward radiation
Hence, long wave upward radiation can be expressed as.
R↑L = σεsT4s
In this case, surface emissivity (εs) is estimated using NDVI and an empirically-derived
method in SEBAL (Van de Griend and Owe, 1993), as following.
εs = 1.009 + 0.047 In(NDVI)

(5)

208

where NDVI > 0
And, surface temperature (Ts) was computed based on formulation proposed by Price (Price,1984). The formula is expressed as follows:
Ts = BT31 + 3.33(BT31 – BT32)(6)

where BT31 and BT32 brightness temperatures from MODIS channels 31 and 32, respectively.
Long wave downward radiation
Further, we used which Prata (1996) developed a parameterization scheme for long wave
downward radiation using vapor pressure and air temperature.
Prata’s downward long wave radiation (R↓L) is defined as
R↓L = σεaT4a(7)

where εa = [1-(1+ξ) exp{-(1.2+3ξ)1/2}, and ξ=46.5eo /Ta

IMAGE ANALYSES AND RESULTS
MODIS TERRA data (22 July, 5 and 25 August, 4. 14, 21, 28 September) were used in thisstudy.
We used 1 km resolution data and broad bands albedo for ch. 1-5, 7; brightness
2002/07/22
temperature for ch. 31, 32 of MODIS TERRA for the image processing. The images were
processed 7 times during the period of end of July to September in 2002. Also we havedetermined and processed that images as follows:a) Land surface albedo α, for estimation shortwave downward radiationb) NDVI, surface emissivity εs, surface temperature Ts for estimation longwave upward
radiation
In the table 1. illustrated estimated values NDVI, surface albedo α, surface emissivity εs,surface temperature Ts on the selected points of the Dundgovi province from processed
images on dated 22 July, 25 August and 21 September, 2002
Figure 2 shows a distribution of shortwave downward radiation in 22 July, 25 August and 21
September, 2002 by MODIS TERRA. Around in this dated, the shortwave downward
radiation value was around 704.1 – 969.8 Wm-2. The shortwave downward radiation higher
value shows in July and low value in September.
Figure 3 shows a distribution of longwave upward radiation in 22 July, 25 August and 21
September, 2002 by MODIS TERRA. In the images shortwave downward radiation value
was around 325.8 – 549.9 Wm-2. The longwave upward radiation higher value shows in
August and low value in July.

209

The instantaneous distribution of net radiation in the above mentioned dated by MODISTERRA showing in Fig. 4. As seen this figure the net radiation value was around 432.6 –817.6 Wm-2. The net radiation value in July seems larger than in September for homogeneous
area of Dundgovi province.

2002/07/22

2002/08/25

969.8 Wm-2 834.3

931.6

2002/09/21

877.0 Wm-2 704.1

753.8 Wm-2

Fig. 2. Instantaneous shortwave downward radiation from MODIS-TERRA data

2002/07/22

2002/08/25

512.6 Wm-2 404.0

325.8

2002/09/21

549.9 Wm-2 363.8

454.3 Wm-2

Fig. 3. Instantaneous longwave upward radiation from MODIS-TERRA data

2002/07/22

558.9

2002/08/25

817.6 Wm-2 467.3

2002/09/21

620.2 Wm-2 432.6

Fig. 4. Instantaneous net radiation from MODIS-TERRA data

210

561.3 Wm-2

Tab.1. Estimated land surface parameters using MODIS TERRA data on the selected points.
Lat/
Long

PointsMandalgovi

45.46
106.1745.44
103.8946.22
105.4645.45
108.0044.56
106.20

Saikhan-Ovoo

Adaatsag
Bayanjargalan
Olziit

8/25
0.17

9/21
0.16

7/22
0.17

8/25
0.17

9/21
0.14

7/22
0.93

8/25
0.92

9/21
0.91

Temperature,Ts, C0
7/22
8/25
9/21
27
41
23

0.16

0.16

0.15

0.10

0.07

0.08

0.90

0.89

0.89

49

29

22

0.15

0.16

0.14

0.24

0.26

0.16

0.94

0.94

0.92

28

43

26

0.14

0.16

0.13

0.12

0.15

0.13

0.91

0.92

0.91

33

40

25

0.16

0.16

0.15

0.08

0.11

0.10

0.89

0.90

0.90

41

40

23

7/22
0.18

Albedo, α

NDVIEmissivity, εσFig. 5. and Fig. 6 shows trend of estimated and observed downward radiation fluxes from 22
July to 21 September, 2002. According to the scatter graphs for comparison of estimation andobserved downward short and long wave radiation shown in Fig.7 and Fig 8. A better
relationship is seen in the between estimated and observed short and long wave downward
radiation in study area. But should be noted that relationship is only for value at experimental
station in Mandalgovi. The regression line in the above mentioned dated is E = 1.1308x +50.357 and R2 = 0.8341 for shortwave downward radiation and E = 0.9182x + 29.02, and R2

.2
IX

8

.2
IX

1

.1

4

.4

IX

IX

II.
VI

VI

II,

2

5

25

250
200
150

VI
I.2

radiation, Wm -2

Longwave downward

Rl-Obs.

Fig.5. Estimated and observed shortwave
downward radiation from July to September in2002 at the experimental station in
Mandalgovi.

Fig.6. Estimated and observed longwave
downward radiation from July to September in2002 at the experimental station in
Mandalgovi.
Estimated longwave
downward radiation,
Wm-2

.2
IX

8

.2
IX

4
.1
IX

Rl-Est .

400
350
300

Estimated Shortwave
downward radiation,
Wm-2

IX

1

Rs-Obs.

.4

Rs-Est.

I .2
2
VI
I I,
5
VI
II.
25

1150
950
750
550
350
150
VI

Shortwave downward
radiation, Wm-2

= 0.7246 for longwave downward radiation.

110 0

y = 1.1308x - 50.357
R2 = 0.8341

10 0 0
900
800
70 0
600
6 0 0 6 50 70 0 750 8 0 0 8 50

400

2 00 2. 0 7. 2 22 00 2. 0 8. 0 5
2 00 2. 0 8. 2 5
2 00 2. 0 9. 0 4
2 00 2. 0 9. 1 4
2 00 2. 0 9. 2 1

9 0 0 9 50

2 00 2. 0 9. 2 8

y = 0.9182x + 29.02
R2 = 0.7246

2 00 2. 0 7. 2 2

3 50

2 00 2. 0 8. 0 5
2 00 2. 0 8. 2 5300

2 00 2. 0 9. 0 4
2 00 2. 0 9. 1 4

2 50

200
200

2 00 2. 0 9. 2 1
2 00 2. 0 9. 2 8

2 50

300

3 50

400

Observed Longwave downward radiation,

Observed Shortwave downward

Wm-2

-2

radiation, Wm

Fig.7. Comparison of estimated and observed
shortwave downward radiation from July to
September in 2002 at the experimental stationin Mandalgovi.

211

Fig.8. Comparison of estimated and observed
longwave downward radiation from July to
September in 2002 at the experimental stationin Mandalgovi.

CONCLUSION
In the present study, MODIS TERRA satellite and ground data were used in estimating the
instantaneous distribution of the net radiation in homogeneous area of Mongolia using theparameterization schemes for short wave radiation fluxes downward and long wave radiation
fluxes downward.
We obtained better results between estimated short wave downward radiation (R2=0.8341)
and longwave wave downward radiation (R2=0.7246) with the observed data. However, we
did not compared the estimated net radiation, due to insufficient of real measured net
radiation data on the ground. To overcome this problem, measured value of net radiation on
the ground are required to validate the results of estimation instantaneous net radiation using
satellite data.
The study was one of first attempth to estimate instantaneous distribution net radiation using
satellite and ground data. However, the study results are gave better understanding of theinstantaneous distribution of net radiation in homogeneous area of Mongolia. We should
improve the research for net radiation using satellite data in further.
Acknowledgements
We would like to thank to Prof. Takamura Tamio and Mr. Itari Okada of Atmospheric
Radiation Division of CEReS of Chiba University, for providing with the observed data at theExperiment Station in Mandalgovi province of Mongolia.
References
1. Batjargal, Z. (2001). Desertification in Mongolia. RALA Report No.200
2. Diak, G. R., & Gautier, C. (1983). Improvements to a simple physical model for
estimating insolation from GOES data. Journal of Climate and Applied Meteorology,22,505-508.
3. Gautier, C., Diak, G., & Masse, S. (1980). A Simple Physical Model to Estimate incident
Solar Radiation at the Surface from GOES Satellite Data. Journal of Applied Meteorology,
19, 1005-1012.
4. Ellingson, R. G. (1995). Surface Longwave Fluxes from Satellite Observations: A criticalreview. Remote Sensing of Environment, 51, 89-97.
5. Gautier, C., Diak, G., & Masse, S. (1980). A Simple Physical Model to Estimate Incident
Solar Radiation at the Surface from GOES Satellite Data. Journal of Applied Meteorology,
19, 1005-1012.
6. Jacobs, J. M., Myers, D. A., Anderson, M. C., & Diak, G. R. (2000). GOES surface
insolation to estimate wetlands evapotranspiration. Journal of Hydrology, 266, 53-65.
7. Ma, Y., Su, Z., Li, Z., Koike, T., & Menenti, M. (2002). Determination of regional net
radiation and soil heat flux over a heterogeneous landscape of the Tibetan Plateau.
Hydrological Processes, 16, 2963 - 2971.
212

8. Niemelä, S., Räisänen, P., & Savijärvi, H. (2001a) Comparison of surface radiative flux
parameterizations Part I: Longwave radiation. Atmospheric Research, 58, 1-18.
9. Niemelä, S., Räisänen, P., & Savijärvi, H. (2001b). Comparison of Surface radiative flux
parameterizations Part II: Shortwave radiation. Atmospheric Research, 58, 141- 54.
10. Pinker, R. T., Frouin, R., & Li, Z. (1995). A Review of Satellite Methods to Derive
Surface Shortwave Irradiance. Remote Sensing of Environment, 51, 108-124.
11. Prata, A. J. (1996). A new long-wave formula for estimating downward clear-sky
radiation at the surface. Quarterly Journal of the Royal Meteorological Society, 122,
1127-1151.
12. Price, J. C., (1984) Land surface temperature measurements from the split window
channels of the NOAA 7 Advanced Very High Resolution Radiometer. Journal ofGeophysical Research, 89, 7231-7237.
13. Tugjsuren, N. (1991). Solar radiation distribution feature in Mongolia. Bulletin of Scienceand Information Center, Mongolia, 150/1.
14. Tugjsuren, N., Takamura, T. (2001). Investigation for photosynthetically active radiation
rejime in the Mongolian grain farm region, Journal of Agric. Meteorol., 57(4), 201-207.
15. Van de Griend, A. A., and Ome, M. (1993). On the relationship between thermal
emissivity and the normalized difference vegetation index for natural surfaces. Int. Jour.
of Remote Sensing, vol 14(6), 1119-1131.

213

P-6

Measurement of NO2, SO2, O3, H2O and aerosol in the troposphereusing differential optical absorption spectroscopy (DOAS)
Toru Kimura, Toyofumi Umekawa
Kyoto Electronics Manufacturing Co.,Ltd.
68 Ninodan-cho, Kisshoin Minami-ku, Kyoto 601-8317, JapanSi Fuqi, Hiroaki Kuze, Nobuo Takeuchi
Center for Environmental Remote Sensing, Chiba University
1-33 Yoyoi-cho, Inage-ku, Chiba 263-8522, Japan
Abstract
We report on air-pollution monitoring campaign in the Kyoto and Chiba area
during 2004. With differential optical absorption spectroscopy (DOAS) method,
average concentrations of NO2, SO2, O3, H2O and aerosol particles can be measured.
For NO2, SO2, O3 and H2O, the DOAS results are found to be consistent with theresults of the ground sampling measurements. For aerosol, the optical thickness at 550
nm is correlated with the concentration of ground-measured suspended particulatematter (SPM), leading to a good correlation between the DOAS and SPM data. Also,
the mass extinction efficiency (MEE) is analyzed for aerosol particles in the lower
troposphere. From Chiba measurements, it is found that the average MEE is about 7.6
m2g-1, and the value exhibits a good inverse correlation with the particle size.
Kyewords: air pollution, mass extinction efficiency, Aerosol, Extinction coefficient1.

Introduction

The technique of differential optical absorption spectroscopy (DOAS) is widelyused to monitor atmospheric pollutant species such as NO2, NO, SO2, and O3.1-3)
These molecular species exhibit characteristic absorption patterns in the visible andUV parts of the spectrum. By using the path length of a few kilometers, and by
extracting the absorption features that vary rapidly with the wavelength, the DOAS
method enables the retrieval of molecular concentrations of the order of ppb.
In the present paper, we describe our recent DOAS results measured in Chiba
and Kyoto urban regions. The Chiba data are based on the novel method of pulsed
DOAS, in which an aviation obstruction light is utilized as the light source.3) Figure 1
shows a schematic of the experimental setup. The path length is about 5.5 km, and thewavelength range covered in this experiment is about 300-800 nm. From Chiba data,
the absorption of NO2, H2O, and the extinction due to aerosol particles areinvestigated. The measurement of aerosols is feasible by monitoring the change in thereceived intensity, since if the alignment of the system is sufficiently stable, theintensity change is brought about solely from the variation of aerosol quantity along
the light path. The monitoring of H2O along the path is important from the viewpointof the increase in the aerosol extinction efficiency due to high relative humidity.4) The214

Kyoto system, on the other hand, is operated with a cw light source dedicated to the
DOAS measurement, with a distance of 2.4 km. The operational wavelength range is
extended to 280 nm, thus making it possible to observe the absorption of SO2, O3, andNO2 simultaneously.
T e le s c o p e

5500 m
E n tr a n c e s lit
G ra tin g

L in e a r C C D a rr a y

U S B 2 0 0 0 S p e c tr o m e te r

Height 67.5m
Osaka
H e igGas
h t 1 3facility
0 m

N o te b o o k P C
H e ig h t 3 1 m
C E R e S , C h ib a U n iv e r s ity

In c in e r a to r s ta c k

Fig. 1 Experimental setup for measuring NO2 , H2O, and aerosol extinction.

2.

DOAS spectrum

2.1 Retrieval of NO2, SO2, O3 and H2O retrieval
In the Chiba measurement (resolution = 0.3 nm/channel), the wavelength range of
400-450 nm is used for NO2, 500-550 nm for aerosol, and 710-740 nm for H2O. In the
Kyoto measurement (resolution = 0.11 nm/channel), NO2 is measured in a wavelength
range of 350-360 nm, O3 in 280-290 nm, and SO2 in 295-305 nm.
The analysis of the DOAS spectra is based on the Beer-Lambert’s law expressed as

I (λ ) = kI 0 (λ )e − Lσ (λ )n ,(1)

where k is the system constant, I(λ) is the measured intensity, I0(λ) the unattenuatedreference intensity, L the path length, σ(λ) the wavelength-dependent absorption crosssection, and n the number density of the species. The dimensionless quantity τ = L σ n
is referred to as the optical thickness.
In the real atmosphere both molecules (Rayleigh scattering) and aerosols (Miescattering) contribute to the radiation extinction. Adding these contributions to eq. (1)givesI (λ )= L σ (λ )n + ε M (λ ) + ε A (λ ) − ln k .
I (λ )τ (λ ) = ln 0

(

)

(2)

Here ε M(λ) is the molecular extinction coefficient which is the product of themolecular cross section, σM(λ), and the number density of air molecules, nm. Similarly,
the aerosol extinction coefficient, εA(λ), is defined as σA(λ)na, where σA(λ) is the aerosol
cross section, and na the number density of the aerosol particles.
Cross sections in eq.(2) can be separated into two components, slowly varying one,σ is (λ ) , and rapidly varying one, σ i, (λ ) , with wavelength. Both the molecular and215

aerosol cross sections are slowly varying components of the optical thickness. Hence
we obtain
⎛

⎞

⎝ i

⎠

τ (λ ) = L∑ σ i, (λ )ni + L⎜ ∑ σ i s (λ )ai + σ M (λ )n M + σ A (λ )n A ⎟ − ln k ,i(3)where subscript i denotes trace gas species such as NO2, O3 or SO2. On the right-handside of eq. (3), the first term describes the rapidly varying part, and the second theslowly varying part.
2.2 Aerosol retrieval and MEE
The aerosol optical thickness αa can be defined asτa = α a L = ln kI 0 (λ ) − ln I (λ ) − τ Rayleigh (λ ) − τgas (λ ) ,(4)

where I, τ Rayleigh and τ gas can be obtained from the analysis of DOAS signals. Afterremoving contributions of the gas absorption and Rayleigh scattering, we candetermine the aerosol optical thickness if the correlation factor k is known. The
visibility at 550 nm, V, can be calculated from Mie extinction coefficient using thewell known Koschmieder relationship:
V = 3.912 / α a .

(5)Here we assume that the visibility of V = 50 km represents a typical condition for a
relatively clear atmosphere. Since in the Chiba case, the distance of the DOAS
measurement is 5.5 km, V=50 km leads to the aerosol optical thickness of τa = 0.43.
This value is used to empirically determine the system constant k. It is noted thatwhen the value of V is changed by ±10%, for instance, the resulting value of kchanges only slightly (k=9.0±0.2).
The basic principle of the aerosol retrieval was described in our previous paper(Yoshii et al., 2003). The following dependence on the wavelength λ is assumed forthe aerosol optical thickness τa(λ):τ a (λ ) = B (

λ

(6)

)−A

550
Here, constants A (Angstrom exponent) and B (turbidity constant, i.e. the aerosol
extinction coefficient corresponding to the wavelength of 550 nm) are to be
determined by fitting this expression to the optical thickness derived from the DOAS
spectra. The aerosol extinction coefficient at 550 nm is calculated by the relationshipof αa (550) =B/L, and the mass extinction efficiency (MEE) is obtained by

MEE= αa (550) / ρ ,(7)whereρis the SPM concentration measured by the ground sampling (with a β-ray
detector).

216

3. Results and discussion
3.1 Concentration of NO2 from DOAS and ground point measurements
In the Kyoto measurement, we measured O3, SO2, NO2, and aerosols. The Chiba
measurement, on the other hand, is used for the retrieval of NO2, H2O, and aerosols.
Figure 2(a) shows the NO2, SO2, O3 data observed for one day in Kyoto, and Fig. 2 (b)shows H2O and absolute humidity observed for one month in Chiba. From thesefigures, reasonable correlations are found between the result of the long-path DOAS
measurement and the data from ground stations below the optical paths.

60

O3 DOAS
SO2 DOAS
NO2 DOAS

24

O3 Point system
SO2 Point system
NO2 Point system

DOAS
Ground System(b)

AH (g/m3)

20

30

16

12

0
8

2004-10-29 0:00

Time

2004-10-29 23:00

9.1/7:00

9.15/10:00

9.30/17:00

Date-Time

Fig. 2. (a) One day change of NO2, SO2 and O3 concentration observed in the Kyoto
measurement on 29 October 2004. (b) One month change of absolute humidity
measured by the DOAS and ground systems in Chiba.

3

0.06

3

2

1

0.03

0
07:00

12:00

Optical thickness at 500nm

SPM
OT(a) Oct. 11

Extinction Coefficient (m-1)

3.2 Aerosol retrieval

SPM (mg/m )Concentration (ppb)(a)(b) Oct. 11
0.00040.0003y=0.0056x
R=0.920.00020.0001
0.02

0.03

0.04

0.05

SPM concentration (mg/m3)

17:00

Time

0.06

Fig. 3. (a) Optical thickness at 550 nm data and SPM data on 15 December 2004, and(b) the correlation plot to derive the MEE value (5.6 m2g-1).
Here we describe the aerosol retrieval from the Chiba data. Figure 3(a) shows thecorrelation between the SPM concentration and aerosol optical thickness at 550 nm
and Fig. 3(b) the correlation between the SPM concentration and aerosol extinction
coefficient in a day (daytime). For both cases, good correlations are evident, and theMEE value is calculated to be about 5.6 m2g-1. The variations of MEE and the217MEE (m2g-1) Angstrom exponent

Angstrom exponent during one month are shown in Fig. 4, in which a data point
represents an average of the diurnal data. Also a good correlation can be foundbetween these two parameters. The Angstrom exponent is a good measure of theparticle size, high and low values suggesting the dominance of fine and coarse
particles, respectively. This result indicates well that the particle size is influential tothe value of MEE, as recently proposed as the size effect on the MEE value.4)
2.0

Sept.

1.6
1.2
0.8
0.4

16

0

5

10

15

20

25

30

0

5

10

15

20

25

30

12
8
4
Date

Fig. 4 One month variation of the MEE and Angstrom exponent, leading to acorrelation coefficient of about 0.83. The average value for the MEE data is about 7.6
m2g-1 and that for the Angstrom exponent is 1.1.

4. Conclusion
We have applied the pulsed DOAS technique to the measurement of NO2, SO2, O3,
H2O and aerosol particles. Through the Kyoto and Chiba campaigns, reasonable
correlations have been found between the long-path and ground-based observations of
the NO2, SO2, O3 and H2O concentrations. For aerosol retrieval, the optical thickness
at 550 nm is correlated with the concentration of ground-measured suspended
particulate matter (SPM), also leading to a good correlation between the DOAS andSPM data. This correlation makes it possible to analyze the mass extinction efficiency(MEE) of aerosol particles in the lower troposphere. It is found that the monthly
average value of MEE in Chiba (September 2004) is about 7.6 m2g-1. This relatively
high value of MEE suggests the dominance of fine aerosols, though additional effectof the humidity should also be considered.
Acknowledgements
The authors are grateful to Mr. T. Tsutsui and Mr. K. Yamakawa of Kyoto
Prefectural Institute of Hygienic and Environmental Sciences for the cooperation in
the Kyoto campaign during the winter in 2004. This work is supported by the218Organization for Small & Medium Enterprises and Regional Innovation, Japan.

References
1) J. Stutz, U. Platt, Numerical analysis and estimation of the statistical error ofdifferential optical absorption spectroscopy measurements with least-squares
method. Appl. Opt. 35, 6041–6053 (1996).
2) H. Edner, P. Ragnarson, S. Spannare, S. Svanberg, Differential optical absorption
spectroscopy (DOAS) system for urban atmospheric pollution monitoring. Appl.
Opt. 32, 327–333 (1993).
3) Y. Yoshii, H. Kuze, N. Takeuchi, Long-path measurement of atmospheric NO2 withan obstruction flashlight and a charge-coupled-device spectrometer, Appl.Opt.42,
4362-4368 (2003).
4) N. Lagrosas, H. Kuze, N. Takeuchi, S. Fukagawa, G. Bagtasa, Y. Yoshii, S. Naito,
M. Yabuki, Correlation study between suspended particulate matter and portable
automated lidar data, Aerosol Science, in press (2005).

219

P-7

Analysis of the Asian Dust Aerosol Optical
Properties over the Ocean
Dodi Sudiana1, Mitsuo Minomura2, Hiroaki Kuze2, Nobuo Takeuchi2
1

Graduate School of Science and Technology, Chiba University,
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japane-mail: dodi@ceres.cr.chiba-u.ac.jp
2
Center for Environmental Remote Sensing, Chiba University,
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

Abstract
An algorithm using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiativetransfer code has been developed to analyze the optical properties of aerosols over the ocean using
NOAA/AVHRR data. Visible and NIR channel data was utilized to retrieve aerosol optical thickness at 632 nm
and 843 nm over the ocean around Japan during clear (non-dust) and Asian dust events in March 18-19, 2002.
The sea surface albedo measured during the non-dust events is used as reference, and the relation between thealbedo and the optical thickness is derived. Using Mie calculations and bi-lognormal size distribution parametersinferred from measurements, the correlation between the optical thickness of the Asian dust and the digital
number of visible and NIR channels is analyzed. Good agreements are found between the simulated results andthe optical thickness retrieved from ground-based, Sunphotometer measurements.
Keywords: aerosol optical properties, NOAA/AVHRR, 6S, Asian dust

1. INTRODUCTION
A number of studies have pointed out the significance of aerosols in the climate system[Charlson et al., 1992; Kaufman et al., 2002]. Aerosols can have a profound impact on the
global and regional climate, both by directly interacting with solar radiation and by indirectly
modifying cloud microphysics. One of the key parameters that must be accurately quantified
in measuring the aerosols in the atmospheres is aerosol optical thickness (AOT), which is ameasure of the aerosol extinction on radiative transfer [Charlson et al., 1992; Chylek andWong, 1995; Russel et al., 1997]. Various methods have been developed to measure the
distribution and quantity of AOT using ground-based Sunphotometers (SP) [Holben et al.,1998], lidar [Welton et al., 2002] and aircraft [Russel et al., 1999]. Although thesemeasurements are accurate, their space and time coverage are limited. Satellite measurements,
due to their large coverage and high temporal resolution, are the most effective way to infer
the quantity of AOT. However, in order to enhance the accuracy of aerosol properties fromsatellite measurements, the ground-based and/or aircraft measurements are still needed.
During the spring, a large amount of Asian dust (Kosa, in Japanese) is flown from dust
storms in Gobi and Taklamakan deserts in Mongolia and China. The dust particles have thepotential of affecting global climate by influencing the radiative balance of the atmosphere.
They are initiated by strong winds, caused by cold fronts or low-pressure systems. Thesedusts significantly increase the atmospheric turbidity over East Asia and Pacific regions. In
situ and satellite remote sensing studies have indicated that the dusts from the Asian continent
could be transported over the Pacific Ocean to the western coast of United States [Takemura
et al., 2002; Tratt et al., 2001; Husar et al., 2001], showing that the dust is not limited to the
Asian regional climate. Due to such a great impact of the dust on not only regional but global
climate, there is a stringent need to analyze the optical characteristics of Asian dust particles.
In this research, we analyze the distribution and magnitude of AOT during clear (nondust) and Asian dust events in spring 2002 using visible and near-infrared channels of220

Advanced Very High Resolution Radiometer (AVHRR) data from the NOAA polar orbiting
satellites. This study demonstrates a simple method to infer the AOT by utilizing the Second
Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code [Vermote
et al., 1997] over the ocean. The AOT data concurrently measured by a ground-based sunphotometer are also used to confirm the modeling results.

2. METHODOLOGY
In this research, we use the visible and near-infrared channels (channel 1 and 2) of
NOAA/AVHRR data obtained in March-April 2002. Selected areas over the Sea of Japan
with size 200×200 pixels area are analyzed for clear and dusty/Kosa cases. For the clear case(non-dust, non-cloudy), here we assume the maritime aerosol model in the 6S library to infer
the relationship between the satellite albedo and the AOT at 632 nm and 843 nm. For theturbid (dusty) case, we derive this relationship on the basis of the aerosol model of the bilognormal size distribution. Finally, the relationship between the NOAA/AVHRR albedo and
the AOT of channel 1 and 2 are retrieved using the clear sea surface reflectance achieved
from the ship measurement as reference [Kozai et al., 2000]. The algorithm of the AOT
retrieval is schematically described in Fig. 1.
Atm. Conditions
Aerosol model
AOT(550) from 0.0 to 1.0

NOAA/AVHRR(Visible and NIR
channels data) DNi
Geometric conditions

Radiance
Lobs(DNi)

6S
Total Radiance(Ltotal)|Lobs(DNi) Ltotal|<ε

Modify ρNYρ − τ(λi) for each DN
Clear waterρref(λi)
Map of τ(λi)

Fig. 1. Schematic diagram of AOT retrieval

2.1. 6S
The 6S code is a radiativetransfer code originally developed
to analyze the satellite signal
between 0.25 and 4.0 µm assuming
cloudless atmosphere. It is used fortheatmosphericradiative
calculations, as well as for the
relevant
sensitivity
analysis[Vermote et al., 1997]. Atmosphericvertical profiles of pressure,temperature, concentration of water
vapor and ozone are considered, in
addition to a Lambertian ocean
surface. The input parameters
needed in the calculations are:geometrical conditions, atmosphericmodel for gaseous components,
aerosol model, spectral condition
and ground reflectance.
As illustrated in Fig. 2, the total
radiance that irradiates the satellite
sensor can be formulated as

Ltotal = Latm (τ, Matm) + Ltar (ρ, ρ, τ, Matm)+ Lenv (ρ, τ, Matm)(1)

where Matm stands for the atmospheric model of molecules and aerosols, ρ the target albedo,
ρ environment albedo, and τ the aerosol optical thickness through the entire atmosphere.

2212.2. NOAA/AVHRR
The NOAA-16, which was launched on
September 21, 2000, has 5 channels of
AVHRR: visible, near-infrared, short-wave
infrared, and two thermal-infrared channels. It
has continuously been operated since
February 27, 2001. The satellite radiance canbe calculated by the following equation:
Lobs = (S×DN +I ) Es /100π,(2)where S and I are calibration factors of
AVHRR visible and near-infrared channels,
DN is the digital number, Es is the solar
spectral irradiance expressed in units of
Wm-2sr-1µm-1Fig. 2. The 6S radiative model

For NOAA-16, we employ the albedo Ai ofeach channel using the following equations(http://noaasis.noaa.gov/NOAASIS/ml/n16cal
up.html):
Ai = S i × DN + I i(3)Channel 1:⎧0.0523 × DN − 2.016 for DN < 497.5
A1 = ⎨⎩ 0.1528 × DN − 51.91 for DN > 497.5(4)⎧ 0.0513 × DN − 1.943 for DN < 500.3
A2 = ⎨⎩0.1510 × DN − 51.77 for DN > 500.3(5)

Channel 2:

2.3. Algorithm
First, we select an area of interest which has 200×200 pixels in size (nearly 200 km × 200km). The geometrical conditions are derived from the center point of the area and we employ
atmospheric models of mid-latitude winter and maritime aerosol model as the input
parameters for 6S simulation. AVHRR data on April 10, 2002 is selected as sample data forclear case and the data on March 18, 2001 for dusty/Kosa case. The simulation is performedby entering AOT(550) from 0.0 to 1.0 with the atmospheric and geometrical conditions. The
radiance observed by the satellite sensor is then compared with the radiance calculated by 6S.
For each AOT(550) value, the calculation is repeated by modifying the sea-surface albedo
value if the difference between the two radiances is larger than 10-5 Wm-2sr-1µm-1, arbitrarilty
set as a threshold value. For the range of digital number values that actually occur in the area,
the relationship between the value of AOT and the albedo value is calculated and stored foreach channel of AVHRR. If we can appropriately assume the surface albedo value (see thefollowing), the lookup table calculated in this manner enables the determination of AOT foreach channel (AOT(λi)).
222

Since the albedo of the sea surface (reference albedo) is the key parameter in the present
retrieval, its accuracy directly affects the accuracy of the AOT retrieved from the satellite
images. We used the measurement of clear sea surface albedo as the reference, as performed
by Kozai et al. (2000). The values measured at the effective wavelengths of channel 1(632nm) and channel 2 (843nm) are 0.28% and 0.15%, respectively.

3. RESULTS AND DISCUSSIONS
3.1 Clear case
The algorithm is applied to analyze clear area over the Sea of Japan on April 10, 2002.
The area of interest is shown in Fig. 3(a). The aerosol model is maritime and applied to thecenter point of the area (140.27oE, 46.78oN). For each digital count in the area, we calculate

8000
7000

Ch1
Ch2

Frequency

6000
5000
4000
3000
2000
1000
0
50

60

70

80

90

100 110 120 130

DN

(b)

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.00

DN
85
90
95
100
105
110
115
120
125

0.8

DN
60
65
70
75
80
85
90
95
100

0.7
0.6

τ(843)

τ(632)

(a)

0.5
0.4
0.3
0.2
0.1
0

0.02

0.04

0.06

0.00

0.02

0.04

0.06

ρ

ρ

(c)
(d)
Fig. 3. Simulation results of clear case: (a) area of interest; (b) histogram; simulation results of: (c)channel 1; (d) channel 2, respectively

223

(a)

(b)

Fig. 4. AOT map of: (a) channel 1 (λ=632nm) and (b) channel 2 (λ=843nm), respectively.
the relationship between the albedo and the AOT at 550nm wavelength. For each channel, thehistogram of digital count and the simulation results are shown in Fig. 3(b) and (c)-(d). The
aerosol model used in the simulation is maritime and the atmospheric model is midlatitude
winter. Using the sea surface albedo as reference (ρref), the AOT(λi) maps are retrieved forchannel 1 and channel 2, as shown in Fig. 4(a) and 4(b), respectively.
The simulation results of AOT showed that AOT values of channel 2 (AOT(843)) are
relatively smaller than the AOT values channel 1 (AOT(632)).
3.2 Turbid caseAccording to the observations, Asian dust/kosa events occurred in March-April 2002. For
this case, we apply the algorithm using the bi-lognormal size distribution as reported by Wang
[Wang et al., 2003]. The particle volume distribution can be computed as:2⎡ 1 ⎛ log r − log r ⎞ 2 ⎤
dV
10 vn
⎟⎟ ⎥,
(6)
= ∑ C n exp ⎢− ⎜⎜ 10
log10 σ nd log10 r n =1⎢⎣ 2 ⎝⎠ ⎥⎦where n is the mode number, rvn is the volume mean radius, σn is the standard deviation, andCn is the peak of n-th mode. In this study, we used rvn value of 0.18 µm and 1.74 µm, and σnof 2.16 and 1.78, which are derived by Wang et al. (2003), to represent the first and second
mode, respectively. The refractive index is highly variable depending on the chemicalcompositions of aerosols [d’Almeida et al., 1991]. While the same value of the real part of theeffective refractive index (i.e. 1.50-1.55) is adopted in several literatures, the imaginary partvaries from non-absorbing [Rao et al., 1989] to values considerably absorbing values of
0.003-0.005i [Higurashi and Nakajima, 2002]. A survey of aerosol network AERONET hasreveled that the imaginary part of the refractive index of desert dust and oceanic aerosols
range from 0.0015 to 0.0007 [Dubovik et al., 2002]. In this study we use the refractive index
of 1.53 – 0.002i at 632 nm and 843 nm. Figure 5(a)-(f) shows the area of interest of
NOAA/AVHRR data on March 18, 2002; the histogram; simulation results and AOT(λ) mapof channel 1 and 2, respectively.
224

3000

Ch1

2500

Ch2

Frequency

2000
1500
1000
500
0
100

110

120

130

140

150

160

170

180

DN

(a)

(b)
DN

1

140
145

DN

1

150

0.9

110
115

0.9

120

160

0.8

125

0.7

165

0.7

130

0.6

170

τ(632)

0.8

175

0.5

180

0.4

Ref

τ(843)

155

140

0.5

145

0.4

150

0.3

0.3

0.2

0.2

0.1

0.1

0

135

0.6

Ref

0
0

0.02

0.04

0.06

0.08

0

0.1

0.02

0.04

0.06

0.08

0.1

ρ

ρ

(c)

(d)

(e)
(f)
Fig. 5. The simulation results of dusty case: (a) area of interest; (b) histogram; simulation
result of: (c) channel 1; (d) channel 2, respectively; the AOT(λ) of: (e) channel 1 and (f)channel 2.
2253.3. Validation

In order to check the validity of the simulation, we compare the AOT(λi) with themeasurement of sun-photometers operated at Chiba University (35.62oN, 140.12oE). The
NOAA/AVHRR on March 17, 2002 is used as a sample data. The site of the validation site isshown as a star in Fig. 6 which according to the NOAA header data, was scanned at 12:52:09
JST. The aerosol model used in the simulation is bi-lognormal and ρref is assumed to be
0.28% and 0.15% as mentioned above. Since the sun-photometer bands are centered at 368
nm, 500 nm, 675 nm, 778 nm (each having a width of 5 nm), the AOT at 632 nm and 843 nm
is calculated by interpolation. From the AVHRR channel 1, the resulting value of AOT(632)
is 0.66. In comparison, the concurrent sun-photometer measurement leads to a value(AOTSP632) of 0.66 (relative error ξ = 0.43%). On the other hand, from channel 2, theAOT(843) is 0.57, comparing to AOTSP843 = 0.58 gives the relative error ξ = 3.11%. Using
maritime model, for another validation result on March 19, we obtain AOT(550) of 0.3306,
whereas AOTsp550 is 0.3235 (relative error ξ = 2.2%).
These results show that a ρref(λ1) value of 0.28% and ρref(λ2) value of 0.15% and aerosol
model (maritime, bi-lognormal) utilizing in the 6S code are indeed appropriate for accurately
estimating the AOT.

1.0

0.0

(a)

(b)

Fig. 6. The AOT retrieved from the simulation: (a) AOT(632); (b) AOT(843)

4. SUMMARY
The radiance from the NOAA/AVHRR is simulated using 6S radiative transfer code to
retrieve the aerosol optical thickness over the ocean during clear/non-dust and Asian dust
events. The model based on maritime and bi-lognormal size distribution for clear and turbid
cases, respectively. The simulation results indicate that by using the sea-surface albedo
measured from the ship as the reference, the AOT values derived from the satellite images
satisfactorily agree with the values from ground-based measurements.
226

5. REFERENCESCharlson, R.J., S.E. Schwartz, J.M. Hales, R. D., Cess, J.A. Coakley, J.E. Hansen, and D.J. Hoffman, Climate
forcing by anthropogenesis aerosols, Science, 255, 423-430, 1992.
Chylek, P., and J. Wong, Effect of absorbing aerosols on global radiation budget, Geophys. Res. Lett., 22, 929931, 1995.
d'Almeida, G.A., P. Koepke, and E.P. Shettle, Atmospheric Climatology and Radiative Characteristics, 561 pp.,
A. Deepak, Hampton, Va., 1991.
Dubovik, O., B. Holben, T.R. Ech, A. Smirnov., Y.J. Kaufman, M.D. King, D. Tanre, and I. Slutsker, Variabilityof absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59,
590-608, 2002.
Higurashi, A., and T. Nakajima, Development of two-channel aerosol retrieval algorithm on a global scale using
NOAA AVHRR, J. Atmos. Sci., 56, 924-941, 1999.
Holben, B. N., T. F. Eck, I. Slutsker, D. Tanré, J. P. Buis, A. W. Setzer, E. F. Vermote, J. A. Reagan, Y. J.
Kaufman, T. Nakajima, F. Lavenu, I. Jankowiak, and A. Smirnov, AERONET-A federated instrument
network and data archive for aerosol characterization. Remote Sens. of Env. , 66, 1-16, 1998.
Husar, R.B. et al., Asian dust events of April 1998, J. Geophys. Res., 106, 18,317-18,330, 2001.
Kaufman, Y.J., D. Tanré, and O. Boucher, A satellite view of aerosols in climate systems, Nature, 419, 215-223,
2002.
Kozai, K., K. Ishida, M. Kuskari, M. Sasaki, Evaluation of aerosol effect on the satellite-derived ocean color in
the western equatorial Pacific ocean using 6S code, Umi to Sora, 76(2), 41-44, 2000.
Rao, C.R. L.L. Stowe, and P. McClain, Remote sensing of aerosols over oceans using AVHRR data, theory,
practice and application, Int. J. Remote Sens., 10, 743-749, 1989.
Russel, P.B., S.A. Kinne, and R. W. Bergstrom, Aerosol climate effects: Local radiative forcing and column
closure experiments, J. Geophys. Res., 102, 9397-9408, 1997.
Russel, P.B., J.M. Livingstone, P. Hignett, S. Kinne, J. Wong, A. Chien, R. Bergstrom, P. Durkee, and P.V.
Hobbs, Aerosol-induced radiative flux changes off the US mid-Atlantic coast: Comparison of values
calculated from Sun photometer and in situ data with those measured by airborne pyranometer, J. Geophys.
Res., 104, 2289-2307, 1999.
Takemura T., I. Uno. T. Nakajima, A. Higurashi, and I. Sano, Modeling study of long-range transport of Asian
dust and anthropogenic aerosols from east Asia, Geophys. Res. Lett., 29(24), 2158,
doi:10.1029/2002GL016251, 2002.
Tratt, D.M., R.J. Frouin, an D.L. Wesphal, April Asian dust event: A southern California perspective, J. Geophys.
Res., 106, 18, 18,371-18,379, 2001.
Vermote, E.F., D. Tanré, J.L. Deuzé, M. Herman, and J.-J. Morcrette, Second simulation of the satellite signal in
the solar spectrum, 6S: An overview. IEEE Trans. on Geosci. and Remote Sens., 35, 675-686, 1997.
Wang J., S.A. Christopher, F. Brechtel, J. Kim, B. Schmid, J. Redeman, P.B. Russel, P. Quinn, and B. Holben,
Geostationary satellite retrieval of aerosol optical thickness during ACE-Asia, J. Geophys. Res., 108(D23),
8657, doi:10.1029/2003JD003580, 2003.
Welton E.J., K.J. Voss, P.K. Quinn, P. Flatau, K. Markowics, J. Campbell, J.D. Spinhirne, H.R. Gordon, and J.
Johnson, Measurements of aerosol vertical profiles and optical properties during INDOEX 1999 using
micropulse lidars, J. Geophys. Res., 107(D19), 8019, doi:10.1029/2000JD000038, 2002.

227

P-8

Retrieval of aerosol optical properties over Chiba
land area from Landsat/TM imagery- Part II: Determination of aerosol size distribution Koji Asakuma*, Mitsuo Minomura**, Hiroaki Kuze** and Nobuo Takeuchi***Faculty of Bio-industry, Tokyo University of Agriculture
196 Yasaka, Abashiri 099-2493, Japan** Center for Environmental Remote Sensing (CEReS), Chiba University,
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Key Words: Atmospheric correction, Aerosol optical properties, Aerosol optical thickness, Landsat/TM

Abstract:
We present a case study in which aerosol optical properties over Chiba land area are derived fromLandsat/TM visible channel data. First, the surface albedo value is obtained for each pixel from theatmospherically corrected TM image (January 14, 1999) with relatively small aerosol optical thickness(AOT). After the correction using the 6S code, this ‘clean’ image provides the “ρ map” that can be used
as a reference. Second, a test image (December 13, 1998) with relatively large AOT is compared with theclean image to study the aerosol optical properties. By changing the aerosol particle radius, this test imageis also subjected to the atmospheric correction, and the resulting surface albedo is compared with the ρmap. The parameter optimization is carried out on the basis of the standard WMO-WCP55 continental
aerosol model, with the contribution from WMO-WCP112 water-soluble particles. The aerosol particle
radius over Chiba area on December 13, 1998 is found to be 4.0×10-3 µm, slightly smaller than theradius of the standard water-soluble particles.

1. INTRODUCTION
Atmospheric correction is indispensable to retrieve accurate surface albedo fromsatellite images in the visible and near infrared bands. The essential parameter for thecorrection is the accurate value of aerosol optical thickness (AOT) [1-3]. The
wavelength dependence of AOT is also important, and in general, this is automaticallygiven by the aerosol model (such as the continental, maritime or urban aerosol model)[2,3]. The actual aerosol property at the time of the satellite overpass, however, is
unknown for most cases. On the other hand, simulation studies indicate that therelationship between the ground albedo and the AOT can vary significantly inaccordance with the aerosol properties such as the size distribution and the refractive228

index. Thus, even if the value of AOT is given from the ground observation (e.g. bymeans of a sun photometer), different assumption about the aerosol model can lead to adifferent value of the surface albedo as a result of the atmospheric correction [3].
In this paper we present an algorithm of deriving the aerosol size distribution fromLandsat/TM images. The algorithm is demonstrated on the image observed over the
Chiba land area. By means of the radiative transfer calculation, the aerosol size
distribution is determined from the condition that the apparent albedo at the top of theatmosphere agrees with the “reference” surface albedo. This reference albedo is
obtained from the atmospheric correction of the ‘clear’ image (see Part I: Determinationof spatial distribution of aerosol optical thickness). On the other hand, the apparent
albedo is obtained from the TM image with ‘turbid’ atmospheric condition. As for the
AOT values, we make use of the values measured by a sun photometer operated on thecampus of Chiba University.

2. METHODOLOGY
We employ the 6S radiative transfer code [4] for determination of the aerosol size
distribution. The code gives the apparent albedo once the surface albedo, AOT and theaerosol model are specified. In addition to the seven standard models such as thecontinental, maritime, and urban models, the 6S code allows the user to specify
detailed aerosol parameters in a form of the multi-modal log normal distribution, themodified gamma distribution, or the Junge power-law distribution. In the standard
aerosol model equipped in the 6S code, the user can also modify the volumetricpercentage of four basic components (dust-like, water-soluble, oceanic and sootaerosols). Table 1 shows the default percentage of these components [5], given as the
WMO-WPC55 standard aerosol models. While the continental and maritime models
have large influence of the water-soluble component, the urban aerosol model isremarkably contributed by soot aerosols with large absorption.
As explained in Part I, the analysis of TM data over Chiba land area indicates thatreasonable values of the AOT can be retrieved when continental or maritime aerosol
models are employed, but not with the urban model. In view of this effect, here we relyon the continental model, and the surface albedo is simulated for various values of theparticle radius RM of the water-soluble component. Table 2 shows the specification offour basic components given in WMO-WCP112, where the mean particle radius of thewater-soluble component is 0.005 µm. Here we change the value of this parameter in arange of 0.001 to 1.0 µm. In order to focus on the effect of this size distribution, otherparameters including the reflective index are fixed in the present simulation.

229

Table 1. Volumetric percentage for WMO-WPC55 standard aerosol models.
Dust Like
Continental
Maritime
Urban

2.265×10

Water Soluble-6--1.651×10-7

0.9383

Oceanic

Soot---

0.06170.99964.208×10

0.5925----4--0.4075

Table 2. Specifications of the four basic components in WMO-WCP112:
RM is the mean radius,
the standard deviation, V the volumeconcentration, N the particle number concentration, and nr-ini therefractive index.
Dust Like

Water Soluble

Oceanic

Soot

RM [ m]

0.500

0.005

0.300

1.180×10-2

σ

2.99

2.99

2.51

2.00

1.140×102

1.140×10-4

5.114

5.978×10-5

N [cm-3]

5.473

1.869×106

2.761×102

1.806×106

nr at 488 nm

1.530

1.530

1.377

1.750

ni at 488 nm

8.00×10-3

5.00×10-3

1.38×10-8

4.50×10-1

V [ m3/cm3]

The best value of the particle radius RM is sought under the condition that ground
albedo values in the clear-day image agree with the values from the turbid-day image
after the atmospheric correction. The target pixel is the one that corresponds to thelocation of the Chiba University. From the data on January 14, 1999 (clear day), we
obtain ρ(C)485 = 0.14 and ρ(C)560 = 0.16 for TM channel 1 and 2, respectively. The turbid
image is observed on December 13, 1998 with AOT values (observed at Chiba
University) of τ485 = 0.294 at 485 nm and τ560 = 0.241 at 560 nm.

3. RESULTS AND CONCLUSION
Figure 1 shows the relationship between the aerosol particle radius RM and theground albedo ρ(T)485 at 485 nm (TM channel 1) obtained from the turbid image. Thisvalue has been obtained through the radiative transfer calculation by assuming that thecalculated AOT at 485 nm (τ’485) is equal to the value from the sun photometer (τ485). In
the calculation, the AOT value at 550 nm (τ550) was changed from 0.0 to 0.4. The digital
number (DN) of the target pixel is 75 for channel 1. The ground albedo ρ(T)560 at 560 nm(TM channel 2) is also obtained from the same calculation with the corresponding pixel
DN value of 31. In Fig. 1, the calculated albedo ρ(T)485 for channel 1 agrees with thereference albedo ρ(C)485 = 0.14 at RM = 4.0×10-3 and RM = 4.7×10-2 µm. Similarly, we
obtain ρ(T)560 = ρ(C)560 = 0.16 for channel 2 for the particle radius of RM = 4.0×10-3
and RM = 6.5×10-2 µm. Consequently it is likely that the aerosol model over Chiba area
230

0.30
ρ(T)485
ρ(T)560

0.28
ground albedo ρ

0.26
0.24
0.22
0.20
0.18
0.16
0.14
0.12
0.10
0.001

0.004

0.047 0.065

0.010
0.100
particle radius (µ m)

1.000Figure 1: Relationship between the aerosol particle radius λm andatmospherically corrected ground albedo. The solid line shows theresult at 485 nm (TM channel 1) and dotted line is at 560 nm(channel 2).
on December 13, 1998 can be described as the continental aerosol model with theparticle radius of 4.0×10-3 µm. This value is only slightly smaller than the standard
water-soluble particle which is the main component of the continental model.

RREFERECES
1. H. Oudirari and E. F. Vermote, Operational atmospheric correction of Landsat TM
data, Remote Sensing Env. 29, pp. 4 – 15, 1999.
2. D. Tanre, Y. J. Kaufman, M. Herman and S. Mattoo, Remote sensing of aerosol
properties over oceans using MODIS/EOS spectral radiances, J. Geophys. Res. 102(D14), pp. 16971 – 16988, 1997.
3. H. Kuze, M. Minomura, Y. Furusawa, Y. Todate and N. Takeuchi, Atmosphericcorrection of satellite data over Chiba area, Proceedings of the CEReS international
symposium on remote sensing ‘Monitoring of environmenta change in ASIA’, pp. 139– 144, 2003.
4. E. F. Vermote, D. Tanre, J. L. Deuze, M. Herman and J. J. Mocrette, Second
simulation of the satellite signal in the solar spectrum, 6S: An overview, IEEE Trans.
Geosci. Remote Sensing 35 (3), pp. 657 – 686, 1997.
5. E. F. Vermote, D. Tanre, J. L. Deuze, M. Herman and J. J. Mocrette, 6S User Guide
Version 2 ‘Subroutine Mie and Exscphase’, pp. 109 – 127, 1997.

231

P-9

Micro Pulse Lidar Observation of Low Relative
Humidity Layer
Jin-jia Guo Zhi-shen Liu

Zhao-ai Yan

Ocean Remote Sensing Laboratory of the Ministry of Education of China
Ocean Remote Sensing Institute, Ocean University of China, Qingdao, 266003, China
Email: guojj@orsi.ouc.edu.cn

Tel: 86-532-2032901-809 FAX:86-532-2032992

Abstract: A very clear layer from 3km to 5km was observed by Micro pulse lidar(MPL) on April 14th, 2004 in Qingdao. This was in agreement with the measurement
results of High Spectral Resolution Lidar (HSRL). From the raw data of radiosondes,
we found from 3km to 5km the relative humidity (RH) was very low and kept as aconstant. This low RH layer was also found in a large area mainly in Shandong
peninsula and west coast of Korea.

Key Words: lidar, radiosond, relative humidity
Introduction: Lidar is a unique remote-sensing technique to obtain the verticalprofiles of various optical properties of atmospheric aerosol and meteorologicalinformation. A eye-safe micro pulse lidar(MPL) and a High Spectral Resolution Lidar(HSRL) were used to make regular measurements every night in Ocean Remote
Sensing Laboratory of the Ministry of Education of China, Ocean University of China,
Qingdao, China. On April 14th when we observed with the MPL at 21:00 (local time),
we found the backscattering signal from 3Km to 5Km was very weak (Fig 1). At first
we thought it was caused by pulse pile up because the micro pulse lidar worked at
photon counting mode and the backscattering signal between 3Km to 5Km was toostrong to count. We checked the High Spectral Resolution Lidar signal which worked
at analog mode. And also found the backscattering signal from 3Km to 5Km was very
weak. From the radiosond data at 20:00 (local time) on April 14th, we found therelative humidity was very low from 3Km to 5Km and kept as a constant (2%). And
from the radiosond data (Fig 2) we found this low relative humidity layer not only
existed in Qingdao(120:19E,36:04N) but also in Jinan (117:02E, 36:40N). So we
assumed this low RH layer was in a large scale area. From the radiosond network (Fig.
3) data, we found our assumption was correct. And the low RH layer was mainly in
Shandong peninsula and west coast of Korea (Fig. 4).

232

Fig 1 A very clear layer was found by MPL at 21:00 in Qingdao on April 14th from 3Km to 5Km.
10000

Jinan

Qingdao

Altitude(m)

8000

6000

4000

2000

0
0

20

40

60

80

100

Relative Humidity

0

20

40

60

80

100

Relative Humidity

Fig. 2 Radiosond data showed that Qingdao and Jinan both had the low RH layer from 3 Km to
5Km, which was in agreement with the results of lidar observation.

233

42

90
80

Latitude

40

70
60

38

50

36

40
30

34

20

32
112 114 116 118 120 122 124 126 128 130 132

100Longitude
Fig. 4 Relative Humidity field distribution in 700hpa pressure. It showed that the low RH layer
exist in a in a large area, mainly in Shandong peninsula and west coast of Korea. The red points
show the location of radiosond station.

234

P-10

Cloud optical thickness and effective particle radiusderived from transmitted solar radiation
measurements: Comparison with cloud radarobservations
Nobuhiro Kikuchi, Hiroshi Kumagai and Hiroshi Kuroiwa
National Institute of Information and Communications Technology

Teruyuki Nakajima
Center for Climate System Research, University of Tokyo

Akihide Kamei
National Institute for Environmental Studies

Ryosuke Nakamura
Japan Aerospace Exploration Agency
A method is presented for determining the optical thickness and effective particle radius of stratiform clouds
from transmitted solar radiation measurements. The procedure compares measurements of the transmissionfunction at water-absorbing and non-absorbing wavelengths with lookup tables of the transmission function
precomputed for plane-parallel, vertically homogeneous clouds. Cloud thermodynamic phase may also be
inferred from transmission function measurements at 1.02, 1.6, and 2.2 µm. The cloud optical thickness
determined from transmission function measurements may be used to derive vertical profiles of cloud
microphysics in combination with radar reflectivity factor. We have also developed an algorithm for solving theradar equation with a constraint of cloud optical thickness at visible wavelength. Observations of clouds wereconducted during August and September in 2003 at Koganei, Tokyo, Japan, using a PREDE i-skyradimeter anda 95-GHz cloud radar SPIDER. Optical thickness and effective particle radius of water clouds were derived fromi-skyradiometer observations. Then, utilizing the optical thickness obtained from i-skyradiometer observations,vertical profiles of the effective particle radius and liquid water content were derived from SPIDER observations.
We found that the effective particle radius derived by using these two instruments were in good agreement.
Key Words: cloud remote sensing

1. INTRODUCTION
It is commonly accepted that the cloud radiative effect is one of the most uncertain factors in
predicting future global warming. Cloud radiative properties depend largely on the optical
thickness and particle size. So far, several methods to remotely sense these parameters havebeen proposed, in which solar radiation reflected by clouds is measured at visible andnear-infrared wavelengths from aircrafts or satellites (e.g., Nakajima and King 1990;
235

Nakajima and Nakajima 1995).
This paper deals with ground-based measurements of transmitted solar radiation by amulti-spectral radiometer. As in the solar reflectance method, the principle of a method
presented in this paper to infer cloud optical thickness and particle size is the fact that thetransmission function of clouds at non-absorbing wavelengths is primarily a function of the
optical thickness, whereas the transmission function at water-absorbing wavelengths is
primarily a function of the particle size.
In deriving the cloud optical thickness and particle size from reflected/transmitted solar
radiation measurements, clouds are usually assumed to be vertically homogeneous. In reality,
water clouds contain significant vertically inhomogeneity, which may influence the retrieval
results. To address this issue, 95-GHz cloud radar observations were also made. We alsopresent an algorithm for solving the radar equation with a constraint of cloud optical thickness
at visible wavelength, by which we can derive vertical profiles of cloud microphysics.

2. CLOUD OPTICAL THICKNESS AND EFFECTIVE RADIUSDERIVED FROM TRANSMISSION FUNCTION MEASUREMENTS
Let us consider the solar radiation incident on a plane-parallel atmosphere. We express the
diffusely transmitted radiation at the bottom of the atmosphere as I(τc,µ,φ). The transmissionfunction T(τc;µ,µ0,φ) is then defined by
πI (τ c , µ ,φ )T (τ c ; µ , µ0 ,φ ) =
.
(1)
µ0 F0

In this expression, τc is the optical thickness of the atmosphere (or cloud), µ is the absolutevalue of the cosine of the observation angle measured with respect to the positive τc direction,
φ is the relative azimuth angle between the direction of propagation of the emergent radiationand incident solar radiation, µ0 is the cosine of the solar zenith angle, and F0 is the incident
solar flux density. Since observations presented in this paper were made at zenith, we will notconsider the dependence of the transmission function on µ and φ.
For a cloud particle size distribution, we adopt a log-normal distribution of the formn( r ) =⎡ (ln r − ln r0 ) 2 ⎤N
exp ⎢−
⎥,
2σ 2
2π σr
⎦
⎣

(2)

where N is the total number concentration, r0 is the mode radius, and σ is the standard
deviation. In terms of these parameters, the effective particle radius may be expressed as

re = r0 exp(5σ 2 / 2) .
In this paper, we assume σ = 0.35.
We computed the transmission function for various values of τc, re, and the solar zenith

236

(3)

angle θ0 using rstar-4b, which is an accurate and efficient atmospheric radiation transfer code
(Nakajima and Tanaka 1986, 1988). Figure 1 demonstrates the principles of the simultaneous
determination of τc and re. The left panel shows computed relationships between thetransmission function at 1.02 and 1.6 µm for water clouds when θ0 = 30°. The dashed lines
represent the transmission function that results for specified values of τc, whereas the solidlines represent the transmission function that results for specified values of re. If cloudthermodynamic phase is known a priori, τc and re may be determined from transmissionfunction measurements at these two wavelengths.
In general, cloud thermodynamic phase is not known. Therefore, transmission function
measurements at 1.02 and 1.6 µm alone will not suffice to unambiguously determine τc and re.
This ambiguity may be reduced by the additional 2.2 µm channel. The right panel of Fig. 1 issimilar to the left panel, but for computed relationships between the transmission function at
1.02 and 2.2 µm. If the cloud we now observe is actually a water cloud, and if we use lookup
tables computed for water clouds, then almost same values of τc and re should be derived fromanalysis using either 1.6 µm or 2.2 µm. On the other hand, if the cloud is composed of ice
particles, we should get better agreement between 1.6 µm and 2.2 µm from lookup tables
computed for ice particles. This is the principle for inferring cloud thermodynamic phase.

Figure 1. Computed relationships between the transmission function at 1.02 and 1.6 µm (left) and1.02 and 2.2 µm (right) for various values of the cloud optical thickness and effective particle radius.

3. CLOUD VERTICAL PROFILES DERIVED FROM RADAROBSERVATIONS
The radar signal P(R) from the range R can be written in units of power asP( R) =RC
Z e ( R) exp ⎡− 2∫ σ ext ( R′)dR′⎤ ,
2
⎢⎣
⎥⎦
0
R

237

(4)

where Ze is the effective reflectivity factor and σext is the extinction coefficient. For a cloudwith particle size distribution n(r), Ze and σext are expressed as
Ze =

λ4

∞

∫C
π K
2

5

0

back

(r )n(r )dr ,

(5)

and
∞

σ ext = ∫ Cext (r )n(r )dr ,0(6)where λ is the radar wavelength, K is defined using complex refractive index of water as K =(m2−1)/(m2+1), and Cback(r) and Cext(r) are the backscattering and extinction cross section of aparticle with radius r, respectively.
We denote liquid/ice water content by ρ. Then Ze and σext may be written as
Z e = ρς (re ) ,σ ext = ρκ (re ) ,

(7)
(8)

where ζ(re) is the effective reflectivity factor per unit liquid/ice water content, and κ(re) is themass extinction coefficient. For a particle size distribution given by equation (2), ζ and κ may
be computed as a function of the effective radius re.
Kumagai et al. (2000) presented a method for solving equation (4) to obtain vertical profilesof cloud microphysics ρ and re, with a constraint of total liquid water path obtained from a
microwave radiometer. We have developed another method for solving equation (4) with aconstraint of the cloud optical thickness at visible wavelength, which may be obtained fromtransmission function measurements as described in Section 2. Details of our method will be
described elsewhere.

4. RESULTS
Ground-based observations of clouds were conducted during August and September in 2003
at Koganei (35.71°N, 139.49°E), Tokyo, Japan. The instruments used were a PREDE
i-skyradiometer and a 95-GHz cloud radar SPIDER.
Figure 2 shows cloud optical thickness (top panel) and effective particle radius (bottom
panel) derived from the transmission function observed by the i-skyradiometer. For the time
period shown in Fig. 2, our algorithm suggests that cloud particles were in liquid water phase.
At the same time, SPIDER detected a cloud layer at about 1 km height, indicating that thecloud thermodynamic phase was properly determined by our algorithm.
The cloud optical thickness shown in Fig. 2 was used, in combination with radar reflectivity
factor taken by SPIDER, to derive vertical profiles of cloud microphysics using the algorithm
presented in Section 3. Figure 3 shows the time-height profile of the effective particle radius
for the same time period as Fig. 2.

238

Figure 2. Cloud optical thickness (top) and effective particle radius (bottom) derived fromtransmission function measurement.

Figure 3. Effective particle radius derived from SPIDER with a constraint of cloud optical thickness
obtained from i-skyradiometer.

239

From this analysis, minimum and maximum values of the effective radius are determinedalong the vertical direction, which are denoted by vertical bars in bottom panel of Fig. 2.
Comparing the effective radius derived by the two different algorithms, we find good
agreement between them.

5. CONCLUSIONS
We have developed an algorithm to derive the optical thickness and effective particle radiusof stratiform clouds from measurements of the transmission function at near-infrared
wavelengths. A method for deriving cloud vertical profiles from cloud radar observations has
also been introduced, in which cloud optical thickness at visible wavelength is used as aconstraint in solving the radar equation. Good agreement was obtained between these two
methods for the effective particle radius, suggesting that the method to solve the radar
equation with a constraint of visible optical thickness is also promising.

REFERENCES
Kumagai, H., H. Horie, H. Kuroiwa, H. Okamoto and S. Iwasaki, 2000: Retrieval of cloud microphysics using
95-GHz cloud radar and microwave radiometer, Proc. SPIE, 4152, 364−371.
Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and effective radius of clouds fromreflected solar radiation measurements. Part I: Theory. J. Atmos. Sci., 47, 1878−1893
Nakajima, T., and M. Tanaka, 1986: Matrix formulation for the transfer of solar radiation in a plane-parallel
scattering atmosphere, J. Quant. Spectrosc. Radiat. Transfer, 35, 13−21.
Nakajima, T., and M. Tanaka, 1988: Algorithms for radiative intensity calculations in moderately thickatmospheres using a truncation approximation, J. Quant. Spectrosc. Radiat. Transfer, 40, 51−69.
Nakajima, T. Y., and T. Nakajima, 1995: Wide-area determination of cloud microphysical properties fromNOAA AVHRR measurements for FIRE and ASTEX Regions, J. Atmos. Sci., 52, 4043−4059

240

AUTHOR INDEX

Akita,

K.

160

Kouga, I.

196

Asakuma, K.

228

Kozai,K.

48

Asano,

S.

150

Kubo,

H.

160

Asaoka, Y.

196

Kuji,

M.

166

Baasandai, E.

100

Kumagai, H.

154, 160, 235

Bagtasa, G.

200

Kuroiwa, H.

235

Batbayar, J.

206

Kuze,

58, 196, 200, 214, 220, 228

Batmunkh, T.

136

Lagrosas, N.

200

Dim, J. R.

19, 51

Li, A.

118

Dulam, Jugder

100, 136

Liu, B.-Y.

65

Fukagawa, S.

196, 200

Liu, G.-G.

185

Guo, J.-J.

232

Liu, J.-G.

118

Hamada, S.

136

Liu, W.-Q.

118

Hara, K.

85

Liu, Z.-S.

65, 232

Hayasaka, T.

132

Lu, Y.-H.

118

Hu, B.

185

Ma, Z.-Q.

185

Iino, N.

136

Minomura, M.

58, 220, 228

Ishikawa, H.

12

Mueller, D.

73

Ishizaka, Y.

91

Mukai, S.

158

Iyengar, GR.

142

Murayama, T.

73

Kamei, A.

235

Naito, S.

200

Katagiri, S.

32

Nakajima, Takashi Y.

32, 51

Kawamoto, K.

132

Nakajima, Teruyuki

1, 32, 160, 235

Kawamura, Y.

160

Nakamura, R.

235

Kawata, Y.

43Nakanishi, Y.

160

Khatri, P. W. Jr.

91

Ogawa, S.

196

Kikuchi, N.

166, 235

Okada, I.

19, 51

Kikukawa, H.

136

Okada, Y.

158

Kimura, T.

214

Okayama, H.

175

Kinoshita, K.

136

Oku, Y.

12

Kobayashi, H.

85

Pinker, R.

123

Koike, T.

26

Qiu, J.-H.

83

Kojima, M.

150

Rajan, D.

142

241

H.

Raschke, E.

35

Yang, K.

26

Rossow, W. B.

5

Yasumoto, M.

158

Saitoh, Y.

73

Yoshii, Y.

200

Sano, I.

158

Zhang, G.

136

Sasaki, A.

48

Zhang, Y.-C.

5

Sasaki, M.

196

Zhang, Y.-J.

118

Schutgens, N.

154

Sekiguchi, M.

73

Shi, G.-G.

132

Shiobara, M.

85

Si , F.-Q.

214

Stackhouse, P. W. Jr.

5

Su, Z.-B.

12

Sudiana, D.

220

Sun, Z.-B.

65

Takamura, T.

19, 51,91, 150, 160

Takano, T.

160

Takemata, K.

43

Takenaka, H.

19, 51

Takeuchi, N.

58, 196, 200, 214, 220, 228

Todate, Y.

58

Tugjsuren, N.

206

Tuya,

206

S.

Uchiyama, A.

166

Umekawa, T.

214

Umeki, K.

43

Wada,

M.

35

Wada,

K.

73

Wandinger, U.

115

Wang, N.

136

Wang, Y.-S.

109, 185

Xie, P.-H.

118

Xu, J.-Q.

132

Yabuki, M.

85, 200

Yamanouchi, T.

35

Yan, Z.-A.

65, 232242