Proceedings of the 16th CEReS International Symposium on
Climate Change Studies through Activities of
SKYNET and Virtual Laboratory for Climate Diagnostics

October 21-23, 2010
Nago, Okinawa, Japan

The 16th CEReS International Symposium has been
supported and sponsored by the National University
Corporation Chiba University and the Japan Society of thePromotion of Science (JSPS) under the CEReS researchframes of the Virtual Laboratory for the climate
diagnostics, SKYNET observation activity and CEReS
cooperative program.
Published byCenter for Environmental Remote Sensing (CEReS),
Chiba University, Japan
1-33 Yayoi-cho, Inage, Chiba, 263-8522 Japan

This compilation ©2010, CEReS, Chiba University
Authors Retain All Rights to Individual Manuscript.

Proceedings of the 16th CEReS International Symposium on
Climate Change Studies through Activities of SKYNET and
Virtual Laboratory for Climate Diagnostics

October 21 – 23, 2010
Nago, Okinawa, Japan

Organized by

Japan Society for the Promotion of Science
Center for Environmental Remote Sensing (CEReS), Chiba University, Japan
SKYNET
Formation of a Virtual Laboratory for Diagnosing the Earth’s Climate System(Editors)
M. K. Yamamoto, A. Higuchi, and T. Takamura

Contents

Evaluation and data assimilation of aerosol distributions and climate effects simulated by
SPRINTARS using remote sensing observations in the Asia region
Takemura, T. (Invited) ------------------------------------------------------------------------------------

1

The use of SKYNET observations for aerosol assimilation
Schutgens, N. -----------------------------------------------------------------------------------------------

3

Estimation of radiation budget using geostationary satellites
Takenaka, H. ------------------------------------------------------------------------------------------------

7

Retrieval methods of microphysical and optical properties of cloud vertical profiles fromradiances measured by ground-based observations at a specific point
Sakami, T. ---------------------------------------------------------------------------------------------------

11

High resolution Doppler observations of clouds with the millimeter-wave CPR FALCON-I
Takano, T. ---------------------------------------------------------------------------------------------------

13

Evaluation of cloud observations by FMCW CPR FALCON-I at Hedo station
Yamaguchi, J. ----------------------------------------------------------------------------------------------

17

The analysis of Skyradiometer observation data by using SKYRAD.PACK and MRI-MLM
Yamazaki, A. ------------------------------------------------------------------------------------------------

23

Seasonal variation of aerosol optical properties in Qingdao
Sheng, L.-F. -------------------------------------------------------------------------------------------------

25

Current status of cloud-resolving model for simulations of high-impact weather systems
Tsuboki, K. (Invited) ---------------------------------------------------------------------------------------

27

Diurnal variation of cloudiness over South Asia using satellite observations
Bhatt, B. C. --------------------------------------------------------------------------------------------------

33

Validation of cloud detecting algorithm CLAUDIA using CALIPSO and Cloudsat cloud mask
Matsui, T. ----------------------------------------------------------------------------------------------------37

A statistical analysis for the characteristics of cloud/precipitation system from Cloudsat data
Yamamoto, M. K. ------------------------------------------------------------------------------------------

39

SKYNET activities in Korea
Sohn, B.-J. ---------------------------------------------------------------------------------------------------

43

The SKYNET observation in Mandalgobi semiarid region
Batbayar, J. -------------------------------------------------------------------------------------------------

53

Characterics of atmospheric aerosols at the Observatory for Atmospheric Research at
Phimai, Thailand, a station of SKYNET
Thana, B. ----------------------------------------------------------------------------------------------------

57

An investigation of SSA retrieval using simulated SKYNET observations
Hashimoto, M. ----------------------------------------------------------------------------------------------

61

Development of SKYNET community aerosol inlet and SSA obtained from surface aerosol
data at Cape Hedo and Fukue Island sites
Kaneyasu, N. -----------------------------------------------------------------------------------------------

63

The influences of meteorological parameters and PM10 concentrations on visibility ofKathmandu, Nepal
Khanal, S. N. ------------------------------------------------------------------------------------------------

65

Long-term variation of aerosol optical depth in China based on meteorological horizontal
visibility observations
Shi, G.-Y. ----------------------------------------------------------------------------------------------------

75

Aerosol and climate changes in China
Li, Z. (invited) -----------------------------------------------------------------------------------------------

81

Aerosol optical characteristics observed by sky radiometer over Loess Plateau in China
during 2009
Bi, J. -----------------------------------------------------------------------------------------------------------

91

Characteristics of dust aerosol derived from sky-radiometer over Loess Plateau ofNorthwest China
Liu, Y (Huang, J) -------------------------------------------------------------------------------------------

95

Influences of aerosol optical thickness and single scattering albedo on the dimming andbrightening in Japan
Kudo, R. ------------------------------------------------------------------------------------------------------ 101

Spatial contrast of Asian dust concentration between windward and leeward of mountains
in Japan during 1-2 April 2007
Hayasaki, M. ------------------------------------------------------------------------------------------------- 103

Influence of spatial patterns of future atmospheric warming on Asian dust emission
Tsunematsu, N. --------------------------------------------------------------------------------------------- 107

Evaluation of MODIS aerosols properties based on SKYNET ground measurements:
Implications on the quality of prospective satellites' data
Dim, J. R. ----------------------------------------------------------------------------------------------------- 111

Air pollution problems in Ulaanbaatar, Mongolia
Tugjsuren, N.-U. -------------------------------------------------------------------------------------------- 119

Aerosol radiative properties over Hefei during 2007-2010
Wang, Z. ------------------------------------------------------------------------------------------------------ 125

Aerosol-cloud interactions derived from remote sensing and in-situ aircraft measurements
Pandithurai, G. ---------------------------------------------------------------------------------------------- 133

Aerosol optical and radiative properties: synergy between sky radiometer and lidar
Kim, S.-W. ---------------------------------------------------------------------------------------------------- 137

Lidar network observations of tropospheric aerosols in East Asia
Sugimoto, N. ------------------------------------------------------------------------------------------------- 143

Shortwave versus longwave aerosol radiative forcing over an urban Environment
Panicker, A. S. ---------------------------------------------------------------------------------------------- 147

Development of correction method for integrating nephelometer and recent trend of aerosol
optical properties based on ground-based measurement at Tsukuba
Uchiyama, A. ------------------------------------------------------------------------------------------------ 151

Use of spectral irradiances measured at surface to retrieve aerosol optical parameters
Pradeep, K. -------------------------------------------------------------------------------------------------- 155

Evaluation and data assimilation of aerosol distributions and climate effects simulated by
SPRINTARS using remote sensing observations in the Asian region

1

Toshihiko Takemura1
Research Institute for Applied Mechanics, Kyushu University
6-1 Kasuga-koen, Kasuga, Fukuoka 816-8580, Japantoshi@riam.kyushu-u.ac.jp

Abstract
A global aerosol transport-climate model, SPRINTARS, has been developed to simulate aerosol global
distributions, radiative forcing, and climate effects. Remote sensing data from the surface and satellites are used tocheck simulated aerosol optical properties and to assimilate SPRINTARS. Quality-assured and long-term
observations are essential in order to use in researches on the climate change and data assimilation.
Keywords : aerosol, SPRINTARS, radiative forcing, climate change, forecast, data assimilation
1. Introduction
There are still uncertainties in estimating aerosol effects
on the climate system (IPCC, 2007). A global aerosol
transport-climate model, SPRINTARS, has been developed
to simulate aerosol global distributions and climate effects,
which are the direct, semi-direct, and indirect effects andtheir feedback effects.
2. Model description
SPRINTARS (http://sprintars.net/) is coupled with
MIROC which is a Japanese general circulation model(GCM) (K-1 Model Developers, 2004). The horizontal andvertical resolutions are T106 (approximately 1.1 by 1.1
degrees in longitude and latitude) and 56 layers, respectively.
SPRINTARS includes the transport (emission, advection,diffusion, sulfur chemistry, and deposition), radiation, cloud,and precipitation processes of all main tropospheric aerosols(black carbon, organic matter, sulfate, soil dust, and sea salt)
to simulate aerosol distributions and climate effects. Aerosol
optical properties, i.e., optical thickness, Ångström exponent,and single scattering albedo, are also calculated at specificwavelengths to compare with remote sensing data. The
model treats not only the aerosol mass mixing ratios but also
the cloud droplet and ice crystal number concentrations as
prognostic variables, and the nucleation processes of cloud
droplets and ice crystals depend on the number
concentrations of each aerosol species. Changes in the cloud
droplet and ice crystal number concentrations affect thecloud radiation and precipitation processes in the model. The
detailed models description in Takemura et al. (2000, 2002,
2005, 2009a).
3. Discussion

SPRINTARS has been improved referring a lot of aerosoland cloud parameters measured by remote sensing fromsatellites (e.g., AVHRR, MODIS, and CALIOP) and thesurface (e.g., AERONET and SKYNET). Simulated aerosol
optical properties capture observational characteristics, forexample, high optical thickness and high Ångström
exponent in urban areas and biomass burning areas/seasons,high optical thickness and low Ångström exponent in dusty
areas/seasons, and low optical thickness and low Ångström
exponent over remote ocean. There is, however, a systematicdifferences in the global mean optical thickness between
simulations and satellite retrievals although anthropogenic
optical thickness agrees well (Kaufman et al., 2005). Thismay be because satellite sensors cannot detect thin optical
thickness over remote ocean. To understand and reduce
uncertainties in simulated aerosol distributions and effects,
the global aerosol model intercomparison project, AeroCom,
is acting, comparing with various aerosol observations(http://nansen.ipsl.jussieu.fr/AEROCOM/).
SPRINTARS can calculate aerosol radiative forcing due
to the direct, indirect, and semi-direct effects (Takemura etal., 2002, 2003, 2005). MIROC coupled with SPRINTARS
also estimated the trend of radiative forcings from the year
1850 to present day due to various climate forcing agents,
i.e., long-lived greenhouse gases, ozone, aerosol, volcanic
activity, and land use change (Takemura et al., 2006). It isessential to use long-term observational data for comparing
with simulations on the climate change.
As an application of using SPRINTARS, a weekly aerosol
forecasting system has been developed (Takemura et al.,2009b). The forecasting system is automatically performed
every day and simulated results are uploaded to the
SPRINTARS homepage (http://sprintars.net/) about 06JST－ 1 －(23 UTC). The other application is construction of data
assimilation system. Quality-assured observational data isessential to check the forecasting system and to drive theassimilation system.

Acknowledgements
I would like to thank the contributors of development of
SPRINTARS and MIROC. The simulation in this study was
performed on the NIES supercomputer system (NECSX-8R). This study is supported by the Grant-in-Aid forYoung Scientists (21681001) of the Ministry of Education,
Culture, Sports, Science, and Technology of Japan,
Environment Research and Technology Development Fund
(RF-0901) by the Ministry of the Environment, Japan, andMitsui & Co., Ltd. Environment Fund (R08-D035).
References
1) IPCC, 2007: Climate Change 2007: The Physical Science
Basis. Contributing of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on
Climate Change, edited by S. Solomon, D. Qin, M.
Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor,and H. L. Miller, 996 pp., Cambridge Univ. Press,
Cambridge, United Kingdom and New York, NY, USA.
2) K-1 Model Developers, 2004: K-1 coupled GCM(MIROC) description. edited by H. Hasumi and S. Emori,K-1 Tech. Rep. 1, 34 pp., Center for Climate System
Research, University of Tokyo, Tokyo, Japan.
3) Kaufman, Y. J., O. Boucher, D. Tanre, M. Chin, L. Remer,
and T. Takemura, 2005: Aerosol anthropogenic
component estimated from satellite data. GeophysicalResearch
Letters,
32,
L17804,
doi:10.1029/2005GL023125.
4) Takemura, T., H. Okamoto, Y. Maruyama, A. Numaguti,
A. Higurashi, and T. Nakajima, 2000: Global
three-dimensional simulation of aerosol optical thickness
distribution of various origins. Journal of GeophysicalResearch, 105, 17853–17873.
5) Takemura, T., T. Nakajima, O. Dubovik, B. N. Holben,
and S. Kinne, 2002: Single-scattering albedo andradiative forcing of various aerosol species with a global
three-dimensional model. Journal of Climate, 15,
333–352.
6) Takemura, T., T. Nakajima, A. Higurashi, S. Ohta, and N.
Sugimoto, 2003: Aerosol distributions and radiative
forcing over the Asian-Pacific region simulated bySpectral Radiation-Transport Model for Aerosol Species(SPRINTARS). Journal of Geophysical Research,

108(D23), 8659, doi:10.1029/2002JD003210.
7) 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. Journal of GeophysicalResearch, 110, D02202, doi:10.1029/2004JD005029.
8) Takemura, T., Y. Tsushima, T. Yokohata, T. Nozawa, T.
Nagashima, and T. Nakajima, 2006: Time evolutions ofvarious radiative forcings for the past 150 years estimatedby a general circulation model. Geophysical Research
Letters, 33, L19705, doi:10.1029/2006GL026666.
9) Takemura, T., M. Egashira, K. Matsuzawa, H. Ichijo, R.
O'ishi, and A. Abe-Ouchi, 2009a: A simulation of the
global distribution and radiative forcing of soil dust
aerosols at the Last Glacial Maximum. Atmospheric
Chemistry and Physics, 9, 3061–3073.
10) Takemura, T., 2009b: Development of Forecasting
System for Atmospheric Aerosols. Tenki, 56, 455–461 (inJapanese).

－ 2 －The use of SKYNET observations for aerosol assimilation

1

Nick Schutgens1, Takamichi Iguchi2, Teruyuki Nakajima1
AORI, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan, schutgen@aori.u-tokyo.ac.jp
2
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA, iguchi@agnes.gsfc.nasa.gov

Abstract
Assimilation of global aerosol is a new technique to circumvent certain limitations in our knowledge of theaerosol system. By assimilating observations of aerosol in a global transport model, uncertainties in e.g. aerosol
emissions may be reduced. Due to rapid economic growth, the Asian region has become an important source
region for aerosols, and the interest in its effects on human health and the Earth’s climate are only increasing.
SKYNET provides observational coverage of this important region and we will show several examples of the useof SKYNET data in aerosol assimilation.
Keywords : SKYNET, aerosol, assimilation
1. Introduction
To understand the climatological impact of aerosols or toforecast air-quality, it is indisputably necessary to resort tomodels to obtain a spatially and temporally consistentpicture of the aerosol system. Observations will always belimited in their coverage. Unfortunately, global and regional
aerosol modeling is hampered by poorly known emission
scenarios of both man-made and natural aerosols.. The
emission of sea-salt and mineral dust is usually describedthrough parametrisations (in mainly 10m windspeed, e.g.
[1]), based on limited observational campaigns orincomplete models. More-over, there is a scale problem forin particular global models that have to assume constantemissions over several 100’s of kilometers. The emission ofman-made aerosol is usually based on conversions ofgovernmental statistics of energy use by various industries,
e.g. [2]. In addition to the obvious limitation of questionable
representativeness, these emission scenarios are often
monthly if not yearly averages only.
Assimilation [3] is a common denominator for a variety
of techniques to combine aerosol simulations andobservations in a more reliable and comprehensive dataset.
During assimilation, a variety of observations are used to
improve the model state of an aerosol transport model. A
new, flexible and physically consistent assimilation scheme
is the so-called ensemble Kalman filter [4] that uses anensemble of model simulations to represent model
prediction uncertainty which may then be compared toobservational errors. In this paper, we apply this ensemble
Kalman filter to the global aerosol transport model
SPRINTARS [5,6].
SKYNET (http://atmos.cr.chiba-u.ac.jp/) is a network ofphotometers dedicated to aerosol observations in Asia. The
network provides AOT (aerosol optical thickness), AE(Angstrom exponent) and SSA (single scattering albedo) for
clear sky observations, every 15 minutes.
In this paper, we show several examples of the use of
SKYNET observations for aerosol assimilation. We start by
discussing the validation of assimilated AOT and AE, andcontinue with an example where SKYNET data themselves
are assimilated. Finally, we discuss the possibility ofassimilating SKYNET SSA.

2. The Local Ensemble Kalman filter
If we define the state x of our model as a vector withthe mixing ratios of simulated aerosols all over the globe,
we can define a model prediction error asP  x  x  x  x  ,T

wheredenotes an ensemble average. The membersof the ensemble use perturbed emission scenarios that
represent our uncertainty in source strengths. The
covariant matrix P then represents the uncertainty in
mixing ratios at a certain moment, with of course
associated uncertainties in e.g. AOT and AE. Now if wehave a set of observations y , with an error covariant
R , defined similarly to P , and simulated observations
H x , we may combine our model and observational
information into an optimal new state that is, broadly
speaking, a average between the original state and theobservations, each weighted by their relative errors(covariants). The original state is usually called thebackground, apriori or forecast state x f and the newstate is usually called the analysis state x a , and they are－ 3 －connected through the well-known Kalman filter equationx  x  P H R y  Hx 
a

f

a

T

1

P a  I  P f HT R1H P f
1

We use a 24-member ensemble for perturbed emissionsof fine (sulfate & carbon) and coarse (seasalt and dust)
aerosol. The average emission in the ensemble is different
from the standard emission, so that the assimilation has asubstantially different AOT and AE than the standard
simulation, when cloudiness or such temporarily prevents
observations.
3. Validation of assimilation products
In [7], we assimilated AERONET observations of
AOT and AE for the month July 2005. Part of thevalidation was done by SKYNET observations at
Cape-Hedo and Toyama. Since AERONET had only afew sites in Asia in 2005, we cannot expect assimilation
results to be very robust. In Fig. 1, we show AOT from astandard SPRINTARS simulation, observations by
SKYNET and simulated results after assimilation of
AERONET.

Figure 1: Aerosol as observed and simulated at Cape-Hedo.

This comprises an independent validation of theassimilation. Immediately, it is obvious that for the firsttwo weeks both the standard simulation and theassimilation results are quite poor, but they are
significantly better during the last week (remember thatthe assimilation uses different average emissions than thestandard simulation, so the AERONET observations
obviously have a positive effect). The main difference
between these two periods is the dominant wind direction.
During the first two weeks it is mainly South-Easterly,
but later it changes to North-Westerly. That implies thatduring the last week, aerosols from the Asian mainland
dominated while earlier marine aerosol dominated. Or
did they? The SKYNET observations, combined withsurface observations by EANET [8], strongly suggest that
Cape-Hedo had a lot of sulfate aerosol during this period.
As it turns out, the Mt. Anathahan volcano emitted large
amounts of SO2 during this period, as can be clearly seenin OMI imagery (a timeseries of observations is found at
http://so2.umbc.edu/omi/movies/wpac_omso2_1jan-30sep05.
mov). So we conclude that the AERONET assimilation

yields good results at Cape-Hedo, except in the unusualcase of the erupting volcano which is at the moment notaccounted for in our perturbed emission scenarios.
4. Assimilation of SKYNET data
In another experiment, we assimilated SKYNET AOT
and AE, in addition to AERONET and CHSNET AOT
and AE as well as MODIS Aqua AOT over ocean for
March 2005. The combination of these three surface
networks provides very good coverage of the Asian
region (with the exception of India), although the numberof AERONET sites is rather limited and CSHNET (anetwork of handheld photometers in China) is lessaccurate than AERONET and SKYNET. In all,
SKYNET is an important contribution to any experimentthat focuses on the Asian region.
In Fig. 2, we show consistency of the assimilation for
SKYNET. That is: after assimilation ,AOT, monthly
biases should be much reduced. For AERONET, with
globally 216 sites , this is clearly the case. For SKYNET,
the low number of regional sites (10) muddles the picture.
However, when the standard bias is large, the assimilation
can strongly improve the simulation. This suggests boththat transport by SPRINTARS is sufficiently appropriate
and that the assimilation of SKYNET data has an impact.
Systematic errors in both AOT and AE (not shown) are
reduced.

Figure 2: Monthly biases for standard simulation andassimilation for the AERONET and SKYNET sites.

At the moment, there are two different applications of
the resulting assimilated aerosol fields. First, such fields
can be used to initialize cloud-resolving models that study－ 4 －the interrelation of aerosols and meteorologicalconditions in cloud formation. Fig. 3 shows an exampleof the simulated water cloud distribution over Asia using
assimilated aerosol fields in the Japan Meteorological
Agency Non-Hydrostatic Model (JMA-NHM) with
spectral bin cloud microphysics.

Figure 3: Distribution of water cloud optical thickness in acloud model with bin-resolved cloud microphysics.

These cloud simulations may subsequently be compared to
MODIS observations for verification (ongoing work).
Second, the assimilated aerosol fields can be used to
study vertical profiles of aerosols. In Fig. 4, we show 4
examples of averaged (March-May 2005) attenuatedbackscatter profiles in Asia. As is clear from Fig. 2 (leftpanel), the original simulation already provided AOT and
AE quite close to the SKYNET observations. Yet the
LIDAR observations by ADnet are clearly different. Thissuggests a problem with the extinction-to-backscatterratio, or in other words, the aerosol scattering properties(ongoing work).

Figure 4: LIDAR profiles over Asia; observed (blue),standard simulation (red) or assimilation (orange).

6. Assimilation of SSA
In principle, there is no problem in assimilating SSA as
an independent piece of information. Assimilating SSA isactually very interesting, as it will allow the distinction ofcarbon and sulfate aerosol. More-over, SSA is an
important parameter in the calculation of direct aerosol
radiative forcing. Yet, there are a few issues that makeassimilation of SSA more daunting than the assimilationof AOT or AE. The first is the poorly constrained(imaginary) refractive index of carbon aerosols (thisissues includes the difficult question of their mixing state).
According to [9], carbon absorptive characteristics varyover a large range. But to simulate observations from themodel ensemble, it is necessary to choose a characteristic
value. Secondly, correct assimilation requires a realistic
estimate of the error in observed SSA. After all, it is theweighting between observational error and model
prediction error that determines how much theassimilation system will modify the state because of acertain observation.
Finally, there is the issue of source location. Our
assimilation system in effect assumes source locations
and then adjusts source strengths based on observations.
However, forest fires are spatially and temporally highlyvariable. Correct assimilation of SSA, with the purpose of
improving carbon simulation, would benefit greatly from
improved emission source locations based on fire maps
(e.g. from MODIS).
7. Summary
We have discussed current and future use of SKYNET
observations for an aerosol assimilation system. Since
SKYNET fills a geographic data gap of surface based
aerosol observations in an important natural andman-made aerosol source region (Asia), its importancecan not be overstated. On the contrary, it would be
beneficial if the network is expanded, in particular into
India. Finally, for optimal assimilation it is desirable that
more efforts at observational error characterization are
made, either through comparison with co-located
AERONET sites or through radiative transfer
simulations.
Acknowledgements
Part of this research was supported by funds from
MOE/GOSAT, MOE/GER fund B- 083, JST/CREST,JAXA/EarthCARE, MEXT/VL for climate diagnostics, andMEXT/Innovative Program of Climate Change Projection－ 5 －as well as the NIES GOSAT project team.

References
1) Gong S.L.: A parametrization of sea-salt aerosol source
function for sub- and super-micron particles, Glob. Biochem.
Cycles 17, 2003.
2) Streets D.G., Yan F., Chin M., Diehl T., Mahowald N.,
Schultz M., Wild M., Wu Y., Yu C.: Anthropogenic andnatural contributions to regional trends in aerosol opticaldepth, J. Geophys. Res. 114, 2009.
3) Bouttier F., Cortier P.: Data assimilation concepts andmethods, ECMWF training course, 1999.
4) Hunt, B., Kostelich, E., and Szunyogh, I.: Efficient data
assimilation for spatiotemporal chaos: a Local Ensemble
Transfom Kalman Filter, Physica D, 230, 112–126, 2007.
5) Takemura, T., Okamoto, H., Maruyama, Y., Numaguti,A., Higurashi, A., and Nakajima, T.: Global threedimensional simulation of aerosol optical thickness
distribution of various origins, J. Geophys. Res., 105,
17,853–17,873, 2000.
6) Takemura, T., Nakajima, T., Dubovik, O., Holben, B.,
and Kinne, S.: Single-scattering albedo and radiative forcing
of various aerosol species with a global three-dimensionalmodel, J. Clim., 15, 333–352, 2002.
7) Schutgens N.A.J., Miyoshi, T., Takemura, T., Nakajima,
T.: Applying an ensemble Kalman filter to the assimilationof AERONET observations in a global aerosol transportmodel, ACP 10, 2010.
8) EA team: Data report on the acid deposition in the East
Asian region, Tech. rep., Network center for EANET,
http://www.eanet.cc/, 2005.
9) Bond T.C., Bergstrom R.W.: Light absorption bycarbonaceous particles: an investigative review, Aer. Sci. &tech. 40, 27-67, 2006.

－ 6 －Estimation of radiation budget using geostationary satellites
Hideaki Takenaka, Munehisa K Yamamoto, Masamitu Hayasaki, Atsushi. Higuchi,
Naoko Saitoh, Hiroaki Kuze, Fumihiko Nishio, Tamio Takamura (1),Satoru Fukuda, Teruyuki Nakajima (2),
Arata Okuyama, Yuki Kosaka, Ryuichiro Nakayama, Hiromi Owada,
Kenji Date, Tomoaki Ono (3)(1) Center for environmental remote sensing, Chiba university(2) Atmosphere and Ocean Research Institute, Tokyo university(3) Meteorological Satellite Center, Japan Meteorological Agency

Abstract
SW radiation budget is estimated using geostationary satellites ( GMS-5, GOES-8, GOES-10, METOSAT-5,
METEOSAT-7). Each satellite data are calibrated, and optics properties of the cloud are retrieved. An accurate
calibrated data propose the better accuracy for analysis of cloud and radiation budget. In this study, cloud and SW
radiation budget analysis are done by CAPCOM algorithm and EXAM SYSTEM. On the other hand, aerosol
optical thickness is retrieved by L-REAP algorithm. We discuss the possibility of aerosol-cloud-radiationinteraction in north Pacific ocean and north Atlantic ocean.
Keywords : Radiation budget, Aerosol, Cloud, Radiation, Interaction

1. Introduction
Clouds can cool the Earth by reflecting solar
radiation and also can keep the Earth warm byabsorbing and emitting terrestrial radiation. They areimportant in the energy balance at the Earth surface
and the Top of the Atmosphere (TOA) and areconnected complicatedly into the Earth system as wellas other climate feedback processes. Aerosols reflects
solar radiation and cools the earth, and it is called a
direct effect. Moreover, aerosols influences thecondensation of the cloud particles by indirect effect.
Thus, cloud and aerosol are one of the importantelement in Earth energy system, and it's important tobe estimate radiation budget to better understandclimate and environmental change.
Wetherald and Manabe discussed the cloud feedback
process using a General Circulation Model (GCM)[Wetherald and Manabe, 1988]. Tsushima and Manabetested the cloud feedback sensitivity to global meansurface temperature based on explicit definition of
feedback processes. GCM has strong sensitivity for
global mean surface temperature although analysis ofsensitivity based on observations are negligible[Tsushima and Manabe, 2001]. Cloud modeling is a

big uncertainty for the climate model and long term
analysis for the global change would be estimated. It is
important to evaluate the influence of cloud for
Earth's radiation budget based on observations.
Geostationary satellite observations are useful for
estimating the upward and downward radiationbudget at the surface and the TOA over wide
regions and at high temporal resolution. We develop
a vicarious calibration technique for the global
analysis. An accurate calibrated data propose thebetter accuracy for analysis of cloud and radiationbudget. (In this study, four satellites: GMS-5,
GOES-8, GOES-10, METOSAT-5, METEOSAT-7
are used for analysis). An accurate calibrated data
propose the better accuracy for analysis of cloudand radiation budget. Additionally, the possibility ofaerosol-cloud-radiation interaction is discussed.

2. Methods

2.1.Calibration of satellites observation data
The geostationary satellite has a big advantage for
estimation of radiation budget because of highly timeresolved data. One of key factors in the estimation is a

－ 7 －sensor calibration. It has some issues in the calibration
procedures. In this study, five geostationary satellites(GMS-5, GOES-8, GOES-10, METEOSAT-5,
METEOSAT-7) is used for radiation budget analysis.
Each satellite were calibrated by Global Space basedInter-Calibration System (GSICS) proposed method
based on three component using the radiative transfer
code RSTAR6. The satellites data calibrated in highaccuracy achieves a accurately radiation budget
analysis.

2.2.Retrieval of cloud optical properties
We used the Comprehensive Analysis Program for
Cloud Optical Measurement (CAPCOM) [Nakajima
and Nakajima, 1995; Kawamoto et al., 2001]. Thisalgorithm was also adopted as one of the standard
algorithms for the Advanced Earth Observing Satellite
II/Global
Imager(ADEOS-II/GLI)
products[Nakajima, 1999]. we applied CAPCOM to fivegeostationary satellites data to retrieve only the cloudoptical thickness using the VIS channel

2.3.Estimation of radiation budget
We develop a high speed and accurate algorithm
based on Neural Network (NN). The advantages of theNN approach are to be speed of the computations andallows to produce numerous parameters since it does
not require a large data base. Figure 1 indicates a threelayers network structure. Neuro-link Network solver(NN solver) is built by improved learning algorithm“Dist.-BP” that has an anti-local minimum and asurvival rule of neuron depending on nerves activities[Takenaka et al., 2009; 2010]. The NN approach isone of the solution to following problems. In general,
satellite based estimate methods often use a Look-up
Tables (LUT). Since pre-calculated values are used,the LUT methods are effective for large amount ofdata processing. However, if the effects of absorbing
gasses and the particle optical characteristics areincorporate dprecisely, LUT becomes huge volume.
An increase in parameter needs not only the increaseof LUT volume but also complex interpolation of
LUT. The NN solver traces radiative transfer code
System for Transfer of Atmospheric Radiation(RSTAR) [Nakajima and Tanaka, 1986, 1998] for highspeed and accurate computation. The Extreme speed
and Approximation module Multiple drive System(EXAM SYSTEM) controls NN solvers by multi-

threading. EXAM SYSTEM applies to MTSAT-1R,
estimates the solar radiation at the TOA and thesurface with semi-real time, and evaluated against in
situ observations. The EXAM SYSTEM uses eight
Central Processing Unit (CPU) cores, And logicallysliced target area

3. Result and discussion
Radiation budget product is generated based on
five geostationary satellites (Fig 2). Each satellites
products are smoothly connected by accurate
calibration (Fig. 3). In past report, we pointed out
the possibility of aerosol-cloud-radiation interaction
in north Pacific ocean [Takenaka, 2009; 2010]. It
indicate aerosol and thin cloud has a same pattern.
They kept by strong diffuse component in
the east Asian to North Pacific ocean region. On theother hand, those characteristics (direct and diffuse
component) showed the same trend in North
Atlantic Ocean in ADEOS-II/GLI result [Takenaka,
2009; 2010]. In this study, we focus the North
Atlantic Ocean region using developed radiation
product. Figure 4 and Figure 5 indicate thedownward SW flux at the surface diffuse component
and direct component (June and September, 2002).
We found the strong diffuse component in northAtlantic ocean in June 2002, And direct componentis blocked. Those trend are not shown in September.
Figure 6 shows the influence of cloud on downward
SW flux. Diffuse and direct component trend arecaused by low optical thickness cloud. Moreover,aerosol optical thickness increases in north Atlantic
ocean (Fig.7). These trends shows the same trend ineast Asia to North Pacific ocean.

- Formation of a Virtual Laboratory for
Diagnosing the Earth's Climate System In order to diagnose the earth's climate system under
severe stress such as a global warming, the cooperative
research centers (CCSR, HyARC, CAOS, and CEReS,) construct "Virtual Laboratory", and research climate
and environmental studies cooperatively withproperties of each center. CEReS activities are
Geostationary satellites global data archives andconstruction of Satellite information data base.
Moreover, development of atmospheric radiationbudget product. We aim at the contribution to a climate
model and the better understanding of the climate－ 8 －References

system.

Wetherald, R. T. and S. Manabe (1988), Cloud feedback
processes in a general circulation

model , J. Atmos. Sci.,

45(8), 1397-1415.
Tsushima, Y. and S. Manabe (2001), Influence of cloud
feedback on annual variation of

global mean surface

temperature , J. Geophys. Res., 106(D19), 22,635-22,646.
Nakajima, T. Y., and T. Nakajima (1995), Wide-area
determination of cloud microphysical properties from NOAAAVHRR measurements for FIRE and ASTEX regions, J.

Figure 1: Schematic illustration of EXAM SYSTEM.
NN solvers traces radiative transfer calculation.

Atmos. Sci., 52, 4043-4059.
Kawamoto, K., T. Nakajima, and T.Y. Nakajima, (2001), A
global determination of cloud microphysics with AVHRR
remote sensing, J. Climate, 14, 2054-2068.
Nakajima, T., and M. Tanaka (1986), Matrix formulations forthe 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 thick atmospheres using a
truncation approximation, J. Quant. Spectrosc. Radiat.
Transfer, 40, 51-69.
Takenaka, H., T. Y. Nakajima, A. Higurashi, A. Higuchi, T.

Figure 2: Test product of SW radiation budget productusing five geostationary satellites. September 23 , 2002.

Takamura, R. T. Pinker, and T. Nakajima 2010: Estimation ofSolar radiation using a Neural Network based on Radiative
Transfer, J. Geophys. Res., (second revised)

Figure 3: Averaged Downward SW flux at the surface
and Upward SW flux at the TOA.
－ 9 －

Figure 6: Influence of cloud on downward SW flux atthe surface direct and diffuse component.

Figure 4: Downward SW flux at the surface diffuse
component. Upper side: June 2002. Lower side:
September 2002.

Figure 7: Aerosol optical thickness retrieved from
GOES-8. Upper side: June 2002. Lower side:
September 2002.

Figure 5: Downward SW flux at the surface directcomponent. Upper side: June 2002. Lower side:
September 2002.

－ 10 －

Retrieval methods of microphysical and optical properties of cloud vertical profiles fromradiances measured by ground- based observations at a specific point

T. Sakami, A. Uchiyama, A. Yamazaki, and R. Kudo
Meteorological Research Institute, Japan Meteorological Agency,
1-1, Naganine, Tsukuba, Ibaraki, JAPAN 305-0052.
E-mail:tsakami@mri-jma.go.jp
Abstract
To estimate microphysical and optical properties of cloud vertical profiles from radiances measured byground-based observations at a specific point, retrievals of the Bayesian estimate using the maximum likelihood
method and estimate using the Kalman filter are developed. In addition, the estimate system based on thesedeveloped retrievals is expanded in principle from one estimate cycle to four estimate cycles. Through ideal
experiments, it is founded out that a coupling among different retrieval methods is likely to improve theaccuracies of estimates.
Keywords : sky radiometer; radiance; microphysical property; optical property; cloud vertical profile;
Kalman filter; Bayesian estimate; maximum likelihood method
1. Introduction
The Meteorological Research Institute (MRI, Japan) has
developed the basis of technology to understand climatechange in atmospheric radiative properties to be caused byclouds and aerosols. Through guessing microphysical andoptical properties of clouds by some radiances at some
visible and near-infrared wavelengths, azimuth angles andzenith angles measured by a Sun-sky scanning photometer
called i-skyradiometer, manufactured by PREDE Co. Ltd.
( product name POM02 ), their properties of clouds linked
with observations contribute to estimates of uncertainties of
the radiative forcing in the present climate or the projection
using climate models. Furthermore, through guessing theirproperties of cloud vertical profiles, their properties of cloudvertical profiles linked with observations could contribute to
the data assimilation system in the weather prediction model.
To estimate microphysical and optical properties of cloudvertical profiles from information of radiances measured byground-based observations at a specific point, various
retrieval methods are developed. Moreover results of ideal
experiments are showed using these developed retrievals.
2. Methods
To estimate microphysical and optical properties of cloudvertical profiles from radiances measured by ground-based
observations at a specific point corresponds to solving theinverse problem to a one-dimensional vertical atmosphericradiative transfer model. In this study, to solve its inverse
problem, some retrievals of the Bayesian estimate using the

maximum likelihood method and estimate using the Kalmanfilter are newly developed. In addition, the estimate system
based on these developed retrievals is expanded as showed
by Fig.1. X   is a set of variables to estimate, Λ and Z areindex sets. Λ represents a set of physical variables, such as
cloud liquid water mixing ratio, cloud ice mixing ratio, mode
radius and standard deviation of cloud droplet or ice crystal
distribution. Z represents a set of atmospheric heights at
which variables to estimate are deposited. Ak kK is a set of
new variables through a basis transform. K is a index set
linked with Λ and Z through a basis transform. N
represents a truncated index number. The over-bar andover-tilde represent sets to which Ak k  N and Ak k  N are
mapped by an inverse basis transform. The superscript,
estimate represents a set which is estimated from solving theinverse problem. It is the standard estimate that X   ismapped to ln X   , ln X l estimste
is deduced from solving thelZ
estimste
inverse problem using ln X  , is mapped to X l lZ ,X   is equal to X l estimste, and until estimated radiances arelZ
close to measured radiances, this process is repeated. A
series of this process is called estimate cycle. ln X   istransformed to new variables Ak kK by a basis transform.
Then, a new estimate cycle to pass Ak estimate
is introduced in
kK



A
the estimate system. k kK is divided between Ak k  N and Ak k  N .
This means that dominant component and anomaly
component are represented in the estimate system. In thisstudy, the former is preserved, and the latter is estimated.
Then, two estimate cycles to pass Ak estimate
or ln X~  arek N
newly introduced in the estimate system.
l


l Z

l

－ 11 －


l Z

l


l Z


l Z

l

l

l


l Z


l Z



l

estimstel Zdeviation, and mode radius. In these experiments, themaximum likelihood method poorly has the accuracies of
estimates, and the Kalman filter estimates the standard
deviations better. Each retrieval method does not highly
have the accuracies of estimates for all variables to estimate.
However, by averaging over all methods, the estimates trend
to come close to the true values. Some coupling methods
among different retrieval methods, named ensemble retrieval
method are likely to improve the accuracies of estimates.
（a ）0.0008

Fig.1 Schematic overview of the expanded estimate system. Variables to estimate
are transformed to a vector spanned by a basis set, and the vector filtered out isestimated as new variables.

Cloud mixing ratio ( kg/kg )0.00070.0006The 1st layer
The 2nd layer
The 3rd layer
The 4th layer
The 1st layer/cloud water
The 2nd layer/cloud water
The 1st layer/cloud ice
The 2nd layer/cloud ice

0.00050.00040.00030.0002Standard deviation

3. Experimental design
0.0001
0
1
2
3
4
5
6
7
8
Two different cases are experimented that the cloud liquid
Retrieval method
water is deposited at 4 levels (2km, 3km, 4km, and 5km),
and the cloud liquid water and cloud ice are deposited at 2 （ b ）1.65
levels (2km and 3km). In these cases, some radiances atwavelengths of 0.315nm, 0.34nm, 0.5nm, 0.675nm,
1.61.627nm, and 2.2nm and zenith angles of 0°and 60°are
1.55
measured. The true values to estimate are assumed such as
the cloud mixing ratio is 5.0×10-4 kg/kg, the mode radius
1.5
and standard deviation of the log-normal distribution are 1.2
1.45
×10-3cm and 1.6 with respect to the cloud liquid water, 3.0
×10-3 cm and 1.6 with respect to the cloud ice. For the
1.4
0
1
2
3
4
5
6
7
8
radiative transfer model which is forward model, RSTAR6B
Retrieval method(Nakajima and Tanaka, 1986) is used. The experimental（c ）numbering is made as follows.
0.004
1.The maximum likelihood method.
0.0035
2.The maximum likelihood method including the Fourier
0.003
transform as a basis transform for estimated variable.
0.0025
3.The maximum likelihood method including the Fourier
0.002
transform as a basis transform and high-pass filter for
0.0015
estimated variables.
0.001
4.The Kalman filter.
0.0005
5.The Kalman filter including the Fourier transform as a
0
1
2
3
4
5
6
7
8
Retrieval methodbasis transform for estimated variables.
6.The Kalman filter including the Fourier transform as a Fig.2 Estimates using various retrieval methods, (a)cloud mixing ratio(kg/kg),
basis transform and high-pass filter for estimated (b)standard deviation, (c)mode radius (cm).
variables.
7.Average over all methods.
Reference
Nakajima, T., and M. Tanaka (1986), Matrix formulation for
the transfer of solar radiation in a plane-parallel scattering
4. Results and Discussion
Fig.2 shows estimates of the cloud mixing ratio, standard atmosphere, J. Quant. Spectrosc. Radiat. Transfer, 35, 13– 21.
The 1st layer
The 2nd layer
The 3rd layer
The 4th layer
The 1st layer/cloud water
The 2nd layer/cloud water
The 1st layer/cloud ice
The 2th layer/cloud ice

Mode radius ( cm )The 1st layer
The 2nd layer
The 3rd layer
The 4th layer
The 1st layer/cloud water
The 2nd layer/cloud water
The 1st layer/cloud ice
The 2nd layer/cloud ice－ 12 －

High Resolution Doppler Observations of Clouds with
the Millimeter-wave CPR FALCON-I
TAKANO Toshiaki1, YAMAGUCHI Jun2, ABE Hideji1, SUZUKI Yohei1, MORIYA Shusuke1,
NISHINO Daichi1, KAWAMURA Yohei1, NAKANISHI Yuji3, and TAKAMURA Tamio3
1
Graduate School of Engineering, Chiba University; Inage, Chiba 263-8522, Japan, takano@faculty.chiba-u.jp
2
Japan Aerospace Exploration Agency
3
Center for Environmental Remote Sensing, Chiba University
Abstract
High resolution Doppler Observations of clouds have been done with the millimeter-wave cloud profilingradar (CPR) FALCON-I developed at Chiba University. FALCON-I operated at 95 GHz has a high spatial
resolution of 15 m in the ranging direction and of 0.2°in the perpendicular directions. Precise observations ofmelting layers at the bottom of clouds reveal that rain drops whose diameters would be around 1 mm are
generated and accelerated up to 7 m/s downward in quite thin layer of about 200 m. Doppler Observations ofinterior of cumulonimbus suggest existence of small structures of about 1 km in which abrupt up and downward
flow occurs. These results will be useful to investigate characteristics of clouds in various places and oceans inorder to make global model of atmosphere.
Keywords : Cloud Profiling Radar, Millimeter Wave Radar, Cloud Properties, Height Distribution of Clouds,
Structure of melting layer, dynamics of interior clouds, Global Model of Atmosphere.
1. Introduction
It is getting more important to know the global
environment and the global change of climate for the humanbeings. It is necessary to know balance of solar energy
coming to the Earth and cycle of water for thecomprehension and to solve severe problems such as thegreenhouse warming, the drying, the ozone holes and so on.
One of the most significant features to know them is cloud.
Information on 3-dimensional structures of clouds, sizes anddistribution of cloud particles, dependence on size of opticalcharacteristics of cloud particles, motions of particles inclouds, and so on are all desirable to determine the role ofclouds.

Observations of clouds with radars would be the mostpowerful method to derive the information because of thefollowing advantages: a) radio waves do not suffer fromheavy extinction such as visible light, and consequently caninvestigate the interior of clouds, b) the radar technique,which is an active sensing method, has great advantage toinvestigate interior structures of clouds to passive methods
such as total power observations of irradiance of clouds, c)
Doppler measurements of clouds is applicable only to radio
frequency waves.
We have originally designed and developed a cloudprofiling radar at 95GHz. We adopt a frequency-modulated
continuous wave (FM-CW) radar rather than a pulse radar
because the former can easily achieve more sensitive andhigh-resolution system than the latter. Whole view of thedeveloped radar, which is named “FALCON-I” is shown in
Fig.1 and its parameters and performance of are summarizedin Table 1. Detail of FALCON-I is described in the papers[1]-[4].
Table 1. Parameters and performance of FALCON-I.

Fig.1. Cloud profiling radar FALCON-I. It has two 1-m
diameter antennas which transmit and receive radio waves at 95
GHz.
－ 13 －

Antenna Diameter
Frequency
Output PowerBeam Width
Range Resolution
Direction of Antennas
Polarization
Temporal Resolution
Sensiutivity (at 5 km)
Doppler Velocity Range

1m × 2
94.79 GHz
0.5 W (+27 dBm)
0.18 degree
15 m
Zenith1 Linear
1 msec (Typical)-32 dBZ (Typical)±3.2 m/sec (Typ)2. Observations with FALCON-I
Using FALCON-I, we observed clouds in various regions,at Chiba (Fig.2), at cape Hedo in Okinawa (Fig.3), andoceans on board MIRAI (Fig.4): a Japanese scientificresearch vessel operated by Japan Agency for Marine-Earth
Science and Technology (JAMSTEC). FACLON-I is a
mobile facility which is able to operate through the internet
connection with the commercial power supply of about
100V 15A. During these observations, FALCON-I worked
stable and well and showed good performance [5]-[7].
3. Observations of cumulonimbus
3-1. Reflectivity profiles
We continuously observed clouds in summer 2010 at
Chiba University. High resolution Doppler Observations ofclouds have been obtained as well as reflectivity profiles

for several large cumulonimbus during the period.
Fig.5. shows time-height diagram of reflectivity for a
cumulonimbus on July 25, 2010. The anvil of the
cumulonimbus began to be seen around 9:30 UT (18:30
JST) from about 12 km in height and gradually came down
to lower height. The main part of the cumulonimbus wasshown in 10:40-11:20 UT at 4-9 km in height. Abrupt
increase of observed reflectivity by going lower height isshown at 4 km. This increase of reflectivity should be causedwith growth of diameters of raindrops in the melting layer.
At lower frequency, for example, X-band (10 GHz)
observations, strong enhancement of reflectivity called“bright band is usually recognized in the melting layer. At 95
GHz, however, bright enhancement cannot be seen but
abrupt increase of reflectivity is only seen.

Fig.2. FALCON-I installed on a truck in the campus of Chiba
University. The container behind the truck is housing of
FALCON-I with two Teflon sheet windows on the ceiling for
MIRAI observations (see Fig.4).

Fig.3. FALCON-I set in the Cape Hedo Station in Okinawa,
Japan, of National Institute for Environmental Studies during
the campaign in spring, 2008.

Fig.4(a). Japanese scientific research vessel, MIRAI, operated
by Japan Agency for Marine-Earth Science andTechnology (JAMSTEC).

Fig.4(b). Container of FALCON-I settled on the upper deck of
MIRAI.

－ 14 －

3-2. High resolution Doppler observations
Fig.6 shows Doppler spectral diagrams of the
cumulonimbus on July 25, 2010 observed with FALCON-I
at Chiba University. This cumulonimbus was generated
several hours before observations in the northwest direction
and moved toward Chiba area. We obtain one Doppler
spectral diagram in every minute with 250 ms observations.
Because Doppler velocity is derived by measuring phaseshift of return signal scattered by objects, there areambiguities of 2π. This causes that, for example, an echoobserved to be 0 m/s may have real vertical velocity of ±6.38 ×n m/s, where n is an integer.
We found, first, the anvil of the cumulonimbus around
9:30 UT at about 12 km in height. Vertical velocity of theanvil was about 0 to -1.5 m/s at 10:08 UT, where negative
velocity means downward motion of the matter. Such rangeof small negative velocities is usually observed even for
quiet and stable clouds. This fact can be explained that only
largest cloud particles would be able to detect with
FALCON-I, which have slight downward velocities about 1
m/s. In the lower part of the anvil shown at 10:29 UT, thinup and downward structures were seen projected form themain echo of the cloud. Thin upward cloud of 0 to +1 m/s
was revealed at the height of 8.0 km as well as thindownward clouds of -1.5 to -2.5 m/s at 7.3 km and -2 to -3
m/s at 8.3 km. These clouds are as thin as about 100 m in thevertical direction. We can find such thin structures in othertemporal data in this cloud and these do not maintain its
structure in the next minute data. These facts suggest thatthese thin structures have also horizontal sizes of about 100m if we assume the horizontal velocity is 1 to 2 m/s and/orlife time of the structure is less than 1 minute.

Fig.5. Time-height diagram of reflectivity observed with FALCON-I on July 25, 2010 at Chiba University. The anvilof the cumulonimbus began to be seen around 9:30 UT (18:30 JST) from about 12 km in height and graduallycame down to lower height. The main part of the cumulonimbus was shown in 10:40-11:20 UT at 4-9 km inheight. Rain started around 10:50. The intensity scale is dB in an arbitral unit.

Fig.6. Doppler spectral diagrams of the cumulonimbus shown in Fig.5 observed with FALCON-I on July 25, 2010 at
Chiba University. Panels show the velocity structure of matter in the anvil at 10:08 UT, lower part of the anvil at 10:29
UT, at start of the rain at 10:44 UT, and during hevy rain at 10:58 UT.

－ 15 －

Rain started at around 10:44 UT as shown in Fig.5 andcorresponding Doppler spectral diagram is the third panel in
Fig.6. We can see thin line structure of a few hundred
meters of vertical width at 4.2 km in height whose verticalvelocities are from -0.7 to +3.16 m/s in Doppler diagram at
10:44 UT. It is obvious that this thin line is the echo of thevery beginning of precipitation when we look successive
spectral diagrams. The real vertical velocities of this line
structure should be from -7 to -3.16 m/s instead of from –0.7
to +3.16 m/s, i.e. 2π= 6.32 m/s should be subtracted. Theline structure should be connected to the left end of the panel
and continued to the bottom of the main body ofcumulonimbus. We notice a small velocity gradient in theline structure in the vertical direction: the height of the line isabout 3.9 km at the lowest velocity of -7 m/s, and is about
4.2 km at the velocity of -3.16 m/s. The whole structure of
the thin line is followings: it starts from about -2 m/s on thebottom of the cumulonimbus at 4.3 km in height, andcontinues up to -7 m/s at 3.9 km. Turbulent up and down
ward motions of ±few m/s also exist in the main body ofthe cumulonimbus as shown in the Doppler diagrams at
10:44 and 10:58 UT.

3-3. Discussion
Observing frequency ν of FALCON-I is 94.79 GHz
which correspond to the wavelength λ= 3.16 mm. For the
wavelength, spherical particles whose diameter D is smaller
than about 1 mm scatter the radio wave with Rayleighscattering region, in which scatter cross section σ is in
proportion to ν4. Particles larger than 1 mm scatter thewave with Mie scattering region, in which σ changes up
and down resonating with the particle diameter andconverges in its geometric cross section. A rough estimationof the raindrop size distribution N(D) would be in proportionto D-3 if we assume total volume of each size is constant.
We may, therefore, consider that the most effective
diameters of raindrops for scattering the wave of FALCON-I
are 1 ±0.5 mm.
The thin and sharp line structure shown in Fig.6. at 10:44
UT, which is the echo by the beginning of the precipitation,suggests following facts as well as related structures. Raindrops of about 1 mm diameter are rapidly generated in thethin sheet of less than 100m thick in the melting layer at thebottom of the cumulonimbus. They are accelerated from -2
up to -7 m/s during 400 m drop, where this downward
velocity corresponds with a usual terminal velocity for 1 mm
diameter raindrops. The up and down motions in the anvil
and the main body of the cumulonimbus suggest theexistence of turbulences of several 100m in size incumulonimbus

Acknowledgements
The authors thank KOBAYASHI Fumiaki of National Defense
Academy for presenting information about cumulonimbus during thecollaborative observations in summer 2010. This work was supportedwith GEOSS (Global Earth Observation System of Systems) by theJapanese Ministry of Education, Culture, Sports Science andTechnology (MEXT), and with the Grant-in-Aid for Scientific Research
by MEXT #14380241, #18310006 and #20310005. It was also
supported with Research Projects for Promoting Technological Seeds
by Japan Science and Technology Agency (JST) Innovation Satellite
Ibaraki in 2007, 2009, and 2010.

References
1) TAKANO Toshiaki, YAMAGUCHI Jun, ABE Hideji, FUTABA
Ken-ichi, YOKOTE Shin-ichi, KAWAMURA Yohei, TAKAMURA
Tamio, KUMAGAI Hiroshi, OHNO Yuichi, NAKANISHI Yuji,
NAKAJIMA Teruyuki; “Development and Performance of theMillimeter-Wave Cloud Profiling Radar at 95 GHz: Sensitivity andSpatial resolution”, IEEJ Trans., FM, Vol.128, No.4, pp.257-262,
2008 (in Japanese), translated to Electronics and Communications inJapan, Vol.93, No.3, 2010.
2) ABE Hideji, YAMAZAKI Fumihiro, YAMAGUCHI Jun, KAWAMURA Yohei, NAKANISHI Yuji, TAKANO Toshiaki, TAKAMURA Tamio, OHNO Yuichi; “Doppler Observations with theMillimeter-Wave Cloud Profiling Radar FALCON-I”, IEEJ Trans.,
FM, Vol.129, No.4, pp.177-182, 2009 (in Japanese).
3) YAMAGUCHI Jun, TAKANO Toshiaki, NAKANISHI Yuji, ABE
Hideji, KAWAMURA Yohei, YOKOTE Shin-ichi, KUMAGAI
Hiroshi, OHNO Yuichi, HORIE Hiroaki; “Evaluation of Radar
Reflectivity (Z) for FMCW Millimeter-Wave Cloud Radar
FALCON-I”, IEEJ Trans., FM, Vol.129, No.4, pp.183-189, 2009 (inJapanese).
4) YAMAGUCHI Jun, TAKANO Toshiaki, TAKAMURA Tamio,
NAKANISHI Yuji, KAWAMURA Yohei, ABE Hideji, HORIE
Hiroaki, OHNO Yuichi; “Evaluation of Parallax on the Dual-Antenna
95 GHz MMCR FALCON-I”, IEICE Trans, Vol.J92-C, No.12,
pp.806-812, 2009 (in Japanese).
5) TAKANO Toshiaki, FUTABA Ken-ichi, ABE Hideji, YAMAGUCHI Jun,HIRAI Akihito, KAWAMURA Youhei, KUMAGAI
Hiroshi, OHNO Yuichi, FUJIYOSHI Yasushi, and OKAMOTO
Hajime; “Observations of Clouds on MIRAI in the Pacific Ocean
with the Millimeter-wave FM-CW Radar at 95GHz”, The
International Society of Optical Engineering, Vol.6362, No.30,
pp.636230・1-8, 2006.
6) G.Pandithurai, T. Takamura, J. Yamaguchi, K. Miyagi, T. Takano,
Y. Ishizaka, S. Dipu, A. Shimizu; “Aerosol effect on cloud droplet
size as monitored from surface-based remote sensing over East China
Sea region, Geophysical Research Lett., VOL.36, L13805, doi:10.
1029/2009 GL038451, 2009.
7) J.Suzuki, M.Fujiwara, A.Hamada, Y.Inai, J.Yamaguchi, R.Shirooka,
F.Hasebe, T.Takano; “Cloud-Top Height Variability Associated with
Equatorial Kelvin Waves in the Tropical Tropopause Layer during theMirai Indian Ocean Cruise for the Study of the MJO-Convection
Onset (MISMO) Campaign”, SOLA (Scientific Online Letter on the
Atmosphere), Vol.6, pp.097-100, 2010.

－ 16 －

Evaluation of Cloud Observations by FMCW CPR FALCON-I at Hedo Station
Jun Yamaguchi1, Toshiaki Takano2, G. Pandithurai3, Hideji Abe2, Youhei Kawamura2
Hideaki Takenaka4, Tamio Takamura4, Toshiyoshi Kimura1, Yutaka Ishizaka5
1
JAXA, Tsukuba Space Center, Sengen 2-1-1, Tsukuba, Ibaraki, Japan, yamaguchi.jun@jaxa.jp
2
Chiba University Graduate School of Engineering, Yahoichou 1-33, Inage, Chiba, Japan
3
Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune 411008 India
4
Chiba University CEReS, Yahoichou 1-33, Inage, Chiba, Japan
5
Aichi Gakuin University,, Yoshiike 12, Iwasaki, Nisshin, Aichi,, Japan
Abstract
CPR (Cloud Profiling Radar) is expected to reveal the inside structure of clouds. Using vertical profiles ofradar reflectivity factor, vertical profiles of cloud microphysical properties (e. g., cloud effective radius, liquidwater content, cloud optical depth) can be retrieved with data of other instruments, which leads to better
understanding of cloud radiative forcing and the earth's energy balance. Chiba University has developed thesolid-state ground-based FMCW CPR FALCON-I to achieve higher range and time resolution with lower power.
In this study, sensitivity and accuracy of FALCON-I are evaluated at the Hedo observation in 2008.
Keywords : CPR (Cloud Profiling Radar), radar reflectivity factor, microphysical properties, FMCW, FALCON-I,
radiative forcing
1. Introduction
It is clearly reported in IPCC AR4 that clouds and aerosols
are one of the important components of radiative forcing of
the earth for better understanding of global warming [1].
Although the contributions to radiative forcing from carbon
dioxide and methane known as the greenhouse gas are
estimated with small error bars from numerical simulation, it
is very difficult to estimate the contributions from cloud andaerosols [2]. They are many types of aerosols, and the
combined aerosols direct radiative forcing is estimated at-0.50 ± 0.40 Wm-2, and cloud albedo radiative forcing is -0.7
Wm-2 as the median with a 5 to 95% range of -0.3 to 1.8
Wm-2. There are still much to study to solve aerosol indirect
effect. Making a progress of this estimation needs filling the
gap between observation works and modeling in spatial andtemporal distribution. Both of more frequent observation ofaerosols and clouds in global and local scales are expected.
CPR is the one of most effective remote sensing
instrument to study cloud inside structure by observing
cloud vertical profiles of radar reflectivity factor and doppler
velocity, which supply important information for cloud
distribution and cloud microphysical properties. Since theconcept of CPR in millimeter-wavelength was proposed in1981 to detect nonprecipitating clouds [3], several CPRs at
35 GHz, 78 GHz and 95 GHz have been developed in the
U.S., the U.K., Germany and Japan. Observation fromSpaceborne CPR called CloudSat has began in 2006 by
NASA (National Aeronautics and Space Administration)

and CSA (Canadian Space Agency) to map 3D distributionof cloud in global scale [4]. EarthCARE project, which is
conducted by JAXA (Japan Aerospace Exploration Agency),
NICT (National Institute of Information andCommunications Technology) and ESA (European Space
Agency), will be a next big step for aerosol and cloud
observation. JAXA and NICT are developing 95 GHz CPR
for EarthCARE project which is the first spaceborne doppler
CPR at 95 GHz [5]. Chiba University in Japan has
developed a solid-state ground-based FMCW 95 GHz CPR
named as FALCON-I (FMCW Radar for Cloud
Observations) since 1999, evaluated theoretically [6] andsystematically [7]. FMCW system achieves high range
resolution, and FALCON-I is expected to reveal moredetailed cloud vertical structure than pulsed CPR.
FALCON-I is also expected to calibrate the CPR of
EarthCARE as one of the ground calibration system.
FALCON-I is expected to contribute to cloud microphysics
which reveals the physical process from formation, growth,precipitation and extinction of cloud.
The primary aim of this study is to evaluate the sensitivityof FALCON-I, comparing cloud vertical profiles with
CloudSat. The second aim of this study is to evaluate cloud
microphysical properties retrieved from radar reflectivity
factor to estimate FALCON-I's accuracy by comparing theestimated downward radiation flux from FALCON-I andthe observed downward radiation flux by Pyranometer.
In this paper, we evaluated correlation of the estimated cloud－ 17 －TABLE I
SPECIFICATIONS OF FALCON-I

optical thickness by FALCON-I and the observed shortwave
downward radiation by Pyranometer for the evaluation as afirst step. We also evaluated retrieved cloud droplet effective
radius, using cloud cendensation nuclei observed by CCN
counter.

NASA developed a spaceborne CPR at 94.05 GHz,
loaded on CloudSat which launched in 2006. CloudSat isorbiting as a member of A-Train with Aura, Glory,
PARASOL, CALIPSO, Aqua and OCO with approximate
altitude of 700km [4]. The specifications of CloudSat are in
Table 2.
Microwave radiometer is used to measure emission at
micrometer, millimeter and sub-millimeter wavelength.
WVR-1100 manufactured by Radiometrics Corporation has
two channels; the channel of 23.8 GHz to estimate
column-integrated amounts of water vapour (g/cm2), thechannel of 31.4 GHz to estimate the amounts of liquid water(g/cm2), and the specifications of MVR-1100 are shown in
Table 3 [10]. In this study, the channel of 31.4 GHz is used
to observe LWP (Liquid Water Path), and it is operated in
Hedo Station by Chiba University CEReS. It is said thattypical retrieval uncertainties of LWP is 25-30g/m2.
CCN is a particle that can form into a cloud droplet. The
CCN counter CCN-100 can measure CCNs at several
supersaturations manufactured by Droplet Measurement
Technology [11]. Aichi Gakuin University operated the
CCN counter during Feb.-Apr. in 2008. The specifications of
CCN-100 are: 1) continuous flow, 2) single supersaturationand 30 seconds supersaturation change, 3) supersaturation
range 0.1 - 2.0%.
Pyranometer is the instrument to measure the upward anddownward solar irradiance on a planar surface to estimate
the solar radiation flux density (W/m2). The pyranometer
CM21 manufactured by Kipp & Zonen is operated for awhole year by Chiba University CEReS. The specificationsof CM21 are: 1) spectral range 305-2800 nm (50% points)
and 335-2200 nm (95% points), 2) sensitivity 7-17 μV/Vm2,
3) directional error 10W/m2, 4) viewing angle 2π sr [12].

2. Instrumentation
We used Z (radar reflectivity factor) of FALCON-I toretrieve cloud microphysical properties in this study.
FALCON-I operates at 94.79 GHz with 20 MHz frequency
modulation. The specifications of FALCON-I are shown in
Table 1, and the outlook is shown in Figure 1. FALCON-I
achieved long range observation upto 20 km with high range
resolution (about less than 20m) with FMCW system.
FALCON-I also has doppler velocity observation function[8]. Although bistatic antenna system causes reduction ofreceiving power, it is corrected with correction index [9].
Atmospheric attenuation, multi-scattering effect andattenuation in clouds were ignored to estimate radarreflectivity factor.

3. Methodology
Cloud observations were done at CHAAMS (Cape Hedo
Aerosol and Atmosphere Monitoring Station) owned by
NIES (National Institute for Environmental Studies) which
locates on 26.87° N, 128.26° E in Cape Hedo, Kunigami,
Okinawa, Japan [13]. Okinawa normally experiences
temperatures above 20°C for most of the year, categorized in
subtropics.
Although WVR-1100 and CM21 operate for whole year,
FALCON-I and CCN-100 joined observation at CHAAMS
for a short term. FALCON-I operated at this station fromFebruary 17th until May 4th in 2008 and CCN-100 operated
from February 18th until April 18th in 2008. Temporal
resolution is 1 minute at FALCON-I, 3 minutes at

Range Resolution

15 m

Temporal Resolution

1minute (at Z mode)

Transmitting Power

27 dBm (0.5 W)

Antenna System

Bistatic Cassegrain

Antenna Beamwidth

0.18 deg (Full Angle)

Sensitivity-30 dBZe at 5 km

TABLE IISPECIFICATIONS OF CLOUDSAT
Range Resolution

485 m

Temporal Resolution

0.16 sTransmitting Power (Peak, Mean)

1800 W, 25 W

Antenna System

Monostatic Offset

Antenna Beamwidth

0.108 Deg

Sensitivity-30 dBZeTABLE III
SPECIFICATIONS OF WVR-1100
Liquid Water Sensing Channel
Frequency

31.4 GHz

Bandwidth

0.4 GHz

Beamwidth (3dB)

4.6 deg

Common Characteristics
Temporal Resolution

3 min－ 18 －

WVR-1100, 30 minutes at CCN-100, and 10 seconds at
CM21.
Re (cloud droplet effective radius) is retrieved from Z andcloud droplets number concentration (N) as below:
1/ 6

1  Z (h) re ( h)  
2  N ( h) exp(0.5 x ) ,2(1)

where h is height, and σx is the logarithmic spread of thecloud droplet size distribution and is 0.34 in this study whichis the average spread of marine stratus clouds [14]. In case of
stratus clouds, although CCN-100 was set on the ground, we
can assume N as CCN number concentration which isconstant with height [15], [16], [17].
Although we cannot avoid assuming that N and σx areconstant with height, the effective radius can be also
retrieved in terms of variable h by using vertical profiles of Z
and Q (LWP) as shown by Eq. 2 [14]:
1/ 3

1/ 3

Re (h) 

Z 1 / 6 (h)     i  m 1 / 2   Z hi h 
1/ 3 
2Q  6   i 1exp(2 x ) , (2)2where ρ is the water density, hi is height, i represents
measurement bin of Z, Δh is the range of 1 bin (9.155 m at
FALCON-I).
LWC (Liquid Water Content) qi can also be retrieved
from Z and LWP shown by Eq. 3 [18]:qi  m

QZ i

Z
i 1

1/ 2

1/ 2
i

h

,

(3)

COT (cloud optical thickness) τ can be written in terms of
Q and Re by Eq. 4 [19]:3 Q  ,
2  Re   

(4)

Thus, we get cloud optical depth at each radar observation
bin and integrated COT retrieved from Z of FACLON-I.

4. Comparison of Ze from Simultaneous Observation
CloudSat has passed over 5 times within a 5 km radius of
CHAAMS while FALCON-I was operating. The both CPRs
detected clouds in 4 of 5 cases, and the 4th case at 4:54,
April 11th (UTC), showed high consistency of the profilesof Ze shown in Fig. 1. The green dots are the Ze of CloudSat
from 2B-GEOPROF file of CloudSat data set withcorrection of gas attenuation, the yellow dots are the Ze of

Fig. 1.
Comparison of Ze verical profiles of FALCON-I and
CloudSat at 4:54Z, April 11th, 2008 at CHAAMS.
1) Blue dots: Ze of FALCON-I
2) Green dots: Ze of CloudSat

FALCON-I, and the blue dots are the Ze of FALCON-I withgas attenuation correction of CloudSat. The cloud observedby the both CPRs is single layered ice cloud with thicknessof approximate 5 km, high homogeneous in temporal andspatial. The starting location of the CloudSat's path of thisprofile is 624 m apart, and the ending location is 470 m apartfrom CHAAMS. The both profiles match well at the cloudbottom and inside of the cloud, whereas FALCON-I's
gradient of the profile is steeper than CloudSat. This conflictis not understood and still under consideration.

5. Retrieval of Microphysical Properties
The temporal changes of Q, N, Z, Re and aerosol size
distribution were discussed [20]. Fig. 2 (a) shows the
temporal change of the vertical profiles of retrieved effective
radius at 8:00Z on April 10th, with the black dots and10:00Z with the red dots by the method of Z and N shown in
Eq. 1. The most important differences of the two profiles arethat the size of radius increased at any height from 8:00Z to
10:00Z. Although cloud bottom height rose up about 70 m
and the cloud top height rose up about 100 m from 8:00Z to
10:00Z, the geometrical thickness didn’t change much. N at
7:56Z was 276 cm-3 and 306 cm-3 at 9:56Z. On the other
hand, Q was 0.036 g/cm2 at 7:58Z and 0.0312 g/cm2 at
9:59Z. The increment of N was 10.9% and of Q was -8.7%.
Increase of CCN number retrains cloud droplets to growbigger if liquid water amount is constant because each
droplets scramble for water. In this case, Q decreased from
8:00Z to 10:00, which can encourage miniaturization ofdroplets, and this can explain the change of the profiles ofretrieved effective radius in Fig. 2 (a).
Fig. 2 (b) shows the change of aerosol size distribution at
8:00Z in the black line and dots and 10:00Z in the red line
and dots. The OPC (Optical Particle Counter) Model 237B
of Pacific Scientific Instruments is operated by Chiba
University CEReS at CHAAMS. The OPC has 5 channels－ 19 －

Fig. 2.
Vertical Profiles of Retrieved Droplet Effective Radiusand Observed Aerosol Particle Radius Distribution [20]:1) Black dots: at 8:00Z, April 10th2) Red dots: at 10:00Z, April 10th3) Fig. (a): Averaged effective radius proflies during 20 minuteswith standard deviation
4) Fig. (b): Aerosol radius distribution with 5 channels by OPC

of size resolution. From the temporal change from the black
dots to the red dots, it is clear that the number of aerosol
increased at any size from 8:00Z to 10:00Z. This aerosol
increase corresponds to the increase of CCN in this case, andit implies the Aerosol Indirect Effect.
COT from cloud bottom to cloud top were estimatedunder the condition of single layered, non-precipitating andliquid water cloud. We compared the estimated COT withobserved SWD by the pyranometer to evaluate the estimatedmicrophysical properties by FALCON-I. We had 4 cases ofsteady clouds which matches all conditions in daytime:
Term 1 is Feb. 24th 08:00-10:00Z, Term 2 is Feb. 25th
08:00-11:30, Term 3 is Feb. 28th 09:00-11:00, Term 4 is Mar.
30th 14:00-17:00. Fig. 3 shows the correlation of theestimated COT and the observed SWD.
The observed intensity after a cloud path I can be writtenwith the variable τ:I  I 0 e ,(5)where I0 is the intensity of radiation source.
Thus correlation between SWD and COT should show
the relation expressed by Eq. (5), Fig. 3 shows that SWD isan exponential function with variable COT. Spread of theplots and overestimation of COT should be evaluatedcarefully. Temporal change of SWD was relatively steady,comparing with temporal change of COT. Estimation ofnoise floor of FALCON-I and MVR-1100 influences valuesof estimated microphysical properties, and it might cause theinstability of COT. This matter is under study, focusing
errors of LWP and Z of raw data.

Fig. 3.
Correlation of the Retrieved Cloud OpticalThickness of FALCON-I and the Short-wave Downward Flux
Observed by Pyranometer:
1) Term1: 08:00-10:00 (JST), February 24th2) Term2: 08:00-11:30 (JST), February 25th3) Term3: 09:00-11:00 (JST), February 28th4) Term4: 14:00-17:00 (JST), March 30th

6. Conclusion and future work
In this study, vertical profiles of Re were retrieved from Z
observed by FALCON-I with CCN number concentration
by CCN counter and LWP by microwave radiometer.
Temporal change of the retrieved Re was discussed with thechange of aerosol, which implies aerosol indirect effect. The
increase of CCN number concentration and aerosol size
distribution and the decrease of liquid water path wererelevant to the temporal change of the profiles in the case.
Estimated COT from retrieval of LWC and Re was
evaluated by comparing correlation with observed SWD.
Although it showed a theoretical relation of exponential,
spread of the distribution is large, thought to be derived fromerror range of LWP and Z.
Although the several steady cases from the CHAAMS
observation were discussed and evaluated in this study, this
discussion has to be evaluated statistically with enoughamounts of data and cases. Especially, systematic errors of
the each instrument must be considered and separated fromthe discussion of microphysical phenomenon.
Acknowledgements
The authors wish to acknowledge the support of NIES for
providing us an opportunity of cloud observation at the
CHAAMS. The authors also thank the students of Prof.
Takano, Prof. Takamura and Prof. Ishizaka who arranged
and set the instruments and data acquisition.
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IPCC Fourth Assessment Report, Working Group I Report,
2005.
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Intercomparison of Equilibrium Climate Change in
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de l'IPSL 18., Oct. 2000.
3) Lhermitte R., “Millimeter Wave Radar,” AMS, 20th
Conference on Radar Meteorology, pp.744-748, Nov.-Dec.
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M. Rokey, D. Reike, P. Partain, G. G. Mace, R. Austin, T.
L'Ecuyer, J. Haynes, M. Lebsock, K. Suzuki, D. Waliser, D.
Wu, J. Kay, A. Gettelman, Z. Wang and R. Marchand,“CloudSat Mission: Performance and Early Science after theFirst Year of Operation”, AGU, Journal of GeophysicalResearch, Vol.113, D00A18, pp.1-18, Dec. 2008.
5) Takahashi N., T. Kimura, Y. Ohno, H. Horie, H.
Nakatsuka, Y. Seki, K. Sato, K. Okada and Y. Sakaide,“Current Status of EarthCARE/CPR Development,” IEICE,Technical Report, Vol.109, pp.13-17, 2010.
6) Takano T., J. Yamaguchi, H. Abe, K. Futaba, S. Yokote, Y.
Kawamura, T. Takamura, H. Kumagai, Y. Ohno, Y.
Nakanishi and T. Nakajima, “Development andPerformance of the Millimeter-wave Cloud Profiling Radarat 95GHz -Sensitivity and Spatial Resolution-,” IEEJ Trans.,
Vol.128, No.5, Apr. 2008, pp.132-139.
7) Yamaguchi J., T. Takano, Y. Nakanishi, H. Abe, Y.
Kawamura, S. Yokote, H. Kumagai, Y. Ohno and H. Horie,“Evaluation of Radar Reflectivity (Z) for FMCW
Millimeter-Wave Cloud Radar "FALCON-I",” IEEJ Trans,
vol. 129, No. 4, Apr. 2009, pp.183-189.
8) Abe H., Yamazaki F., Yamaguchi J., Kawamura Y., Ohno
Y., Nakanishi Y., Takano T. and Takamura T, ”Doppler
Observations with the Millimeter-Wave Clouds Profiling
Radar FALCON-I”, IEEJ, FM, Vol. 129, No.4, April 2009,
pp.177-182.
9) Yamaguchi J., Takano T.,Takamura T., Nakanishi
Y.,Kawamura Y., Abe H., Horie H. and Ohno
Y., ”Evaluation of Parallax on the Dual-Antenna 95GHz
MMCR FALCONI”, IEICE, C, Vol. 92-C, Dec. 2009,
pp.806-812.
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for Observations of Total Column Precipitable Water Vapor
and Cloud Liquid Water Path,” AMS 5th Symposium on
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Streamwise Thermal-Gradient CCN Chamber for
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－ 21 －

The analysis of Skyradiometer observation data by using SKYRAD.PACK and MRI-MLM
Akihiro YAMAZAKI1, Akihiro UCHIYAMA1, Eriko KOBAYASHI2,
Rei KUDO1, and Tomonori SAKAMI1
1
Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba 305-0052, JAPAN,
akyamaza@mri-jma.go.jp, uchiyama@mri-jma.go.jp, reikudo@mri-jma.go.jp, tsakami@mri-jma.go.jp
2
Aerological Observatory, Japan Meteorological Agency, 1-2 Nagamine, Tsukuba 305-0052, JAPAN,
eriko-kobayashi@met.kishou.go.jp
Abstract
SKYNET is the aerosol-cloud monitoring network in East Asia which uses PREDE skyradiometer. The
aerosol optical properties which skyradiometer observes are retrieved by SKYRAD.PACK in SKYNET. We
developed the software (MRI-MLM) to apply the inversion method to the maximum likelihood method based on
SKYRAD.PACK version 4.2. We analyzed skyradiometer data observed in Tsukuba, JAPAN by using
SKYRAD.PACK and MRI-MLM, and investigate the difference of retrieved results.
Keywords : skyradiometer, SKYNET, aerosol, maximum likelihood method
1. Introduction
Aerosol optical properties are very important in the studies
of global and regional climate changes. SKYNET(Nakajima et al., 2003) ground based observation network is
well known aerosol-cloud monitoring network in East Asia
which uses skyradiometer (POM-01 or POM-02
manufactured by PREDE Co., Ltd.) for the purpose ofaerosol radiative forcing studies. The aerosol opticalproperties which skyradiometer observes are retrieved by
SKYRAD.PACK (Nakajima et al., 1996).
We improved SKYRAD.PACK version 4.2 anddeveloped software (MRI-MLM) to apply the inversion
method to the maximum likelihood method (Kobayashi et al.,2006). In this study, we compare the aerosol opticalproperties retrieved by using SKYRAD.PACK and
MRI-MLM, respectively.
2. Skyradiometer observation data
PREDE skyradiometer POM-02 was installed and started
observation in 2002 at Meteorological Research Institute(MRI) in Tsukuba (36.056N, 140.125E), Japan.
Skyradiometer has been calibrated by inter-comparing it to
the spetctoradiometer (SVC GER2600 manufactured bySpectra Vista Co., Ltd.) in every winter season. The
spectroradiometer was calibrated annually by the Langleymethod using the data obtained at Mauna Loa Observatory(MLO), NOAA ESRL-GMD.
2. Data analysis
We analyzed skyradiometer data observed at MRI from2004 to 2010 by using SKYRAD.PACK version 4.2 and

MRI-MLM version 1.1, respectively. The analysis ofskyradiometer needs the total ozone amount and the surfacepressure to take account of ozone absorption and Rayleighscattering of air molecules. The monthly averaged totalozone amount for 39 years (from 1971 to 2009) is taken from
Aerological Observatory close to MRI. The surface pressure
is taken from SYNOP data from same site.
The cloud affected data was detected by CSSR version 1.0(Khatri and Takamura, 2009).

3. Intercomparison of aerosol optical properties
The intercomparisons of aerosol optical properties at wave
length 400, 500, 675, and 870nm between SKYRAD.PACK
and MRI-MLM were based on 12594 retrieved data. Figure
1 (a), (b) and (c) show scatter plots of aerosol optical
thickness (AOT@500) at wavelength 500nm, Ångström
exponent and single scattering albedo (SSA@500) at
wavelength 500nm, respectively. AOT@500 has significant
linear relationship (R=0.99, Slope=0.99, Intercept=0.01)
between SKYRAD.PACK and MRI-MLM. There is good
linear correlation for Ångström exponent between them. On
the other hand, the retrieved single scattering albedo
SSA@500 is little dispersed. SSA@500MRI-MLM isunderestimate in the case of SSA@500SKYRAD.PACK>0.86 and
SSA@500MRI-MLM is overestimate in the case of
SSA@500SKYRAD.PACK <0.86, respectively.
Linear correlations of aerosol optical thickness, single
scattering albedo, and Ångström exponent between
SKYRAD.PACK and MRI-MLM are shown in Table 1.
Aerosol optical thickness at each wavelength has highly
significant linear relationship with correlation coefficient－ 23 －

lager than 0.99 between SKYRAD.PACK and MRI-MLM.
Calculated Ångström exponents by the results of MRI-MLM
are similar to those of SKYRAD.PACK. Correlation
coefficients of single scattering albedo at all wavelengths arelittle dispersed. Single scattering albedo from MRI-MLM(SSAMRI-MLM) is underestimate in the case of that of
SKYRAD.PACK(SSASKYRAD.PACK) is larger than around0.87 at each wavelength and SSAMRI-MLM is overestimate in
the case of SSASKYRAD.PACK is less than around 0.87 at eachwavelength , respectively.

2.5

y = 0.081197 + 0.88868x R= 0.91758

(b)
2

ALPHA
MLM

1.5
1
0.5
0
-0.5
-0.5

Acknowledgement
We are grateful to Open CLASTR project for using
SKYRAD.PACK version 4.2 in this research.

1

0

0.5

1
1.5
ALPHA
SkyradPack2

2.5

y = 0.20553 + 0.76131x R= 0.82367(c)

SSA@500nm
MLM

0.9

References
1) Nakajima, T., Sekiguchi, M., et al.: Significance of direct
and indirect radiative forcings of aerosols in the East China
Sea region, J. Geophys. Res., 108(D23), 8658,
doi:10.1029/2002JD003261, 2003.
2) Nakajima, T., Tonna, G., Rao, R., Holben, B.N.: Use ofsky brightness measurements from ground for remotesensing of particulate polydispersions, Appl. Opt., 35,
2672–2686, 1996.
3) Kobayashi, E., Uchiyama, A., et al.: Application of theStatistical Optimization Method to the Inversion Algorithm
for Analyzing Aerosol Optical Properties from Sun and Sky
Radiance Measurements, J. Meteor. Soc. Japan,Vol. 84, No.6,
1047-1062, 2006.
4) Khatri, P. and Takamura, T.: An Algorithm to Screen
Cloud-Affected Data for Sky Radiometer Data Analysis, J.
Meteor. Soc. Japan, Vol. 87, No. 1, 189-204, 2009.

0.8

0.7

0.6

0.5
0.5

0.6

0.7
0.8
SSA@500nm
SkyradPack

0.9

1

Fig. 1. Scatter plots of aerosol optical thickness(AOT) at wavelength 500nm (a), Ångström
exponent (ALPHA) (b), and single scattering
albedo (SSA) at wavelength 500nm (c) between
SKYRAD.PACK and MRI-MLM.

Table 1. Regression curves coefficients of aerosol
optical thickness (AOT), single scattering albedo(SSA), and Ångström exponent (ALPHA) between
SKYRAD.PACK and MRI-MLM.
y = 0.007717 + 0.99608x R= 0.99957

2.5

(a)

AOT@400
AOT@500
AOT@675
AOT@870
SSA@400
SSA@500
SSA@675
SSA@870
ALPHA

AOT@500nm
MLM

2

1.5

1

0.5

0

0

0.5

1
1.5
AOT@500nm
SkyradPack2

2.5－ 24 －

Slope
0.986
0.996
0.996
0.996
0.793
0.761
0.724
0.663
0.889

Intercept
0.005
0.008
0.007
0.007
0.180
0.206
0.236
0.300
0.081

R
0.999
1.000
0.998
0.998
0.833
0.824
0.818
0.744
0.918

Seasonal variation of aerosol optical properties in Qingdao
Li-Fang SHENG1, Akihiro YAMAZAKI2, Akihiro UCHIYAMA2, and Guang-Yu SHI3
1
Ocean University of China, 238 Songling Road , Qingdao 266100, CHINA,
shenglf@ouc.edu.cn
2
Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba 305-0052, JAPAN,
akyamaza@mri-jma.go.jp, uchiyama@mri-jma.go.jp
3Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, CHINA,
shigy@mail.iap.ac.cn
Abstract
Aerosol optical properties are very important in the studies of global and regional climate change.
Skyradiometer is a useful instrument to obtain the ground based monitoring of aerosol optical properties. We
analyzed skyradiometer data observed in Qingdao, China by using SKYRAD.PACK version 4.2 and investigate
the retrieved aerosol optical properties.
Keywords : skyradiometer, aerosol, SKYNET
1. Introduction
Aeolian Dust Experiment on Climate impact (ADEC) was
conducted from 2000 to 2004 to investigate physical andchemical properties of aeolian dust by the collaboration ofChina and Japan of the observation network. In this project, askyradiometer POM-02 (manufactured by PREDE Co., Ltd.)was installed at Ocean University of China (OUC),
Qingdao in 2002 and started observation to measure aerosol
optical properties (Uchiyama et al., 2005). After ADEC
project, OUC and Institute of Atmospheric Physics (IAP)
have continued to collaborate with Meteorological ResearchInstitute (MRI), JAPAN, and observed continuously by
skyradiometer in Qingdao. On the other hand, SKYNET(Nakajima et al., 2003) has supported our continuous
observation from 2008.
This paper presents preliminary results of retrieved aerosol
optical properties in Qingdao.
2. Instrumentation
PREDE skyradiometer POM-02 was installed and started
observation in 2002 at OUC (36.070N,120.333E) which
located near Yellow Sea in Qingdao, China. Skyradiometerhas been calibrated by inter-comparing it to thespetctoradiometer MRI calibrated annually by the Langleymethod using the data obtained at Mauna Loa Observatory(MLO), NOAA ESRL-GMD.
2. Data analysis
We analyzed skyradiometer data observed at OUC in
Qingdao from 2002 to 2009 by using SKYRAD.PACK
version 4.2 (Nakajima et al., 1996). In the analysis ofskyradiometer observation data, we need the total ozone
amount and the surface pressure. They take account of ozone
absorption and Rayleigh scattering of air molecules,
respectively. The monthly averaged total ozone amount for
10 years (from 2000 to 2009) is taken from Institute ofAtmospheric Physics, Chinese Academy of Sciences which
is a site (STN208) of World Ozone and Ultraviolet Radiation
Data Centre (WOUDC). The surface pressure is taken fromSYNOP data from Meteorological Observatory in Qingdao.
We use CSSR version 1.1 developed by SKYNET (Khatri
and Takamura, 2009) for cloud-screening procedure in thisstudy.

3. Retrieved aerosol optical properties
In this study, we show preliminary results of retrieved
aerosol optical properties from skyradiometer observation
data in Qingdao.
Figure 1 shows scatter plots in the respective season
between aerosol optical thickness at wavelength 500nm(AOT@500nm) and Ångström exponent. And Table 1
shows seasonal mean values of aerosol optical thickness at
wavelength 500nm (AOT@500nm) and Ångström
exponent.
Higher AOT@500nm was observed in summer, andlower AOT@500nm was observed in winter. Ångström
exponent was almost observed more than 0.5 for 3 seasons(JJA, SON, and DJF). Low Ångström exponent haverelatively higher optical thickness in spring (MAM).
Acknowledgement
Observation of skyradiometer has been supported by－ 25 －

SKYNET from 2008.
We are grateful to Open CLASTR project for using
SKYRAD.PACK version 4.2 in this research.

2.5

SON

(c)

Angstrom exponent

2
1.5
1

..

References
1) Uchiyama, A., Yamazaki, A., et al.: Characteristics ofAeolian dust observed by sky-radiometer in the ADEC
Intensive Observation Period 1 (IOP1), J. Meteorol. Soc.
Japan, 83A, 291-305, 2005.
2) Nakajima, T., Sekiguchi, M., et al.: Significance of direct
and indirect radiative forcings of aerosols in the East China
Sea region, J. Geophys. Res., 108(D23), 8658,
doi:10.1029/2002JD003261, 2003.
3) Nakajima, T., Tonna, G., Rao, R., Holben, B.N.: Use ofsky brightness measurements from ground for remotesensing of particulate polydispersions, Appl. Opt., 35,
2672–2686, 1996.
4) Khatri, P. and Takamura, T.: An Algorithm to Screen
Cloud-Affected Data for Sky Radiometer Data Analysis, J.
Meteor. Soc. Japan, Vol. 87, No. 1, 189-204, 2009.

0.5

○

0
-0.5

0

0.5

1

1.5
2
AOT@500nm

2.5

3

2.5

DJF

(d)

1.5
1

..

Angstrom exponent

2

0.5

○

0

2.5

MAM

(a)

-0.5

0.5

1

1.5
2
AOT@500nm

2.5

3Fig. 1. Scatter plots of aerosol optical thickness at
wavelength 500nm (AOT@500nm) and Ångström
exponent in the respective season.
(a) Spring (MAM), (b) Summer (JJA), (c) Autumn(SON), and (d) Winter (DJF).

1
0.5

○

0
-0.5

0

0.5

1

1.5
2
AOT@500nm

2.5

3Table 1. Seasonal mean values (Mean) and standard
deviation values (σ) of aerosol optical thickness at
wavelength 500nm (AOT@500nm) and Ångström
exponent in the respective season.

2.5

JJA

(b)
2
1.5

MAM
JJA
SON
DJF
mean σ mean σ mean σ mean σ
AOT@500nm 0.69 0.40 0.80 0.55 0.55 0.40 0.43 0.29
Ångström Exp. 1.04 0.31 1.16 0.27 1.33 0.23 1.29 0.26

1..

Angstrom exponent

0

1.5

..

Angstrom exponent

2

0.5

○

0
-0.5

0

0.5

1

1.5
2
AOT@500nm

2.5

3－ 26 －

Current Status of Cloud-Resolving Model for Simulations ofHigh-Impact Weather Systems
Kazuhisa Tsuboki
Hydrospheric Atmospheric Research Center (HyARC), Nagoya University
Furo-cho, Chikusa-ku, Nagoya, 464-8601, JAPANe-mail: tsuboki@rain.hyarc.nagoya-u.ac.jp

1. Introduction
One of the most important objectives of regional models
is a high-resolution simulation of high-impact weather
systems, which can be used for detailed studies and accurate
predictions. High-impact weather systems are mostsignificant phenomena in the atmosphere and sometimes
cause huge disasters to human society. Understanding theirmechanisms and structures is necessary for prediction andprevention/reduction of disasters. Most high-impact weather
systems that cause heavy rainfalls and/or violent winds
consist of cumulonimbus clouds and their organized systems.
They are usually embedded within a larger weather systemand occasionally have a multi-scale structure, ranging fromcloud-scale to synoptic-scale systems. These weather
systems in East Asia include the Baiu front, typhoons, andwinter snowstorms associated with a cold-air outbreak.
In order to perform simulations and numericalexperiments of high-impact weather systems, we have beendeveloping the cloud-resolving numerical model named
CReSS. Since the multi-scale structure of the weather
systems has a wide range of horizontal scales, a large
computational domain and a very high-resolution grid toresolve individual classes of the multi-scale structure arenecessary to simulate the evolution of the weather systems.
In particular, an explicit calculation of cumulonimbus clouds
is important for a quantitative simulation of precipitation
associated with the high-impact weather. It is also required
to formulate accurately cloud physical processes as well as
the fluid dynamic and thermodynamic processes. For thistype of computation, a large parallel computer with a hugememory is necessary.
The purpose of the present-day research is to explicitly
simulate clouds and their organized systems in a large
domain (larger than 1000 x 1000 km) with resolved
individual clouds using a very fine grid system (less than 1
km horizontally). This will clarify a detailed structure of thehigh-impact weather systems and permits a more quantitative
prediction of the associated precipitation. It will contribute to
the accurate prediction of precipitation and the reduction ofdisasters caused by the high-impact weather systems.

2. Brief description of CReSS
The formulation of CReSS is based on the
non-hydrostatic and compressible equation using
terrain-following coordinates. Prognostic variables are
3-dimensional velocity components, perturbations ofpressure and potential temperature, water vapor mixing ratio,
sub-grid scale turbulent kinetic energy (TKE), and cloudphysical variables. A finite difference method is used for the
spatial discretization. The horizontal domain is rectangular,
and variables are set on a staggered grid: the Arakawa-C gridin the horizontal and the Lorenz grid in the vertical. For time
integration, the mode-splitting technique (Klemp andWilhelmson 1978) is used. Terms related to sound waves ofthe basic equation are integrated with a small time step, andother terms with a large time step.
Cloud physical processes are formulated by a bulk
method of cold rain, which is based on Lin et al. (1983),
Cotton et al. (1986), Murakami (1990), Ikawa and Saito(1991), and Murakami et al. (1994). The bulk
parameterization of cold rain considers water vapor, rain,
cloud, ice, snow, and graupel. The microphysical processes
implemented in the model are described in Fig.1.
Parameterizations of the sub-grid scale eddy motions in
CReSS are one-order closure of Smagorinsky (1963) or the
1.5-order closure of turbulent kinetic energy (TKE). In thelatter parameterization, the prognostic equation of TKE isused. All numerical experiments in this textbook use
three-dimensional 1.5-order closure scheme. The surface
process of CReSS is formulated by a bulk method, whosebulk coefficients are taken from Louis et al. (1981).
Several types of initial and boundary conditions areavailable. For a numerical experiment, a horizontally
uniform initial field provided by a sounding profile will be
used with an initial disturbance of a thermal bubble or
random temperature perturbation. The boundary conditions
are one of the following types; rigid wall, periodic, zeronormal-gradient, and wave-radiation types.

－ 27 －

Fig.1: Diagram describing water substances and cloud microphysical processes
in the bulk scheme of CReSS
CReSS can be nested within a coarse-grid model for a
prediction experiment. In such an experiment, the initial
field is provided by interpolating grid-point values and theboundary condition is provided by the coarse-grid model.
For a computation within a large domain, conformal map
projections are available, which include the Lambertconformal projection, the polar stereographic projection, andthe Mercator projection.

For parallel computing of a large computation, CReSS
provides two-dimensional domain decomposition in thehorizontal direction (Fig.2). Parallel processing is performed
using the Massage Passing Interface (MPI).
Communications between individual processing elements(PEs) are performed by exchanging data of the twooutermost grids. The OpenMP is also available.

Fig.2: Schematic representation of two-dimensional domain decomposition and thecommunication strategy for parallel computations using MPI.

－ 28 －

3. Simulation of Tornado within a Supercell
The supercell is highly three-dimensional and its
horizontal scale is several tens of kilometers. A large domain
in the order of 100~km is necessary to simulate a supercell
using a cloud model. A tornado, on the other hand, has ahorizontal scale of a few hundred meters. The simulation of
the tornado requires fine horizontal grid spacing in the orderof 100~m or less. In order to simulate a supercell and its
associated tornado by a cloud model, a huge memory andhigh-speed CPU are crucial.
Numerical experiments of a supercell thunderstorm using
a cloud model have been performed during the past 20 years(Wilhelmson and Klemp, 1978; Weisman and Klemp, 1982,
1984). Recently, Klemp and Rotunno (1983) attempted toincrease the horizontal resolution to simulate the finestructure of a meso-scale cyclone within the supercell. An
intense tornado occasionally occurs within a supercell
thunderstorm. It is, however, more difficult to resolve a
tornado. To overcome this difficulty, Wicker andWilhelmson (1995) used an adaptive grid method, atwo-way nesting technique, to simulate tornado genesis.
Their grid spacing of the fine mesh was 120 m. Grasso andCotton (1995) also used a two-way nesting procedure of acloud model to simulate a tornadic vorticity. Nesting
methods introduce complications of communication
between the coarse-grid model and the fine-mesh model atthe boundary. A cloud-resolving model does not require any
nesting methods. We simulate both the supercell and thetornado using a uniform grid, therefore avoid any
complication of the boundary communication. The
computational domain of the present simulation is about 50
x 50 km and the grid spacing is 100 m. The integration time
is about 2 hours.
The basic field is give by a sounding at Shionomisaki,
Japan at 00 UTC, 24 September 1999. The initial
perturbation is a warm thermal bubble placed near thesurface, which can induce an initial convective cloud. One
hour from the initial time, a quasi-stationary supercell issimulated by CReSS (Fig.3). The hook-shaped precipitation
area and the bounded weak echo region (BWER), which arecharacteristic features of a supercell, are formed in thesimulation. An intense updraft occurred along the surface
flanking line. At the central part of BWER or of the updraft,a tornadic vortex was formed 90 minutes from the initialtime.
A close view shows closed contours in the central part ofthe vorticity. The diameter of the vortex is about 500 m andthe maximum vorticity is about 0.1-1, similar to the observed

tornado. The pressure perturbation also shows closed
contours that correspond to those of the vorticity. This
indicates that the flow in the vortex is in cyclostrophic
balance. The vertical cross section of the vortex shows thatthe axis of the vorticity and the associated pressure
perturbation is inclined to the left hand side and extends to aheight of 2 km. At the center of the vortex, the downward
extension of cloud is simulated.
Though this is a preliminary result of a supercell andtornado, some characteristic features of the observation are
successfully simulated. The important point of thissimulation is that both the supercell and the tornado are
simulated with the same grid size. The tornado is produced
purely by the physical processes in the model. A detailed
analysis of the simulation will provide us important
information on tornado genesis within a supercell

Fig.3 Horizontal display at 600~m of thesimulated supercell at 5400 seconds from theinitial. Mixing ratio of rain (gray scales, g kg-1,
vertical velocity (thick lines, m s-1, the surface
potential temperature at 15m (thin lines, K)
and horizontal velocity vectors.

4. Typhoons and the associated heavy rainfall: T0418
and T0423
Typhoons develop by close interaction between alarge-scaledisturbanceandembeddedintensecumulonimbus clouds. The horizontal scale of typhoons
ranges from several 100 km to a few 1000 km while that of
the cumulonimbus clouds is an order of 10 km. Typhoons
often bring a heavy rain and a strong wind. The heavy rain is
usually localized in the eyewall and spiral rainbands which－ 29 －

develop within typhoons. Since cumulonimbus clouds are
essentially important for typhoon development, thecloud-resolving model is necessary for a detailed numericalsimulation of typhoons.
Some typhoons usually attain Japan and its surroundings
and cause severe disasters. In particular, ten typhoons landed
over the main lands of Japan in 2004. In the present section,
we show two simulation experiments of the typhoons. One
is the typhoon T0418 which brought a very intense wind andcaused huge disasters due to the strong wind. The other isthe typhoon T0423 which brought heavy rainfalls andcaused severe floods.
Typhoon T0418 moved northwestward over thenorthwest Pacific Ocean and passed Okinawa Island on 5
September 2004. Its center passed Nago City around 0930
UTC, 5 September with the minimum sea level presser of
924.4 hPa. When T0418 pass over Okinawa Island, double
eyewalls were observed. This is a distinctive feature of thetyphoon. T0418 was characterized by strong winds andcaused huge disasters due to the strong winds over Japan.
The main objectives of the simulation experiment of
T0418 are to study the eyewall as well as spiral rainbands,
and to examine structure of the strong wind associated withthe typhoon around Okinawa Island. The simulation
experiment of T0418 started from 0000 UTC, 5 September
2004. The simulation experiment shows very detailed
structure of the eye and the spiral rainbands (Fig.4).
Individual cumulus clouds are resolved. They are simulated
within the eyewall and along the spiral rainband. A weak
precipitation forms around the central part of the eye. The
maximum tangential velocity is present along the eyewall
and at a height of 1 km. It is larger than 70 m -1. The
high-resolution experiment shows detailed structure of thecloud and precipitation systems associated with the typhoon,and simulates the overall structure of the typhoon and its
movement.
Typhoon T0423 moved along the Okinawa Islands on 19
October 2004 and landed over Shikoku Island on 20 October.
In contrast to T0418, T0423 is characterized by heavy
rainfall over Japan. Heavy rainfalls associated with T0423
occurred in the eastern part of Kyushu, Shikoku, the east
coast of the Kii Peninsula, and the Kinki District. They
caused severe floods and disasters in these regions.
The purpose of the simulation experiment of T0423 is to
study process of the heavy rainfall. At the initial time of1200 UTC, 19 October 2004, T0423 was located to the
NNE of Okinawa.

Fig.4: Surface pressure (contour lines; hPa) andrainfall intensity (color levels; mm hr-1) of thesimulated Typhoon T0418 at 0830~UTC, 5
September 2004.

The movement of T0423 and the rainfall weresuccessfully simulated. In the simulation, a northward
moisture flux is large in the east side of the typhoon center.
When the large moisture flux reaches to the Japanese Islands,
heavy rainfalls occur along the Pacific Ocean side. The
heavy rainfall moves eastward with the movement of thetyphoon from Kyushu to Shikoku. When the typhoon
reaches to the south of Shikoku, heavy rainfall begins in the
Kinki District (the rectangle in Fig.5) and intensifies at 0630
UTC, 20 October (Fig.5). The distribution and intensity of
precipitation well correspond to those of the radarobservation.

－ 30 －

Fig.5: Same as Fig.4 but for the Typhoon T0423
at 0630~UTC, 20 September 2004. Arrows arehorizontal wind velocity at a height of 974 m andwarmer colored arrows means moister air. The
rectangle indicates the region of Fig. 6

along the Baiu front.
The initial and boundary condition were provided by the
JMA Regional Spectral Model (RSM). Initial time is1200~UTC, 12 July 2004. The simulation showed that the
SSL moved eastward along the Baiu front.
Figure 7 shows that the SSL reaches Japan at 0020~UTC,
13 July 2004. Moist westerly wind is intense to the south of
the SSL. Large precipitation extends to the east of the SSL.
On the other hand, a very intense rainband forms to thesouth of the SSL. Enlarged display (Fig.8) of the rainband
shows that it extends from the northern part of the Noto
Peninsula and reaches Niigata with intensification. The
rainband forms between the southwesterly and westerly
winds at the low level. The rainband is composed of intense
convective cells. It maintains until the SSL moves to the
Pacific Ocean. The long time maintenance of the intense
rainband results in the severe flood in Niigata Prefecture.

Fig.6: Mixing ratio of precipitation (color levels;gkg-1) and horizontal velocity (arrows) at a height of6142~m at 0630~UTC, 20 September 2004.

The close view of northern Kinki shows that a large
amount of solid precipitation is present around a height of 6
km (Fig.¥ref{fig:T0423_small}). The heavy rainfall in theregion forms below the large mixing ratio of the solid
precipitation. The heavy rainfall along the Pacific Ocean side
moved eastward, while that in the Kinki District lasted until
12 UTC, 20 October. After the typhoon moved to the east of
the Kinki District, the northeasterly was intensified
significantly. Consequently, orographic rainfall formed in thenorthern Kinki District. As a result, the accumulated rainfall
became a large amount and the severe flood occurred.

5. Localized heavy rainfall
Precipitation systems associated with the Baiu front
occasionally show a multi-scale structure. The Baiu front
extends zonally for several thousand kilometers while a
localized heavy rainfall has a horizontal scale of a few
hundred kilometers. To clarify the water circulation process
and the role of each class of the multi-scale systems, we
performed a simulation experiment of the heavy rainfall in alarge domain and with a high resolution. The explicit
representation of cumulonimbus clouds in the model isessentially important for accurate and quantitative simulationof the localized heavy rainfall.
The localized heavy rainfall occurred in Niigata andFukushima prefectures on 13 July 2004. Radar observationof the Japan Meteorological Agency (JMA) showed that an
intense rainband extended zonally and maintained for morethan 6 hours. The Baiu front was present to the north of
Niigata and a sub-synoptic scale low (SSL) moved eastward－ 31 －

Fig. 7: Surface pressure (contour lines; hPa) and rainfall
intensity (color levels; mm hr-1) and horizontal velocity(arrows) at a height of 1610 m at 0020 UTC, 13 July
2004. Warmer colored arrows mean moister air. The
rectangle indicates the region of Fig. 8.

Fi.8: Same as Fig.7 but for the region of therectangle in Fig.7 and at a height of 436 m.

6. Summary
High-impact weather systems occasionally cause hugedisasters to human society owing to heavy rainfall and/or
violent wind. They consist of cumulonimbus clouds andusually have a multi-scale structure. High-resolution
simulations within a large domain are necessary forquantitatively accurate prediction of the weather systems andprevention/reduction of disasters. For the simulations, wehave been developing a cloud-resolving model named the
Cloud Resolving Storm Simulator (CReSS). The model isdesigned and optimized for a parallel computer. The purposeof the present research is high-resolution simulations ofhigh-impact weather systems in a large calculation domain
with resolving individual cumulonimbus clouds using theCReSS model. Characteristic high-impact weather systems
in East Asia are the Baiu front, typhoons, and wintersnowstorms. The present paper describes simulations ofthese significant weather systems. We have chosen for thecase study of the Baiu front the Niigata-Fukushima heavy
rainfall event on 13 July 2004. Typhoons for simulations are
T0418 which caused a huge disaster due to strong wind, andT0423 which caused severe flood over the western Japan in
2004. These experiments clarified both the overall structuresof weather systems and individual clouds. The
high-resolution simulations resolving individual clouds
permit a more quantitative prediction of precipitation. They
contribute to accurate prediction of wind and precipitationand to reduction of disasters caused by high-impact weather
systems.

References
Tsuboki, K. and A. Sakakibara, 2002: Large scale parallel
computing of Cloud Resolving Storm Simulator. High
Performance Computing, Springer, H. P. Zima et al.
Eds, 243-259.
Tsuboki, K., 2008: High-resolution simulations ofhigh-impact weather systems using the cloud-resolving
model on the Earth Simulator. High Resolution
Numerical Modeling of the Atmosphere and Ocean,
Springer, New York, Kevin Hamilton and Wataru
Ohfuchi (Eds), 141-156.
Liu. A. Q., G. W. K. Moore, K. Tsuboki and I. A. Renfrew,
2006: The effect of the sea-ice zone on thedevelopment of boundary layer roll clouds duringcold air outbreaks. Boundary-Layer Meteorology,
Vol.118, No.3, 557 - 581.
Maesaka, T., G. W. Kent Moore, Liu, A. Q., and K.
Tsuboki, 2006: A simulation of a lake effectsnowstorm with a cloud resolving numerical model.
Geophysical Research Letters, Vol.33, L20813.
Wang, C. C., G. T. J. Chen, T. C. Chen and K. Tsuboki,
2005: A numerical study on the effects of Taiwan
topography on a convective line during the Mei-yu
season. Monthly Weather Review, Vol.133, 3217 3242.

Acknowledgements
The present study of CReSS development is supported by
the KAKUSHIN program. The simulations of the present
work were performed by the Earth Simulator and mainframe
computer of the computer center at Nagoya University.

－ 32 －

Diurnal variation of cloudiness over South Asia using satellite observations
B. C. Bhatt1, T.-Y. Koh1, M. K. Yamamoto2, K. Nakamura3
1
Nanyang Technological University, 9th storey BorderX block, RTP, 50 Nanyang Drive Singapore 637553
bcbhatt@ntu.edu.sg
2
Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japanmkyamamoto@faculty.chiba-u.ac.jp
3
Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601 Japan
nakamura@hyarc.nagoya-u.ac.jp

Abstract
The climatological features of the diurnal cycle of cloudiness were investigated over three convective centersof interest over South Asia: over the Himalayas; north India and the Bay of Bengal (BOB). Three hourly
blackbody brightness temperature (TBB) data from the METEOSAT-5 and rainfall data (3B42) from the TropicalRainfall Measuring Mission (TRMM) were utilized. Analysis was concentrated on pre-monsoon and the summer
monsoon season. Two methods were used to detect cloudiness: one method counted cloud clusters (CC) andutilized cloud tracking technique to identify time clusters (TC); and the other method computed cloud coverfrequency (CCF). CC was defined as a contiguous area of pixels with TBB lower than the threshold of 219 K. The
CCF was derived using 235 K as the threshold.
From the analysis, it was noted that there were many CC over north India in the pre-monsoon season
especially during the late afternoon hours. But deep convective activity was weaker over the region during thesummer monsoon season. Bimodal diurnal cycle in CC occurrence was noted over the Himalayas during thesummer monsoon season. It was noted that the peak convective activity occurred at 6 LT for the Bay of Bengal
during both the summer monsoon and the pre-monsoon season. The life cycle of cloud clusters during summer
monsoon season was also investigated. The diurnal cycle varied with location. There appeared a gradual delay inthe preferred time of initiation, attainment of maximum area and dissipation as one progresses northward from
BOB region through north India to the Himalayas.
Keywords: diurnal variation; climatology; South Asia

1. Introduction
Atmospheric convection seems ubiquitous over South
Asia. The widespread cloud and precipitation activity during
monsoon period has long been recognized over the region.
The frequency of convection is modulated by a variety ofphenomena including monsoon depressions, intraseasonal
active-break phases and the local diurnal circulations such as
land-sea breezes and mountain-valley winds.
Changes in convective activity on the diurnal time scale
have been observed in many places around the globe. If weexamine the South Asian region covered by previous
cloud/precipitation studies, Indian Ocean have been
explored more than the adjoining landmasses (Gambheer
and Bhat 2001). Convective activity over domains within
South Asia has not been sufficiently researched, although

there were some studies on diurnal variations. Kodama et al.
(2005) reported maximum lightning activity over the east
coast and north part of India during the pre-monsoon season.
In the Himalayas, bimodal diurnal cycle of rainfall has beenreported from limited ground-based and satellite
observations (e. g., Bhatt and Nakamura 2006). Over thenorthwest side of Bay of Bengal (BOB), Zuidema (2003)
documented a high degree of convective activity withmorning maximum in cloudiness in the summer monsoon
season. The monsoon depressions from BOB usually movenorthward or northwestward accompanied by the onshoretransport of moist air. There is substantial convective
organization. The associated rainfall is distributed acrossthree regional maxima centered separately in BOB, northIndia and the southern slopes of the Himalayas (Xie et al.
2006). The diurnal variability over the centers of heavy－ 33 －

rainfall in BOB, north India and the Himalayas has not been
sufficiently researched and deserve further attention. Basedon the observations reported by Xie et al. (2006), three
convective centers of interest (Fig. 1) are identified for thiswork: region A in the Himalayas; region B in north India andregion C in the BOB to examine fine-scale variations ofconvective activity.
The aim is to delineate the climatological diurnal
convective activity over three convective centers during the
pre-monsoon and especially in the summer monsoon
seasons. We use high spatial resolution infrared (IR) data
denoting cloud fields from METEOSAT-5. We also
performed a simple validation of METEOSAT results with
Tropical Rainfall Measuring Mission (TRMM) data fromover ten years.

12 months of data from both datasets were divided into two
seasons: pre-monsoon season for March, April and May(MAM); and summer monsoon season for June, July andAugust (JJA).
Two methods were used to detect cloudiness from
METEOSAT-5 data. One utilized cloud cluster (CC)technique while the other computed cloud cover frequency(CCF). In the CC technique, deep convective clouds weredetected as patches of very low TBB. A CC was defined as acontiguous area larger than 2000 km2 and with a brightness
temperature lower than 219 K. The cloud tracking procedureused here was similar to the one described by Williams andHouze (1987). To detect convective clouds in the CCF
technique, it was assumed that cloudy sky has not only
cooler but also less homogeneous distribution of TBB thanclear sky. The CCF was derived using 235 K as thethreshold.

3. Results and Discussion

Figure 1: Terrain height (units: m) over South Asia. The
three regions used to study cloud diurnal variations are:region A, region B and region C.

2. Data and Method
Two sets of satellite data were used. One was TRMM rain
product (3B42) available on 0.25º x 0.25º grid with temporal
resolution of 3 hours from 1999 to 2008. This product isfrom multiple satellite microwave and infraredmeasurements (Huffman et al. 2007). The other was
METEOSAT-5 IR brightness temperature (TBB) data
available with spatial resolution of 5 km and temporal
resolution up to 1 hour for 1999 and 2000 but data at hand
was limited to 0, 3, 6, 9, 12, 15, 18 and 21 Local Times(LT) to be comparable with the TRMM dataset. Local times
are defined as Universal Time Coordinate (UTC) + 6 hours.

3.1 METEOSAT-5 data
The selected two years, 1999 and 2000 are notexceptional with regards to inter-annual climatic variationsin the region. The space-time variability of deep convective
clouds during the pre-monsoon and summer monsoon will
be described. We examined areal representation of thediurnal cycle of cloudiness without considering each CC’slife span over South Asia in the three regions. Figure 2ashows the diurnal variation of the normalized number of CC
over a day for the pre-monsoon season. The average numberof CC in every 3-hour interval in local time has beennormalized by the respective total number of CC in a day foreach region so that the relative variation stands out. The peakoccurrence of CC over region B and the region A is in the
mid-afternoon (15 LT). On the other hand, in region C, themaximum occurs in the early morning (6 LT). Thus, thephase of the diurnal cycle differs greatly between land andsea. The enhanced CC distribution over north India
including region B was nearly oriented in NE-SW direction
as it was noted in the spatial distribution of CC (not shown).
Figure 2b shows the diurnal variation of the normalized
number of CC over a day for the summer monsoon season.
The peak appears earlier in this season at 12 LT over region
B, while the region A peak remains at 15 LT. The shift in thetime of maximum convective activity from early afternoon
in the region B to late afternoon over the region A may－ 34 －

suggest northward migration of deeply convecting MCSs. It
is remarkable that the peak convective activity occurs at 6
LT for region C during both the summer monsoon and thepre-monsoon. These results are in agreement with diurnal
cycle studies by Ohsawa et al. (2001), Yamamoto et al.
(2008) and Zuidema (2003) over the BOB region. From thespatial distribution, it was noted that deep convective activity
was weak during the summer monsoon (not shown).
We further focused on the summer monsoon season andinvestigated the life time of cloud clusters over threedomains (Fig. 3). For the Himalayan region, time cluster(TC) mostly form in the early afternoon (12-15 LT), attaintheir maximum area around 15 LT and then decay away by
the early evening (18-21 LT). In region B, initiations tend tooccur in the early morning (6-9 LT) but maximum area isonly attained around noon (12 LT) followed by dissipation in
the early afternoon (12-15 LT). The region C stands out
among the three regions in that initiation, attainment of
maximum area and dissipation all tend to take place before
noon (around 3-6 LT, 6 LT and 9-12 LT respectively).

Figure 2: Diurnal variation of the number of CC
normalized by the total number of CC over a day for eachregion during MAM 1999-2000 (a) and during JJA
1999-2000 (b). The black, gray and white bars denote results
for the regions A, B and C respectively.

Figure 3: The number of TC distributed over their local timeof initiation (left column), attainment of maximum area
(middle column) and dissipation (right column) in JJA
1999-2000. The first, second and third rows correspond to
the regions A, B and C respectively.

3.2 TRMM data
Ten years of TRMM 3B42 rainfall data (1999-2008) was
analyzed. The findings have the statistical significance toestablish the robustness of the same phenomena that was
revealed in the shorter METEOSAT-5 dataset. The spatial
distribution of normalized differential rainfall index (NDRI)
during MAM and JJA of 1999-2008 is computed(Yamamoto et al. 2008). Moderate to large NDRI overregion C suggests relatively strong diurnal variations (Fig. 4).
Over regions A and B, the NDRI values are more variable.
During the pre-monsoon season in region A, the low
nighttime rainfall in the TRMM observations does notcorrespond with the high nighttime occurrence of CC notedin Meteosat-5 results. For the summer monsoon season,
there is concentration of nighttime intense rainfall over theregion A. The nighttime occurrence of CC, which could
imply the likelihood of rain, is evident in the METEOSAT-5
data analysis. Results from Meteosat-5 for region B and C
also agree well with TRMM rainfall analysis.

－ 35 －

Acknowledgements
This symposium is sponsored by the Ministry of
Education, Science, Sports and Culture, Japan (MEXT), andNational University Corporation Chiba University.

Figure 4: Normalized Difference Rainfall Index (NDRI)
derived from TRMM (3B42) dataset: (a) for MAM
1999-2008, and (b) for JJA 1999-2008. Regions marked 1 to
3 represent regions A, B and C respectively.

4. Summary
The climatological features of the diurnal cycle and spatial
variability of cloudiness were investigated over South Asia
using data from the METEOSAT-5 during March-May andJune-August of 1999 and 2000. Analysis of ten years of
TRMM rainfall product (3B42) was performed to enhance
the confidence in 2-year METEOSAT-5 results. Three
convective centers of interest were investigated: region A in
the Himalayas; region B in north India and region C in the
Bay of Bengal (BOB). For pre-monsoon season, thepreferred region of occurrence of CC was over region B. In
the pre-monsoon season, there are many CC over region B
and the region A in the mid-afternoon (15 LT). In thesummer monsoon season, the peak occurrence of CC
appears around 12 LT over the region B and still around 15
LT over the region A. For region C, CC is most numerous
around 6 LT in both seasons, showing clear contrast with theother two land regions.

References
1) Bhatt, B. C. and K. Nakamura, 2006: A
climatological-dynamical analysis associatedwithprecipitation around the southern part of the Himalayas. J.
Geophy Res. 111, D02115
2) Gambheer, A. V., and G. S. Bhat, 2001: Diurnal variationof deep cloud systems over the Indian region using
INSAT-1B pixel data. Meteorol. Atmos. Phys. 78, 215-225.
3) Huffman, G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J.
Nelkin, K. P. Bowman, Y. Hong, E. F. Stocker, and D. B.
Wolff, 2007: The TRMM multi-satellite precipitation
analysis: quasi-global, multi-year, combined-sensor
precipitation estimates at fine scale. J. Hydrometeor. 8,
38-55.
4) Kodama, Y.-M., A. Ohta, M. Katsumata, S. Mori, S.
Satoh, and H. Ueda, 2005: Seasonal transitions ofpredominant precipitation type and lightning activity overtropical monsoon areas derived from TRMM observations.
Geophys. Res. Lett. 32 (L14710).
5) Ohsawa, T., H. Ueda, T. Hayashi, A. Watanabe, and J.
Matsumoto, 2001: Diurnal variations of convective activity
and rainfall in tropical Asia. J. Meteorol. Soc. 79, 333-352.
6) Williams, M. and
R. A. Houze Jr., 1987:
Satellite-observed characteristics of winter monsoon cloudclusters. Mon. Wea. Rev. 115, 505-519.
7) Xie, P., H. Xu, N. H. Saji and Y. Wang, 2006: Role ofnarrow mountains in large-scale organization of Asianmonsoon convection. J. Climate 19, 3420-3429.
8) Yamamoto, K. M, F. A. Furuzawa, A. Higuchi and K.
Nakamura, 2008: Comparison of diurnal variations inprecipitation systems observed by TRMM PR, TMI, and
VIRS. J. Climate 21, 4011-4028.
9) Zuidema, P., 2003: Convective clouds over the Bay of
Bengal. Mon. Wea. Rev. 131, 780-798

The life cycle of cloud clusters during summer monsoon
is worthy of note: statistically speaking, there is a gradualdelay in the preferred time of initiation, attainment of
maximum area and dissipation as one progresses northwardfrom regions C through B to A.

－ 36 －

Validation of cloud detecting algorithm CLAUDIA using CALIPSO andCloudsat cloud mask
1

Takashi Matsui1, Haruma Ishida2, Takashi Y. Nakajima1
Reasearch and Information Center, Tokai University, 2-28-4 Tomigaya, shibuya-ku, Tokyo 151-0063, Japan
pine@tokai-u.jp, nkjm@yoyogi.ycc.u-tokai.ac.jp
2
Yamaguchi University Faculty of Engineering, 2-16-1 Tokiwadai, Ube, Yamaguchi 755-8611, Japanishidah@yamaguchi-u.ac.jp

Abstract
CLAUDIA is a cloud detection algorithm developed for the arbitrary multispectral imagers. A validation of
CLAUDIA using the CALIPSO and Cloudsat observations is presented. A comparison between CLAUDIA and
MODIS cloud mask algorithm (MOD35) is also presented. The validation reveals about the accuracy of the cloudmasks generated by CLAUDIA and the dependency of the detection accuracy on several surface types. The result
indicates that CLAUDIA identifies the clear pixels more accurately than MOD35 and CLAUDIA is more neutralthan MOD35. In other word, CLAUDIA is not biased to either clear or cloudy.
Keywords : cloud mask, MOD35
1. Introduction
The cloud mask is one of the standard products produced
from the satellite imagers. It is used as the preprocessing forthe high-order products. Cloud and Aerosol Unbiased
Decision Intellectual Algorithm (CLAUDIA) is a clouddetection algorithm developed for the arbitrary multispectral
imagers1). It is now used to generate the cloud mask products
from the GOSAT/CAI observations and will be adapted to
the Japanese future Earth observing missions,
GCOM-C/SGLI and EarthCARE/MSI. In this study, thevalidation of CLAUDIA using the CALIPSO and Cloudsat
observations are presented. The comparison of CLAUDIA
with MOD35 is also presented.
2. CLAUDIAThe CLAUDIA was developed under the concept of theneutral cloud detections. “Neural” means the discriminations
are not biased either clear or cloudy. It consists of several
pixel-by-pixel threshold tests that output the Clear
Confidence Level (CCL) that indicates the probability ofcloud existence through a value from 0.0 (cloudy) to 1.0(clear) (Fig.1). CLAUDIA calculates the final CCL from the
CCL values of the tests using the following formulation:mQ1  1  (1  q11 )(1  q12 )L (1  q1m )(1)n
Q2  q21  q22 L q2n(2)

Q final  Q1  Q2

(3)

where qgi is the CCL of the i-th threshold test in group-g. Qg

represents CCL values of the group-1 or 2. Qfinal is the final
CCL of CLUAIDA. The group-1 consists of the suitable
tests for detecting the clear pixels (Table 1) and Eq. (1) is
considered to be “clear conservative’’. In contrast, thegroup-2 consists the suitable tests for detecting the cloudy
pixels and Eq. (2) is considered to be ‘‘clear conservative.’’
The concept of the CLAUDIA is referred to that of
MOD35. MOD35 also calculates CCL values, but theformulation to combine the CCL values and the concept of
the grouping are different from CLAUDIA2). The
formulation of MOD35 tends to bring “cloudy bias” .

3. Validation of CLAUDIA using CALIPSO-Cloudsat
The radar and lidar satellite can be used to validate thecloud masks generated from imager data because thedetections by the former are generally more accurate thanthe latter. For example, Ackerman et al. used the CALIPSO
observations to validate MOD354). In this study, the cloudmasks generated using CLAUDIA from Aqua/MODIS data
in January 2008 to November 2008 are validated based on
the combined Cloudsat-CALIPSO cloud masks6).
Aqua/MODIS, Cloudsat and CALIPSO consist the“A-train” and the their observations are overlapped. The
MOD35 overlapped with them are also validated.
The outputs of Cloudsat-CALIPSO cloud mask are thecloud existences in each vertical bin. For this validation, thepixels are labeled as “cloudy” if one or more cloudy bins
exist. The outputs of MOD35 are “Cloudy”, “Probably
cloudy”, “Probably clear”, or “Clear”. For the comparison
with MOD35, 0-0.25, 0.25-0.5, 0.5-0.75, 0.75-1.0 of the－ 37 －

CLAUDIA CCL values were labeled as “Cloudy”,“Probably cloudy”, “Probably clear” or “Clear”, respectively.
The occurrence rate images according to CLAUDIA and
MOD35 outputs when the Cloudsat-CALIPSO cloud masks
indentified as “Clear (Cloudy)” were shown in Fig. 3 (Fig.4).
In Fig.3 CLAUDIA and MOD35 identified the 78% and
55% pixels as “Clear” and misidentified the 10% and 17%
pixels as ”Cloudy” on average. In Fig.4 CLAUDIA and
MOD35 identified the 70% and 89% pixels as “Cloudy” andmisidentified the 14% and 6% pixels as ”Clear” on average.
The result indicates that the discrimination of MOD35 tend
to be biased to “Cloudy” whereas CLAUDIA can identifies
both “Clear” and “Cloudy” in almost the same accuracy.
Therefore CLAUDIA is more neutral than MOD35.
The images and histograms of the averaging NDVI
calculated referring to Cloudsat-CALIPSO, CLAUDAI or
MOD35 are shown in Fig.5. The histogram of CLAUDIA ismore similar to that of Cloudsat-CALIPSO than that of
MOD35.
References

Fig. 2 An example of CLAUDIA outputs. (left : MODIS
RGB, right : CLAUDIA out put)

Fig. 3 The occurrence rate images according to CLAUDIA
and MOD35 outputs for “Clear” pixels.

1) Ishida, H. and T. Y. Nakajima (2009), Development of an unbiased clouddetection algorithm for a spaceborne multispectral imager, J. Geophys. Res., 114,
D07206, doi:10.1029/2008JD010710.
2) Ackerman, S. A., K. I. Stabala, W. P. Menzel, R. A. Frey, C. Moeller, and L. E.
Gumley (1998), Discriminating clear sky from clouds with MODIS, J. Geophys.
Res., 103, 32,141–32,157, doi:10.1029/ 1998JD200032.
3) Ackerman, S. A., R. E. Holz, R. Frey, E. W. Eloranta, B. Maddux, and M. J.
McGill (2008), Cloud detection with MODIS: Part II. Validation, J. Atmos.
Oceanic Technol., 25, 1073–1086.
4) Hagihara, Y., H. Okamoto, and R. Yoshida (2010), Development of a
combined CloudSatCALIPSO cloud mask to show global cloud distribution, J.
Geophys. Res., 115, D00H33, doi:10.1029/2009JD012344.

Fig. 4 The occurrence rate images according to CLAUDIA
and MOD35 outputs for “Cloudy” pixels.

Fig. 1 The concept of the clear confidence level in tests
Table 1 Threshold tests in CLAUDIA and main targets
Group
1

2

Threshold tests

Main target

R(0.86m)

Optically thick clouds over ocean

R(0.66m)

Optically thick clouds over land

R(0.86m)/ R(0.66m)

Optically thick clouds

NDVI

Clouds over deep forests

R(0.86m)/ R(1.6m)

Clouds over bright desert

BT(10.8m)

High clouds

R(1.38m)

Thin cirrus

BT(10.8m)- BT(3.9m)

Optically thick clouds

Fig.5 The images and histogram of NDVI made referring to
Cloudsat-CALIPSO, CLAUDAI or MOD35 cloud masks.

－ 38 －

A statistical analysis for the characteristics of cloud/precipitation systemfrom Cloudsat data
Munehisa K. Yamamoto, Atsushi Higuchi, and Masamitsu Hayasaki
Center for Environmental Remote Sensing, Chiba University, Japan, mkyamamoto@faculty.chiba-u.jp
Abstract
Global distributions of cloud largely effect earth radiation budgets. The heating / cooling effect differs
depending on a type of cloud due to the differences of the characteristics of radiative process. Thus, it is importantto understand cloud distributions classified into some cloud types. A cloud type classification method has beendeveloped using multiple bands in visible and infrared using geostationary satellite. However, it was hard toclassify the cloud types because of limit of information from optical thickness and cloud top height.
In 2006, Cloudsat satellite carrying the Cloud Profiling Radar (CPR) was launched, and its observation
enables us to find vertical distributions of cloud globally. This study tried to characterize cloud / precipitation
characteristics and classify cloud types from vertical distributions of clouds observed by Cloudsat CPR. This
study applied a base of vertical-method of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar(PR) 2A23 algorithm to the vertical distributions of reflectivity factor (Z) from CPR in order to detect the brightband height (BBH) and cloud types. The detected BBH exists under 250-500 m from freezing height derived froma re-analysis data. The cloud types were classified into convective with large Z, stratiform with BBH, and others.
In this presentation, we will also report the characteristics of global cloud distributions with / without
precipitation, with shallow or anvil, and so on.
Keywords : Remote sensing, Cloudsat CPR, cloud distribution
1. Introduction
Recently, the global warming attracts big attention. One of
the biggest concerns is the change of the global cloud
distributions. Global distributions of cloud largely effectearth radiation budgets. The heating / cooling effect differs
depending on a type of cloud due to the differences of thecharacteristics of radiative process such as reflection fromshort wave radiation and absorption and emission from longwave radiation. Since these processes depend on a cloud
thickness and cloud temperature, it makes difficult toestimate and predict the global radiation budget. Therefore, it
is important to understand cloud distributions classified into
the cloud types.
A cloud type classification method has been suggested
using multiple bands in visible and infrared using
geostationary and low-orbit satellites. For example, Inoue(1987) developed a simple cloud type classification based on
split-window technique. Brightness temperature difference
between visible and infrared channels has good relation tosome cloud types. However, it was hard to classify the cloudtypes because of limit of information from optical thickness
and cloud top height. Using precipitation radar technique, on
the other hand, rain type classification method has beendeveloped to estimate precipitation because drop size
distributions are largely different with rain types. The

Tropical Rainfall Measuring Mission (TRMM) provides
standard products of rain characteristics fromthree-dimensional radar reflectivity. In 2006, Cloudsat
satellite carrying the Cloud Profiling Radar (CPR) was
launched, and its observation enables us to find vertical
distributions of cloud globally. This study tried tocharacterize cloud / precipitation characteristics and classify
cloud types from vertical distributions of clouds observed by
Cloudsat CPR.
This study presents the characteristics of global cloud
precipitation distributions and the classification of cloudtypes from Cloudsat CPR.

2. DataThis study mainly used Cloudsat CPR 2B-GEOPROF
product provided by the Cloudsat Data Processing Center
for June-July August (JJA) 2007–2009. This productprovides radar reflectivity factor and significant echo andcloud flag observed by CPR, MODIS, and ECMWF data. In
order to compare the simultaneous observation data between
Cloudsat CPR and TRMM PR, we used 2D-CLOUDSATTRMM product. This product summarizes a basic nearestneighbor interpolation of the TRMM data on to the Cloudsat
data’s along track resolution, and generated for intersects
with an overpass time difference of 50 minute or less.

－ 39 －

Fig. 1 Appearance number of CPR radar reflectivity
factor at TRMM stormH.

3. Results
3.1 Instantaneous Cloudsat CPR vs TRMM PR
Radar reflectivity factor Z is given by the sixth-powers ofthe diameters of all the drops contained in a unit volume ofspace. However, equivalent radar reflectivity factor (Ze), Z
derived from radar received power, is affected by
precipitation attenuation depending on radar frequency.
Under the simultaneous observation, Ze from Cloudsat CPR
and TRMM PR should be different value. When storm top
height (stormH as the top height of precipitation) is
identified from Cloudsat CPR, this difference must be
considered. According to the appearance number of
Cloudsat CPR Ze at the stormH from TRMM PR (about 17dBZ) in the simultaneous observations (Fig. 1), Ze isconcentrated around 10 dBZ. This difference is explained by
the radar frequency, horizontal resolutions, and overpass
time difference. From this result, hereafter stormH fromCloudsat CPR is defined by the top height of 10 dBZ.
Figure 2 shows the appearance frequency of radar echo
from Cloudsat CPR and TRMM PR at the same pixel.
When both of the echoes are detected in the pixel, cloudwith precipitation would exist. If the Cloudsat CPR only
observes radar echo, this pixel would contain
non-precipitating cloud. There are few pixels just TRMM
PR echo detected. Over 90% of the radar echo detected
pixels exists non-precipitating clouds except for tropics. The
appearance frequency of precipitating clouds in the tropics ishigher than those in the mid-latitude regions. This implies
that shallow and weak precipitation frequently occurs in the
mid-latitude ocean.
3.2 Global distributions of cloud / precipitation
parameters
Historically cloud type and cloud top height detections
from satellite sensors have been used by visible and infrared

Fig. 2 Appearance frequency of cloud / rain detected
pixels with Cloudsat CPR (black) and with both CPR
and TRMM PR (white) displayed by cumulative bar
chart.
channels. However, it is hard to estimate them precisely due
to lack of vertical resolutions. Moreover, it is not easy task tomatch up simultaneous data of both of cloudH and stormH
unless TRMM PR and VIRS observations. Assuming thecorrespondence of stormH from Cloudsat CPR mentioned
the precious subsections, relationship between stormH andcloudH should be further understood.
Figure 3a shows mean cloudH from Cloudsat CPR. Overocean, cloudH over 12 km appears over the maritime
continent, the Bay of Bengal, and the west coast of the
Mexico. High cloudH regions correspond to large amount of
rainfall. Over land, cloudH also exceed 10 km over tropical
Africa and south-east Asia. However, cloudH over land isgenerally lower than that over ocean. From the difference
between the ascending and descending passes (Fig. 3b),cloudH over ocean (land) in the early-afternoon pass ishigher (lower) than that in the mid-night pass due to diurnal
variations of cloud activity. Mean stormH distributions (Fig.
3c) apparently similar to the mean cloudH. However, highstormH regions are relatively different particularly over land(e.g. the Tibetan Plateau, western India, and North America).
The ascending-descending contrast between land and ocean
is apparently obscured, but stormH over the Rocky
Mountains is dominant in nighttime. These diurnal
variations correspond to those from TRMM PR and VIRS
(Yamamoto et al. 2008).

－ 40 －

(a)

(c)

(b)

(d)

Fig. 4 Horizontal distributions of (a) Mean cloudH in the daily passes (b) difference of mean cloudH between the daily passand nocturnal pass, and (c) (d) those for mean stormH observed by Cloudsat CPR in 2007-2009 JJA.
(a)

(b)

Fig. 5 Horizontal distributions of (a) median of stormH / cloudH and (b) occurrence frequency in stormH / cloudH in thedaily pass observed by Cloudsat CPR in 2007-2009 JJA.
Frequency of occurrence with stormH to cloudH is shownin Fig. 4. The high frequency regions where cloud system islikely to cause precipitation occur over the Bay of Bengal
and the west coast of Mexico. Over the mid-latitude region,
large differences of the frequency are not found.
As convective system develops, storm height generally
increases. A ratio of stormH to cloudH would be one of theindices of convective activity. Figure 4 illustrates median of
all pixels with stormH / cloudH. StormH reaches above 80%
of cloudH in the Sahara, Middle-east, the Tibetan Plateau,
and Rocky Mountains. Over ocean, tropic region becomes
higher ratio than in mid-latitude. Regions with relatively thehigh ratio such as south Indian Ocean and south-east Pacificcorrespond to small precipitation amount.

3.3 Cloud type classification from Cloudsat CPR
In order to identify further cloud / precipitation

characteristics, a classification method based on the
V-method of TRMM PR2A23 algorithm (Awaka 2007) wasapplied to the Cloudsat CPR data except for somemodifications (Table 1). For the freezing height estimation
vertical distributions of temperature and geopotential heightfrom the Japanese 25-year Reanalysis (JRA-25) and JMA
Climate Data Assimilation System (JCDAS) data instead ofmonthly mean surface temperature and constant lapse rate.
Figure 6 shows vertical distributions of radar echoes withfreezH, BBH, and cloud type observed by Cloudsat CPR
and those observed by TRMM PR at a case of overpass.
BBH exists under 250-500m from freezH with small
variations. From the vertical distributions of radar echoes
BBH is reasonably detected. Compared to the rain type from
TRMM PR, further cloud type characteristics are detected
due to finer resolution.
In the cloud type classification by infrared sensors, it is－ 41 －

Table 1 Parameters from Cloudsat CPR developed in this study and modifications from PR2A23 standard product.
TRMM PR
rainFlagCloudsat CPR
cloudFlag

rainType

cloudTypefreezH
HBB
stormHfreezH
HBB
cloudH

Modification
Cloud certain / possible was idenitfied from CPR_Cloud_mask
V-method was only applied to cloudType. Convective cloud was
identified from Zmax > 15 dBZ
JRA25 data instead of monthly mean surface temperature
freezH1km, Zmax > 10 dBZ
The highest altitude of CPR_Cloud_mask

less than 30% and more than 90% of fulfillment ratio
regardless of the cloudH. 10-14 km of cloudH,
corresponding to the tropopause, fulfillment ratio has large
variations. These distributions are similar to that over the
mid-latitude oceans except for lower cloudH shifts.

Fig. 3 Contoured appearances of altitude diagram offulfillment ratio from cloudH observed by Cloudsat CPR
in 2008 over ocean in 20ºS–20ºN (left) and 25º–55ºN(right).

4. Summaries
This study investigated cloudH and stormH characteristics
from Cloudsat CPR. Compared to the TRMM PR and VIRS
observations, more accurate relationship are obtained
particularly in cloudH owing to 2-demensional (vertical)
observation. Moreover, drop size distributions, attenuationfrom precipitation and cloud will be clarified by proceeding
to the comparison.
Acknowledgements
This symposium is sponsored by the Ministry of
Education, Science, Sports and Culture, Japan (MEXT), andNational University Corporation Chiba University.

Fig. 6 Vertical distributions of radar reflectivity factor in(a) TRMM PR and (b) Cloudsat CPR (gray image),freezH (dotted lines), bright band height (bold solidlines), and rain (cloud) type (solid line in the upper partof each panel) over 16°N and 96°E at 19:25UTC
overpass in 1 September, 2007.
hard to distinguish between cirrus and cumulus cloud due tosimilar low brightness temperature from high cloudH. On
the contrary, since cloud radar can detect vertical
distributions of cloud echoes, their classification would beeasier. Figure 5 shows relationship between cloudH andfulfillment ratio of radar echo from the surface. Over thetropical ocean (Fig. 5a), large amount of pixels concentrates

References
Awaka, J., T. Iguchi, and K. Okamoto (2007): Rain typeclassification algorithm. V. Levizzani, P. Bauer, and F. J.
Turk (ed.), Measuring Precipitation from SpaceEURAINSAT and the Future, Advances in Global
Change Research, 28, Springer, 213-224.
Inoue, T. (1987): A cloud type classification with NOAA 7
split-window measurements. J. Geophy. Res. 92, D4,3991-4000.
Onogi, K. and co-authors (2007): The JRA-25 Reanalysis. J.
Meteor. Soc. Japan, 85, 369-432.
Yamamoto, M. K., F. A. Furuzawa, A. Higuchi, and K.
Nakamura (2008): Comparison of diurnal variations inprecipitation systems observed by TRMM PR, TMI, and
VIRS. J. Climate, 21, 4011-4028.

－ 42 －

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－ 52 －

The SKYNET observation in Mandalgobi semiarid region
Jadamba Batbayar1, Nas-Urt Tugjsuren2
Mongolian University of Science and Technology, E-mail: tchbayar@yahoo.com
2
Mongolian University of Science and Technology, P.O.B-157, Ulaanbaatar-46, Mongolia,
E-mail: tugjsurn@must.edu.mn
1

Abstract
As for today, there are a number of uncertainties of atmospheric aerosols physical and optical properties due
to insufficient observation network over Mongolia. However, one of the observation sites of the ground-based
Skyradiometer Network (SKYNET) for Eastern-Asia has been operating since 1997 in Mandalgobi province ofMongolia. This study focused on more detailed analyses aerosol characteristics (Optical thickness, Angstrom
exponent, Single scattering albedo etc.) using data sets collected at Mandalgovi sites (45.711N, 106.265E) of theSkyradiometer Network. For this purpose, Skyradiometer observation data are processed and analyzed. In addition,some preliminary results of aerosol properties analyses of the skyradiometer observation in Mongolia arepresented.
1. Introduction
The atmospheric radiation and related atmosphericparameters are of fundamental importance to climate change
studies. Ground based network effort promotes a large
number of fundamental researches on the atmosphericparameters in any given region. SKYNET is an observation
network to understand aerosol-cloud-radiation interaction in
the atmosphere. SKYNET has maintained Sky radiometer
observation site since 1997 at Mandalgobi, Mongolia, incollaboration with the Chiba University. This site has been
instrumented mainly a sky radiometer and radiation
instruments for continuous measurements of atmosphericparameters such as aerosol, cloud and radiation. The
SKYNET project aims at a better understanding of long-term
variability in the radiation budget and atmospheric parameters
over the eastern Asia and its attributes based on the analysis ofground-network data. As for this objective, both regional andlocal analyses are needed to investigate the aerosol effects on
climate change. SKYNET data at Mandalgobi site provide us
with valuable information of earths and atmosphericparameters. The data are also important for extracting precise
knowledge on the Earth atmosphere, as well as on physicalprocesses of aerosol-cloud-radiation interactions.
Accurate information of aerosol optical properties definedby analyses using data collected by the sky radiometer of
SKYNET network has been involved research activities inthe semiarid region of Mongolia. These efforts has allowed an
opportunity to obtaining comprehensive information ofaerosol optical properties that will helpful to recognize theaerosol effects on regional climate change and future climate

scenarios.

2. Data and study site description
The data used in the present study are from theSkyradiometer observations at Mandalgobi (45.711N and
106.265E) for spring, 2007. The data observed at Mandalgobi
site are compiled and archived in the SKYNET server in
Chiba University. The data provided from SKYNET serverof Chiba University have been treated with data quality
control procedure. We used Level 2.0 data including retrieved
parameters such as Aerosol optical thickness (τ), Angstrom
Exponent (α) and Single scattering albedo (ω) at wavelengthsof 500nm.from SKYNET archive at Chiba University(http://atmos.cr.chiba-u.jp).
The observation site of SKYNET is located in the
Mandalgobi. The Mandalgobi is one of the administration
unite in the central part of Mongolia and is lifted at relatively
high altitude 1,393m above the sea level.
3. Results and discussion
The aerosol optical properties data used in the present study
were compiled from the skyradiometer data of Mandalgobi
SKYNET site; the data are being archived at Chiba
University. Table 1 summarizes statistical characteristics ofthe aerosol optical properties in Mandalgobi semiarid region
which analyses have been performed in the current study. It isgave an opportunity to evaluate the backgroundcharacterization aerosol optical thickness, Angstrom exponent
and Single scattering albedo in semiarid region of Mongolia.
The optical thickness is with mean values of τ (500nm)－ 53 －

Table 1. Statistical characteristics of Aerosol optical thickness, Angstrom exponent and Single scattering albedo
Year/Months

N

τ

στ

α

σα

ω

σω

March, 2007

5

0.18

0.07

1.28

0.42

0.94

0.03

April, 2007

18

0.17

0.04

1.27

0.31

0.99

0.02

May, 2007

24

0.18

0.05

0.78

0.18

0.96

0.03

around 0.17-0.18 and standard deviation of 0.04 to 0.07. The
Angstrom exponent ranging 0.78 to 1.28 with standard
deviation 0.18- 0.42 and single scattering albedos are around0.94-0.99 at 500nm with standard deviation 0.02-0.03.
Figure 1 illustrates the daily averaged aerosol optical
thickness (a), Angstrom exponent (b), single scattering albedo(c) and standard deviations of daily τa (500nm) (d) for period
from March to May in 2007 at Mandalgobi SKYNET site.
From Figure 1a the daily average aerosol optical thickness
values at 500 nm are higher in the second half of the spring
season than first half. The aerosol optical thickness values are
generally, between 0.05 and 0.20, however some cases has
exceed this values. As for the monthly variation of the daily
average aerosol thickness showed maximum value 0.53 in thefirst decade of May. Thus, daily mean values of τa (500nm)

b)
0.6
0.5
0.4

Angstrom exponent, α

AOT, τ (500nm)

a)over Mandalgobi show the spring seasonal peaks in May,
2007. In the majority of cases the computed standard
deviations of daily τa (500nm) range below less than 0.07,
however, occasionally can far exceed this value (Fig.1d).
Figure 1b presents the daily averaged values of Angstrom
exponent, α for Mandalgobi site. The Angstrom exponent
values showed significant variability which values from 0.13
to 1.89 while AOT values are between 0.05 and 0.60 andlarge day to day variation in the study period.
Furthermore, the optical observation data collected in
Mandalgobi SKYNET site provides by single scattering
albedo data for this study. As for daily average single
scattering albedo ω, at 500nm, values range from 0.87 to 0.99
(Fig.1c). However, mostly values of single scattering albedo
observed in the range between 0.95 and 0.99. These values

0.3
0.2
0.1
0
10

20

March

31

10

20

April

30

10

20

1.5
1
0.5
0

10

31

May

20

March

c)

31

10

20

April

30

10

20

31

May

d)
0.2

0.94
0.89
0.84
0.79
10 20
March

31 10 20 30 10 20
April

31

Daily st.d, τ (500nm)

0.99
SSA, ω

2

0.15
0.1
0.05
0
10 20
March

May

31 10 20 30 10 20
April

May

Figure 1. Daily mean values of aerosol thickness at 500nm (a), daily mean values of Angstrom exponent (b), daily meanvalues of single scattering albedo (c), daily standard deviations of τ (500nm) (d) for Mandalgobi site (45.711N, 106.265E)－ 54 －31a)Frequency ofoccurrences, %

60

N=47

50
40
30
10
0.1

0.2

0.3
0.4
0.5
AOT, τ (500nm)

0.2
0.4
0.6
Aerosol optical thickness, τ(500nm)

Figure 3. Scattergram of Angstrom exponent versus
Aerosol optical thickness in spring, 2007 for Mandalgobi site.

0.6

b)
20

2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0

20
0

Frequency ofoccurrences, %trend of increasing values of Angstrom exponent, α as τ
(500nm) decreases over study site. There is a most large
variation of aerosol optical thickness values when Angstrom
exponent values are smaller than about 0.8 in spring season.
Therefore, Mandalgobi site shows negatively correlation
between AOT and Angstrom exponent.
Lastly, the analysis of the aerosol optical properties should
be continued with the more continuous observational data andto elaborate and improve the analyses for different seasonsand more optical and physical parameters such as volume size
distributions and refractive index.

Angstrom exponent, α

indicate that are close to values in clean region. The single
scattering albedo obtained in this region at 500nm is similar to0.95 in Nagasaki (Nakajima et al., 1989). But some values ofsingle scattering albedo as well as 0.87 and 0.89 obtainedsome times for this period that are similar to 0.89 in
Dunhuang (Kim, D-H. et al., 2005).
The frequency of occurrence distributions for aerosol
optical thickness τ, and Angstrom exponent α are presented in
Figure 2. The frequency histograms of τ (500nm) for
Mandalgobi site demonstrate the majority of values (75%) areless than 0.20 (Fig.2a) and other values (25%) are around0.30-0.60. The most frequently occurring values of τ (500nm)
are about 0.20 for this site in spring 2007.
The Angstrom exponent frequency for Mandalgobi site
shows relatively broader distributions. The frequency
histogram has higher Angstrom exponent’s peak frequency
around between 0.7 and 1.3.

N=47

15
10
5
0
0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9
Angstrom exponent, αFig. 2. Frequency of occurrences of aerosol optical thickness at
500nm (a) and Angstrom exponent, α (b) for Mandalgobi site.

The scattergrams of Angstrom exponent, α versus aerosol
optical thickness, τ (500nm) for spring, 2007 are shown in Fig.
3. As shown on these scattergrams for Mandalgobi site has awide range of Angstrom exponent values (0.13-1.89) at low
aerosol optical thickness (0.05-0.20). It shows a reasonable

4. Conclusion
The conclusions of this analysis are summarized as follows:
1. Aerosol optical properties (aerosol optical thickness,
Angstrom exponent, and single scattering albedo) oversemiarid region were analyzed for spring, 2007, using
measurements of the Skyradiometer Network(SKYNET). The aerosol optical thickness values are
generally, between 0.05 and 0.20, however some cases
has reached up to 0.53 in spring. Mandalgobi site haslarge Angstrom exponent ranging between 0.13 and 1.89
due to fine particles. And there also exist dust particles
with Angstrom exponent values around 0.13-1.00. The
single scattering albedo values mostly range from 0.95 to
0.99. These values indicating close to the values inrelatively clean region.
2. Future work is needed to better utilize the SKYNET
observation data for more detailed aerosol opticalproperties analysis, such as volume size distributions andrefractive index.

－ 55 －

References
1. Dubovik, O., B. N. Holben, T. F. Eck, A. Smirnov, Y. J.
Kaufman, M. D. King, D. Tanre and I. Slutsker, 2002:
Variability of absorption and optical properties of key
aerosol types observed in worldwide locations, J. Atmos.
Sci., 59, 590-608.
2. Kim, D-H., J.Sohn, T. Nakajima, T.Takamura, Toshiko
Takemura, Byoung-Cheol Choi, and Soon-Chang Yoon,
2004: Aerosol radiative forcing over East Asia
determined from ground based sky radiation
measurement. J. Geophys. Res., Vol. 109
3. Nakajima, T., M. Tanaka, M. Shiobara, K. Arao, andY.Nakanishi, 1989: Aerosol optical characteristics in theyellow sand events observed in May, 1982 at Nagasakipart II. Models. J. Meteor.Soc. Japan, 67, 279-291
4. Takamura, T., Nakajima, T., Okada, I., Uchiyama, A.,
Sugimoto, N., Shi, G.Y., Zhou, J., 2002: Aerosol-cloudradiation study using the SKYNET data. The first ADEC
Workshop, Tokyo, Japan; http://www.aeoliandust.com.
5. Xuehua Fan, Hongbin Chen, Philippe Goloub, Xiango
Xia, Wengxing Zhang and Bernadette Chatenet., 2006:
Analysis of column-integrated aerosol optical thickness
in Beijing from AERONET observations, China
Particuology, Vol. 4, No. 6, 330-335.

－ 56 －

Characterics of atmospheric aerosols at the Observatory for Atmospheric Research at Phimai,
Thailand, a station of SKYNET
Boossarasiri Thana1, Thawatchai Sudjai1, Jinchula Chotpitayasunon1,
Haruo Tsuruta2, Tamio Takamura3, Teruyuki Nakajima2
1

Department of Geology, Chulalongkorn University, Phayathai road, Bangkok 10330, THAILAND:
boossara@geo.sc.chula.ac.th, thawatchai@geo.sc.chula.ac.th, share7@hotmail.com
2: Center for Climate System Research, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba,
JAPAN: haru2004@ccsr.u-tokyo.ac.jp
3
Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522,
JAPAN, takamura@faculty.chiba-u.jp

Abstract
Aerosols play an important role in the global climate, the radiative forcing of the climate, and the Earth's
radiative balance. The aerosols were measured at the Observatory of Atmospheric Research, in Phimai, Thailand
where is a member of SKYNET, during 2006-2008. The amount aerosols were much higher in the dry seasons
than in the wet seasons due to monsoon. The aerosols were collected during 2007-2008 and the records of fineparticles found that ammonium sulfate, and elementary and organic carbons were major components especiallyin dry season. Aerosol optical depth by sky radiometer was about 0.90 in wet seasons, and much higher than in
the dry seasons. In dry season were many fires activity due to the biomass burning from agricultural harvest
almost all over Thailand. These results clearly indicate that the aerosols emitted from biomass burning could
affect on atmospheric.
Keywords : Aerosol, aerosol optical depths, single scattering albedo, biomass burning, chemical composition

1. Introduction
Aerosols influence both direct and indirect the radiation
balance.
Direct scattering and absorbing interaction
between atmospheric aerosols and incoming solar radiation
may influence the radiative forcing. The aerosol scatteringand absorption depend on their chemical physical properties.
The important of aerosol types such as black carbon fromagriculture or human activities are key of climate changing.
Chemical and optical properties of atmospheric aerosols in aregional scale are expected to be much differences because
there are many different sources of aerosols in natural andanthropogenic area. The purpose of this study focus on the
results from the aerosol measurements at Phimai(15.18oN ,102.56oE), Thailand, in Southeast Asia whereaffected by the aerosols emitted by biomass burning of crop
residues in regional scale.
2. Methods
Atmospheric aerosols were observed at the Observatory
for Atmospheric Research in Phimai which is SKYNET
station in Southeast Asia. This station is located innortheastern Thailand surrounding with agriculturalactivities. The location is located on Korat platau where
the main component of rock is called rock salt. The opticalproperties were measured by Skyradiometer (POM-02).
The instrument was manufactured by PREDE. The opticalproperties data were screened cloud-affected analysis by
Khatri and Takamura (2009). Some uncertainties will be
introduced. Columnar aerosol optical depths (AODs) were
estimated and to determine the Ångström exponent, a basic
measure related to the aerosol size distribution will be
investigated. In general, the Ångström exponent ranges from
0.0 to 2.0, with smaller Ångström exponents corresponding tolarger aerosol particle sizes [Dubovik et al., 2002; Kim et al.,2004]. During June 2006 through August 2008, thechemical properties were measured. The samples werecollected twice a day within three days continuously every
two weeks by using multi-nozzle cascade impact (MCI)
samplers on the filter of 47 mm in diameter (d). The sizesof aerosols were collected as d < 1.0µm, 1.0µm < d < 2.5µm, 2.5µm < d < 10 µm, and 10 µm < d in respectively. In
this paper will be focus only on PM2.5. Chemical analysis－ 57 －

was made for OC and EC by optical thermal OC/EC
analyzer.

1.5

1.5
1.0

Dry

Wet

Dry Wet

Dry

1.0

0.5

0.5

0.0

0.0

Date

Angstorm
Exponent

2.0

2.0

1/
6/
20
08
9/
9/
20
08

AOD (550 nm)

To reduce uncertainties in the quantitative assessment ofaerosol effects on regional climate and environmental
changes, extensive measurements of aerosol opticalproperties were made with skyradiometer at Phimai
observatory for atmospheric research at Phimai were usedduring 2006-2009.
The variations of atmospheric aerosol is large differencein concentration. The daily average of aerosol opticaldepth (AOD) were between 0.2-0.7 in winter (dry season)and the AOD increasing at the end of dry season in summer
cause of wind direction changed. The AOD were higher
than 0.5 up to 1.4 in summer. Meanwhile the AOD islower than 0.5 during rainy season of 2006, but the value ishigher and increase nearly to 1.0 in 2007 and 2008. Figure
1 shows the time series of daily mean AOD at 500 nm . At
Phimai, a range of AOD, implying different types of aerosols.
There were an increasing in small aerosol particles from winterto summer due to regional biomass burning. In the summer
seasons, large sea salt aerosol particles seems to be dominated.
This suggests that more than one type of aerosol are mixed in
the climatic effects.
The increasing AOD in summer may cause of biomassburning activities. The Ångström exponent during wet
season was clear that the value is larger than 1.3. On theother hand during dry period, the value of Ångström
exponents were variety from near zero up to 2.0. The
Ångström exponent is often used as a qualitative indicator ofaerosol particle size (Angstorm, 1929), with values greaterthan 2 indicating small particles associated with combustion
by products, and values less than 1 indicating large particles
like sea salt and dust. Values of Ångström exponent 1
indicate size distributions dominated by coarse mode aerosols(radii  0.5 mm) that are typically associated with dust and sea
salt, and values of Ångström exponent  2 indicate size
distributions dominated by fine mode aerosols (radii  0.5 mm)that are usually associated with urban pollution and biomassburning [Eck et al., 1999; Westphal and Toon, 1991].
Therefore the size distribution in this area were mixed
between coarse and fine particle during dry season
especially in summertime as show in figure 2 which is thedaily average of AOD and Ångström exponent. The graphshows that the fine parcels caused of increasing AOD.

2.5

5/
9/
20
05
14
/1
2/
20
05
24
/3
/2
00
6
2/
7/
20
06
10
/1
0/
20
06
18
/1
/2
00
7
28
/4
/2
00
7
6/
8/
20
07
14
/1
1/
20
07
22
/2
/2
00
8

3. Results and Discussion

AOD (500) vs Angstorm Exp.

AOT(550nm)

Ang. E xp.

Figure 1. Time series of daily mean aerosol optical depth andÅngström exponent at 500 nm during 2006-2008.

Figure 2. Time series of daily mean single scattering albedo
500 nm during 2006-2008.
The single scattering albedo (SSA: ) is the important
optical characteristic of absorbing properties of aerosol
particles, which determines the ratio between scattering andabsorption in the total extinction: = / (+ ) where isthe scattering coefficient, and is the absorption coefficient.
In figure 2. shows the time series of daily mean SSA at 500
nm. are not much different. SSA is between 0.9-1.0 except
some value in rainy season, the SSA decreased to 0.7-0.8.
It means that most of particles scattered the radiation. For
the decreasing values of SSA may cause from the difference
type of aerosol came to the area. For example in 2006 thedifference particles appeared during the end of wet season(figure 2). SSA is about 0.7. It seems to be that the single
scattering albedo is slightly difference in wet season as theincreasing humidity in the atmosphere.
By using the backward trajectory model to track the air
masses arrived at Phimai as shows in figure 3. (HYSPLIT
Trajectory: http://www.arl.noaa.gov/ready/hysplit4.html).
The movement majority route of the aerosol came from thedifferent ocean in both monsoon seasons. Even though thewind blew from the difference direction. Except the SSA
values were sometimes decreased, it might regard to theactivity that happen locality. For example as shown infigure 2, at the end of wet season in 2006, SSA decreased
significantly. An alternative reason is the variation ofsouthwest monsoon. In Phimai, there are substantially morebiomass burning and adding more absorbing soot and organic
aerosols to the atmospheres.

－ 58 －

Daily OC concentration (ug m-3)

25

20

10>d>2.5
2.5>d>1.0
1.0>dOCJuly 200 7-March 2008

Wet Season

Dry season

15
D1

D2

10

5

0
J J J A A A S S S O O O N N N D D D J J J F F F M M M

Figure 3. The backward trajectory for 2 different monsoon.

Figure 4. Daily mean OC concentrations in PM2.5 in Phimai(July 2007- March 2008)10
Daily EC concentration (ug m-3)

The SSA values are not clear that the aerosol type are notso difference properties between dry and wet season.
During mid 2007 to early 2008, the chemical experiment
was done. The samples were collected for 72 hours
continuously twice a month. The period for collecting data
is 11 months by multi-nozzle cascade impact samplers withspeed 20 liters per minutes. The chemical compositions ofaerosols were determined by and optical thermal OC/EC
analyzer for organic carbon (OC) and elementary carbon(EC). The daily mean EC and OC concentration in dry
season was clearly shown that the value was much higher
than in the wet season (Figure 4 and 5), the mean total
concentration of OC was about 3.5 times higher than that of
EC in the whole period. The mean EC in PM2.5 were about
90 and 95 % of the mean total concentration in the wet anddry season, respectively, while the mean OC in PM2.5 hadabout 65 and 80 % of the total in the wet and dry season,
respectively (Tsuruta et al.; 2008). During rainy season theaerosol concentration were washed out from the atmosphere.
This result is shown the increasing of PM2.5 again after
winter monsoon onset completely. The ratio of aerosol in
the whole periods are the same as mention above. This
result corresponds to the main activities in this area where is
agriculture such as cassava. Therefore the aerosols in thisarea were emitted by biomass burning of crop residuals
especially in dry season. Phimai is located in Korat Platau
where the main rock in this area is rock salt then the aerosol
samples tend to be large size from salt.

samplin g period (month)8610>d>2.5
2.5>d>1.0
1.0>d

Wet season

EC
July 2007-March 2008

Dry season

42

0
J J J A A A S S S O O O N N N D D D J J J F F F M M M

Sampling period (month)Figure 5. Daily mean EC concentrations in PM2.5 in
Phimai (July 2007- March 2008)
Phimai affected by two difference direction of monsoon.
In summer, the southwest monsoon from Indian ocean will
dominate. Then the seasalt aerosol will be transport to theregion. The opposite direction is the northeast monsoon
from east Asian continent during winter. The source ofwind direction will be the cause of particle types.

Conclusion
The aerosol particle in Phimai is almost the same type
except at the end of southwest monsoon season. The
optical properties were changed from scattering property to amore absorption. This result was confirmed by the size ofaerosol which is more variety during the same period. The
chemical component was shown the large concentration oforganic carbon much more than elementary carbon. The
size of PM2.5 in elementary carbon were high concentrationin both winter and summer. The changing of monsoon
affected to the type of aerosol in the region.

－ 59 －

Acknowledgements
We would like to express our great thanks to many staffs
in Department of Geology, Chulalongkorn University,
Thailand for an excellent implementation of the Observatoryof Atmospheric Research at Phimai and also the SKYNET
project which their techniques and financial support andcloud screening data which we use in this paper.
References
1) Angstrom, A. (1929), On the atmospheric transmissionof Sun radiation and on dust in the air, Geogr. Ann.,11, 156– 166.
2) Dubovik, O., B. N. Holben, T. F. Eck, A. Smirnov, Y. J.
Kaufman, M. D. King, D. Tanre, and I. Slutsker (2002),
Variability of absorption and optical properties of key
aerosol types observed in worldwide locations, J.
Atmos. Sci., 59, 590– 608..
3) Eck, T., B. N. Holben, J. Reid, O. Dubovik, A. Smirnov,
N. O’Neill, I. Slutsker, and S. Kinne (1999),
Wavelength dependence of the optical depth ofbiomass burning, urban, and desert dust aerosols, J.
Geophys. Res., 104(D24), 31,333– 31,349.
4) Khatri, P. and T. TAKAMURA (2009), An Algorithm
to Screen Cloud-Aคected Data for Sky Radiometer
Data Analysis, J. Met. Soc. of Japan, Vol. 87, No. 1, pp.
189—204. DOI:10.2151/jmsj.87.189.
5) Kim, D.-H., B.-J. Sohn, T. Nakajima, T. Takamura, T.
Takemura, B.-C. Choi, and S.-C. Yoon (2004), Aerosol
optical properties over east Asia determined fromground-based sky radiation measurements, J. Geophys.
Res., 109, D02209, doi:10.1029/2003JD003387.
6) Mattoo, S., and B. N. Holben (1994), Size distribution
and scattering phase function of aerosol particles
retrieved from sky brightness measurements, J.
Geophys. Res., 99(D5), 10,341– 10,356.
7) Tsuruta, H., J. Chotpitayasunon, B. Thana, P. Khatri,
T. Takamura, Hayasaka, T. Nakajima. (2008),
Characterization of atmospheric aerosols at theObservatory for Atmospheric Research at Phimai,
Thailand, a station of SKYNET. presented in CEReSInternational Symposium/ SKYNET Workshop, Chiba
University, Nov. 13-14, 2008.
8) Westphal, D., and O. Toon (1991), Simulations of
microphysical, radiative, and dynamical processes in acontinental-scale forest fire smoke plume, J. Geophys.
Res., 96(D12), 22,379–22,400.

－ 60 －

An investigation of SSA retrieval using simulated SKYNET observations

1

Makiko Hashimoto1and Teruyuki Nakajima2
Atmosphere and Ocean Research Institute, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568,Japan.
1
m_hashi@aori.u-tokyo.ac.jp, 2teruyuki.nakajima@aori.u-tokyo.ac.jp

Abstract
Single scattering albedo (SSA) is a key parameter to estimate aerosol direct radiative forcing and tounderstand earth’s climate. This study focused on SSA retrieval by a software package, SKYRAD.pack, which has
been developed by University of Tokyo but shown a tendency of overestimates of retrieved SSA suggested bycomparison with AERONET results in Beijing. In order to investigate the origins of the errors, we did sensitivity
tests for various parameters (ground albedo, calibration constant, gas absorption) associated with the algorithm
program in SKYRAD.pack. The results showed that overestimate of retrieved SSA is attributed by underestimateof calibration constant and/or ground surface albedo. The consideration of gas absorption is not a possible reasonof the error but an important step for SKYRAD.pack to more accurately retrieve SSA.
Keywords : SSA; SKYRAD.pack; algorithm

1. Introduction
Aerosol optical properties observed by SKYNET are
retrieved using a software package, SKYRAD.pack,developed by the University of Tokyo. However, it has
been pointed out that the value of the single scattering
albedo (SSA) in Beijing site was systematically larger
than that of AERONET (Che et al., 2008). A closeintercomparison of the SKYNET and AERONET results
indicates that SSA from SKYNET has a tendency torapidly become unity with increase in wavelength, when
SSA value is close to unity. And there is also a tendencyof larger size distribution for coarse mode aerosols withradius larger than several microns. The same happened in
Phimaiobservation
site(Tsuruta,personal
communication, 2010).
There are several candidates for the cause: Ignoranceof gaseous absorption by SKYRAD.pack; the minimum
observable scattering angle (3 degrees) is not smallenough for retrieving the coarse mode aerosol size
distribution; error in the improved Langley method(Nakajima et al., 1996); error in the solid angle
measurement and so on. We, therefore, made a numericalexperiment using the Rstar6b radiative transfer code tosimulate observed data of PREDE skyradiometer andinvestigate the effects of various observational andinstrumental parameters that affect the SKYNET
retrievals.

2. Methods
Observation data was simulated by the radiation model
assuming the gaseous constituent profiles of “US
standard atmosphere” and the optical properties of “Rural
aerosol type” with a solar zenith angle of 60 degree, andground surface albedo of 0.2. The rural aerosol typeincludes water-soluble (70 %) and dust-like particles(30 %). We used five channels at 400, 500, 670, 870, and1020 nm. In the analysis, we gave normal random errorsof ±5%, ±10%, and ±50% to the ground surface albedo
and ±1%, ±3% and ±5% to the calibration constant for
the test data to be analyzed by SKYRAD.pack. We alsochanged the minimum observable scattering angle from
1.5 to 3 degrees. Furthermore, we made experiments withand without gas absorption in the inversion.
3. Results and Conclusions
From Figs. 1 and 2, SSA was overestimated when thecalibration constant or ground surface albedo given in theanalysis was lower than in reality. Particularly, a small
difference in the calibration constant had a significant
impact on the SSA retrieval. It was also found that thesmaller minimum observable scattering angle improved
the retrieval of the volume size distribution for large
particles (not shown), but the retrieved SSA rarelychanged its value. From Fig. 3, SSA was improved whengas absorption is included in the analysis, but the
improvement is small and difficult to explain the large
difference in the retrieved SSA values between SKYNET－ 61 －

and AERONET. That is, it is considered that a cause oflarge SSA in Beijing is underestimate of calibration
constant and/or ground surface albedo. In the future study,
we will investigate impacts of other candidates toretrieved SSA, especially in terms of the differences in
SSA between different wavelengths.

Fig. 3 Same as in Fig 1, but for gas absorption effects

Fig. 1 SSA dependence on wavelengths ranging from
0.4 to 1.02 micron for various ground albedo (Ag)values

Acknowledgements
We would like to thank SKYNET and SKYRAD.pack
developers, especially Miho Yamano and Kathri Pradeep.
We were supported by projects from JAXA/EarthCARE,
MEXT/VL for Climate System Diagnostics, MOE/Global
Environment Research Fund B-083, NIES/GOSAT and
JST/CREST.
References
Che, H., G. Shi, A. Uchiyama, A. Yamazaki, H. Chen, P.
Goloub, and X. Zhang, 2008: Intercomparison between
aerosol optical properties by a PREDE skyradiometerand CIMEL sunphotometer over Beijing, China.
Atmos. Chem. Phys., 8, 3199–3214.
Nakajima, T., G. Tonna, R. Rao, Y. Kaufman, and B. Holben,
1996: Use of sky brightness measurements fromground for remote sensing of particulate
polydispersions. App. Opt., 35, 2672-2686.

Fig. 2 Same as in Fig 1, but for various calibration
constant (F0) values－ 62 －

Development of SKYNET community aerosol inlet and
SSA Obtained from Surface Aerosol Data at Cape Hedo and Fukue Island Sites
Naoki Kaneyasu
National Institute of Advanced Industrial Science and Technology
16-1 Onogawa, Tsukuba 305-8569, Japan, kane.n@aist.go.jp
Abstract
A community inlet was deployed at each SKYNET supersite by 2007 for the in-situ monitoring of surface
aerosol optical/microphysical properties Periodical calibration of Integrating Nephelometers have been carriedout since 2008. Upgrading of the monitoring infrastructure enabled us to compare the measured aerosol optical
data at different sites in SKYNET. As a case study, single scattering albedos of surface aerosols at Cape Hedo
and Fukue Island are calculated after addressing several correction procedures.
Keywords : optical properties, aerosol inlet, single scattering albedo, absorption coefficient, scattering coefficient

1. Introduction
Data obtained from SKYNET in-situ surface aerosol
monitoring instruments had been used for the calculation ofaerosol optical properties.
However, air inlets that
introduce ambient air into aerosol monitoring instruments
were different site by site in design and shape. They are notproperly characterized in particular for their size separation
abilities.
In addition, Integrating Nephelometers in
SKYNET sites have not been calibrated periodically before
2007 except that in Miyako site. Data obtained fromfilter-based absorption photometers such as PSAP need to becorrected for multiple scattering by filter fibers, particles
retained on the filter fibers, and the “shadowing” effect ofdeposited particles. Previous calculations for aerosols
optical parameter such as single scattering albedo (SSA)
from SKYNET in-situ data did not consider these problems.
The author designed a common inlet system for
SKYNET and has installed it in super sites (Cape Hedo,
Fukue Is., Miyako Is. Hefei, and Phimai) since 2007
(Kaneyasu et al., 2009).
Periodical calibration ofIntegrating Nephelometers has been carried out since 2008.
Addressing above issues enables us to compare the data at
different SKYNET sites. The design of SKYNET
community aerosol inlet is briefly described here. In addition,
preliminary results of SSA calculation at Cape Hedo andFukue are compared.
2. SKYNET community aerosol inlet
SKYNET community aerosol inlet is designed inaccordance with the guideline proposed by World
Meteorological Organization/Global Atmosphere Watch
project. The basic structure consists of a vertical pipe

installed through the roof of the laboratory so that the air isbrought from the inlet top (>4.5 m from ground level) to the
air splitter attached at the bottom of pipe with minimalbending parts (Fig.1).

Fig.1 Basic structure of SKYNET community inlets installedat (a) Cape Hedo site, and (b) Fukue, Miyako, Hefei, andPhimai sites. *Fig. 2 Illustrative schematics of PM10 impactor-head of
SKYNET community aerosol inlet (Left), and its
cross-sectional drawing (Right). *－ 63 －Aerosols are pre-separated at the inlet top with a PM10
impactor head newly designed for SKYNET super sites (Fig.
2). The size separation characteristic of the inlet was tested
with poly-dispersed fly-ash particles in a factory. The
experimentally determined 50% cut-off point was 9.4-10.3
μm in aerodynamic diameter. Air splitters that distributes
air into each aerosol instruments are designed as iso-kinetic
type which adjust the air speed in main pipe and in narrowtubes connected to each aerosol instruments (Fig.3). This
enables to avoid unwilling size separation of particles at the
splittercorrected for truncation error in this manner.
In Cape Hedo and Fukue, in-situ aerosol absorption
has been continuously measured with Aethalometers(Magee AE-32). A numbers of correction schemes havebeen proposed to retrieve aerosol absorption coefficientfrom the attenuation data by Aethalometer. In this study,
the correction scheme of Coen et al.(2009) was applied.
Fig.4 compares the monthly average aerosol SSA (drystate) at Cape Hedo and Fukue in April 2009, April 2010,
and August 2010. Regardless of the large difference inpollution concentrations between two sites, dry SSA of
surface aerosols at Cape Hedo and Fukue does not differ
greatly.

Fig. 3 Iso-kinetic air splitter of SKYNET community aerosol
inlet.

3. A case study: calculation of SSA at Fukue andHedo sites.
Integrating Nephelometers physically integrate thescattered light by particles inside a cavity column.
However, they cannot measure scattered light at forward
extreme and backward extreme angles that leads to anerror in the measurement of aerosol light scattering
coefficient. This is called truncation error. Truncation
error is well characterized for TSI 355x/356x seriesnephelometers (e.g., Anderson et al., 1996). For theaerosol light scattering coefficient at Cape Hedo, datafrom a TSI nephelometer is used and corrections for thetruncation error were applied according to this paper.
On the other hand, the evaluation of truncation error
for Radiance M903 nephelometer is rather limited(Heintzenberg et al, 2006). The author theoreticallyevaluated the truncation error of Radiance nephelometer,
which is often deployed in SKYNET sites. Mie
scattering calculations were made with aerosol size
distributions measured at Cape Hedo site with an
Aerodyne Aerosol Mass Spectrometer (A. Takami,
personal correspondence) and with the refractive index ofsulfate ammonium. Aerosol light scattering coefficientin Fukue measured with a Radiance nephelometer isFig. 4 Comparison of monthly average single scattering
albedos at Cape Hedo and Fukue sites in April 2009, April 2010,
and August 2010.

Acknowledgements
This study was conducted under GEOSS/SKYNET
program funded by the Ministry of Education, Science,Sports and Culture, Japan (MEXT). Figures marked with* are reproduced from figures in Kaneyasu et al.(2009).
References
Anderson, T. L. et al., Performance characteristics of ahigh-sensitivity, three-wavelength total scatter/backscatter
Nephelometer, J. Atmos. Oceanic Tech. 13, 967-986(1996).
Ceon, M. C. et al., Minimizing light absorption
measurement artifacts of the Aethalometer: evaluation offive correction algorithms, Atmos. Meas. Tech. Discuss., 2,1726-1770 (2009).
Kaneyasu, N. et al., Designing of GEOSS-SKYNET aerosol
inlet and its evaluation using poly-disperse fly-ash
particles, J. Jpn. Assoc. Aerosol Sci. Tech., 24, 201-208, inJapanese (2009)
Heintzenberg, J. et al., Intercomparisons and aerosol
calibration of 12 commercial integrating Nephelometersof three manufactures, J. Atmos. Oceanic Tech. 23,
902-914 (2006).

－ 64 －

The influence of meterological parameters andPM10 concentrations on visibility of Kathmandu Valley
Shashank Pandey (1), Sanjay Nath Khanal (2) Dhiraj Giri (3), Ashish Singh (4)Corresponding Author address: sanjay@ku.edu.np
1. VSBK/CESEF Project, Nepal(Government of Nepal/Swiss Developement Cooperation),Kathmandu Nepal
2. Department of Environmental Science and Engineering, Kathmandu University, Kathmandu, Nepal
3. Department of Natural Science, Kathmandu University, Kathmandu, Nepal
4. Environment and Public Health Organization, Kathmandu, Nepal
Abstract
Increasing experience of periods of impaired visibility is assumed to be due to increased air pollution in
Kathmandu valley. It is observed that the number of clear days in Kathmandu Valley is decreasing since the past
two decades. In Nepal, very few studies have focused on the status of visibility in Kathmandu Valley. This study
tries to find a linkage between Visibility, Particulate matter 10 (PM10) and Meteorological parameters in the
Kathmandu valley.
In this study we have considered daily 24-hour average PM10 concentration recorded in six air monitoringsite in Kathmandu Valley as the PM10 concentration of Kathmandu Valley. The duration of the PM10 data ranges
from 2003 to 2005. Similarly, meteorological data were obtained from the weather monitoring station of the
Department of Hydrology and Meteorology located at Tribhuvan International Airport (TIA) for the period 2003
to 2005. For better representation of the influence of PM10 and meteorological parameters, only those days wereconsidered with data from 6 air monitoring sites, and meteorological data from the weather station.
The trend in the mean visibility is increasing as we go from 2003 to 2005 i.e. 7.5467 km to 8.1267 km
respectively. The mean visibility is highest in monsoon 8.6131 km, which decreases with post monsoon 7.8781
km, pre monsoon 7.0356 km and winter 6.5363 km respectively in a descending order. The Chi-square test of
independence at 0.05 level of significance, reveled that mean visibility in the Kathmandu Valley depends on the
different seasons of Kathmandu Valley. In the case, of variation of visibility, with the time of the day, it was foundto increase gradually with the commencement of day peaking at 2:45 P.M and declining with the onset of night.
Irrespective of season, it is observed that as the day progresses the temperature increases, the wind becomesturbulent, humidity decreases and foggy weather disappears. This might be the reason for highest visibility during
the noon time. The analysis of variance of mean visibility with different time of the day shows that the mean
visibility was found to differ significantly with the different time of the day. Meteorological parameters like
temperature (minimum and maximum), rainfall, wind speed, pressure, total cloud and humidity were also found toaffect mean visibility.
Keywords : PM, Visibility, Meteorology, Kathmandu.
Introduction
Besides, the unequivocal admission of health effects ofincreasing air pollution levels in Kathmandu Valley,
increasing experience of periods of impaired visibility is also
assumed to be due to increased air pollution. It is observedthat the number of clear days in Kathmandu Valley isdecreasing since the past two decades. Aesthetically, for the
resident of the Valley, such impaired visibility andappearance of haze, is perhaps the most obvious adverse
effect of air pollution.
Visibility impairment is perhaps one of the most wellunderstood effects of air pollution, occurring when particles

and gases in the atmosphere scatter and absorb light.
Generally fine particles from anthropogenic sources arecommonly responsible for visibility impairment. Throughcomplex chemical reactions, gases react to form such fineparticles, e.g. sulphur dioxide reacts to form ammonium
sulphate, nitrogen oxides react to form ammonium nitrateand hydrocarbon gases can convert into carbon particles.
Some of these particles are hygroscopic in nature, andabsorbing water enhances their ability to impair visibility.
Hence, when relative humidity is high, the visibility
impairment capability of certain particles such as sulphates
increases. Correspondingly, Visibility is a good and simple－ 65 －

indicator for air quality. Increase in the amount of fineparticulate and gases in the atmosphere are associated with
reductions in the ability of the human eye to see through theatmosphere or identify an object at a distance. Commonly
applied measurement of visibility degradation is visual range
in miles or km, (higher values mean better visibility). It canbe measured easily even without aid of any instruments.
Numerous studies carried out all over the world havegiven ample evidence that visibility impairment is largely
due to the light scattering of ambient aerosols for which
concentrations are governed by meteorological conditions
and the emission source strength. Visibility impairment,
which results from the scattering and absorption of light byparticles and gases in the atmosphere, is considered as adirect effect of air pollution. Many have shown correlation
between meteorological parameters, particulate matters andvisibility. In Nepal, very few studies have focused on
visibility in Kathmandu Valley. This study examines theinfluence of meteorological parameters such as, maximum
and minimum temperature, rainfall, relative humidity,
atmospheric pressure, total cloud coverage, wind speed,
wind direction and PM10 concentration on visibility.

Materials and Methods
Study area
KathmanduValley
Kathmandu Valley is an oval shaped, flat bottomed basin
which occupies about 351 square kilometer. It is about
1300m above mean sea level and is surrounded by high hill
range whose altitude from the valley floor varies from 500m

to 1400m. The cross section of the oval structure of the
Kathmandu Valley extends 20 km from north to south and
30 km from east to west. Kathmandu Valley is influenced bymonsoon. The general pattern is characterized by a windy
hot and humid climate in March to May, followed by awell-defined monsoon is approximately from June to the
middle of September. About 82 per cent of the rain falls
during the period of monsoon. So, the remainder of the year
is dry with cold winter in December, January and February.
Autumn is the most pleasant season with the pleasanttemperatures and occasional short bursts of rain; wintertemperatures drop to freezing with a high level of snowfallin the mountains. Wind is ordinarily light throughout theyear, but there is a strong wind in and around the March toMay. Major part of the winds blowing over the valley issouthwesterly and northwesterly. Wind speed lies in therange of 0.5-7.5 m/s. The relative humidity reaches morethan 80 % during rainy season but decreases to about 50 %during dry season. Fog is common in the morning during themonths of October to February. A high altitudinal variation
with extreme diurnal radiation leads to a potentially strong
cooling system in the night and a warming in the day.
During the dry season, cooling in the morning and lateafternoon causes the formation of deep inversion layers.
When the inversion layer is deep enough, insulation may notbreak through the inversion. Due to topographical specificity,
once the pollutants have entered into the valley, they cannot
easily escape and may remain into the atmosphere forextended period of time.

Figure 1 : Geographic View of Kathmandu Valley－ 66 －

PUTALI SADAK
THAMELBHAKTAPUR
MATSYAGAONPATAN HOSPITAL

TRIBHUVANUNIVERSITY

Figure 2: Air Quality Stations at Kathmandu Valley
Monitoring Sites
Air Quality Monitoring Sites
In Kathmandu Valley daily 24-hour continuous air
monitoring is carried out at six permanent sampling stations,
namely urban roadside station, (urban traffic place,
Putalisadak and urban hospital place, Patan Hospital), urban
residential station, (city core residential place, Thamel),urban background station (Tribhuvan University, and
Bhaktapur) and valley background station (Matsyagaon).
The air monitoring stations thus were located in all
directions and represented all predominant urban areas
associated with high, medium and low human activities(Figure-1). This was done as to get a better spatial
representation of the valley.
Air Monitoring Instrument for PM10
In these stations particulate matter concentration ismonitored by standard method on daily basis. A Low
Volume Sample (LVS) for PM10 and PM2.5 without
pneumatic movement of filters is used. The PM10/PM2.5
sampler was type M/S Instrumatic, Denmark, Model 85-02,
designed according to the EN12341 standard.
Meteorological data
Meteorological data recorded by weather monitoring
station at Tribhuvan International Airport (TIA), was used
for the study. TIA is the only monitoring station in
Kathmandu Valley that measures visibility together withmeteorological variables including wind direction, windspeed, temperature, relative humidity, cloud cover,atmospheric pressure and precipitation.
Visibility
Visibility is an observational data. TIA station has
developed a map of Kathmandu valley with overlaying
concentric circles starting with a 500 meters radius, covering
the total area of Kathmandu valley. With an increase of 500
meter, a concentric circle is present with a reference point
place. The center of all circles is TIA station. In the case ofvisibility distance falling between the circles, approximation
is done. The unit of visibility is Km.
Data
PM10 and meteorological data were obtained for theperiod of 2003 to 2005. For better representation of theinfluence of PM10 and meteorological parameters, only thosedays were considered with data from all 6 air monitoringsites, and meteorological data from the weather station. The
main computational tools used were Microsoft Excel andstatistical tool, SPSS. Means, standard deviations,correlation analysis, regression, Chi- Square and ANOVA
test were done to show the statistical significance of thedifferences. To study nature of distribution of visibility,
probability distribution curve was plotted using Best Fit tool.
Results
Temporal Variation of Visibility
Mean visibility was found increasing from 2003 (7.5467km) to 2005 (8.1267 km) (Table: 1).

－ 67 －

Table 1: Mean, Standard Deviation, Maximum and Minimum value, and Range
Year
N
Mean
Std. Deviation
Minimum
Maximum
Range
(km)
(km)
(km)
(km)
305
7.54671.4428
3.66
12.53
8.87
2003
2004

366

7.26671.4964

3.24

10.89

7.64

2005

347

8.12671.7909

2.25

12.13

9.88

Total

1018

7.64371.6281

2.25

12.53

10.28

Table 2: Seasonal Variation of Visibility in Kathmandu Valley
N
Mean
Std. Deviation
Minimum
Maximum
(km)
(km)
(km)
276
7.0356
1.43103.56
11.75
Pre-monsoon
Season

Range
(km)
8.19

Monsoon366

8.61311.1340

5.44

12.53

7.08

Post-monsoon

171

7.87811.6896

4.12

12.13

8.01

Winter205

6.53631.5198

2.25

11.38

9.13

Total

1018

7.64371.6281

2.25

12.53

10.28

It was recorded highest in monsoon (8.6131 km),followed by post monsoon (7.8781 km), pre monsoon(7.0356 km) and winter (6.5363 km) respectively. It can beinferred that different season has an effect on the mean
visibility in Kathmandu Valley. Monsoon season is recordedwith highest rainfall and with the lowest value of PM10 as
well. Winter season is observed to have the lowest rainfall,
more fog, effect of inversion layer and highest PM10 value
might be the reason for the lowest mean visibility. Mean
visibility in the Kathmandu Valley was dependent on
different seasons and differ significantly in different seasons.
Mean visibility in the Kathmandu Valley was better in themonth of June (8.328 km) to October (8.5325 km). The
highest mean visibility was in the month of September(8.7559 km) and lowest in the month of January (5.7753
km). Mean visibility was dependent on different months
and differ significantly within the months.
Mean visibility was found to be more in Dashain andTihar (long festival)period (8.59kms), whereas it was lowest,(7.65 km) during working days. This might be due to thedecrease in PM10 concentrations by reduced vehicular
movement during this time. It was to differ significantly withthe different days. Visibility was found to increase graduallywith the commencement of day peaking at 2:45 P.M anddeclining with the onset of night. Irrespective of season, it isobserved that as the day progresses the temperature
increases, the wind becomes turbulent, humidity decreases
and foggy weather disappears. Mean visibility was found todiffer significantly with the different time of the day.

Influence of Meteorological Parameters on Visibility
In Kathmandu valley, average visibility was foundmaximum in maximum air temperature range of 32oC andabove, and the visibility was lowest at the temperature range
below 16 oC This trend was evident in every season. With
the minimum temperature, maximum mean visibility of
8.6691 km was observed in the range of 20-25oC, and themean visibility reaches to a minimum of 6.3497 km when
the temperature reaches the lowest range of -0.5oC.
Wash out effect of particulates by rainfall is also evident
with the visibility change during rainy and non-rainy periods.
It is observed that the mean visibility is higher during theperiod of rain compared to period of no rain. However,during the rainfall period, the visibility is highest in rainfall
range of (10mm - 30mm). Mean visibility was at lowest
with relative humidity 40-60% (6.711 km) and as therelative humidity grows to 80% and above range, the mean
visibility reaches the maximum of 7.6968 km. The amountof relative humidity in the atmosphere is found dependent on
rainfall. Therefore, it can be implied that increase inhumidity means increase in rainfall and which directly
increase the visibility. With atmospheric pressure, the datashows inverse relationship with the visibility. Though, thetrend in the data is not as clear, but the general trend seems toshow that the increase in pressure causes lowering ofvisibility. Such inverse relationship may be due to fact thatthat as the pressure goes up, the atmosphere becomes astable one, which characterizes low or no wind and rainfall.

－ 68 －

Table 2: Average concentration values determined within selected ranges of temperature, precipitation,
atmospheric pressure, wind speed and relative humidity in Kathmandu Valley
Tmax
(0C)

N

Mean
Tmin
Vis.(kms) (0C)

N

Mean
Rain
Vis.(kms) (mm)

N

Mean
AP
N
Vis.(kms) (mmhg)

Mean
Total cloudVis.(kms) (OKTA)

N

Mean
RH
Vis.(kms) (%)

< 11

2

5.7049-0.5-18

6.3497

No rain 624

7.3017<855.06

7.3417

Clear sky

2

3.718811-16

84.41001-5

1196.5958259

8.28778.38840-2

186

7.521381

6.02645-10

200

6.968361

8.1434319

8.06862-4281

7.171520-24 1946.9657

10-15 216

7.279436

8.0479412

7.24794-6283

7.803224-28 306

7.711915-20 2568.129419

7.5695136

7.05876-8262

8.110228-32 393

8.257620-25 219

8.6691

6

7.6394855.0860.0
860.0865.0
865.0870.0
870.0875.0≥875.0

143

16-20

2

8.8750≥8 a46.6406≥32

8.53200.00110.0
10.020.0
20.030.0
30.040.0
40.050.0≥50

137.879534

Wind Speed N(m/s)
No wind
43

Mean

0-2

970

7.65952-3

59.0000Total

1018 7.6437

N

Mean
PM10

40.00- 24 6.7118
60.00
60.00- 362 7.6129
80.00
≥80
632 7.6968

7.1309The relation of visibility with cloud cover elucidate thatmaximum mean visibility of 8.1102 kms is seen with hightotal cloud range of 6-8 OKTA; where as the minimum
mean visibility of 3.7188 kms is seen when there is clear sky.
The overall trend, however, depicts direct effect of cloud
cover on visibility.
Maximum mean visibility (9.0000 km) was seen with thehighest wind speed range of 2-3 m/s; where as the minimum
mean visibility (7.1309 km) is seen when there is no wind.
This means that wind helps in increasing the visibility, andthis is due to the ability of high speed wind to blow away theparticles.
It was found that wind blows mainly from two directions;west and south of the Kathmandu Valley. Irrespective of theseason westerly and southerly wind was predominant.
However, during the monsoon easterly wind and in winternorthwesterly wind were in account. Wind direction in

sixteen compass point directions for the period 2005 indicatethat the highest mean visibility in Kathmandu Valley was
associated with wind direction NNE (11.5 Km). However,considering different seasons, highest mean visibility was
associated with wind direction NNE, SSE, WSW and WNWin pre-monsoon, monsoon, post-monsoon and winterrespectively.
Similarly in Kathmandu Valley lowest mean visibility was
associated with the wind direction North, ENE, SSE andENE respectively in pre-monsoon, monsoon, post-monsoon
and winter.
In general, the visibility in the Kathmandu Valley
depends on the ambient air temperature, precipitation,
atmospheric pressure, wind speed, relative humidity andpredominant wind direction and differs significantly withthese parameters.

－ 69 －

PREMONSOONPREMONSOON

NORTH
NNW

100

NNE

80

NW

NORTH

60

WNW

6

WNW

20

2

WEST

ESE
SW

WSW

ESE
SE

SW

SSE

Frequency

SSW

SOUTH

SOUTH

MONSOON

NORTH
NW

80

NORTH

NNE

70

12

NNW

NE

60

ENE

6

WNW

20

2

EAST

0

WSW

WEST

ESE
SW

SE
SSW

WSW

ESE
SW

Frequency

SE
SSW
SOUTH

POSTMONSOONPOST MONSOON

NORTH

30

NORTH

NNE

14

NNW

NE

20

WNW

Mean

SSE

SOUTH

NW

EAST

0

SSE

NNW

ENE

4

10

WEST

NE

8

40
30

NNE

10

NW

50

WNW

Mean

SSE

MONSOON

NNW

EAST

0

SE
SSW

ENE

4

EAST

WSW

NE

8

ENE

0

NNE

10

NW

40

WEST

12

NNW

NE

12

NW

NNE
NE

10
8

ENE

10

6

WNW

ENE

4
2

0

WEST

WEST

EAST

WSW

WSW

ESE
SW

SE
SSW

ESE

SSW

SSE

SSE
SOUTH

WINTERWI N TER

NORTH

NORTH
NW

80

NNW

NNE

40

NE

6

WNW

ENE

ENE

4
2

WEST

EAST

0

WSW

ESE
SW
SSE

EAST

0

WSW

SE
SSW

NNE

8

20

WEST

12
10

NW

NE

60

WNW

Mean

SE

SW

Frequency

SOUTH

NNW

EAST

0

ESE
SW

Fr equency

SE
SSW

SOUTH

Mean

SSE
SOUTH

Figure 3: Prevailing wind directions in different seasons and corresponding visibility in Kathmandu Valley－ 70 －

Table: 3. 24 hour PM 10 data from monitoring stations of Kathmandu valley.
N
Mean
Std.
Minimum
Maximum
Range
PM10 (g/m3)
Deviation
Good (0 -60)
96
8.23781.1471
5.44
11.63
6.18
Moderate (60-120)406

8.64141.2333

4.94

12.53

7.58

Unhealthy (120-350)

514

6.76381.4447

3.24

11.63

8.38

Very Unhealthy (350- 425 )22.7688

.7336

2.25

3.29

1.04

1018

7.64371.6281

2.25

12.53

10.28

Total

Influence of PM10 on Visibility
Government of Nepal has categorized five different typesof air quality based on levels of PM10. The categories
prescribed are range 0-60 g/m3 as “Good”, 60-120 g/m3
as “Moderate”, 121-350 g/m3 as “Unhealthy”, 351- 425
g/m3 as “Very Unhealthy”, and >425 g/m3 as “Hazardous”.
In this study the same categories have been maintained forthe purposes of interpretation statistical analysis.
Table: 3 shows that the mean visibility increases withdecrease in PM10 concentration. The mean visibility ishighest i.e. 8.6414 km between 0-120 (g/m3) of PM10
concentration and low i.e. 2.7688 km in highest
concentration of 351-425 (g/m3) of PM10. Increase in PM10
density in the atmosphere results in the increased scatteringof light, reducing visibility thereby. Mean visibility in the
Kathmandu Valley depends on the PM 10 concentrations andit was found to differ significantly with PM10 concentration.
Mean visibility varies significantly with the change ofPM10, and also with the change of wind speed, barometerpressure, total cloud, temperature ( maximum andminimum), humidity, year, month and season. However, thechange in the day of the week was found not significantly

affecting the visibility. From this analysis, it can be inferredthat PM10 is not the only factor determining visibility. It canbe implied that while relating PM10 with the visibility, theabove meteorological factors play a significant role and must
be considered for analysis.

Correlation Analysis
Table 4 depicts the correlation of visibility and different
meteorological parameter during the study period.
Visibility in the valley has positive relationship withmaximum temperature, minimum temperature, rainfall,wind velocity and total cloud. This implies that as thevisibility increases, the maximum temperature, minimum
temperature, rainfall, wind speed, and total cloud are higher
and vice versa. However, the Pearson’s coefficient isrelatively strong with maximum and minimum temperature.
However, visibility in the valley has negative relationshipwith atmospheric pressure and PM10. As the atmosphericpressure and PM10 increases visibility in Kathmandu Valley
decreases and vice versa. R-square value of -.606, signifies astrong relation of PM10 with visibility.

Table 4: Pearson’s Coefficient of Correlation between Visibility with
Selected Meteorological Parameter and PM10

Visibility

PearsonCorrelationSig.
(2-tailed)
N

MAX.
TEMP.
.453**RAINFALL.081*

HUMIDITY.018

ATMOSPHERICPRESSURE-.262**

WIND
VELO.
.066*

TOTAL
CLOUD
.177**

MIN
TEM
.446*

PM10
-.606**

.000

.010

.561

.000

.035

.177

.000

.000

1018

1018

1018

1018

1018

1018

1018

1018

** Correlation is significant at the 0.01 level (2 -tailed)

Table 5: Pearson’s coefficient of correlation between Visibility in Kathmandu Valley andselected meteorological parameter in different seasons

Visibility

PearsonCorrelationSig.
(2-tailed)
N

MAX.
TEMP.
.070

RAINFALL.081*

HUMIDITY.018

ATMOSPHERICPRESSURE-.262**

WIND
VELO.
.066*

TOTAL
CLOUD
.177**

MIN
TEM
.446*

-.606**

.000

.010

.561

.000

.035

.177

.000

.000

1018

1018

1018

1018

1018

1018

1018

1018

－ 71 －

PM10

Regression Analysis
Considering that the mean visibility in Kathmandu Valley
depends on maximum temperature, minimum temperature,
rainfall, relative humidity, atmospheric pressure, wind speed,
total cloud coverage and the PM10 concentration linear
regression analysis was done. Regression analysis is foundto be significant at 0.05 level of significance. It was also
observed that except rainfall and wind speed, othermeteorological parameters are found to be significant
predictor of visibility. Further it was observed that R2 value
is 0.563 i.e. 56.3% of variation in dependent variable has
been explained by independent variable.

Probability Distribution
The frequency distribution of visibility is useful inunderstanding the statistical characteristics of air quality. In
Kathmandu Valley the visibility data follows the Weibull
probability distribution. The probability graph shows that themean visibility lie in the range of 4.73 km to 10.21 km with90% confidence interval. Maximum number of observations
was found between 7 to 9 kms, with a probability of around
0.25.

Table 6: Regression Coefficient
Un-standardized
Std.
Standardized
Coefficients
Error
Coefficients
B
Beta

T

Sig.

5.188

0.0000(Constant)

45.4648.764

Maximum Temperature

0.07

0.023

0.177

3.009

0.003

Minimum Temperature

0.004

0.02

0.015

0.191

0.849

Rainfall-0.0120.004-0.081-3.4790.001

Humidity-0.0650.008-0.313-8.2510.0000Atmospheric Pressure-0.0360.01-0.097-3.5540.0000Wind Speed-0.213

0.111

-0.05

-1.9180.055

Total Cloud-0.1290.034-0.165-3.7350.0000

Average PM10

-0.0240.001-0.818-26.890.0000

0.25

0.20

0.15

0.10

0.05

0.00 2

4

<

5.0%

6

8

10

90.0%
10.21

4.73

12

14

5.0%

>

Figure 4: Frequency distribution of visibility in Kathmandu valley.

－ 72 －

Conclusions
1. Monsoon season was found to have the highest
visibility with winter having the lowest of all.
2. The mean visibility in the Kathmandu valley isbetter in the month of June 8.328 km to October
8.5325 km as compared with other months.
Visibility was less during January (5.7753 km).
3. Mean visibility was found increased during thenon-working days long festival time (Dashain andTihar) than that of working days and week end
days. Within a day, It was observed that thevisibility increase gradually with the day whichpeaks at 2:45 P.M and declines with the onset ofnight.
4. Mean visibility was found dependent with different
year, different seasons, and in different months.
5. Meteorological parameters like temperature(minimum and maximum), rainfall, wind speed,pressure, total cloud and humidity were found toaffect mean visibility. Mean visibility is dependent
on the above parameters. Change in temperature(minimum and maximum), rainfall, wind speed,pressure, total cloud and humidity changes themean value of visibility. Visibility in the valley has
positive relationship with maximum temperature,minimum temperature, Rainfall, wind velocity andTotal cloud. The analysis of visibility with the winddirection for the year 2005 shows the highest mean
visibility in Kathmandu Valley was associated withwind direction NNE.
6. PM10 concentration was found to affect mean
visibility and is dependent on PM10. Mean
visibility varies significantly with the change ofPM10, and also with the change of meteorologicalparameter.
7. Visibility in the valley has negative relationshipwith atmospheric pressure and PM10. Except
rainfall and wind speed, other meteorologicalparameters are found to be significant predictor ofvisibility. The probability distribution of thevisibility was found to follow the Weibull’s
distribution, where maximum observed value lie in
the range of 7-8 kms. This implies that maximum
number of day in Kathmandu has thecorresponding visibility range.

Acknowledgements
This study was funded by the Kathmandu University. We
thank the Air Quality Department of the Ministry ofEnvironment Science and Technology, Department ofHydrology and Meteorology, and Tribhuwan International
Airport for assistance in technical issues and for providing
pollution and meteorological data.
This paper is presented in the 16th CEReS International
Symposium on climate change studies through activities of
SKYNET and Virtual Laboratory for climate diagnostics(October 21-23, 2010), Chiba University, Nago, Okinawa,
Japan with support from Japan Society for Promotion of
Science (JSPS).
Selected References
1. Charlson, R. J. and Heintzenberg, J, (1995). Aerosol
Forcing of Climate, John Wiley & Sons, Chichester, pp.
191–194.
2. Davis, J.M., Eder B. K., Nychka D., Yang Q.,(1998). Modeling the effects of meteorology on ozone
in Houston using cluster analysis and generalized
additive models. Atmospheric Environment, 32,
2505–2520.
3. ENPHO, (1993 b). Air Quality Assessment in
Kathmandu City. Environment and Public HealthOrganization, Kathmandu, Nepal.
4. Gautam, C., (2002). “Ambient Air Quality, Inventory of
Pollutants and Future Ambient Air Quality Monitoring
Programme of Kathmandu Valley”, Proceedings of One
Day Seminar on Air Quality Management ofKathmandu Valley: Challenges and Opportunities,
Kathmandu, 2002.
5. Gautam, C, Sharma, S., and Fuglsang K., (2004). Air
Quality Monitoring and Management in Kathmandu,
Nepal. International Conference on Better Air Quality
Management in Asia (BAQ 2004), December 2004,
Agra, India.
6. Giri, D., Murthy. V. K., Adhikary, P.R., Khanal, S. N.,(2007). Cluster analysis applied to atmospheric PM10
concentration data for determination of source andspatial patterns in ambient air quality of Kathmandu
Valley, Current Science, 93, No.5.
7. Giri, D., Murthy. K., Adhikary, P.R., Khanal, S. N.,(2006). Estimation of number of deaths associated with
exposre to excess ambient PM10 air pollution. Int. J.
Environ. Sci. Tech., 4(2), 183-188.

－ 73 －

8. Giri, D., Murthy. K., Adhikary, P.R., Khanal, S. N.,(2006). Ambient air quality of Kathmandu Valley
as reflected by atmospheric particulate matter
concentration (PM10). Int. J. Environ. Sci. Tech., 3(4),403-410.
9. Giri, D., Murthy. K., Adhikary, P.R., Khanal, S. N.,(2004). “Spatial and seasonal variation of ambient
airborne particulate matter (PM10) mass concentrationsin Kathmandu Valley” Proceeding of International
conference on better air quality management in Asia,
from December 6-8, 2004, Agra, India.
10. Lee, D.O., (1990). The influence of wind direction,
circulation type and air pollution emissions on summer
visibility trends in southern England. Atmospheric
Environment 24A, 195–201.
11. Lee, D.O., (1994). Regional variations in long-term
visibility trends in the UK, 1962–1990. Geography 79,
108–121.
12. Liou, K. N., (1996), An Introduction to Atmospheric
Radiation, Academic Press, California, pp. 50–86.
13. Malm, W.C., (1999). Introduction to Visibility. Air
Resources Division National Park Service.
14. MOPE, (2003). National Ambient Air Quality
Standards for Nepal. Ministry of Population andEnvironment. HMG/ Nepal.
15. Pandey, R.K., (1987). Geography of Nepal. Center for
Altitude Geography, Kathmandu, Nepal.
16. Sapkota, B.K., Sharma, N.P., Poudel K. and Bhattarai,B., (1997). “Particulates Pollution Levels in Kathmandu
Valley”, Environment, 3:2, Ministry of Population andEnvironment, Kathmandu.
17. Van de Hulst, H. C., (1987). Light Scattering by Small
Particles, Dover Publication, New York, pp. 383–453.

－ 74 －

Long-term Variation of Aerosol Optical Depth in China Basedon Meteorological Horizontal Visibility Observations*
Validation and Analysis of Aerosol Optical Depth Derived
from Meteorological Horizontal Visibility in China*
QIN S. G.1,2 SHI G.Y.2 # CHEN L2,3 WANG B.2 ZHAO J. Q.2 YU C. W.4 YANG S.2
1 Meteorological Observation Center, China Meteorological Administrator, Beijing, China
2 State Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
3 National Satellite Meteorology Center, China Meteorological Administrator, Beijing, China
4 Climate Center of Hebei Province, Shijiazhuang, China
Abstract
After treating with the visibility data inhomogeneity source from inconsistent record format at the beginningof the year 1980, we obtain the long-term time series of visibility, from which atmospheric aerosol optical depth
over China during 1961 to 2005 are derived. By validation, it shows well consistency with former research andobservation on long-term variation of aerosol optical depth over China. It is found that aerosol optical depth over
China showed an increasing trend but a slowdown upward trend in the post-1985, especially in megacity regions.
The aerosol optical depth are low in the northwestern China and high in the southeastern China where aerosol
optical depth is generally greater than 0.4, and the maximum of more than 0.8 locates in the Sichuan Basin.
Keywords：Visibility Aerosol Optical Depth
1. Introduction
Atmospheric aerosol plays an important role in energybalance of Earth-Atmosphere system and thus can influence
the global climate change significantly through direct,
semi-direct and indirect effect. Aerosol Optical Depth(AOD) is one of the primary parameters to determine theaerosol radiative effects which can be obtained throughground-based measurement and satellite retrieval (Dubovik
et al., 2000; Chu DA et al., 2003), however, due to highly
temporal and spatial variability of aerosols, there is still lackof long time series of AOD on global or even regional scale.
Horizontal Visibility is an indicator reflecting theatmosphere transparency which is highly negative correlatedwith AOD. Studies by Wang et al. (2009) indicate that AOD
derived from meteorological horizontal visibility has aconsiderable accuracy with satellite retrieval product. This
article is to validate AOD estimated with visibility data
observed at routine meteorological stations and to give
spatial and temporal variations of AOD over China.

2. Data and Methods
2.1 Data information
Meteorological horizontal visibility was observed in 753
routine meteorological stations from 1961 up to 2005. 639
stations more than 35 years are used in this paper, where are
select to estimate AOD. Note that the visibility record format
begins to change from 1980. Before 1980 visibility was
divided into ten levels based on different visibility distance
range while after 1980 was given the actual visibility
distance (unit: km). Routine meteorological stations in China
are regularly observed at least 4 times a day, at 2:00am,
8:00am, 14:00pm and 20:00pm (Beijing time, the same
below). In order to eliminate the visibility target
inconsistencies in night time at 2:00am and 20:00pm andpossible fog influence at 8:00am, the one observed at
14:00am was chosen to represent the daily visibility in thispaper. In addition, for the purpose of AOD estimating, thevisibility observed in some weather conditions, such as
precipitation, fog and high humidity (RH> 90%) wasremoved.

 This article is based on a study first reported in Chinese Journal of

2.2 Visibility data processing methods
As mentioned formerly, visibility had different record
format before and after 1980. If want to derive long-termtime series, it is required to assign a reasonable visibility
distance for level in record format before 1980. In theory,

Atmospheric Sciences, 2010, Vol. 34(2): pp449-456.
 This article is based on a study first reported in Chinese Journal of
Atmospheric Sciences, 2010, Vol. 34(2): pp449-456.
＃

Corresponding author, supervision for this study－ 75 －

Table 1 Visibility interval and estimation for visibility levels

paper, the AOD is derived based on the visibility obtain byestimates for whole period from 1961 to 2005.

Visibility Level

Visibility
interval / km

Estimates forcorresponding level / km

0

<0.05

0.025

1

0.05~0.2

0.1

29

2

0.2~0.5

0.28

28

3

0.5~1.0

0.7

4

1.0~2.0

1.41

5

2.0~4.0

2.67

6

4.0~10.0

6.77

7

10.0~20.0

13.58

8

20.0~50.0

29.04

9

≥50.0

54.27

Visbility / km30

 3.912
 0.55 
 0.0116  V  

2  v*

27
26
25
24
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

daily visibility data in level format can be assigned any value
in the interval corresponding to the level, e.g. the median;
however, because of non-equidistance intervals for levels, it
may lead to inhomogeneity for time series. In this paper,
daily visibility data from 1980 to 2005 are transferred tolevels format data firstly, and then under the limit of eachlevel, daily visibility is averaged as the estimates for the tenlevels (hereinafter called estimates). Table 1 gives thevisibility levels, intervals and estimates for corresponding
level respectively, in which estimate is median of interval forlevel_0, that is 0.025km. It can be seen that the estimates fora visibility level is less than the median of interval, and thelevel is larger, the difference is more significant.
Using the estimates to replace daily visibility data before
1980 in format of level, the monthly and annually averagevisibility can be obtained. Figure 1 shows long-term
variation of visibility over China from 1961 to 2005
averaged by 639 stations data, in which two processing
results are given at the beginning of 1980. One is to get theannual average using the visibility observations directly (thecircles), the other is to convert visibility to levels, then obtain
the annual average according to processing method for data
before 1980 (black dots). As shown in Figure 1, the visibility
data processing method used in this paper may effectively
guarantee the continuity of the visibility variation over China
when record format were altered from 1980, and at the sametime, keep a small relative errors between the estimates toobservations after 1980 with the errors range form -1.5% to
1.7%, which is the slightly influence on long-term trend
analysis. In order to keep the continuity of data, later in this  

Estimates
Obseration

Fig.1. Variation of annually averaged visibility over China
during 1961 to 2005

2.3 The method to derive AOD based on visibility
According to the definition of meteorological visibility V,
Visibility is inversely proportional to the atmospheric
extinction coefficientσ0.55 at the wavelength of 0.55m,which can be expressed by equation (1):

V

3.912

 0.55(1)

In atmospheric standard conditions (T = 15 ℃, P =1013hPa), assuming aerosol obey Junge distribution, in
terms of the relationship between atmospheric verticalattenuation with visibility in Elterman (1970), and aerosol
particle concentration distribution with height inducted by
McClatchey et al. (1971), AOD (τ λ) can be derived fromvisibility (V) based on the equation (2),in Equation (2),Z is the altitude of meteorological station(km), H1=0.886+0.0222V km ， H2=3.77km ， ν*= -2 ，λ=0.55m.
In this paper, the surface vapor pressure is introduced torevise the result of AOD according to the method developedby Qiu and Lin (2001), more detail in the reference.

3. Results
According to the method introduced in section two, thedaily AOD in meteorological station is derived based on
visibility, thus monthly and annually average for station or
China is also obtained for the period from 1961 to 2005.

5.5
5.5
5.5
   HZ
 H1  e 1  e H1   12.5e H1  H 2 e H1 

 



－ 76 －

(2)

.55

AOD

.50

Reference in Luo (2000)
This paper

.45
.40
.35
.30
1960

1965

1970

1975

1980

1985

1.4

This paper
AERONET_Beijing

1.2
1.0
.8

AOD

3.1 Validation of aerosol optical depth results
Two time series of annually averaged AOD from 1961 to1990 over China are shown in Figure 2. The line with solid
dots shows the result of this paper, while the line withtriangles show ones in Luo et al. (2000) which is obtained by"broadband extinction method" with 44 stations direct solar
radiation data in China. It can be seen that two results arehighly consistent in variation characteristic with a correlation
coefficient of 0.9. Annually averaged AOD in this paper isslightly higher compared to result in Luo et al. (2000) andthe possible reason is that AOD in this paper is obtained at
the wavelength of 0.55m, while equivalent to wavelengthof 0.75m in Luo et al. (2000), assuming aerosol obey Junge
distribution, the wavelength smaller, the AOD greater.
AERONET is a global aerosol monitoring network
established under the leading of NASA, whose aim is toacquire global aerosol optical properties using the
ground-based sun photometer equipped at baseline or
representative regional, further to validate and assess theaccuracy of satellite retrievals (Dubovik et al., 2000). Here,one of AERONET sites Beijing which has the longest
available data in China is selected for validation. From thevariation of monthly average AOD from January 2002 to
December 2005 in Beijing given in figure 3, we can see
similar variation characteristic for two series with acorrelation coefficient of 0.54. There are indications that thevariation of monthly average AOD in Beijing derived byvisibility is mild and relative larger differences for extreme
AOD values compare to sun photometer observation,
probably reason is that the visibility is artificial observation,
which the sensitivity and accuracy can not be compared withthe sun photometer instruments.

.6
.4
.2
0.0
01-2002 07-2002

01-2003 07-2003

01-2004 07-2004

01-2005 07-2005

01-2006

Month / Year

Fig.3. Variation of monthly averaged AOD in Beijing
comparison to AERONET (Jan. 2001-Dec. 2005)

MODIS(Moderate
Resolution
Imaging
Spectroradiometer) is a key instrument aboard the NASA
Terra satellites which can be used to retrieve aerosol opticaldepth with "dark-target” method proposed and developed by
Kaufman et al (1997). In this paper, the MOD 08 Level 3
monthly average AOD Gridded product from 2001 to 2005
is also used to validate the AOD derived from visibility.
Figure 4 shows the scatter plots and line regressive ofcomparison in the spring (March, April and May), summer(June, July and August), autumn (September, October andNovember) and winter (December, January and February)
seasons, respectively. Seen from the linear regression
equations and correlation coefficients at the bottom rightcorner, the seasonal correlation coefficient of visibility
derived AOD and MODIS AOD are all higher than 0.6, withthe maximum of 0.7 in the fall, and the minimum of 0.6 inthe summer which may be the reason of higher water vapor
or more concurrency of clouds and rain. Further, thevalidation shows that visibility derived AOD is usuallyhigher than the satellite retrieval for low AOD, while lower
for high AOD. One reason may be visibility derived AOD is
not satisfactory for extreme AOD as motioned formerly.
Additional reasons may be that the applicability of MODIS
retrieval aerosol products in China has geographical andseasonal difference (Wang et al, 2007), and the global
MODIS retrieval aerosol optical depth over land issignificantly higher (Xia, 2006).

1990

Year
Fig.2. Variation of annually averaged AOD for 44 stations in
China－ 77 －

2.0

2.0

JJA

MAM
1.5

VAOD

VAOD

1.5

1.0

1.0

.5

.5

y=0.23+0.39x
R=0.67

0.0
0.0

.5

2.0

1.0

MOD08_AOD

1.5

y=0.27+0.33x
R=0.60

0.0

2.0

0.0

1.0

MOD08_AOD

1.5

2.0

DJF

SON
1.5

VAOD

1.5

VAOD

.5

2.0

1.0

1.0

.5

.5

y=0.24+0.53x
R=0.70

0.0
0.0

.5

1.0

MOD08_AOD

1.5

2.0

y=0.28+0.55x
R=0.63

0.0
0.0

.5

1.0

MOD08_AOD

1.5

2.0

Fig. 4. Scatter plots for comparison of visibility derived AOD with MODIS retrieval AOD for each season

studied in this paper using annually averaged AOD datafrom 2001 to 2005, and shown in figure 6. It shows that thepattern is mainly characterized by feature of a higher insoutheast and a lower in northwest. There are several
significant high-value areas in China such as Sichuan,
Henan, North China and Yangtze River regions with AOD
magnitude to 0.6 and with a maximum greater than 0.8 in
Sichuan Basin. The result is consistent with Li et al (2003)
study, which is obtained by the use of MODIS aerosol
products.
.42.40

1.57e-3

AOD

.38
.36
.34
.32
.55

1.06e-3
2.61e-3.50

AOD

3.2 Temporal and Spatial characteristics of AOD in
China
Using visibility derived AOD data, temporal and spatial
characteristics of AOD is studied in this paper. Figure 5
shows the long-term variation characteristic of AOD over
China from 1961 to 2005, in which the top figure is annually
averaged by 639 stations data for more than 35 years, while
the bottom figure is averaged by 31 stations data located at
the capital cities of provinces, autonomous regions andmunicipalities on behalf of megacity mean. In figure 5, thelinear trends of AOD variation for two periods (1961 to
1985 and 1986 to 2005) are also shown by solid bold linewith the slope labeled on the upper left and lower rightcorner, respectively. It can be understand that AOD over
China keep increase for the whole period from 1961 to 2005
but with a slow down increasing rate at the period of
post-1986. This characteristic is especially significant inlarge urban areas with the rate reduces more than an order ofmagnitude as shown in the bottom of figure 5. Aerosols
emission caused by rapid increasing fossil fuel consumption
is likely account for AOD variation tendency in China. The
situation is particularly evident in large urban areas before
1985, where the increase rate of AOD is largest. But with thedevelopment of the state and residents environmental
awareness at the beginning of 1990s, more strength emission
standards is executed for particle pollution control, which iscarried out better in large urban cities than others in China,
thereby cause significant difference in variation of AOD
increase rate in megacity areas and other areas.
Averaged AOD distribution pattern in China is also

.45
.40

0.139e-3

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Fig.5 Variation and line trend of annually averaged AOD
over China
Top: The curve is the annually averaged time series of AOD with 639
stations data for more than 35 years, the two lines are the linear trend of
AOD from 1961 to 1985 and from 1986 to 2005, respectively. Theirchange slope is marked at upper left and lower right corner of the figure;
Bottom: same with top but with 31 stations data, locate in capital cities
of provinces, autonomous regions and municipalities－ 78 －

50N
0.6

0.5

Latitude

40N

0.4

30N

0.3

0.2

20N
80E

90E

100E

110E

120E

130E

0.1

Longitude

Fig.6 Distribution pattern of averaged AOD over China (2001 to 2005). The bold curve is the isoline.

4. Summary and Discussion
By using the processing method introduced in this paper,
visibility data inhomogeneity source from inconsistentrecord format from 1980 is well disposal. Validation byformer study, ground-based measurement data and satellite
retrieval products are indications that it is possible to derived
AOD by meteorological visibility and visibility derived
AOD can well reflect distribution pattern and long-term
variation characteristic
Wild et al. (2005) find that the surface solar radiation inmost sites of the world has experienced a reversal tendency
from reduced to increase named by from “dimming” to"brightening" at early 1990s. Shi et al. (2008) study showsthat similar phenomenon is also happened in China at the
beginning of 1990s. Although the reason of “brightening”
needs further study, there are some evidences indicate thatthe reduction of atmospheric aerosol content and its
interaction with the clouds is the most likely factors (Wild etal., 2005). Most solar radiation observation sites in China
locate in the cities, 31 capital cities of provinces,autonomous regions and municipalities selected in this paper
are actually equipped with instruments for solar radiation
observations, so to some extent, “brightening” occurrence in
China have a certain relationship with AOD increase rate
slowing down in megacity areas. Certainly, further researchis needed to verify it.

Acknowledgment
The visibility and other meteorological data used in thisstudy are obtained from CMA/ National Meteorological
Information Center, which is highly acknowledged. We also
would like to thank related staff for the use of AERONET
data and MODIS aerosol products.
References
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Dubovik O , Smirnov A , Holben B N , et al . 2000.
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－ 80 －Aerosols and Climate Changes in China
Zhanqing Li
Dept of Atmospheric and Oceanic Sciences and Earth System Science and Interdisciplinary Center
University of Maryland, College Park, MD
Abstract
As the most populated country of the world, China is a major source of aerosols which may contributesignificantly to the regional climate and its changes. Following a series of extensive observation campaigns, awealth of general and specialized measurements were acquired to allow extensive and close-up investigations of
the optical, physical and chemical properties of anthropogenic, natural, and mixed aerosols; their sources,
formation and transport mechanisms; horizontal, vertical and temporal variations; direct and indirect effects andinteractions with the East Asian monsoon system. Particular efforts have been made to advance our understandingof the mixing and interaction between dust and anthropogenic pollutants during transport. Modeling studies werealso carried out to simulate aerosol impact on radiation budget, temperature, precipitation, wind and atmosphericcirculation, fog, etc. Major observation campaigns and scientific achievements towards these goals are reviewed
in this paper.
1. Overview of Climate Changes in China
Perhaps the most noticeable change of climate in China isa reduction of 35% in visibility from the 1960s to the 1980s.
During this period, the amount of direct solar radiation
reaching the ground decreased by about 8.6% [Luo, 2001;
Liang and Xia, 2005; Shi et al., 2008], while global total
solar radiation decreased by about 4.6% per decade [Shi etal., 2008]. Sunshine duration also decreased significantly
over a large portion of China in the second half of the past
century [Kaiser and Qian, 2002].
The decrease in solar radiation is at odds with a generaldecrease in the annual mean cloud cover (1-3%/decade) andrainy days (1-4% /decade) observed at many ground stations[Kaiser, 1998; Liang and Xia, 2005], which is consistent
with changes in the frequencies of cloud-free sky andovercast sky [Qian et al., 2006]. While temperature has
shown a general increasing trend over much of China,
significant cooling trend was observed surrounding the
Sichuan Basin and central eastern China from 1960s to
1990s [Qian and Giorgi, 2000; Xu et al., 2006]. Thesestudies suggest that increased atmospheric pollutants fromhuman activities during the past several decades may haveproduced a fog-like haze that resulted in less sunshine anddecreased solar radiation reaching the surface, reduced
evaporation, and moderated the warming trend over heavily
polluted areas.
Changes in precipitation follows a general trend of“southern flood and northern drought”. By means of model
sensitivity tests and data analysis, Xu [2001] and Menon et al.
[2002] argued that the change in precipitation pattern islinked to the aerosol direct effect. By using the observed
trend of AOD [Luo et al., 2001] and an assumed constant ofaerosol single scattering albedo (o) of 0.85, Menon et al.
[2002] obtained similar gross trends in modeled andobserved temperature and precipitation trends. It is
worthnoting that the o as retrieved from a combination ofsatellite and ground observation is highly variable and themean value across China is close to 0.90 [Lee et al., 2007;
Zhao and Li, 2007]. Thus additional mechanisms appear
to be at work as well to explain the observed climate change
trends.

2. Major Observation Campaigns in China
Various observation activities have taken place since 2004
with the following overarching goals:(1) to acquire essential aerosol and meteorologicalmeasurements for investigating the impact of aerosols
on regional climate,(2) to understand the mechanisms governing aerosolclimate interactions and how aerosols impinge on theregional impact under diverse atmosphericenvironments ,(3) to examine the roles of aerosols in affecting
atmospheric circulation particularly on the East Asianmonsoon system.
The first observation initiative is the East Asian Studies
of Tropospheric Aerosols: an International Regional
Experiment (EAST-AIRE) (Li et al. 2007a). In the firstphase of the study, focus has been placed on obtaining in situ

－ 81 －

Figure 1. Observation stations and instruments for the EAST-AIRE project.
and remote sensing measurements of aerosols and pollutant
gases near or down wind of major source regions throughout
inland China. Both routine and intensive observation
campaigns (IOC) were conducted with ground-based andairborne measurements. Extensive measurements weremade at 2 baseline stations measuring aerosol, cloud andradiation quantities, 25 stations measuring AOD at multiple
wavelengths; and ground-based and airborne IOCs
measuring the physical and chemical properties of aerosol
particles and precursor gases, and cloud and radiation

quantities. The observation stations and instruments areshown in Figure 1.
The second-phase of the study was conducted from
April-December 2008 at multiple sites in China with thecentral station equipped with the Atmospheric Radiation
Measurement (ARM) Mobile Facility (AMF) at Shouxian in
Anhui province of China, approximately 500 km west of
Shanghai in the Jiang-Huai prairie region between the Huai
and Yangzi rivers, together with three ancillary sites asshown in Figure 2.

Figure 2. Observation sites in the 2nd phase of study with the ARM mobile facility at Shouxian, Anhui, China.
－ 82 －

AMF provides virtually complete description of the stateof the atmosphere, the surface and the boundary layer to help
understand various atmospheric processes. It consists of asuite of in-situ, passive and active remote sensing
instruments to characterize the properties, location andevolution of cloud, aerosol, and meteorological profiles.
Cloud radar observes cloud location, reflectivity, particle
vertical velocity, and velocity distribution above the facility.
Aerosol optical/physical properties and vertical profiles were
measured, together with solar/terrestrial upwelling anddown-welling radiation, surface latent, sensible, and CO2
fluxes and standard surface meteorological variables.
Thanks to the unprecedented operation of 95-GHz cloudradar in China, the vertical structure of clouds is observed forthe first time. The cloud radar data are used to train and test
an algorithm for estimating cloud layers using radiosondedata to detect cloud layers was tested, modified and applied
to the data acquired from May to Dec., 2008 [Zhang J. et al.,2010]. To complement AMF’s comprehensive observations,a station situated at Taihu was substantially enhanced in
instrumentation. Established as an EAST-AIRE baseline
station [Li et al., 2007b], fundamental aerosol, cloud, andradiation variables have been measured continuously since2005. Taihu is surrounded by several large cities in the
Yangtze delta region: Shanghai, Hangzhou, Nanjing, Suzhou
and Wuxi (the closest).
In addition to the two southern sites, the NASA’s
SMART-COMMIT was deployed at two northern sites near
the Gobi desert (Zhangye) and Beijing (Xianghe). SMART
consists of more than 30 remote-sensing instruments
measuring the optical and physical properties of aerosols,
cloud, precipitation and radiaiton, while the COMMIT
contains 20-some in-situ instruments to measure trace-gas
concentrations, fine and coarse particle size, aerosol mass
(PM1, PM2.5 and PM10) and chemical composition. It was
first deployed at Zhangye from April to June, and moved to
Xianghe from June to end of October in northeastern China
to enhance the observations during the Olympic Games.

3. Aerosol Properties and Radiative Forcing
Through the EAST-AIRE, we have obtained (1) the
temporal and spatial distributions of aerosol loading andprecursor gases [Li C. et al., 2007], (2) aerosol single
scattering albedo (o) [Zhao and Li, 2007; Lee et al., 2007;
Chaudhry et al., 2007], (3) aerosol direct radiative effects at
a few sites [Li et al., 2007a, Xia et al., 2007a,b,c], (4)validation results of satellite products [Li et al., 2007c; Mi etal., 2007], and (5) transport mechanisms [Dickerson et al.,2007]. Lee et al. [2010] proposed a new Langley method
to be able retrieve AOD under variable atmosphericconditions that was applied to derive annual mean AOD at
three sites during the 2008 campaign: 0.99, 0.87 and 0.84
respectively for Xianghe, Taihu and Shouxian.
Using ground-based aerosol optical depth [Xin et al.
2007] and single scattering albedos retrieved from a
combination of satellite and ground-based observations [Leeet al., 2007], the first observation-based estimates of aerosol
radiative forcing at the top, bottom and inside of theatmosphere were made across [Li et al. 2010]. On average,
aerosol-induced cooling at the surface is compatible with thewarming in the atmosphere, leading to virtually nil effect for
the atmosphere-surface system.
An important source of sulfate was discovered in springthat has been previously overlooked [Guo et al. 2010]
following sulfur isotope analysis and applying scanningelectron microscope (SEM) imaging technique to thesamples acquired in China. The same aerosol samples wereanalyzed in a chemical lab to study the concentrations ofmajor elements and water-soluble ions at the Xianghe site
[Li C. et al. 2010b], leading to a finding of unexpected highlevel of lead (mean: 0.28 μg/m3) for this non-urban site.
The lead was enriched by over 100 fold relative to the
Earth’s crust.
During the AAF field experiment in the spring of 2008,
three Micro-Pulse Lidar (MPL) systems were deployed innorthwestern China near the major desert source regions.
Analyzing these measurements, Huang Z. et al. [2010]
attribute the observed high-altitude dust layer over Zhangye
to the site’s proximity to dust sources.
Li C. et al. [2010c] investigated several trace gases andaerosol properties were measured during the AAF
deployment from April to June, 2008 at Zhangye, a site
located within the Hexi Corridor in northwestern China.
Logan et al. [2010] linked air-borne dust measurements inthe central Pacific to the dust observed on the ground in
China, a signal of long-range transport in far downstream
regions.

4. The Roles of Aerosols on Climate Changes in China
The global trend of dimming to brightening was also
found in China from 1950s to 2000 Wang and Shi [2010]. To
understand the trend, the spatial and temporal variability ofsunshine duration and total/low cloud cover across China
was examined in Xia [2010]. A significant decreasing trend(>-1%/decade) in total cloud cover was found although aslightly increasing trend in low cloud cover was seen.

－ 83 －

Annual sunshine duration decreases by -1.7% per decade,which is consistent with the results of Kaiser and Qian
[2002].
Given the well-established long-term trend, a key
question was addressed whether current global climate
models can reproduce the trend. Dwyer et al. [2010] foundthat all of the GCMs reproduce the dimming trend over
China before 1990 consistent with the observations, but themagnitude of simulated trends is much less than thatobserved. Yet, models cannot reproduce the brightening
trend after 1990. Long-term (1956 to 2005) declining trendof light precipitation was attributed to increasing aerosol
loading by Qian et al. [2009]. Niu et al. [2010] revealed adoubled increase of winter fog events in central eastern
China during past 30 years where aerosol loading is higher
and experienced more rapid changes than many otherregions. By means of controlled and experimental model(NCAR CCM3) runs, they found that increase inatmospheric aerosols can generate a cyclonic circulation
anomaly to offset the predominant Siberian high-pressure
system and thus reduces the flow of dry and cold air toeastern-central China, more favorable for fog to form.
In western China, previous studies found a decreasing
trend for the dust events [Qian et al., 2002]. The reduced
dust activities are corroborated with increasing precipitation
in western China, as shown in Gu et al. [2010]. In an
attempt to understand the causes for the decadal changes,
they carried out model simulations and concluded thatlight-absorbing aerosols are the primary cause for
precipitation increase which leads to the reductions in dust
storm frequency and intensity [Gu et al. 2010].

5. Conclusions
The sources, distribution, and effects of aerosols over East
Asia, particularly China, have drawn much attention over the
past several years. Thanks to the intensive observation
campaigns as outlined here, significant progress has been
made towards better understanding of various aspects of theEast Asian aerosols, including their spatiotemporalvariations, optical and microphysical characteristics, andchemical composition of both anthropogenic and natural
aerosols, enabling the estimation of the aerosol radiative
effects. These coordinated new measurements also allow for
more in-depth investigations into the key processes
governing aerosol-climate interactions, long-range transportand evolution of aerosols, the interaction of various aerosol
species, and the source-receptor relationship on regional
scales. The data and knowledge learned from the field

experiments have been proven highly valuable for
conducting regional and global model simulations to address
the climatic impact of East Asian aerosols on regional andglobal climate.

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－ 89 －Aerosol Optical Characteristics Observed by Sky Radiometer over Loess Plateau in China
during 2009
Bi J. R.1, J. P. Huang1, Y. Z. Liu1, Z. W. Huang, G. Y. Shi2, and T. Takamura3
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education and College of Atmospheric Sciences,Lanzhou University, Lanzhou, 730000, PRC
2
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG),
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, PRC
3
Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522,
JAPAN

1

Abstract
Aerosol optical characteristics observed by a POM-01 sky radiometer over Loess Plateau in China during theperiod from March 15th to October 31th 2009. A good agreement was implemented by the improved Langleymethod. The measured and retrieved AOD from the PREDE sky radiometer are very consistent with the measured
by Cimel sunphotometer’s results. We find that the daily average values of AOD500 and Ångström exponent ( α )both show very large day-to-day variations, especially during the springtime. The relationship between AOD500
and Ångström exponent shows obvious negative correlation during the whole period. Dust aerosol particles are
dominant during springtime at SACOL, and fine mode particles are predominant during summertime due to theimpact of the rainy season.
Keywords : Aerosol optical characteristics, Loess Plateau, improved Langley method, Ångström exponent*Corresponding Author: Mr. Jianrong Bi，email: bijr@lzu.edu.cn
Brief instruction of the author: Jianrong Bi, Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, P.R.China.
(o):+86-931-5292611, E-mail: bijr@lzu.edu.cn. Now specializing in aerosol-cloud-radiation interaction with theclimate change.

1. Introduction
Aerosol particles can scatter and/or absorb shortwave andlong-wave radiation in the atmosphere, and then play animportant role in the global and regional climate (Huang etal., 2006a,b; Li et al., 2007). There was extensive research to
been conducted in climatic effects of aerosols, these studies
are vital to understand the characteristics and variations ofaerosol and we can evaluate more accurately the effects ofaerosols on global and regional climate change in the future.
However, aerosol radiative forcing and their climatic effects
still contain considerable uncertainties due to both aerosol
loading and properties exhibit great spatial and temporalvariations (Intergovernmental Panel on Climate Change,2007). Satellite remote sensing and ground-based
observations are two effective ways to understand accurately
knowledge of aerosol loading and properties. Satellite
retrievals need some assumptions and using ancillarymeteorological data which might cause errors in the results(Takamura et al., 2009). Comparing with satellite remotesensing, ground-based measurements is more accurate and it
can improve the climate model, validation of space-borne
observations as well as (Huang et al., 2008).
The SKYNET is a well known ground-based observation
network to understand aerosol-cloud-radiation interaction in
the atmosphere (Takamura et al., 2009). The main
instruments consist of a sky radiometer and radiation
instruments such as a pyrenometer and pyrgeometer.
SKYNET operates sky radiometers in the Asia-Pacificregion for many years. The objective of this work is to
examine aerosol optical characteristics over Loess Plateau in
Northwester China based on continuous spectral solar direct
irradiance and scattering radiance measurements by a sky
radiometer during March 15th to October 31th, 2009.

2. Instrument and measurement
A sky radiometer (also known as aureolemeter) Model
POM-01, produced by PREDE Co. is deployed to SACOL
Observatory (Lat: 35.95oN, Lon: 104.13oE, Alt: 1970m)－ 91 －

over Loess Plateau in Northwestern China since March 15th
2009. SACOL is a rural site and located on the top ofTsuiying mountain, which is about 50 km southeast ofLanzhou city at the southern bank of Yellow River (Huang
et al., 2008). The detailed descriptions of the instrument anddata acquisition procedures were given by Aoki et al. (2003).
The radiometer takes measurements of the solar direct
irradiance and sky scattering radiance distributions and has
seven interference filters. And we just utilize five
wavelengths (400, 500, 675, 870, and 1020 nm) to retrieved
aerosol optical properties.

3. Data inversion
The aerosol optical characteristics (including aerosol
optical depth, Ångström exponent, volume size distribution,
single scattering albedo, normalized phase function,complex refractive index of real and imaginary part) can be
retrieved by using the “SKYRAD.pack Ver. 4.2” inversion
algorithm developed by Nakajima et al. (1996). And a cloud
screening method developed by Khatri et al. (2009) was
employed firstly to eliminate the cloud contamination data
and artificial observations. An important features of theinversion algorithm is that an improved Langley plot method
has been used to calculate the calibration constants V0 of
instrument (Nakajima et al., 1996; Boi et al., 1999) in theequation, V=V0×exp(-m× τ ), where V, m and τ represent
the measured radiance, air optical mass and total extinction
optical depth respectively. In the normal Langley method,
the calibration constants are obtained by performing alogarithmic plot of the optical mass (m) versus measured
solar direct irradiance when assuming that the aerosol opticaldepth remains stable during the whole calibration period.
However, this is not satisfied at most locations and undermost situations. Accordingly, an modified Langley method(MLM) was been developed (Tanaka et al., 1986), in which
temporarily variable atmospheric turbidity was taken into
account. Nakajima et al. (1996) extended Tanaka’s method(MLM). Firstly, an inversion with only forward scattering
intensity data (scattering angle: 3～30o) is performed. Thenderived temporarily variable aerosol optical depths (AOD)
are multiplied by the corresponding air optical mass (m) andused to obtained values of the abscissa for a V-(m×τ a )scattering graph. Finally, the calibration constant is obtained
through a linear fit of the data points. More accurate
calibration is expected since this extension allows therelative size distribution to vary with time. Hence, MLM isutilized in the study of calibrating the sun/sky radiometer.

4. Results
4.1. Calibration result of radiometer
Figure 1 illustrates an example of calibration on May 6th
2009 at SACOL. There was a large difference between thecalibrations in the morning and the afternoon when normal
Langley method was used (see Fig. 1a). Obviously, it was
artificial since the calibrations were carried out at the sameday, so the similar results should be derived. The good
agreement was performed when improved Langley method
was employed as shown in Figure 1b. The time series ofcalibration constants on the basis of MLM is depicted in

Figure 1. Calibration results obtained by using the normal
Langley (left) and improved Langley (right) methods basedon radiometer measurements on May 6 2009 at SACOL.
Figure 2a. indicates that the variations of V0 at five
wavelengths keep stable during the 2009. Figure 2b
illustrates the corresponding monthly averaged values of V0,
which are obtained by taking running and smooth of thetime series data. And there is a corresponding V0 value foreach wavelength of each month to simulate aerosol opticaldepths.

－ 92 －

4.3. Mean daily variation
Figure 4 shows the daily mean variations of aerosol
optical depth at 500 nm (AOD500) and Ångström exponent( α ) from the period of March 15th to October 31th 2009 at
SACOL. The daily average values of AOD500 and Ångström
exponent ( α ) both show very large day-to-day variations,especially during the springtime. It is also obvious that when
the daily mean values of AOD500 are large, the daily
averaged values of Ångström exponent are small, and viceversa.
Figure 2. Time series (a) and monthly averaged (b) of
calibration constants obtained by MLM. Overall average ateach wavelength in parentheses in (b).
4.2. Inter-comparison with sunphotometer’s result
In order to confirm the accuracy of inversion results
obtained from sky radiometer, we compare between theaerosol optical depths derived from sky radiometer andsunphotometer. Figure 3 delineates a comparison between
the AOD measured by a Cimel sunphotometer and themeasured and retrieved AOD from the PREDE sky
radiometer on April 7th 2009 at SACOL. The results showthat AODs derived from PREDE sky radiometer and Cimel
sunphotometer are very consistent.

Figure 4. Mean daily values of aerosol optical depth at 500
nm (AOD500) and Ångström exponent ( α ) during 2009 at
SACOL.
4.4. Relationship between Ångström exponent versus
AOD500Figure 5 depicts the relationship between Ångström
exponent vs. AOD500 during spring and summer 2009 at
SACOL. There is a significant feature that the relationshipbetween AOD500 and Ångström exponent shows obvious
negative correlation during the whole period. Dust aerosol
particles are dominant from March to June at SACOL, andfine mode particles are predominant form July to August due
to the impact of the rainy season.

Figure 3. A comparison of AOD between sky radiometerand sun photometer at 440 nm (a) and 675 nm (b) on April
7th 2009 at SACOL－ 93 －

Figure 5. The relationship between Ångström exponent
versus AOD500 during spring (a) and summer (b) 2009 at
SACOL.

5. Conclusions and Discussions
Solar direct irradiance and sky scattering radiancemeasurements by a sky radiometer have been utilized toretrieve aerosol optical properties during 2009 over Loess
Plateau in China. A good agreement was implemented by
the improved Langley method. The measured and retrieved
AOD from the PREDE sky radiometer are very consistent
with the measured Cimel sunphotometer’s results. The daily
average values of AOD500 and Ångström exponent ( α ) bothshow very large day-to-day variations, especially during thespringtime. The relationship between AOD500 andÅngström exponent shows obvious negative correlation.
Dust aerosol particles are dominant during springtime at
SACOL, and fine mode particles are predominant during
summertime due to the impact of the rainy season.
Acknowledgements
SACOL was sponsored by Lanzhou University through
985 Program. We greatly appreciate Prof. Takamura and Dr.
Khatri (CEReS, Chiba Univ., Japan), as well as Prof. G. Y.
Shi’s (LASG, IAP, CAS, China) support. We are grateful ofProf. Nakajima (CCSR, Tokyo Univ., Japan) for providing
SKYRAD software. Also thanks for Dr. Z. Z. Wang
(AIOFM, CAS, China). Thank you for all the people whoare contributed to this job.
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－ 94 －

Characteristics of Dust Aerosol derived from sky-radiometerover Loess Plateau of Northwest China
Jianrong Bi1,Yuzhi Liu1, Jianping Huang1, Guangyu Shi2, Tamio Takamura3, Zhong wei Huang1,
Pradeep Khatri3, Jinsen Shi1, Tianhe Wang1, Xin Wang1, Beidou Zhang1
1
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education and College ofAtmospheric Sciences, Lanzhou University, Lanzhou, 730000, PRC
2
LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, PRC
3
Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Abstract
This study investigates the characteristics of dust aerosols over Loess Plateu of Northwest China using
observation data collected in this region during 2009 and 2010. As the first part of the research, comparison ofaerosol optical thickness (AOT) and single scattering albedo (SSA) measured by two independent instruments(SKYNET sky radiometer and AERONET sun photometer) is performed. We find very good agreement of AOT,
whereas SSA from the former instrument is systematically higher than the latter on. The possible reasons for suchdifferences are discussed. As the second part of the study, we study the characteristics of dust aerosols in thisregion. We find that strong dust events can carry high concentration of anthropogenic aerosols, such as black
carbon (BC) aerosol, during their transport from the source region. This finding may provide some clues to justify
absorptive Asian dust aerosols observed in the Pacific Ocean region in the past studies. The study also suggeststhat significant dust aerosols may exist in the atmosphere of this region even during no dust event days.
Keywords: dust aerosol, aerosol optical thickness, BC aerosol
1. Introduction
Aerosols are known to alter the atmospheric heat budget
and climate by their direct (scattering and absorption of solar
radiation) and indirect (modification of microphysical andoptical properties of clouds). Despite the importance ofaerosols on climate change, the roles of aerosols on climatechange phenomenon are still not well understood. This is
primarily due to short life time and variability of aerosols on
space and time. In order to improve our understanding
regarding aerosol effects on climate change, it is necessary tocollect information of aerosols of various origins.
Among several types of aerosols, the characteristics ofdust aerosols are still subject of debate (e.g.,Nakajima et al.
2007). It is likely that dust aerosols have different opticalcharacteristics depending on geographical location.
Moreover, when dust aerosols, originated from arid andsemi-arid areas, transport over long distances, they mix withanthropogenic aerosols. Such mixed aerosols can haveimportant effect on both regional and global climate. The
observation data collected near the source region of dust
aerosols may provide valuable clues to solve climatic effectsof such mixed aerosols. As the semi-arid region ofnorthwestern China is close to the Taklamakan and Gobi
deserts, data collected at this region are of great importanceto study several important aspects of dust aerosols. In viewof this recognition, this study is designed to understand thecharacteristics of dust aerosols over Loess Plateau ofNorthwest China.

2. Observation site and instrumentation
Sky radiometer (Model POM-01, Manufacture: PREDE
Co. Ltd. Japan) has been operated at Semi-Arid
Climate Observatory and Laboratory (SACOL)(35.0470N,104.1360E) (http://climate.lzu.edu.cn) fromMarch, 2009 as a part of SKYET actives of SKYNET
network (http://atmos.cr.chiba-u.ac.jp). Since SACOL is arural site, and it is located at southeast of Lanzhou city andsouth edge of Gobi desert, it is possible to observe various
types of aerosols, including anthropogenic aerosol, naturaldust, relatively background aerosol depending on theatmospheric condition and route of air masses. Due to thespecial geographical location and good infrastructure, it has
become a super site for atmospheric study. In order to further
understand the characteristics of dust aerosols in moredetail, POM-02 sky radiometer was operated during an
intensive field observation conducted from 20 April to 20
June, 2010 at Minqin Desert Control Research Institute(38.6070N,102.9590E). This study mainly uses datameasured by such sky radiometers. In addition, we also used
black carbon (BC) mass concentration measured by－ 95 －

aethalometer (Model AE-31; Manufacturer: Magee
Scientific)
The detailed description of the sy radiometer instrument
and data retrieval procedures can be found elsewhere (e.g.,
Nakajima et al., 1996; Khatri and Takamura 2009). In brief,
the instrument measures spectral direct solar irradiance anddiffuse sky irradiances at predefined scattering angles. Suchmeasured data were inverted using SKYRAD.PACK(version 4.2) software (Nakajima et al., 1996) andcalibration constants obtained from improved methods ofNakajima et al. (1996). The retrieved parameters are aerosol
optical thickness (AOT), single scattering albedo (SSA),
and refractive indices at 400nm, 500nm, 675nm, 870nm,
and 1020nm for POM-01 and 340nm, 380nm,
400nm,500nm,675nm,870nm, and 1020nm for POM-02
instrument. In addition, another important retrieved
parameter is volume size distribution at 20 size bins for both
POM-01 and POM-02. Such retrieved parameters were
further processed by using cloud screening algorithm of
Khaitr and Takamura (2009).

3. Results and discussion
3.1. Intercomparison of key aerosol optical parameters
obtained from SKYNET sky radiometer and
AERONET sunphotometer
As SACOL was also equipped with sun photometer of
AERONET network (Holben et al., 2003), which measures
same optical parameters of sky radiometer of SKYNET
network, it was a good opportunity to perform closure studyof key aerosol parameters. Figure 1 shows comparison of
AOTs at four wavelengths (400nm and 440nm, 675nm,
870nm, and 1020nm). As shown in the Figure, thecomparison result is relatively good with data falling around
1:1 line. It is worth to note that the calibration constant for
direct irradiances obtained from Improved Langley (IL)method using surface observation data are used for
SKYNET sky radiometers (Nakajima et al., 1996), whereas
as AERONET sun photometers are relied on calibration
constants determined at high mountains using Normal
Langley (NL) technique. The latter technique isinconvenient and economically expensive. The observed
good agreement in Figure (1) may verify the accuracy of IL
method adopted for SKYNET sky radiometers, and may
suggest that observed AOTs in this study can be interpretedwith much more confidence level. Figure (2) shows
comparison of SSAs obtained from two independent
instruments. In contrast to AOTs, the differences ininstantaneous SSA are considerably large. The possible

reasons for such poor agreement may be explained due tothe difference in retrieval algorithm and measurement
protocol of sky radiances. Since SSA is very sensitive to
AOT, surface reflectance etc., some differences on thoseparameters might have also caused to differ SSA. Despite
such discrepancies, two important conclusions can be drawn
from Figure 2: (i) Using present versions of algorithms,
SSAs from SKYNET sky radiometer are systematicallyhigher than AERONET sun photometer (ii). The averagecolumnar SSAs at all wavelengths are relatively small at thisremote site. This indicates the transportation of lightabsorbing aerosols from urban atmosphere. Taking into
account of the fact that this area is often under the influenceof dust aerosols with higher concentration during spring
season, it can be suggested that such dust aerosols can mix
with strongly light absorbing aerosols transported fromurban areas. Since the surface reflectance of this area isrelatively high, such light absorptive aerosol mixture cantrap radiation energy in the atmosphere more significantly.
As a result, aerosols of this region can have very significant
effect on atmospheric heat budget and regional climate.
Such effects can be numerically evaluated using radiativetransfer model.
3.2. Optical characteristics of aerosols during dust storm
eventsFigure 3 shows aerosol optical thickness at 500nm andAngstrom exponent from 22 April 2010 to 20 June 2010 at
Minqin observation site. During this observation period,
frequent heavy dust events were observed. The observed
dust events are denoted in Figure 3(a). As shown in Figure3(a), during such dust events, AOT at 500nm increased
drastically. Such drastically increased AOTs arecompensated by low values of Angstrom exponent. The
indicates the dominance of coarse mode aerosols incomparison to fine mode aerosols. It is worth to note in
Figures 3(a) and 3(b) that dust events are frequently
observed within the gaps of few days. Another thing to be
noted in Figure 3(a) is the sudden decrease of AOT after dust
event. This suggests that dust aerosols are lifted by strong
winds, suggesting the possibility of reaching such dust
aerosols over the Pacific Ocean and beyond it. As a part ofunderstanding the characteristics of dust aerosols in moredetail, volume size distribution of one typical dust event of
May 3 is compared with relatively clean observation day of
May 22. Table 1 shows geometrical mean radius andvolume concentration of fine and coarse mode aerosols for
those two observation days. As shown in Figure 4 and Table－ 96 －

Figure 1. Comparison of AOTs between sky radiometer of SKYNET network and sun photometer of
AERONET network at (a) 400nm and 440nm, (b) 675nm, (c) 870nm, and (d) 1020nm during 2009.
1 that during the dust event day of May 3, volumeconcentration of coarse mode aerosols largely dominate finemode aerosols. Interestingly, Table 1 shows that coarse
mode aerosols contributed more than 99.5% to total volumeconcentration on dust event day of May 3. Table 1 alsosuggests the presence of relatively large size ranged dust
aerosols on May 3 in comparison to May 22. One
pronouncing feature to be noted in Figure 4 and Table 1 isthat even during the clear day of May 22, coarse mode
aerosols large dominate fine mode aerosols. On this day,coarse mode aerosol contributed nearly 90% to total volume.
This finding indicates that existence of considerable dust
aerosols in the atmosphere even during no dust event casesin this region. Such result was also previously shown by
Takamura et al. (2007) over Jeju island of Korea during
spring season.
Another interesting result observed during intensive
observation period at Minqin site is shown in Figure 5. As
shown in Figure 5, during the typical dust event days, we not
only observed very high concentration of dust aerosols but
also very high concentration of black carbon aerosols. Thevery high concentration of BC mass concentration observedon April 24 at around 20 CST (Chinese standard time) is
even higher than the values of BC mass concentration foundin typical urban atmospheres of the world. This finding has
several important implications for atmospheric researchcommunities. There is a strong debate on light absorptive
capacity of dust aerosols. Kaufman et al. (2001) suggested
that Sarahan are less light absorptive, whereas as Nakajima
et al. (2007) argued that Asian dust are more light absorptive.
The observed result in this study may empathize that fact
light absorbing capacity of Asian dusts are due to the
mixture of BC and dust aerosols during the transportation ofdust aerosols. Taking into account the fact that Minqin isbasically the desert area, if such BC aerosols from urban
areas are transported to desert areas, aerosol mixture can be
more effective to influence the regional climate incomparison to other locations such as urban areas, ocean etc.
Thirdly, such BC aerosols can be uplifted with dust aerosols
up to high altitudes. As a result, such absorptive aerosols athigh altitudes may have interaction with cloud, which may
affect precipitation pattern and efficiency.

－ 97 －

Figure 2. Comparison of aerosol SSAs between sky radiometer of SKYNET network and sun photometerof AERONET network at (a) 400nm and 440nm, (b) 675nm, (c) 870nm, and (d) 1020nm during 2009.

Figure 3. (a) Aerosol opt8ical thickness at 500nm and (b) Angstrom exponent at Minqin site.

Figure 4. Volume size distribution during (a) clear sky day of 2010-05-22 and (b) dust event of 2010-05-03.
－ 98 －

Table 1. Aerosol size distribution parameters for clear sky and dust event days shown in Figure 4.
Obs. dayRm , fine ( m)

R m , coarse ( m)Vm , fine ( m 3 / m 2 )V m , coarse ( m 3 / m 2 )Clear sky day (2010-05-22)
Dust event day (2010-05-03)

0.25
0.17

1.69
3.62

0.003
0.00240.028
0.83

Figure 5. Black carbon mass concentration during dust event day and clear sky day

4. Summary
Dust aerosols originated from the deserts of Asia are
known travel long distances around the world. As a result,
such dust aerosols have strong impacts on not regional sector,
but also on a global scale. This study attempts to characterize
the characteristics of Asia dust aerosols near the source
region over northwest China. The study mainly analyzes
data measured by sky radiometer of SKYNET network. As
the first part of the study, we verified by the results measured
by our key instrument. After that, we discussed about thecharacteristics of dust aerosols by using intensive
observation data. By using collocated measurements of
AOTs from SKYNET sky radiometer and AERONET sun
photometer, we discussed that onsite calibration protocol
adopted for sky radiometer of SKYNET network is not only
convenient and less expensive, but the results are also withreasonably high accuracy. In contrast to AOTs, SSAs from
two independent instruments are systematically different.
The possible reasons for such discrepancies are highlighted.
During the intensive observation period over Minqin in
between 20 April to 20 June 2010, we observed frequent
dust events with the time gap of few days. Those dust eventswere associated with very high AOT, low Angstrom
exponent and dominant coarse mode aerosols. For the

typical dust event day of May 03, we observed that coarse
mode aerosols contributed more than 99.5% of total volume.
For the observation days when dust events are not reported,
we even observed around 90% of total volume as thevolume of coarse mode aerosols. This suggests the existenceof dust aerosols in considerable amount in the atmosphereeven during no dust storm event. The most interestingfinding of this study is the association of extremely high BC
mass concentration during strong dust event cases. The
observed BC mass concentration during strong dust eventcase is even higher than values over typical urban
atmospheres of the world. The study discusses that such dust
aerosols mixed with absorbing BC aerosols can have several
important implications on our atmosphere. This also may
help to shed light on unsolved mysteries of Asian dust
aerosols.

References
Kaufman, Y. J., D. Tanre, O. Dubovik, A. Karnieli, L. A.
Remer, 2001: Absorption of sunlight by dust as inferredfrom satellite and ground‐based remote sensing, Geo.
Phys. Res., 28, 1479-1482.
Khatri, P., and T. Takamura, 2009: An algorithm to screen
cloudaffected data for sky radiometer data analysis, J.

－ 99 －

Meteor. Soc. Japan, 87, 189−204.
Nakajima, T., G. Tonna, R. Rao, Y. Kaufman, and B.
Holben,1996: Use of sky brightness measurements fromground for remote sensing of particulate polydispersions.
Appl. Opt.,35, 2672−2686.
Nakajima, T., S. Yoon, V. Ramanathan, G. Shi, T. Takemura,A. Higurashi, T. Takamura, K. Aoki, B. Sohn, S. Kim, H.
Tsuruta, N. Sugimoto, A. Shimizu, H. Tanimoto, Y. Sawa,
N. Lin, C. Lee, D. Goto, and N. Schutgens, 2007:
Overview of the Atmospheric Brown Cloud East AsianRegional Experiment 2005 and a study of the aerosol
direct radiative forcing in east Asia, J. Geophys. Res., 112,
D24S91, doi:10.1029/ 2007JD009009.
Takamura, T., N. Sugimoto, A. Shimizu, A. Uchiyama, A.
Yamazaki, K. Aoki, T. Nakajima, B. J. Sohn, and H.
Takenaka, 2007: Aerosol radiative characteristics at
Gosan, Korea, during the Atmospheric Brown Cloud East
Asian Regional Experiment 2005, J. Geophys. Res., 112,
D22S36, doi: 10.1029/2007JD008506.

－ 100 －

Influences of aerosol optical thickness and single scattering albedo on the dimming andbrightening in Japan
Rei Kudo1, Akihiro Uchiyama1, Akihiro Yamazaki1, Tomonori Sakami1, and Osamu Ijima2
Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan, reikudo@mri-jma.go.jp
2
Chief of Radiation section, Atmospheric Environment Division, Global Environment and Marine Department,
Japan Meteorological Agency, 1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan
1

Abstract
Surface solar radiation at widespread locations decreased until the mid-1980s (global dimming), andincreased after the decline (global brightening). Since the transition is seen under both cloudy and clear skies, thechange in aerosols is one of the dominant contributors. The radiative effect of aerosols at the surface isapproximately determined by optical thickness and single scattering albedo. Long-term variations of both optical
thickness and single scattering albedo are necessary to evaluate the influences of aerosols on global dimming andbrightening. We have developed the method to estimate optical thickness and single scattering albedo from direct
and diffuse irradiances measured by ground-based broadband radiometers. In the estimation, Junge size
distribution with a fixed shaping parameter is assumed. A scaling parameter of the size distribution and an
imaginary part of the refractive index are optimized to direct and diffuse irradiances. Optical thickness and single
scattering albedo are calculated from these optimized parameters. The method was validated with the sky
radiometer retrievals. The estimated optical thickness and single scattering albedo at 500nm agreed well with thesky radiometer retrievals. The developed method was applied to hourly accumulated direct and diffuse irradiances
under clear skies from 1975 to 2008 at Tsukuba, Japan. Optical thickness increased until the mid-1980s, thendecreased until the late 1990s, and was almost constant in the 2000s. Single scattering albedo was about 0.8 until
the late 1980s, then increased, and was about 0.9 since the mid-1990s. The surface solar radiation calculated fromestimated optical properties indicated clear dimming and brightening. The magnitude of the brightening was
12.7Wm-2, of this, 8.3Wm-2 was due to a decrease in optical thickness, and the remaining 4.4Wm-2 was due to anincrease of single scattering albedo.
Keywords : Aeorosol, Surface solar radiation
1. Introduction
Surface solar radiation at widespread locations decreased
until the mid-1980s (global dimming), and then increased(global brightening). Since the transition is seen under boththe clear and cloudy sky conditions, the change in aerosol
optical properties is one of the dominant contributors. The
radiative effect of aerosols at the surface is approximately
determined by optical thickness and single scattering.
Long-term variations of both optical thickness and single
scattering albedo are necessary to evaluate the influences ofaerosols on the global dimming and brightening.
The aerosol turbidity coefficient has been obtained fromthe broadband pyrheliometer, but single scattering albedo
has not been obtained. In this study, we developed themethod to estimate both optical thickness and single
scattering albedo from broadband pyrheliometer andpyranometer. Using this method, the variations of optical
thickness and single scattering albedo from 1975 to 2008 at

Tsukuba, Japan were evaluated. Furthermore, the influencesof them on the surface solar radiation were investigated.

2. Methods
Estimation of optical thickness and single scattering
albedo are based on the parameter optimization to themeasurements (broadband direct and diffuse irradiances).
This method requires the forward calculation of the direct
and diffuse irradiances at the surface from the aerosolparameters. The real part of the refractive index of aerosols
is fixed at 1.52, and the imaginary part is estimated. The size
distribution is assumed to be a Junge distribution, which isconsist of a scaling parameter and a shaping parameter. The
shaping parameter is related to Angströme exponent, which
is usually obtained from the sunphotometer measurements,
but it does be obtained from the broadband pyrheliometer.
Therefore, the shaping constant is fixed at 3.3, which is anormal value of the continent-urban origins. Two parameters－ 101 －

are optimized to the direct and diffuse irradiances by theGauss-Newton method.
The developed method was validated with the sky
radiometer retrievals. The annual means of optical thickness
and single scattering albedo at 500nm in 2007 were 0.19±
0.08 and 0.93±0.05 for sky radiometer, and 0.18±0.08 and
0.92±0.05 for broadband radiometers (Figure 1). Ourestimated optical thickness and single scattering albedo
agreed well with the sky radiometer retrievals.

optical properties indicated more apparent dimming andbrightening than under the cloudy conditions. The
magnitude of the brightening was 12.7 Wm-2, of this, 8.3
Wm-2 was due to a decrease of optical thickness, and theremaining 4.4 Wm-2 was due to an increase of single
scattering albedo.

3. Results
Our developed method was applied to the measurements
under the clear sky conditions from 1975 to 2008 at Tsukuba,
Japan (Figure 2). The optical thickness increased until the
mid-1980s, then decreased until the late 1980s, and was
almost constant in the 2000s. The single scattering albedo
was about 0.8 until the late 1980s, then increased, and was
almost 0.9 since the mid-1990s.
Using the estimated optical properties, the surface solar
radiation under the clear sky conditions from 1975 to 2008
were calculated (F1 in Figure 3). The surface solar radiation
under the clear sky conditions indicated more apparent
dimming and brightening changes than the surface solar
radiation measured under the cloudy sky conditions (F2).
Therefore, the dimming and brightening at Tsukuba may be
due to the changes in aerosol optical properties, not clouds.
To evaluate only the influence of optical thickness on thesurface solar radiation, the surface irradiances (F1’) were
calculated with an average of the imaginary part of therefractive index in 1975-2008. The increase of F1 was about
12.7 Wm-2 from 1980-1985 to 1995-2000, of this, 8.3 Wm-2
was due to a decrease of optical thickness. The influence of
single scattering albedo was defined by the differences
between F1-F1’ and was about 4.4 Wm-2. The brightening
under the clear sky conditions was due to both optical
thickness and single scattering albedo.

Fig. 1 Optical thickness (circles), single scattering albedo(squares), and asymmetry factor (triangles) estimated from asky radiometer (filled) and broadband radiometers (open).

Fig. 2 Estimated optical thickness (circles) and single
scattering albedo (squares) from 1975 to 2008. Opensymbols are daily means and closed symbols are annual
means.

4. Summary
The method to estimate aerosol optical thickness andsingle scattering albedo from the broadband direct anddiffuse irradiances was developed, and was applied themeasurements under the clear sky conditions at Tsukuba
from 1975 to 2008. Optical thickness increased until the
mid-1980s, then decreased until the late 1990s, and was
almost constant in the 2000s. Single scattering albedo was
low value until the late 1990s, then increased, and was
almost constant in the 2000s. The surface solar radiation
under the clear sky conditions calculated from the estimatedFig. 3 Surface solar irradiances under the clear sky
conditions calculated from estimated optical properties (F1),
the irradiances measured under the cloudy sky conditions(F2), and the irradiances measured under the all sky
conditions (F3). F1’ is the irradiances calculated with anaverage of the imaginary part of the refractive index. F0 is
the irradiances calculated without aerosols.

－ 102 －

Spatial contrast of Asian dust concentration between
windward and leeward of mountains in Japan
during 1–2 April 2007
Hayasaki,1 M., M. K. Yamamoto1 , A. Higuchi1 , A. Shimizu2 , I. Mori2 , and M. Nishikawa2
1: Center for Environmental Remote Sensing, Chiba University, Japan
2: National Institute for Environmental Studies, Japan

1. Introduction
During 31 March–2 April 2007, a massive dust episodeobserved over East Asia. The maximum PM10 concentration exceeded 1000 µg m−3 in Seoul, Korea (Park et al.
2010) and 700 µg m−3 in Banryu, western Japan (Yumimoto et al. 2008). The dust plumes brought extremely
high concentration of dust, however, spatial and temporalvariations of dust concentrations showed complex featuresin Japan.
Takahashi et al. (2010) indicate that delaying dusttransport in Kanto was caused by a stably stratiﬁed layers
near the ground. Tsunematsu et al. (2009) also suggestthat surface-based stable layer prevent a downward transport of elevated dust to the ground level. However, theirconclusion have been deduced from limited observational
data and not fully discussed other processes. As shown in
Han et al. (2004), wet deposition process play an important role for scavenging dust particles in a downstream
area.
The main objective of this paper is to identify major
cause(s) for delaying dust transport in Kanto, Japan. To
show regional-scale characteristics about spatial distributions and its temporal changes of dust concentrations, we
use operational air-quality monitoring data in Japan.

ss
ia
Ru
a

Hokkaido Is.

pa

n

Se

40˚N

Ja

N. KoreaW

H

S. Korea

T
Y

Honshu Is.

SeaF

Shikoku Is.

hina

30˚N

Pacific Ocean

Kyushu Is.
500 km

130˚E

0 km

elevation
500

1000

1500

140˚E

keywords: Asian dust, indirect transport, temperature
inversion

China

E. C

Abstract
During massive Asian dust episode for 1–2 April
2007, mass concentration of suspended particulate matter (SPM) rapidly began to increase in the morning on
1 April from coastal side of the Japan Sea. For the daytime 1 April, while rapid SPM increase and extreme highconcentrations observed in Hokuriku and Tohoku, SPM
concentrations in Kanto remained low level and increased
from late afternoon. Ground lidar observation and rawinsonde sounding in Hokuriku show that the top heightof dust layer (∼ 2 km) approximately correspond to thebase of temperature inversion. In Kanto, SPM increasebegan from east coast and then it advanced westward after 15 LT with easterly wind. The CloudSat/CPR andCALIPSO/CALIOP observations show that clouds overmainland Japan and the Japan Sea was found in theupper-level ( > 6 km). Continuous meteorological observations show that cloud condensation and rainfall was notobserved over mountains for the daytime 1 April. These
results suggest that the temporal delaying of SPM increase in Kanto was caused by an indirect dust transport.

Fig. 1: Geographical locations of observatories. Square symbols: upper-air sounding sites (Fukuoka, Yonago, Wajima,
Tateno (Tsukuba)); Star: Lidar observatories (Toyama andTsukuba). ”H” represents EANET site (Happo).

2. Data
Aerosol mass concentrations of SPM observed about
1300-site in Japan. Due to the diﬀerence of cut-oﬀ property of particles between PM10 (50% cut-oﬀ at 10 µm) andSPM (100% cut-oﬀ at 10 µm), SPM monitoring value isapproximately PM7 (Minoura et al. 2006).
The meso-scale model of grid point value (GPV-MSM)
data distributed by the Japan Meteorological Agency(JMA) is used in this study. The pressure level data have
3-hourly temporal interval and 0.1◦ × 0.125◦ horizontal
resolution around Japan (22.4◦ – 47.6◦ N, 120◦ – 150◦ E).
To identify a vertical proﬁles of cloud and dust
particles, we use a combined products between
CALIPSO/CALIOP and CloudSat/CPR that is provided by the Research Institute for Applied Mechanics(RIAM), Kyushu University (hereafter RIAM-CALIPSO
products). The classiﬁcation procedure for cloud particle
types is fully documented in Yoshida et al. (2010).

－ 103 －

40˚N

21 LT 31Mar2007

SPM; 925V

40˚N

20 m/s
09 LT
01Apr2007

SPM; 925V

40˚N

5 m/s

38˚N

36˚N

20 m/s
21 LT
01Apr2007

SPM; 925V

40˚N

5 m/s

SPM; 925V

20 m/s
5 m/s

5 m/s

SPM ≥ 100 µg/m3

SPM ≥ 100 µg/m3

SPM ≥ 100 µg/m3

80 ≤ SPM < 100

80 ≤ SPM < 100

80 ≤ SPM < 100

80 ≤ SPM < 100

60 ≤ SPM < 80

60 ≤ SPM < 80

60 ≤ SPM < 80

60 ≤ SPM < 80

SPM < 60
38˚N

20 m/s
15 LT
01Apr2007

SPM ≥ 100 µg/m3

SPM < 60
38˚N

Rain ( ≥ 1 mm/hr)

SPM < 60

SPM < 60

Rain ( ≥ 1 mm/hr)Rain( ≥ 1 mm/hr)

38˚N

Rain ( ≥ 1 mm/hr)

A

orography (m)orography (m)orography (m)

1500

1500

1500

36˚N

36˚N

800

B

height (m)
1500

36˚N

800

800
800

0

138˚E

138˚E

136˚E

142˚E

138˚E

0

140˚E

142˚E

136˚E

34˚N

140˚E

142˚E

138˚E

C

0

34˚N

140˚E

142˚E

140˚E

136˚E

34˚N

136˚E

0
34˚N

Fig. 2: Hourly SPM concentration in central Japan at (a) 21 LT on 31 March, (b) 09 LT, (c) 15 LT, and (d) 21 LT on 1 April
2007. Vectors show a 925-hPa horizontal wind of the GPV-MSM data. Rainfall intensity (light blue square) is obtained by
AMeDAS observatories. Gray shade show a topography of GPV-MSM.
Ambient environmental observattion site

2 m s-1

SPM (µg m-3)

10 m s-1

0

10

20

rain (mm/hr)

Heavy dust event for 20070331_00 - 20070404_00

We also use a non-spherical (dust) extinction coeﬃ(a) Hokuriku (57-site)
500
cient of ground lidar observations which is maintained
400by the National Institute for Environmental Studies,
300
Japan. The algorithm and estimation scheme for separating spherical/non-spherical particles is referred from200
Ambient environmental observattion site
Shimizu et al. (2004).
100
0

Heavy
dust eventfor 20070331_00- 20070404_00
Mar 31
Apr 01
Apr 02
Apr 03
3. Results(b) Tohoku (60-site)
10 m s2ms
Figure 2 show a horizontal distribution of SPM con500
0centrations in central Japan (Honshu island). SPM con400
10centrations begin to increase in the morning April 1 over300
20
western Japan and coastal area along the Japan Sea after
200
frontal rain (Figs. 2a, b). At 09 LT (Local time. 9-hour
Ambient environmental observattion site
100
advancing from UTC) on 1 April (Fig. 2b), the maxi−30mum SPM concentration inside the area is 209 µg m
Heavy
dust eventfor 20070331_00- 20070404_00
Mar 31
Apr 01
Apr 02
Apr 03in Hokuriku (Region A shown in Fig. 2d). By 15 LT on
(c)
Kanto
(304-site)
2007-04-01T19:00:00
175 [ 134 - 192]
10
m
s
2
ms
500
0
1 April, high SPM sites (SPM ≥ 100 µg m−3 ) advance2007-04-01T17:00:00
172 [ 147 - 191]
2007-04-01T18:00:00 171 [ 140 - 190]400
eastward extending from western Japan, Hokuriku, and2007-04-01T20:00:00 163 [ 126 - 191]10
2007-04-01T16:00:00 160 [ 135 - 181]
southern Tohoku (Region B). On the other hand, high300
2007-04-01T21:00:00
145 [ 86 - 180]20
2007-04-01T15:00:00 133 [ 109 - 164]
SPM sites are not observed in Kanto (Region C). At 21
200
2007-04-01T11:00:00
126 [ 57 - 175]
LT on 1 April, high SPM sites are widely observed in
2007-04-01T14:00:00 125 [ 89 - 181]100
2007-04-01T12:00:00
124 [ 82 - 173]
Kanto (Fig. 2d). The high SPM sites are ﬁrstly observed0
at 19 LT along the eastern part of Kanto and subsequently
Mar 31
Apr 01
Apr 02
Apr 03
moved to the inland area under easterly wind condition
2007-04-02T22:00:00 118 [ 83 - 162]
2007-04-02T21:00:00 116 [ 80 - 155]
(not shown).
Fig.
3: SPM time series in (a) Hokuriku, (b) Tohoku,
2007-04-02T19:00:00 115 [ 77 - 167]
2007-04-02T17:00:00[ 70 - 164] 31 March – 3 April.
and(c) Kanto115during
Solid andFigure 3 show an areal SPM concentrations in Region
2007-04-01T16:00:00 113 [ 86 - 142]
dashedlines
shows
median,75and25-percentileof SPM
2007-04-02T16:00:00 112 [ 66 - 161]
A–C. The median, maximum, and 25- and 75-percentile
2007-04-02T20:00:00 111[ 79 - 163]
concentrations,
respectively.
Star
mark:
areal
maximum
concentrations are determined in hourly by using all ob2007-04-02T18:00:00 111 [ 72 - 163]
concentration.
Hourlyand2007-04-01T15:00:00 111[ 88 - 141]rain intensity (downward bar)
M. HAYASAKI
servatories inside the area. Hourly rainfall intensity and surface
2007-04-01T14:00:00
109 [ 83 - at135]JMA observatory are also plotted.
wind (barb)
surface wind at the nearest meteorological observatories
from lidar site (Toyama, Sendai, and Tateno in Region
2007-04-02T22:00:00 105 −3- 123]
A, B, and C, respectively) are also plotted. In Region A around
30 µg 103m [[ 87
the daytime and increased over
2007-04-02T23:00:00
86 - during
125]−3
101 [ 85 - 122]
(Fig. 3a), median SPM rapidly increase from 36 µg m−3 602007-04-03T00:00:00
µg
m
after
20
LT
(Fig.
3c).
2007-04-02T21:00:00 101 [ 86 - 122]
(08LT) to 119 µg m−3 (10LT) within two hours. The max2007-04-03T02:00:00 100 [ 83 - 117]
Figure4
show
atime-heightsection of dust extinc2007-04-02T20:00:00
98 [ 82 - 115]
imum median SPM recorded 175 µg m−3 at 19 LT and tion
2007-04-03T01:00:00
82 - 122]
coeﬃcient97 [ atToyama and Tsukuba. Contour lines
2007-04-03T03:00:00
95 [ 80 - 113]
decreased gradually after that. The temporal diﬀerence show
the potential
temperature at the nearest
grid point
2007-04-02T19:00:00
93 [ 79 - 113]
M. HAYASAKI
between the end time of frontal rain and beginning time obtained
2007-04-03T04:00:00[ 70 - 110]
from92GPV-MSM.
At Toyama (Fig. 4a), whichof SPM increase is approximately 10 hours in Region A. locate in a coastal side of the Japan Sea, we can identify
In Region B, which locate in approximately 300 km east two dust layers in the daytime 1 April. The upper one
of Region A and 200 km north of Region C, the median locate above 3 km with weak dust extinction coeﬃcient.
SPM concentrations have also exceeded 100 µg m−3 at 13 The lower one can be clearly identiﬁed below 2 km that
LT and continued to 16 LT. The maximum median SPM potential temperature is colder than 295 K. The dense
in Region B is 113 µg m−3 at 16LT 1 April. The temporal dust layer are identiﬁed below cloud after 03 LT and thendiﬀerence between frontal rain and SPM increase is ap- it come down the ground after 09 LT on 1 April. MeanM. HAYASAKI
proximately 8–10 hours. On the other hand, the median while, two weak dust layers are also identiﬁed at Tsukuba
SPM in Region C have remained low concentration levels (Fig. 4b) in 3–4 km (θ ≈ 310 K) and around 1 km above
-1

-1

-1

SPM (µg m-3)

rain (mm/hr)

SPM (µg m-3)

rain (mm/hr)-1

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2010 Oct 29 10:24:25/home/viper/hayasaki/tex/paper/20101021_CEReS_sympo_Nago/script/timeseries_area_w_meteor.gmt

2010 Oct 29 10:24:27/home/viper/hayasaki/tex/paper/20101021_CEReS_sympo_Nago/script/timeseries_area_w_meteor.gmt－ 104 －

(a) Toyama ( 36.70 N, 137.10 E) : Non-spherical particles(b) Tsukuba ( 36.05 N, 140.12 E) : Non-spherical particles

6000

6000

5000

5000

2000
290 K

3000

1000

1000

0

0
31-March

01-April

02-April

0.2

300 K
0.1

290 K

0.0

31-March(a) Toyama ( 36.70 N, 137.10 E) : Spherical particles

0.3

300 K

0.2

2000

290 K

310 K

310 K

0.3

(km-1)

300 K

300 K

0.4

4000
(km-1)

3000

0.5

0.4

310 K

310 K

4000

altitude (m)altitude (m)

0.5

0.1

0.0

01-April

02-April(b) Tsukuba ( 36.05 N, 140.12 E) : Spherical particles

6000
Fig.
4: Time-height section of dust extinction coeﬃcient6000(color)and θ (contour; 2.5 K interval) at (a) Toyama and 0.5(b) Tsukuba
0.5
Derived from Weather chart CD-ROM of JMA
during18 LT 31
March – 12 LT 2 April 2007. black: cloud,gray: above-cloud.
5000
5000
Vertical profile of θ, θ , θ * profile ; rawinsonde sounding

300 K

300 K

0.4

4000
0.3

3000

(km-1)

3000

0.4

310 K

310 K

310 K
0.3

(km-1)ealtitude (m)altitude (m)e09 LT 01 April 2007 310 K

4000

300 K
on 1 April
in Japan (seeFig. 1). The inversion layers
300 K
(gray
shading)areidentiﬁedby a vertical temperature: 5 m/s
100009 LT 01 April 2007
1000
290 K
lapse rate (∆T /∆z > 0) and its thickness (∆z ≥ 200).
00Strong temperature inversions are clearly found in coastal
31-March
01-April
02-April
31-March
02-April
stations along01-April
the Japan Sea. Thoughbase heights of inversion layers increase with eastward advancing (Fukuoka:
839 m, Yonago: 1405 m, and Wajima (lower layer): 2062m), the inversion base have same potential temperatureapproximately 290 K. The main dust layers are placed
under the inversion base in the side of Japan Sea. Since
the Central Mountains in Japan have 1500–2000 m height,
most of the dust particles can not go across the mountains.
θ, θ , θ [K]Therefore, preventing vertical mixing for the daytime 1
April is not a primary cause for delaying SPM increase in∗
Fig. 5: Vertical profiles of θ (solid), θe (dashed), and θe (dotted)
Kanto.
at four rawinsonde sites at 09 LT on 1 April. Shading represent
temperature inversions (∆T /∆z > 0). Thin inversion layers (∆z ≤Second, we discuss an inﬂuence of wet deposition. In200) are omitted.
the Japan Sea side, westerly wind is dominant in the dust
layer. Since high mountains (approx. 2000 m) in westernand northernpart of Kanto blocked dust contained airM. HAYASAKI
ground (θ ≈ 295 K). Yumimoto et al. (2008) suggest that mass, dust particles may be scavenged by condensation
the upper dust layer is originated from the Taklimakan or rainfall processes along a mountain slope. From visiDesert. Considering a potential temperature of the lower ble satellite image, dense cloud widely covered over Japan
dust layer at Tsukuba, it may be originated from the up- (Fig. 6a). However, precipitation radar and ground-level
rain gauges did not observe rainfall over central Japanper part of the dense dust layer in Toyama
As shown in Figs. 2 and 3, there are two interest- (including Hokuriku and Kanto) during the daytime 1
ing characteristics for SPM concentrations in Kanto: (1) April (not shown). Figure 6b show a vertical proﬁle of
SPM increase delayed compared to the adjacent area, (2) clouds and dust particles. Asian dust, which is categoSPM remained low concentration level during the daytime rized as ”3-D ice” in the RIAM-CALIPSO cloud particle
1 April. The main cause(s) of the spatial and temporal type dataset, locate over the Japan Sea along θ ≈ 290K
variations of SPM concentrations in Kanto will be dis- surface. On the south of Japan, water cloud can be identiﬁed in near-surface level. Height of upper clouds above
cussed in the following section.
mainland Japan and Japan Sea is ranging from 6 to 11km above mean sea level. Due to a ground clutter, Cloud4. Discussion
Sat/CPR can not obtain an eﬀective signal within 1 km
Considering the results mentioned above and previous above ground. Therefore, CloudSat observation is hardstudies, we brieﬂy focused on the following three processes to detect a cloud along mountain slope. Figure 7 showfor occurring a characteristic spatial and temporal varia- that time series of PM , rainfall, wind, and relative hu10
tions of SPM in Kanto: (1) preventing downward trans- midity (RH) at Happo (z = 1850 m). After frontal rainport of dust particles by a stably stratiﬁed layer (Taka- fall, PM increases with time on 1 April. During the10hashi et al. 2010; Tsunematsu et al. 2009), (2) wet depo- daytime, a dry air-mass (RH < 30%) is observed undersition (Han et al. 2004), and (3) indirect transport going westerly-northwesterly wind condition. The dry air conaround the Central Mountains in Japan.
dition is also observed in the other in-situ observatories
Firstly, we examine a possibility of a preventing down- over mountainous area during the daytime (not shown).
ward dust transport from elevated dust layer in Kanto. As These results suggest that wet deposition is not a prishown in Fig. 4a, potential temperature (θ) for the main mary process providing a temporal delay of SPM increase
dust layer at Toyama is cold below θ < 290 K throughout in Kanto during the daytime April 1.
the dust period. Moreover, the main dust layer is cappedby a temperature inversion layer. Figure 5 show vertical
5. Conclusions
proﬁles of θ, equivalent potential temperature (θe ), andDuring the massive Asian dust episode for 1–2 April
saturated equivalent potential temperature (θe ) at 09 LT 2007, SPM concentrations in Kanto showed low-level (∼

2000

290 K

0.2

0.2

0.1

0.1

0.0

0.0

2000

0K
: 2529
m/s

6000

5000

47744
YONAGO
35.43N,133.33E
8m

47807
FUKUOKA
33.58N,130.38E
15 m

47600
WAJIMA
37.40N,136.90E
14 m

47646
TATENO
36.05N,140.13E
31 m

z [m]

4000

3000

2000

1000

0

280

300

320

e

2010 Oct 27 17:56:49

e

340

280

300

320

340

*

/home/viper/hayasaki/paper/submit/20100215_mountain_barrier_01Apr2007/script/upper_sounding/4stn_profiles_1paper_z.gmt－ 105 －

EANET site: Happo ( 36.683N, 137.800E, 1850 m)

Timeseries of meteorological variables and pollutants
Massive Asian dust episode in Japan

Happo ( 36.683N, 137.800E, 1850 m)
600(total rain = 61.5 mm)

2 m/s

10 m/s
0

10

400
20
300

100
80

200

60

40˚N

40

100

RH (%)

PM10 (µg m-3)

500

rain (mm/hr)(a) Aqua/MODIS RGB: 01 April, 200720
0

0
Mar 31

Apr 01

Apr 02

Apr 03Fig. 7: Time series of PM10 (upward bar) and meteorologicalvariables at Happo (36.683◦ N, 137.800◦ E; z=1850m), Japan.
Dashed line: relative humidity (%), downward bar: precipitation intensity (mm/hour), barb: horizontal wind.
30˚N

RIAM-CALIPSO product (CS${yyyy_doy_HHMM}_*.bin; 120m - 19800m; 83-bin; ∆z = 240 m)
No. of (profiles * bins) = 101628 [1882 (30 - 48N) * 54 (sfc - 13.000 km)
140˚E

130˚E

Meridional section of CALIPSO/CALIOP and CloudSat/CPR profile(b) 0435 UTC 01Apr2007
350 K
340 K

12

350 K
340 K

10

tained from the C-type cooperative research project forphotochemical oxidant and particulate matters (NIES,
Japan). We would like to thank Dr. Y. Hagihara and Dr.
H. Okamoto for oﬀering access of the RIAM-CALIPSO
products. The Generic Mapping Tools (GMT; Wessel andSmith 1998) were used for drawing the ﬁgures.

330 K

altitude [km]

M. HAYASAKI

20

82010 Oct 29 11:27:43
REFERENCES/home/viper/hayasaki/tex/paper/20101021_CEReS_sympo_Nago/script/EANET_timeseries_V_RH_prec_SPM.gmt

K

3

0

31

300 K

K

Arao, K., J. Ishizuka, N. Sugimoto, I. Matsui, A. Shimizu, I. Mori,
M. Nishikawa, K. Aoki, A. Uchiyama, A. Yamazaki, H. Togawa
and J. Asano, 2006: Yellow sand dust event on 13 April 2003 overWestern Kyushu, Japan. SOLA, 2, 100–103.

6
320 K

0K

4

310 K

30

0K

2

280 K

45

Minoura, H., K. Takahashi, J. C. Chow and J. G. Watson, 2006: Multiyear trend in fine and coarse particle mass, carbon, and ions in
downtown Tokyo, Japan. Atmos. Environ., 40, 2478–2487.

30

0

K

29

Han, Z., H. Ueda, K. Matsuda, R. Zhang, K. Arao, Y. Kanai and H. Hasome, 2004: Model study on particle size segregation and deposition during Asian dust events in March 2002. J. Geophys. Res.,109, D19205, doi: 10.1029/2004JD004920.

0
30

35

-20

0

40

warm/super-cooled water
3-D ice
2-D ice20

reflectivity [dBZe]

Park, S.-U., A. Choe and M.-S. Park, 2010: Estimates of Asian dust
deposition over the Asian region by using ADAM2 in 2007. Scienceof the Total Environment, 408, 2347–2356.

No. of counts in each particle types

Fig. 6: (a) Aqua/MODIS visible image
on ID:
1 April,2007. (b)(total (type1-6) = 7986)
1: warm water(circle)θ= 710
Vertical proﬁles of radar reﬂectivity ID(color;dBZe),(contour),
2: super-cooled water (cross) = 612
and cloud particle type (symbol; see IDYoshida
et al., 2010) along
ID 3: 3D ice (triangle) = 4524
50˚N
the greenline in Fig. 6a.
ID 4: 2D plate (inverted triangle) = 1763
ID 5: unknown1 (likely ice) (not shown) = 364
ID 6: unknown2 (not shown) = 11
40˚N

Shimizu, A., N. Sugimoto, I. Matsui, K. Arao, I. Uno, T. Murayama,
N. Kagawa, K. Aoki, A. Uchiyama and A. Yamazaki, 2004: Continuous observations of Asian dust and other aerosols by polarization lidars in China and Japan during ACE-Asia. J. Geophys.
Res., 109, D19S17, 10.1029/2002JD003253.
Takahashi, H., H. Naoe, Y. Igarashi, Y. Inomata and N. Sugimoto, 2010:
Aerosol concentrations observed at Mt. Haruna, Japan, in relation
to long-range transport of Asian mineral dust aerosols. Atmos.
Environ., 44, 4638–4644.

120˚E

140˚E

130˚E

40 µg m−3 ) and increased later in the evening under easterly wind condition. The main dust layer in a windward Tsunematsu, N., H. Iwai, S. Ishii, M. Yasui, Y. Murayama and K. Mizutani, 2009: Influence of surface-based stable layer development on
30˚Nof theCentral Mountains is capped by the strong temAsian dust behavior over Tokyo. Boundary-Layer Meteor., 131,
263–275.
perature inversions below 2 km (Fig. 4a and 5). The
capped dust layer are also reported in previous studies Wessel, P. and W. H. F. Smith, 1998: New, improved version of Generic
Mapping Tools released. EOS Trans. Amer. Geophys. Union, 79,(e.g., Arao et al. 2006). Additionally, a dry condition in579.
side
the capped dust layer is hard to form cloud droplets
M. HAYASAKI2010 Oct 29 11:33:49
and rainfall along mountain slope. These results indi- Yoshida, R., H. Okamoto, Y. Hagihara and H. Ishimoto, 2010:
Global analysis of cloud phase and ice crystal orientationcate that temporal delaying of SPM increase in Kanto isfrom Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observation (CALIPSO) data using attenuated backscattering
caused by a indirect transport of low-level dust which isand depolarization ratio. J. Geophys. Res., 115, D00H32,
going around the Central Mountains in Japan. Since thisdoi: 10.1029/2009JD012334.
study is just a case study, the occurrence frequency of an
K., I. Uno, N. Sugimoto, A. Shimizu, Z. Liu and D. M.
indirect transport can not discussed. Further studies will Yumimoto,
Winkler, 2008: Adjoint inversion modeling of Asian dust emission
using lidar observations. Atmos. Chem. Phys., 8, 2869–2884.
be required for using long-term observational data.
/home/viper/hayasaki/tex/paper/20101021_CEReS_sympo_Nago/script/RIAM-CALIPSO_meridional.gmt

Acknowledgements
We thank Dr. Tsunematsu for valuable comments.
The operational air-quality monitoring dataset are ob-－ 106 －

Influence of Spatial Patterns of Future Atmospheric Warming on Asian Dust Emission
Nobumitsu Tsunematsu 1*, Masamitsu Hayasaki 1, Tomonori Sato 2,
Feifei Cui 1, Akihiko Kondoh 1, Hiroaki Kuze 1, Kohei Sakamoto 3
1
Center for Environmental Remote Sensing, Chiba University,
2
Faculty of Environmental Earth Science, Hokkaido University,
3
GPE-Corner, Weathernews Inc.
*
E-mail: tsunematsu@faculty.chiba-u.jp
Abstract
Spatial patterns of future atmospheric warming presented by climate model projections indicate that
atmospheric temperatures of the lower troposphere will considerably increase at high latitudes in the Northern
Hemisphere and also the upper troposphere at low-middle latitudes. In this study, potential impacts of the spatial
patterns of future atmospheric warming on Asian dust emission were investigated by performing numerical modelexperiments with a dynamical downscaling of global-scale climate model (the Model for Interdisciplinary
Research On Climate: MIROC V3.2-hires) projections into regional-scale atmospheric model (the Weather
Research and Forecasting modeling system: WRF V3.1) simulations. After three significant Asian dust outbreaks
were reproduced as the control runs using WRF-Chem, the initial and boundary conditions of the control runs
were updated by adding differences in atmospheric variables between the future climate (2091-2100) and therecent climate (1991-2000) in order to execute the pseudo global warming runs. The results showed that the dust
emission fluxes in the main Asian dust sources (MADSs), i.e., the Taklimakan Desert and the Gobi Desert,
decrease markedly in the future climate condition. The future decreases in the dust emission fluxes would likely
be caused by a combination of the relatively large increases in sea level pressures (SLPs) in MADSs and the small
SLP changes to the north of MADSs, which reduces the meridional SLP gradients between those two areas andconsequently decreases surface wind velocities associated with cold air outbreaks.
Keywords : Global warming, Asian dust emission, Dynamical downscaling, WRF, MIROC
1. Introduction
Climate model projections show spatial patterns of futureatmospheric warming caused by global warming (IPCC
AR4); atmospheric temperatures considerably increase in
the lower troposphere at high latitudes in the Northern
Hemisphere and the upper troposphere at low-middle
latitudes. This leads to future changes in the meridional andvertical atmospheric temperature distributions in the
Northern Hemisphere. The meridional atmospherictemperature gradients are expected to decrease in the lower
troposphere and increase in the upper troposphere.
Atmospheric stabilities are expected to increase at
low-middle latitudes and decrease at high latitudes.
Asian dust phenomenon is one of well-known springtime
atmospheric phenomena in East Asia. Asian dust particles
are emitted mainly from the Taklimakan Desert and theGobi Desert, and then transported to Korea and Japan
mainly by low-pressure systems. The Asian dust emission isfrequently induced by cold air surges originating from therelease of Siberian air masses (e.g., sun et al., 2001).
Therefore, the future Asian dust emission should be

influenced by the remarkable atmospheric warming in thelower troposphere at high latitudes in the Northern
Hemisphere. The future changes in atmospheric stabilities
are also expected to influence the Asian dust emission,
because the dust emission is closely associated withatmospheric stability (e.g., Tsunematsu et al., 2005).
Nevertheless, the number of numerical studies investigating
the relationship between future global warming and Asian
dust emission is still rather small. This study therefore
investigated potential impacts of the spatial patterns of futureatmospheric warming on Asian dust emission by mainly
conducting numerical model experiments.

2. Analysis of Global-Scale Climate Model Output
Output data from the Model for Interdisciplinary
Research On Climate (MIROC V3.2-hires: K-1 model
developers, 2004) were analyzed to obtain the spatial
patterns of future global warming in the atmosphere. The
MIROC model is one of global-scale climate models that arecapable of reproducing past climate and predicting futureclimate. This model has 23 vertical levels and T106

－ 107 －

horizontal resolution (approximately 1.125° x 1.125°).
In this study, the MIROC output data derived from resultsof the 20th Century Climate in Coupled Models (20C3M)
experiment and the 21st century climate projection based on
the Special Report on Emission Scenario (SRES) A1B wereused to obtain “recent climate” for the period of 1991-2000
and “future climate” in 2091-2100, respectively. Using themonthly mean data, differences in atmospheric temperatures,
geopotential heights, and u-v wind components between the
future and recent climate were calculated for each grid of the
MIROC model. Sato et al. (2007) named the differences“Global Warming Monthly mean Differences (GWMDs)”.
Months of the data used for the calculations of the GWMDs
were restricted to March and April when the frequency of
the Asian dust outbreaks is considerably higher than othermonths. The March and April monthly mean data wereaveraged before calculating the GWMDs. The 50°E-130°E
zonal mean GWMDs at latitudes between 20°N and 80°N
were prepared for each vertical level of the MIROC model(Figure 1) to downscale the future changes in the meridionaland vertical distributions of atmospheric temperatures,
geopotential heights, and horizontal winds into dust
simulations conducted by a regional-scale atmosphericmodeling system.

3. Dust Simulations by a Regional-Scale Model
The advanced research version of the Weather Researchand Forecasting (WRF) modeling system V3.1 (Skamarocket al., 2008) was used for numerical experiments on dust.
This modeling system provides an atmospheric chemistrymodel (WRF-Chem V3.1: Grell et al., 2005) that is capableof simulating the regional-scale movements of various gases
and aerosol particles. This study altered source codes of
WRF-Chem to simulate emission, advection, dispersion, anddeposition of soil dust with the Particulate Matter up to 10µm in size (PM10) only, after assuming the PM10 soil dust
to be Asian dust particle.
Prior to performing the main numerical experiments inthis study, a significant Asian dust event that occurred during
the period from March 29 to April 2, 2007 was simulated byusing the WRF-Chem model as the pre-experiment. The
pre-experiment was conducted to confirm whether the
WRF-Chem model could be applied to dust simulations. A
brief outline of this Asian dust event is that the dust particles
were emitted mainly from the Gobi Desert and the
Taklimakan Desert and then transported to a considerablylarge area of East Asia including the megacities, such as
Shanghai, Seoul, and Tokyo (e.g., Tsunematsu et al., 2009).

In the pre-experiment, a calculation domain was given by
160 x 84 horizontal grids centered at 40°N/90°E with thegrid interval of 40 km. The number of the vertical layers was
set at 30. The 6-hourly global analysis data from the
National Centers for Environmental Prediction-FiNaL(NCEP-FNL), which has a 1.0° x 1.0° horizontal resolution,
were used for the initial and boundary conditions. An initialtime of the 72 hours numerical integration was set at 0000
Universal Time Coordinated (UTC) March 29, 2007.
Potential areas of emission of the soil particles were
determined by the U.S. geological survey (USGS:
http://www.usgs.gov) land covers categorized into barren
and sparsely vegetated, shrubland, and grassland. The
particles were emitted from not the ground surface but
the first level of the model in this study. The height of thefirst level is approximately 29 m above ground level (AGL).
The 29 m height difference can be disregarded because thecalculation domain has a horizontal scale of several
kilometers. The hourly emission mass fluxes of the PM10

(a)

(b)

20N
30N
40N
50N
60N
70N
80N

(c)

(d)

Fig. 1 Vertical profiles of the 50°E-130°E zonal mean
“Global Warming Monthly mean Differences (GWMDs)” atlatitudes between 20°N and 80°N of (a) atmospherictemperatures (K), (b) geopotential heights (m), (c) ucomponent of wind velocities (m s-1), and (d) v componentof wind velocities (m s-1).

－ 108 －

soil dust at each grid of the model were calculated byequations presented by Mukai et al. (2004). Also, theemission mass flux was set at zero when the model grid wascovered with snow. The soil moisture content and snow
cover data were calculated from the NCEP-FNL data. The
hourly emission of the soil particles started at the initial timeplus 6 hours due to the spin-up of the model. In addition to
the emission processes, the model includes calculations ofboth dry and wet deposition processes of the soil particles.
To show the reproducibility of the dust simulations, thespatial and temporal distributions of the simulated PM10 soil
dust mass concentrations obtained from results of thepre-experiment were compared with observations ofPM10 and suspended particulate matter (SPM) massconcentrations near the ground surface on the JapaneseIslands, located at far from the Asian dust sources. The
PM10 and SPM observation data were provided by the aciddeposition monitoring NETwork in East Asia (EANET:
http://www. eanet.cc/), and the Ministry of the Environment
and local governments of Japan, respectively. While thePM10 data are collected from a few sites, the SPM
observations are conducted at numerous sites on theJapanese Islands (Figures not shown here).

4. Pseudo Global Warming Experiment
The main numerical experiments for investigating
potential impacts of the spatial patterns of futureatmospheric warming on Asian dust emission were
performed with another calculation domain given by 150 x
150 horizontal grids with the center of 50°N/90°E and thegrid interval of 40 km. This domain is almost centered on
the Asian dust sources, i.e., the arid and semi-arid regions in
China and Mongolia. Also, the domain includes a large area
at high latitudes where Siberian air masses develop andrelease cold air surges that frequently induce Asian dust
emission. In the main experiments, the option of “heat andmoisture fluxes from the surface” in the WRF model was
turned off to remove interaction between the atmosphere andsurface because this study did not consider future changes inthe surface conditions. Other settings of the main
experiments are the same as those of the pre-experiment.
First, we randomly selected the three significant Asian
dust outbreaks, which occurred on April 14, 2002, April 16,
2003, and March 31, 2007, and then simulated those dust
outbreaks as the control runs (CTL-200204, CTL-200304,
and CTL-200703). The initial times of the CTLs were set at0000 UTC April 12, 2002, 0000 UTC April 14, 2003, and0000 UTC March 29, 2007, respectively, prior to the 72

hours numerical integration. Second, the vertical profiles ofthe zonal mean GWMDs of atmospheric temperatures,
geopotential heights, and u-v wind components at latitudes
between 20°N and 80°N, described in Section 2, were added
to each variable of the 6-hourly NCEP-FNL data inproportion to the latitudes and pressure levels in order toupdate the initial and boundary conditions of the CTLs.
Third, the updated initial and boundary conditions were used
to execute the “pseudo global warming runs” (PGW-200204,
PGW-200304, and PGW-200703). In the PGWs, relative
humidity in the future climate was assumed to be equal tothat in the recent climate because atmospheric warming canincrease both the absolute amount of water vapor and thesaturation vapor pressure, preventing relative humidity fromlargely changing. Finally, the future changes in Asian dust
emission were analyzed by comparing results of the PGWs
with those of the CTLs. Many previous studies adopted
this dynamical downscaling method to perform numericalexperiments called the “pseudo global warming
experiments” [e.g., Sato et al., 2007; Kawase et al., 2009].
This downscaling method allows the spatial patterns of
future atmospheric warming, derived from output data fromthe coarse resolution global-scale climate model (MIROC),
to impact on the dust simulations by the fine resolution
regional-scale atmospheric model (WRF-Chem). We canquantitatively estimate the impacts of the spatial patterns of
future atmospheric warming on Asian dust emission fluxes
by executing the CTL and PGW under the same backgroundatmospheric condition and the same dust event. These are
difficult for traditional dynamical downscaling methods that
directly give grid point values of global-scale climate model
projections to regional-scale model simulations.
Results of the experiments showed that the soil dust
emission fluxes in the main Asian dust sources, i.e., theTaklimakan Desert and the Gobi Desert, decrease markedly
in the future climate. In all of the three dust outbreak events,
cold air outbreaks that induce Asian dust emission originatedfrom the vicinities of Lake Baikal, the western part ofMongolia, and the Dzungaria Basin (Figures not shown).
Based on the cold air outbreak routes, we can discuss a
dynamical mechanism of the future decreases in the soil dust
emission fluxes in the Taklimakan and Gobi deserts. Figure
2 shows mean distributions of differences in the daily total
emission mass fluxes of the PM10 soil dust, the daily total
column-integrated mass concentrations of the PM10 soil
dust, the daily mean sea level pressures, and the daily meansurface air temperatures between the PGWs and CTLs on
the three dates: April 14, 2002, April 16, 2003, and March 31,－ 109 －

2007. The large increases in SLPs in the Taklimakan Desert
and the small SLP changes in a region ranging from thevicinity of the Dzungaria Basin to the western part ofMongolia reduce the SLP gradients between the two areas(Fig. 2c). Similarly, the large SLP increases in the easternpart of the Gobi Desert and the small SLP changes in aregion ranging from the vicinity of Lake Baikal to thewestern part of Mongolia reduce the SLP gradients between
those areas (Fig. 2c). The former SLP gradient reduction
results in a weakening of the cold air surges intruding into
the Taklimakan Desert, which usually move across theTienshan Mountains or along the eastern rim of themountains (e.g., Tsunematsu, 2005). The latter reduction
leads to a weakening of the cold air surges moving to theGobi Desert. The resultant decreases in surface windvelocities in the Taklimakan and Gobi deserts (Figures notshown) cause the remarkable reduction in the soil dust
emission fluxes in those deserts (Fig. 2a). It is thus foundthat 1) the small SLP changes in the region located to thenorth of the main Asian dust sources (the Taklimakan and
Gobi deserts) and 2) the large SLP increases in the main dust
sources are two important factors in decreasing the future
dust emission. The small SLP changes corresponded to thelarge increases in surface air temperatures (Figs. 2c-d).

5. Summary
Potential impacts of the spatial patterns of futureatmospheric warming on the Asian dust emission were
investigated by performing the pseudo global warming
experiments for the three significant Asian dust outbreak
events. After the three events were reproduced using
WRF-Chem as the control runs, the initial and boundary
conditions of the control runs were updated by adding thevertical profiles of the 50°E-130°E zonal mean GWMDs of
atmospheric temperatures, geopotential heights, and u-v
wind components at latitudes between 20°N and 80°N tocarry out the pseudo global warming runs. The GWMDs
were calculated from the MIROC outputs of the 20C3M
experiment and the SRES-A1B 21st century climate
projection for the periods of March and April in 1991-2000
and 2091-2100, respectively. Results of the experiments
showed that the future soil dust emission fluxes in the main
Asian dust sources decrease markedly, compared with theemission fluxes in the recent climate. The SLPs in the main
dust sources largely increased in the future climate condition.
However, the future SLP changes in the region located to thenorth of the main dust sources were small. The future
meridional SLP gradients between the two areas therefore

decreased, weakening the cold air surges that induce Asian
dust emission. This mechanism can lead to reduction in the
future dust emission.
(a)

(b)

(c)

(d)

Fig. 2 Mean distributions of the differences in (a) the daily
total emission mass fluxes of the PM10 soil dust (µg m-3),(b) the daily total column-integrated mass concentrations of
the PM10 soil dust (µg m-3) in the atmosphere, (c) the dailymean sea level pressures (hPa), and (d) the daily meansurface air temperatures (K) between the PGWs and CTLs
on April 14, 2002, April 16, 2003, and March 31, 2007.

Acknowledgements
This study was supported by donations from
Weathernews inc. and Grant-in-Aid for Scientific Researchof Ministry of Education, Culture, Sports, Science andTechnology of Japan (No.20403008). The MIROC model
data were provided by a project of Formation of a virtuallaboratory for diagnosing the earth's climate systemwith climate study-related research centers of University ofTokyo, Tohoku University, Chiba University, and Nagoya
University.
References1)Grell, G.A, et al. (2005), Atmos. Environ., 39, 6957-6975.
2)K-1 model developers (2004), K-1 Technical Report, 1, 34pp.
3)Kawase, H., et al. (2009), J. Geophys. Res., 114, D24110.
4)Mukai, M., et al. (2004), J. Geophys. Res., 109, D19204.
5)Sato, T., et al. (2007), J. Hydrol., 333, j.jhydrol.2006.07.023.
6)Skamarock, W. C., et al. (2008), NCAR/TN-475+STR, 113 pp.
7)Sun, J., et al. (2001), J. Geophys. Res., 106, 10,325-10,333.
8)Tsunematsu, N. (2005), Sci. Online Lett. Atmos., 1, 21-24.
9)Tsunematsu, N., et al. (2005), J. Geophys. Res., 110, D21207.
10)Tsunematsu, N., et al. (2009), Boundary Layer Meteorol., 131, 263-275.

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Evaluation of MODIS Aerosols Properties Based on SKYNET Ground Measurements:
Implications on the Quality of Prospective Satellites’Data
Jules R. Dim 1, Tamio Takamura 2, Pradeep Khatri 2
Nobuyuki Kikuchi 3
1
Earth Observation Research Center (EORC), JAXA, Tsukuba, Japan
2
Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba, Japan
3
National Institute for Environmental Studies (NIES), Tsukuba, Japan
Abstract
The quality of Earth observation satellites’ products can be evaluated through the comparison of thesequantities with those derived from ground measurements. In the perspective of the operational start of the Global
Change Observation Mission-Climate (GCOM-C) satellite carrying the Second-generation Global Imager sensor(SGLI) , the quality of aerosols properties retrieved from actual and comparable satellites, such as the Terra- andAqua- Moderate Resolution Imaging Spectroradiometer (Terra- and Aqua-MODIS), is assessed with groundmeasurements from three SKYNET (observation network to understand aerosol-cloud-radiation interaction) sites,
located in the southern part of Japan. In this assessment, two major aerosols properties, the aerosol optical
thickness (AOT) and the Ångström exponent (Alpha), derived from skyradiometers measurements of the
SKYNET sites are compared with the retrievals from two Terra- and Aqua-MODIS satellites’ algorithms for aperiod of 2 years (2008 and 2009). Results of these comparisons show overall similarities in the monthly andseasonal variations of the aerosols quantities between these data sets. However the magnitude of this variationshows some differences between the ground data and one of the MODIS algorithms. The retrievals from thisalgorithm appear consistently overestimated for the AOT (thicker layers) and underestimated for Alpha(dominance of coarser particles). Possible causes of these tendencies are investigated with particular emphasis on
the aerosol size distribution assumption used for the satellite retrievals and the contamination by clouds.
Keywords : SKYNET, Terra- and Aqua- MODIS satellites, aerosols properties, evaluation of satellite retrievals
1. Introduction
Operational remote sensing of aerosols from long-term
satellites provides a means to achieve global and seasonal
characterization of aerosols (Remer et al, 2005). Groundmeasurements, though very dispersed are generally used toevaluate and improve the quality of satellites’ derived
geophysical properties. In the perspective of the future
launch of the GCOM-C/SGLI polar orbiting satellite, theperformance of atmospheric products (the aerosol optical
thickness and the Ångström exponent) derived from actualand comparable (to the GCOM-C/SGLI) satellites areevaluated with ground measurements of three SKYNET
sites located in the southern part of Japan. The satellite
observations used for this purpose are from Terra- and AquaMODIS satellites. The evaluations are aimed at highlighting
and discussing the difficulties, associated with accurate
aerosols retrievals, which should be addressed by the tools
used to derive atmospheric properties of future satellites. The
ground sites, against which the satellites’ retrievals areevaluated, are located on islands and have been recording
atmospheric radiation and climate data on a regular basis forat least 5 years.

2. Observation data
The aerosols properties examined in this study are the
AOT and Alpha. The satellite data used to derive theseproperties are from the Terra- and Aqua-MODIS polar
orbiters while ground measurements are obtained from three
SKYNET sites located close to the sea: Fukue-jima,Hedo-Misaki, and Miyako-jima (to be called here,
respectively Fukue, Hedo and Miyako). Figure 1 shows asatellite image of the AOT distribution around these sites.
The AOT values here are mostly around 0.4. On these sites,
skyradiometers made by PREDE, Japan as well otherobservation instruments record atmospheric radiation andvarious climate variables. The skyradiometers, measure thedirect solar irradiance and diffuse sky radiances at thefollowing wavelengths: 315, 400, 500, 675, 870, 940 and
1020 nm for the POM-01 type radiometer and at 4
additional channels (340, 380, 1627 and 2200 nm) for the
POM-02 type radiometer. These skyradiometers have beencalibrated by the improved Langley method at each site

－ 111 －

(Tanaka et al., 1986; Nakajima et al., 1996a, Nakajima et al.,2003) and the error of the calibration constants is believed tobe less than 2% (Nakajima et al., 2003). Aerosols properties
derived from two algorithms using Terra- and AquaMODIS radiances are evaluated against the SKYNET sites’(to be named here Ground) measurements. These algorithms
are: the National Institute for Environmental Studies, Japanalgorithm (to be named here NIES) and the National
Aeronautics and Service Administration, USA algorithm (tobe named here NASA).

calculations are pre-computed in terms of the aerosol andsurface parameters. For retrievals, the ancillary data needed
include wind velocity at 10 meter height, ozone and water
vapor amount to correct radiance for surface reflectance,ozone and water vapor absorption. The assumption of theaerosol optical model uses a bi-modal log-normal volume
spectrum for modeling the aerosol size distribution:

3. Description of the MODIS aerosol algorithms
The satellites’ aerosols properties evaluated in this study
are the products of two algorithms: NIES and NASA. Eachof these algorithms is indeed two entirely independentalgorithms, one for deriving aerosols over land and thesecond for aerosols over ocean. Over the land the aerosol
optical thickness is derived using the dark target approach.
The NASA land algorithm as described in Kaufmann andTanre (1998) uses the measured radiance at 3 channels: 0.47,
0.66, and 2.13 μm channels, while over the ocean, theaerosol optical thickness is provided in seven wavelengths
from 0.47 to 2.13 μm. The radiances of these channels areinverted into the aerosol optical thickness and the volume
distribution (in the range of 0.08-5 μm radius). In the
inversion, it is assumed that the aerosol size distribution
is bi-modal log-normal. The AOT as well as
secondarily-derived size parameters, among which Alphaare stored on a resolution of 10x10 pixels. The steps in theland retrievals are as follows: to determine the AOT, surface
reflectance (after ozone and water vapor amount corrections)is first estimated (through reflectances at 2.13 μm). Theseestimated surface reflectances at 0.47 and 0.66 μm and themeasured mean-of-atmosphere reflectances at 0.47 and 066
μm are used as input into the chosen model’s LUT toretrieve values for the AOT, fluxes and other parameters. A
full second retrieval is made from the appropriate model’s
LUT. The final step in the process is to interpolate the values
at 0.47 and 0.66 μm using an Ångström law in order toreport optical thickness and flux values at 0.55 μm.
The NIES algorithm retrieves the AOT at 0.50 μm, andAlpha from two channels (visible and NIR) radiance data
(Nakajima, T., and A. Higurashi, 1997, Nakajima, T., and A.
Higurashi, 1998, Higurashi, A., and T. Nakajima, 1998).
Satellite-received radiances are synthesized with fourlook-up tables (LUTs). It is clear that the strategy for theaerosol retrieval is based, as in the NASA algorithm, on
look-up the table (LUT) approach, i.e., radiative transfer,
Where, subscript n indicates the mode number,,are adopted for theparameters of the modeled volume spectrum, and theis used incomplex refractive index ofthis retrieving scheme.

4. Comparisons of aerosols properties
The average aerosols quantity from daily satellite images
in an area of 100*100 km surrounding each SKYNET site ischosen for the comparison with the nearest matching time
ground data record. The analysis covers a period of 2 years(2008-2009). Figure 2 presents the histograms of the
distribution of the aerosols properties as derived from theground observations. The average AOT and Alpha between
the sites are found to vary between 0.27 and 0.36 then 0.97
and 1.05 respectively. The distribution is mostly uni-modal
to bi-modal. The AOT mode is at 0.4 for Fukue and Hedo,
while the 2 modes of Miyako are at 0.7 and 0.4. The
northernmost site, Fukue shows a sharper mode, while thetwo other sites have more flattening in the AOT distribution.
The case of the latter two sites may suggest a mixture ofaerosols of various origins while the former case (bell shape
distribution) would indicate a more unique origin of theaerosols at that site.
Figure 3 presents the correlation between the aerosols
properties measured from skyradiometers and thoseretrieved from the NIES and NASA algorithms. There is abetter 1:1 alignment between the ground data and the
MODIS-NASA properties while the MODIS-NIES
retrievals constantly overestimate the AOT andunderestimates Alpha. Between the three sites, the bestalignment for both AOT and Alpha with the MODIS-NIES
is at the northernmost site of Fukue, which has beenpreviously identified as presenting a likely unique origin in
its aerosols. The worst underestimation of Alpha values inboth algorithms is at the southernmost site of Miyako(probably the most contaminated aerosols). These smaller－ 112 －

Alpha values of the satellite retrievals compared to the sitedata suggest that the aerosols detected by both algorithms
are dominated by larger size particles. In both algorithms thelargest AOT (>0.75) appear to be underestimated comparedto the ground data. The monthly and seasonal change of
AOT and Alpha reflect the differences in the magnitude ofthese parameters between the ground and the satellite
retrievals though the short term variation trends as well as
their peak and low episodes are well reflected on the satellite
data.
Possible factors at the origin of these differences may
range from the size distribution model assumption used in
the retrieval of the aerosols properties to the cloudcontamination..

5. Discussion
The differences in cloud properties between the satellite
retrievals and the ground measurements are discussed interms of the assumed size distribution used in the satellite
retrievals and the cloud contamination.
As noticed earlier both NIES and NASA algorithms use abi-modal log distribution assumption for the aerosols
retrievals. There is a good correlation between the NASAdata and the ground data. This would suggest that the size
assumption used would be valid. This confirms also that the
ground data are of high quality. A lower correlation between
the ground data and the NIES retrievals was noticed earlier(Figure 2). A previous study, comparing the ADEOS-II/GLI
retrievals (using a similar version of the algorithm of
MODIS-NIES) with ground data (Takamura et al., 2009)
confirm these tendencies and, uses the ground size
distribution of the SKYNET sites to improve the initialsatellite retrievals.
The cloud environment surrounding the retrieved aerosols
can affect the proper detection of the aerosols properties. To
study this effect, the clouds encountered in the same area
where the aerosols properties are averaged are compared tothese aerosols. The cloud parameters taken into account are
the cloud fraction (CF), the cloud top pressure (CTP) and thecloud optical depth (COD). The examination of the clouds
occurrence above the three SKYNET sites studied (Fukue,
Hedo and Miyako) shows the dominance of low levelclouds (63, 77 and 80% of total at the respective sites) andmore specifically stratocumulus (37, 56 and 55% of total at
the respective sites). At the same time, the average cloudamount estimated is 51, 55, 64% at the respective sites. It
appears that the strongest discrepancies between the aerosols
properties of the satellite retrievals and those of the groundoccur at the most cloudy site (Miyako).

6. Conclusion
In this study, difficulties in the estimation of theatmospheric aerosols properties were examined. These
pertain to the quality of the aerosol model size distribution
assumption and the impact of clouds. The comparison ofboth the AOT and Alpha retrieved from the satellites withground measurements showed a certain agreement in thevariation trend of these aerosols properties. However themagnitude of this variation seems to differ mostly between
one of the satellites retrievals and the ground measurements.
And these differences do not follow a specific seasonalpattern and seem to be more accentuated in thesouthernmost site. The ground AOT distribution suggests
there may be varied sources in the aerosol load at this site.
Also it is the cloudiest of the three sites examined. This
study suggests that particular attention should be paid to thesize distribution model assumption and the corrective
methods related to the cloud radiation scattering for aerosols
retrievals from future satellites such the GCOM-C/SGLI.
References
1) Higurashi, A., and T. Nakajima, 1998: Development of a
Two Channel Aerosol Retrieval Algorithm on Global Scale
Using NOAA / AVHRR. J. Atmos. Sci., 56, 924-941.
2) Nakajima, T., G. Tonna, R. Rao, Y. Kaufman, and B.
Holben, Use of sky brightness measurements from groundfor remote sensing of particulate polydispersions, Appl. Opt.,
35, 2672– 2686, 1996a.
3) Nakajima, T., and A. Higurashi, 1997: AVHRR remotesensing of aerosol optical properties in the Persian Gulf
region, the Summer of 1991. J. Geophys. Res., 102(D14),
16,935-16,946.
4) Nakajima, T., and A. Higurashi, 1998: A use of
two-channel radiances for an aerosol characterization fromspace. Geophys. Res. Lett., 25(20),3815-3818.
5) Nakajima T., M, Sekiguchi, T. Takemura, I. Uno, A.
Higurashi, D. 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,
No. D23, 8658, doi:10.1029/2002JD003261, 2003

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6) Remer L. A., Y. J. Kaufman, D. Tanre, S. Mattoo, D. A.
Chu, J. V. Martins, R. –R. Li, C. Ichoku, R. C. Levy, R. G.
Kleidman, T. F. Eck, E. Vermote and B. N. Holben.( 2005).
The MODIS aerosol algorithm, products, and validation.
Journal of the Atmospheric Sciences-Special section. Vol 62.
pp 947-973).
7) Takamura T., H. Takenaka, Y. Cui, T.Y. Nakajima, A.
Higurashi, S. Fukuda, N. Kikuchi, T. Nanakajima, I. Sanoand R.T. Pinker, Aerosol and cloud validation system basedon SKYNET observations: estimation of shortwave
radiation budget using ADEOS II/GLI data, J. Remote
Sensing Soc. Japan, 29(1), 40-53, 2009.

8) Tanaka, M., T. Takamura, and T. Nakajima, Refractive
index and size distribution of aerosols as estimated fromlight scattering measurements, J. Clim. Appl. Meteorol., 22,1253– 1261, 1983.
9) Yoram J. Kaufman1 and Didier Tanré. 19998. Algorithm
for remote sensing of tropospheric aerosols from MODIS(Product ID: MOD04, Revised).

Figure 1. Satellite retrieved AOT on 2008.02.19 at 02:05 and SKYNET sites location－ 114 －

AOT (Fukue)

Alpha (Fukue)

25.00%

25.00%

20.00%

20.00%

15.00%

15.00%
AOT

10.00%

Alpha

10.00%
5.00%

0.00%

0.00%

0.1
0.2
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5.00%

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AOT (Hedo)
25.00%

20.00%

20.00%

15.00%

15.00%
AOT

1.1

1.3

1.5

1.7

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Alpha

10.00%
5.00%

0.00%

0.00%

0.1
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5.00%

0.1

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AOT (Miyako)

0.7

0.9

1.1

1.3

1.5

1.7

1.9

Alpha (Miyako)

25.00%

25.00%

20.00%

20.00%

15.00%

15.00%
AOT

10.00%

0.9

Alpha (Hedo)

25.00%

10.00%

0.7

Alpha

10.00%
5.00%

0.00%

0.00%

0.1
0.2
0.3
0.4
0.5
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1
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5.00%

0.1

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0.9

1.1

1.3

Figure 2. AOT and Alpha histogram distributions at the SKYNET sites.

－ 115 －

1.5

1.7

1.9

AOT Hedo vs. MODIS‐NASA (08‐09)

2

2

1.5

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MODIS‐NIES

AOT Hedo vs. MODIS‐NIES (08‐09)

1
0.5

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AOT Fukue vs. MODIS‐NASA (08‐09)

2

2

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MODIS‐NIES

AOT Fukue vs. MODIS‐NIES (08‐09)

1
0.5

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AOT Miyako vs. MODIS‐NIES (08‐09)

AOT Miyako vs. MODIS NASA /08‐09

2

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MODIS‐NASA

MODIS‐NIES

1
Hedo

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MiyakoFigure 3a. AOT comparison between ground measurements (x-axis) and satellite retrievals (y-axis). .

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2

Alpha FUKUE vs. MODIS‐NASA /08‐09

Alpha Fukue vs. MODIS‐NIES (08‐09)

2

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Alpha Miyako vs. MODIS‐NASA/08‐09

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MiyakoFigure 3b. Similar to figure 3a but with Alpha..

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2

Air pollution problems in Ulaanbaatar, Mongolia
Tugjsuren Nasurt
Mongolian University of Science and Technology
E-mail: tugjsuren@must.edu.mn
Abstract
The objective of this report is to provide an analytical basis to underpin discussions on air quality in Ulaanbaatar,
Mongolia and to discuss possible short and long-term strategies for reducing air pollution; given the changing
demographics and growing urbanization and industrialization. These trends have spurred an increase in the demand forenergy in several sectors including transport, construction, heating, industrial production and have resulted in challenges
such as pollution from transport, waste disposal, and natural resource mining among others. A scenario analysis of air
pollution emissions in Ulaanbaatar for the years 2010 and 2020 indicate that unless the government makes a concerted
effort to address the issue at multiple levels, air pollution and its corresponding health impacts will be significant. While
there is no single solution to reduce emissions, a combination of measures ranging from public education and awarenessto strengthening of monitoring and enforcement, to improving technology is necessary, in order to successfully address
the increasing levels of air pollution. This paper presents, multi-pollutant emissions analysis, dispersion results, impact
assessment, and scenario analysis for 2010 and 2020 for Ulaanbaatar.

1. Introduction
Air pollution was one of the critical problems 20 years
ago in Ulaanbaatar, but even now it has not been solved. In
fact, UB is fast becoming the world’s most polluted city; aremarkable achievement considering its population only
hovers around the 1.2 million. The pollution is visibly worsein the wintertime. This is because coal-fueled ger (traditional
felted tents) stoves and boilers used for heating and cooking
produce toxic black smoke plumes that hover like a blanket
over the often windless city. The chokingly-thick pollution isa result of a combination of factors: the poor combustion ofcoal in what are essentially wood stoves, the congested roadtraffic, the dry ground condition and industry. For about
eight months of the year heating is essential for the survivalof residents. 60 percent of the city’s residents live inperi-urban ger districts; areas populated in the main by life
condition not good people from Mongolia’s rural areas who
are arriving in a steady flow. UB’s population has expanded
by 70 percent over the last 20 years and unfortunately thecity’s infrastructure has not been able to keep up with thegrowth. In these areas, which are mainly located upwind of
the city, the only source of heating are poor quality stoves orindividual household boilers fueled by coal, wood and insome cases rubbish-varying from black tar dipped bricks to
old car tires. UB’s remaining citizens live in apartmentbuildings heated by three coal-burning heat and power
stations. All forms of household heating in UB contribute to
the air pollution problem.

The number of cars on the city’s roads seems to be
increasing daily. Drivers beep their horns impatiently andineffectually, hoping for movement, while pumping carbon
dioxide, carbon monoxide, sulphur dioxide and nitrogenoxides into the air and the faces of those braving the cold or
waiting to cross the street. When cars do move, further dust
is released into the air, especially in the ger areas where mostof the roads remain unpaved.
The followings are the main causes of environmental
harm and air pollution of Ulaanbaatar city and generate hugepressure against safety and healthiness.
1. Climate and geography. Extreme and harsh climate(Ulaanbaatar is one of the coldest capital city in theworld and during the coldest months of the year), airflows and stagnancy centralize on incurved area between
four mountains with affections of the air inversion in
could seasons causes high densely pollution and toxin in
air level of people breathing and it reached to dangerous
level to the human health.
2. Increase in demography, population migration andexpansion of ger district. As of 2009, there are 1000.0
thousand residents or 37.0 percent of total population ofMongolia accommodation in UB city, the 470.400
hectares of land. Out of 216.0 thousand families of thecapital city,89.2 thousand or 41.2 percent of families live
in apartments. There are 135000 families are registeredin Ulaanbaatar city, which are considered to be air
polluting sources among more than 130000 counted－ 119 －

sources in 9 districts. Moreover, each families in privatehouses, apartments and in gers, utilizing about 5.3 tonsof coal annually which is responsible for 90 percent of
air pollution in the city.
According to the information released from “Air quality
Professional Agency”, it is mentioned that three power
plants in Ulaanbaatar city burning 3.361.510 tons ofcoal is utilized in 3 power stations as raw material per
annum, in addition to 1378 small and middle sized low
pressure furnaces utilizing 1.1 million tons, are the main
generators of air pollution. 54 small and middle sized
low pressure furnaces do meet the safety requirements.
There are 25 schools and 18 daycares in the city which
are heated by low pressure furnaces with 895000 tons ofcoal annually. In the last decade, the city population
has increased by 300 thousand and the share of ger
district families increased from 48% to 58.6%. The air
polluters, in the center of the city are mostly dairy
product sellers, concession standers and who burn litterfor heating.
City planning and urbanization. It is challenging thatindustrial plants and various services are centered in
Ulaanbaatar city producing large pile of wastes and ashes.
As of 2007, total of 95000 vehicles, 436 motorcycles , 779
tractors and all terrain vecicles, 1626 trailers were registeredin the city. However, approximately 130000 vehicles are
running within the capital city. As of 2010, total of 130000
vehicles were registered in capital city. 64.6% of total
vehicles in Ulaanbaatar city run on gas, 33.1% on diesel and
2.3% on liquid gas fuel. Incomplete gas burn during thetraffic jam or pedaling at cross sections, causes air pollution.

In addition, increasing number of gas stations and car
repair shops in the city also chips in the general state ofenvironmental pollution.

2. Air quality monitoring in Ulaanbaatar city.
There are 4 observing points measuring city air quality.
There are also 22 observing points located in bigger 18 cities
which measure sulfur and nitric dioxide, and conduct air
monitoring and research activities. Daily average of sulfur
dioxide and nitric dioxide in capital city air were higher than
the standards in 12.2-34.5 percent of total observed days in2005 and 14.0-31.0 percent in 2009. Sulfur dioxide usually
increases in October thru April, due to extensive use of coalin winter time. Nitric dioxide has been observed higher than
the standards all season around, usually in the area closer tobigger roads, which can be explained by use of vehicles andtraffic loads. The vehicles in te capital city have increased by
3.3 times and volume ger district has increased by 2.3 times
since 1995. Occasional measurement of sulfur dioxidesurpassed the air quality standard (AQS) in 2007 reached to14.6% of the total average observed, which was more with
0.6% than in 2006; occasions of nitric dioxide surpassed
AQS even reached to 44% which was 12.7% higher than the2006. How is changed this view in the last 2 years?
Occasional measurement of sulfur dioxide surpassed the air
quality standard (AQS) in 2009 reached to 15.5% of the total
average observed, which was more with 0.64% than in
2008; occasions of nitric dioxide surpassed AQS evenreached to 46% which was 13.2% higher than the 2006. As
conclusion, we obviously can see that air pollution has
sudden increase in last years (Fig.1-3 )Fig.1 Number of days having higher-than-the average level of pollutant in the air (by percentage)－ 120 －

3

Fig.2 Statistics on Air polluting substances (mcg/m )Fig.3 Annual average level of the sulfur dioxide-CO2 and nitric dioxide-NO2
in air of Ulaanbaatar city

3. Current state of air pollution in Ulaanbaatar city.
According to the research, 2009 annual average of sulfur
dioxide level in the air was 12.5 mcg/m3 and the highest or
83.0 mcg/m3 of sulfur was recorded in January. According to
the research, 2009 annual average of nitric dioxide level in
the air was 37.5 mcg/m3 and maximum daily average of
99.1 mcg/m3 was recorded at some place in July.
Ulaanbaatar city air quality information reveals that the nitricdioxide level (standard level , 40 mcg/m3) in January 2008
was twice higher with 83mcg/m3 at some places. Sulfur
dioxide amount (standard level, 30mcg/m3) in more air
polluted areas was measured at 38-39mcg/m3, while in otherareas, the pollution was comparatively less. The above
picture was observed in areas with crowded cars, crosssections, and traffic overloads. In 2007, the sulfur gas annual
average in bigger cities and villages of Mongolia was
2-13mcg/m3, the highest has been recorded in Darkhan citywhich was recorded at 461 mcg/m3. The annual averagelevel of nitric dioxide was recorded at 5-39mcg/m3 in
Darkhan city, while the highest was observed as 123mcg/m3,
which is five times higher than the average. The
Government of Mongolia has issued Decree No.218 of 2007
concerning the implementation work of Parliament
Resolution 46 of 28 june 2007 on Specific measures for
reduction of air pollution and Presidential Directive on
Specific measures for reduction of air pollution. By the order229 of 2006 of the Minister for Nature and Environment,
National Air Quality Office has been established with the
purpose of implementing policies on reduction of air
pollution, air quality control and research and fordisseminating information. The City Mayer has issued
Decree No.2/444 of September, 2006, on establishing Air
Quality Unit. Issues of evaluating the current state of air
pollution, measures toward reducing air pollution, requiredbudget, and scientific estimation of implementing suchmeasures, need to be addressed at the government－ 121 －

decision-making level. In connection with this, theParliament of Mongolia has issued decree No.46 onreversing air pollution, which includes total of 13 articles.

4. Measures to reduce of air pollution in Ulaanbaatar
city.
Steps are being taken to implement Point 12 of themillennium Development Goals: “to adapt sustainable
development principles in the national development policies
and programs and to reverse air and environmental pollution
in urban areas, particularly in Ulaanbaatar city”. A feasibility
study has concluded that it would be efficient if plant forecologically friendly fuel were established at Power Plant 2,
a government owned joint stock company. The capacity ofthe plant will be processing 540 thousand tons of coal andproducing 216 thousand tons of smokeless fuel annually.
Calculations also show that the steam ovens converted toutilize the gas separated during the semi coking process,could generate 125.28 million kWh of electricity and 123
thousand Gcal of thermal energy. It was also concluded thatcoking coal is the most feasible solution to reverse air
pollution in the city. Based on these conclusions, theParliament has approved a budget of MNT 50 billion to
alleviate air pollution in Ulaanbaatar city, and additional
MNT 2 billion from state budget to subsidize companies that
produce smokeless condensed fuel. As result of these
activities, NO2 emitted in the air will be decreased by
80-90% and poisonous chemical particles released in theenvironment by 95-98%, and 30 tons of particles andashes produced by power plants annually become a history.
A family living in an average Mongolian ger with 5 walls
and 2 layers will spend approximately 2.5 tons of condensed(smokeless) fuel in winter months, which will cost
approximately 105 thousand tugrugs. If this is the case for
86 thousand families in Ulaanbaatar ger district, we will beable to decrease the smoke coming out of the pipe of eachger by 60 percent. Therefore, in 2007, measures have beentaken toward switching low pressure furnaces that are
currently being used for schools, daycares and family
hospitals, with electric furnaces, and providing 20 thousandfamilies with environmentally friendly condensed fuels,
ground fuel, electric furnace and additional insulting covers.
The Minister for Construction and Urban Development and
The Mayor of Ulaanbaatar city signed a contract onestablishing Apartment Corporation in order to implement“40000 family Apartment Project”. A location of theapartment complex and corresponding urban planning , havebeen already selected. Moreover, it is important to seek forand increase the consumption of alternative energy sources,
such as solar and wind energy, adopt new advanced
technologies in the energy production and to decreaseenergy costs per production unit. The Ministry always
holds a policy to support projects and initiatives such asutilization of gas furnace, liquid gas and utilization of
inexpensive
energy in night hours, production ofcondensed fuel, production of improved ger stove,
experiment of speed fire-starter, installation of SHP-600
filter in automobiles, monitoring of automobile diagnostic
centers and experiment of vehicle emission testers. In orderto provide the public to live and work in healthy and safe
environment and in order to legalize “polluter Pays
Principle”, a law on Air Payment, has been drafted anddiscussed at the Cabinet meeting of the Government, andconsequently, submitted to the Parliament of Mongolia for
final approval. With the aim of establishing legal
environment for determining the roles of the state and local
governments, individuals and companies, in prevention fromair pollution and its increasing level, and of adopting modern
technologies in reduction of air pollution, the draft law onchanges and amendments to the Law on Air and on
Environmental protection have been developed.

5. Conclusion
1. To increase the consumption of alternative energy
sources, such as solar and wind energy, adopt newadvanced technologies in the energy production and to
decrease energy costs per production unit.
2. To support projects and initiatives such as utilization ofgas furnace, liquid gas and utilization of inexpensive
energy in night hours, production of condensed fuel andger stove using new technologies, experiment of speed
fire-starter, installation of SHP-600 filter in automobiles,
monitoring of automobile diagnostic centers andexperiment of vehicle emission testers. In order toprovide the public to live and work in healthy and safe
environment and in order to legalize “Polluter Pays
Principle”
3. With the aim of establishing legal environment for
determining the roles of the state and local governments,
individuals and companies, in prevention from air
pollution and its increasing level, and of adopting
advanced technologies in reduction of air pollution－ 122 －

References
1. Oyun R., Jargalkhuu J., Saijaa N., N. Tugjsuren N.,
Mendbayar B., Sanchir I [2007]: Proseedings of “Airpollution -2007”, Mongolia and Japan Joint Symposium,
Nov. 24, 2007, Ulaanbaatar, Mongolia
2. Report on the state of the environment of Mongolia(2006-2007), Ulaanbaatar, 2008, Mongolia, 114 pp
3. General statistics on environmental mmpact assessments,
1998-2007, 2008-2009, Ulaanbaatar, Mongolia
4. Mongolia national action programme on climate change[2000]: Edited by Batjargal Z., Dagvadorj D., Batima P.
Ulaanbaatar, 2000, 156 pp
5. Greenhouse gases mitigation potentials in Mongolia[2000]: Edited by Batima P., Dagvadorj D., Dorjpurev
J. Ulaanbaatar, 2000, 144 pp
6. Tugjsuren N [2000]: Investigation of polluted cold air in
the atmosphere of Ulaanbaatar city using satellite data.
Proceedings of Mongolia and Russian Joint Symposium,
December, 2009, Ulaanbaatar, Mongolia－ 123 －Aerosol radiative properties over Hefei during 2007-2010
Zhenzhu Wang1, Dong Liu1, Yingjian Wang1, Pradeep Khatri2, Jun Zhou1,
Guangyu Shi3, Tamio Takamura2
1 Key Laboratory of Atmospheric Composition and Optical Radiation, Anhui Institute of Optics andFine Mechanics, Chinese Academy of Sciences, PO Box 1125, Hefei, Anhui 230031, China,
E-mail: zzwang@aiofm.ac.cn
2 Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
3 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Abstract
Aerosol radiative properties are retrieved from the observed data using a ground based sky radiometer. The
seansonal variations are discussed after a good validation using another sunphotometer. The derived results as
input parameters are used to calculated aerosol radiative forcing (RF). The comparison between the measured flux
and the modeled flux shows a good agreement at the surface. The mean values of RF due to aerosol at SFC, TOA,
and in ATM during 2007 to 2010 are -33.9, -23.1, and 10.8 Wm-2, respectively. Because of high value in SSA, therelative RF kept in ATM is not so much though the large value of AOD over Hefei.
1. Instruction
Atmospheric aerosols have a significant impact on
climate due to their imporatant role in modifying
atmosphere energy budget. They are able to alter climate indirect and indirect ways. The direct effect is related to thescattering and absorption of solar and infrared radiation by
aerosols, and the other one is due to changing the
microphysical and chemical properties of clouds by them.
The scattering of solar radiation acts to cool the planet, whileabsorption of solar radiation by aerosols warms the air
directly instead of allowing sunlight to be absorbed by thesurface of the Earth. On global scale, the direct radiatve
forcing is estimated to be in the range of -0.9 to -0.1 Wm-2
for aerosols ( Intergovernmental Panel on Climate Change,
IPCC, 2007)[1]. Yet, these estimats are subject to very large
uncertainties because of uncertainties in spatial and temporalvariations of aerosols. At local scales, as aerosol properties
can vary spatially and temporally, radiative forcing due toaerosols can be also very different and it can exceed theglobal value by an order of magnitude. Hence, there is anurgent need to obtain more informantion on aerosol
properties and radiative forcing due to aerosols in different
regions of climate significance especially in China.
SKYNET is an observation network to study aerosol,
cloud, and radiation interaction in the atmosphere. Data
collected by SKYNET network are very useful to extend ourknowledge regarding the effects of aerosols on regional andglobal climate change. And PREDE Sky radiometer is a key
instrument of this network and is useful to study aerosol
properties. Also, there are other instruments implemented

with, such as radiometers, microwave radiometer et al. Hefei(31.897N, 117.173E) site , as talked about in this paper, is
one of the super ground-base stations in the eastern China.
In this study, aerosol properties using sky-radiometer data
in Hefei are derived, and high quality data including AOD,
SSA, Ang. Index in clear day are obtained. Radiative flux atthe surface is calculated and compared with observed
radiative flux, which is based on inversed aerosol data andtested to be a good agreement. Then direct radiative forcing
due to aerosol is also obtained over Hefei site.

2. Instruments and Methods
The instruments used in this study are installed at Anhui
Institute of Optics and Fine Mechanics (AIOFM), 15 km
west of Hefei city and are listed in Table 1. The PREDE
Sky-radiometer is a key instrument of SKYNET network in
Aisa and is useful to study aerosol properties. The calibration
methodolog, quality control, data reduction precedures andcloud screening for this instrument are presented in [2-4].
The Sky-radiometer is operated continuously everyday
except rain and snow , to acquire direct solar and diffuse sky
radiances at predefined scattering angles. The data have been
analysed by using the SKYRAD.PACK [2] version 4.2 for
retrieval of aerosol optical thickness (AOT), SSA,
asymmetry factor (ASY), volume size distribution (SD),refractive index (Mr,Mi) and angstrom index (  ). Daily
mean AOD, SSA, ASY and M at 340, 380, 400, 500, 670,
870, and 1020 nm wavelengths as well as SD and  arecalculated after cloud-screening [4]. The sunphotometeris used for comparison with the sky radiometer. And the－ 125 －

Table 1 Instruments and parameters
Instruments

Parameters

Sky radiometer POM-02
340,380,400,500,675,870,1020 nm
Pyranometer CM21 305-2800 nm

AOT,SSA,ASY, Ang.Exp.,…
diffuse Flux

Pyrheliometer MS-53 305-2800 nm

direct Flux

Microwave Radiometer WVR-1100

Precipitable water path, Liquid water path

Sunphotometer DTF-5

AOD1050,860,780,670,610,520,400 nm…

Pyranometer, Pyrheliometer, and Microwave Radiometerare used for detecting diffuse, direct flux and water vapor
content, respectively. All of them mentioned above, areoperated simultaneously and data collected during Mar.,2007 to Sep., 2010 are used in this paper. In addition, thefluxes at the surface and top of atmosphere are also
estimated by a radiative transfer calculation using SBDART
model developed by Ricchiazzi et al.[5]. From thesecalculated fluxes, the radiative forcing due to aerosol can be
known. The radiative effect of aerosols on solar radiation
can be expressed in two ways, one the difference between
the actual atmosphere with aerosols and the atmospherewithout aerosols, and the other is the effect of anthropogenic
aerosols only, that is the direct radiative forcing due toaerosols [6]. In present study, the former definition is used toget our results.

3. Results and discusion
The AOD values by skyradiometer, which are obtainedusing both direct and diffuse solar irradiance, are comparedwith the ones by sunphotometer using direct data. Figure 1
shows the correlation between them in clear days of
2008-2010 and a good agreement is given with thecorrelation coefficient of 0.973 and sample number of 2993.
Note here that the sunphotometer DTF-5 is made in AIOFM
and often calibrated on the top of mountain in a verysunshine day. Also, the performance of the sky radiometer
was rountinely checked by professional personnel.

AOD 670 nm (Sun-photometer)

1.2

Y = 0.00565 + 0.00828 * X
R : 0.97326
1.0 SD: 0.03841
N : 2993

0.8
0.6
0.4
0.2
0.0
0.0

Hefei, during 2008-2010
Clear sky

0.2

0.4

0.6

0.8

1.0

1.2

AOD 675 nm (Sky-radiometer)
Fig. 1 Scatter diagram of retrieved AOD for the 675 nm channel by sunphotometerand skyradiometer during 2008 to 2010 over Hefei－ 126 －

20

18
16

14

14

Frequence (%)

16

12
10
8
6

10
8
6
4

2

2

20
18

-1.5

-1.0

-0.5

0.0

0.5

0
-2.0

1.0

ln (AOD) (500 nm)

20

Autumn (154D)
Gauss Fitted

18

16

16

14

14

12
10
8
6

0.0

0.5

1.0

0.0

0.5

1.0

0.5

1.0

Winter (135D)
Gauss Fitted

6

2
-0.5

-0.5

ln (AOD) (500 nm)

8

4

-1.0

-1.0

10

2
-1.5

-1.5

12

4

0
-2.0

Summer (166D)
Gauss Fitted

12

4

0
-2.0

Frequence (%)

20

Spring (214D)
Gauss Fitted

Frequence (%)

Frequence (%)

18

0
-2.0

-1.5

-1.0

ln (AOD) (500 nm)

-0.5

0.0

ln (AOD) (500 nm)

Fig. 2 Frequency distribution of the AOD (500 nm) for the four seasons and the fitted Gaussian model
The AOD is representative of the aerosol loading in theatmosphere column and is important for the identification ofaerosol source regions and aerosol evolution. Figure 2 shows
the frequency distribution of the AOD (500 nm) for the four
seasons. The day number of each season used for analysis is
214 (Spring), 166 (summer), 154 (autumn), and 135 (winter).
The data is also transformed to logarithmic scale and fittedwith the Gaussian model with a form:y  y0 

A
w  2

e

 x  xc 
 2

 w 

2

The lognormal probability distribution best represents thefrequency distribution of AODs in the spring ( R 2 =0.962);in autumn, the fit is also good; but in summer and winter the
fit is not so strong because of the influence of difference
aerosols. So, the the frequency distribution of AODs can becharacterized by lognormal probability distribution during
the same source of aerosol ( e.g., in spring). In spring months,
northern and northwestern cold front activities often attack,and the dust particles can be taken to Hefei along the cold air
mass. Dust particles often were observed over Hefei inspring [7-10].

Table 2 Seasonal statistics of the AOD based on the fitted Gaussian model

y0

A

xc

w

R2

Spring-0.396+0.931

21.283+2.672-0.368+0.028

1.029+0.094

0.962

Summer

0.392+1.728

19.263+4.933

0.023+0.072

0.983+0.200

0.794

Autumn-1.138+2.133

23.916+6.904-0.396+0.045

1.288+0.230

0.910

Winter

-0.027+2.318

20.380+7.178-0.322+0.068

1.185+0.277

0.818－ 127 －

30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
0.0

1.8

30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
0.0

1.8

30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
0.0

Frequency (%)

Spring(214)Gauss Fitted

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Ang. Index ()autumn(154D)
Gauss Fitted

Frequency (%)

Frequency (%)
Frequency (%)

30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Ang. Index ()Summer(166D)
Gauss Fitted

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1.2

1.4

1.6

1.8

Ang. Index ()winter(135D)
Gauss Fitted

0.2

0.4

0.6

0.8

1.0

Ang. Index ()Fig. 3 Frequency distribution of the Angstom index for the four seasons and the fitted Gaussian model
The Angstrom index is determined from the spectral
dependence of the AODs, and is a good indicator of theaerosol size. The coefficient is calculated using regression
analysis, in which the AOD values for 7 wavelengths(340-1020 nm mentioned above). Figure 3 shows thefrequency distribution of  (340-1020 nm) for the four
seasons. Also, the gaussian model is used to fit the frequency
distribution of  . If the value 0.8 is considered to be thepoint that can represent different aerosol type, lager dust
particles often load in spring and sometimes in winter. In
summer and in fall, the aerosols are maily made up of small
particles. The wider spread in frequency distribution is seenin the summer than in the fall. The fitted lines in figure 3
illustrate that the frequency distribution of  follow thenormal probability function to some great degree; thecorrelation coefficients are 0.897 (spring), 0.954 (summer),
2
0.977 (fall), 0.962 (winter). The smallest value in R will be
indicator of big dust aerosol and small anthropologic
aerosol.

Figure 4 shows the monthly mean AOD (500 nm), (340-1020 nm), SSA (500 nm) and PWC over Hefei
from Mar., 2007 to Sep., 2010 with error bars showing thestandard deviation of monthly averaged value. The seasonalvariation in AOD is evident with the biggest value insummer and smallest in fall, where a sharp increasing in
June and decresing in November. The pattern of the monthly
change in  is that smallest value occurs in spring and in
December, which indicates that the aerosol particles arelarge and related to dust events. At the meantime, the insummer and in fall small particles are loaded over Hefei.
And the SSA is another important parameter indicated theaerosol type (absorption or not), which is a key variable inassessing the radiative forcing of aerosols[11]. The main
source of error in the derived SSA is due to the calibration of
the radiative data, and is about +0.03. From figure 4, one cansee that the SSA in summer is much higher than otherseasons and the SSA at end of the fall and at the beginningof the winter is much lower than others. In addition, thewater vapor will affect the aerosol loading in summer season
as shown in figure 4, and may be correlated to SSA andAOD.

－ 128 －



Table 2 Seasonal statistics of the  based on the fitted Gaussian model

y0

A

xc

w

R2

Spring1.368+0.736

7.538+1.015

1.078+0.020

0.398+0.048

0.897

Summer

0.576+0.556

8.984+0.779

1.229+0.013

0.410+0.031

0.954

Autumn0.373+0.430

9.332+0.537

1.267+0.008

0.326+0.018

0.977

Winter

0.878+0.649

8.720+0.800

1.105+0.012

0.380+0.030

0.962

alfa

2.0

AOD (500nm)

1.6

Mar. 2007 - Sep. 2010
Hefei

1.2
0.8
0.4
0.0
2.0

57

79

43

55

56

3

4

78
5

59
6

47
7

60

8

9

10

51
11

45
12

39 Days
1

2

3

4

5

6

7

8

9

10

11

12

1

2

3

4

5

6

7

8

9

10

11

12

1

2

4

5

6

7

8

9

10

11

12

1

2

Ang. Index

1.5

1.0

0.5
1.02
1.00
0.98

SSA

0.96
0.94
0.92
0.90
0.88
8
7

PWC (cm)

6
5
4
3
2
1
0

3

SpringSummerAutumnWinterMonth

Fig. 4 Monthly averaged AOD (500 nm),  (340-1020 nm), SSA (500 nm) and PWC over Hefei from Mar.,2007 to Sep., 2010 by Sky-radiometer and microwave radiometer－ 129 －

900

700

Y=1.00183*X-1.80845
R=0.99733
SD=10.77011
N=10183

Calculated flux (Wm-2)

700
600

Y=1.04441*X+10.65539
R=0.98274
SD=18.66243
N=9836600

Calculated flux (Wm-2)

800

Direct flux (Hefei)

500
400
300
200

500

Diffuse flux(Hefei)
400

300

200

100

100
0

0
0

100

200

300

400

500

600

700

800

900

0

Observed flux (Wm-2)

100

200

300

400

500

600

700

Observed flux (Wm-2)

Fig. 5 Scatter plot for the comparison of observed fluxes with the calculated fluxes forthe direct and diffuse fluxes
30

Radiative Forcing (Wm-2)

20
10

Hefei
Mar.,2007-Sep.,2010
24h Average

0
TOA

SFC

-10

ATM

SpringSummerAutumnWinter

-20
-30
-40
-50
-60

Fig. 6 Seasonal aerosol radiative forcing at the surface, top of the atmosphere,
and in the atmosphere during Mar., 2007 to Sep., 2010 over Hefei.
Figure 5 shows the compared results between calculated
and measured fluxes with the slope and offset values. The
comparison shows a good agreement with the correlation
coefficients of 0.997, and 0.983, for direct and diffuse fluxes,
respectively. And the SD between calculated and measured
surface fluxes ranges from 10 Wm-2 for the direct flux to 19
Wm-2 for the diffuse flux. This discrepency may be causedby errors in the measured or assumed input data as well as
errors in the flux observatios, and especially the input
parameters related to aerosols.

Figure 6 gives the seasonal aerosol radiative forcing (RF)at the surface (SFC), top of the atmosphere (TOA), and in
the atmosphere (ATM). Here, aerosol forcing is integrated
over 24 hours to get daily values and used for computing
seasonal variations. The result shows that the value insummer is biggest, in fall is smallest. The mean values of RF
due to aerosol at SFC, TOA, and in ATM are -33.9, -23.1,
and 10.8 Wm-2, respectively. Because of high value in
SSA, the relative RF kept in ATM is not so much though thelarge value of AOD over Hefei.

－ 130 －

References[1] IPCC. 2007. Climate Change 2007: The PhysicalScience Basic. Contribution of Working Group I
Contribution to the Intergovernmental Panel on Climate
Change Fourth Assessment Report. Solomon S, Qing D
H, Manning M, et al. eds., Cambridge University Press,
Cambridge, United Kingdom and New York, N Y, USA[2] Nakajima, T., G. Tonna, R. Rao, Y. Kaufman, and B.
Holben, 1996: Use of sky brightness measurements fromground for remote sensing of particulate polydispersions,Appl. Opt., 35, 2672-2686.
[3] Kim, D., B. Sohn, T. Nakajima, T. Takamura, T.
Takemura, B. Choi, and S. Yoon (2004), Aerosol
optical properties over east Asia determined fromground-based sky radiation measurements, J. Geophys.
Res., 109,D02209, doi:10.1029/2003JD003387.
[4] Khatri, P. and T. Takamura, 2009: J. Meteor. Soc.
Japan , 87,189-204.
[5] Ricchiazzi, P., S. Yang, C. Gautier, and D. Sowle, 1998:,
Bull. Am. Meteorolo. Soc., 79, 2101-2114.
[6] Remer L A, Kaufman R J. 2006. Aerosol direct
radiative effect at the top of the atmosphere over cloud
free ocean derived from four years of MODIS data.
Atmos. Chem. Phys., 6, 237–253[7] Zhou, J., G, Yue, C. Jin, F. Qi, W. Yi, T. Li,
Y. Chen, L. Xiong, Two-wavelength Mie lidar formonitoring of tropospheric aerosols, ACTA OPTICA
SINICA, 20, 1410-1417, 2000.
[8] Zhou, J., G. Yu, C. Jin, F. Qi, D. Liu, H. Hu, Z.
Gong, G. Shi, T. Nakajima and T. Takamura, Lidar
Observations of Asian Dust over Hefei, China in the
Spring of 2000, JGR, 107, D15, AAC51-58, 2002.
[9] Dong Liu, F. Qi，C. Jin, G. Yue and Jun Zhou,
Polarization lidar observations of cirrus and Asian dust
aerosols over Hefei, Chinese Journal of Atmosphere
Sciences, Vol. 27, 1093-1100, 2003.
[10] Jun Zhou, Dong Liu, Guming Yue, Fudi Qi, andAiyuan Fan (2006), Vertical Distribution and Temporal
Variation of Asian Dust Observed over Hefei, China byusing a Lidar, Journal of the Korean Physical Society,49(1), 320-326.
[11] Dubovik, O., B. N. Holben, T. F. Eck, A. Smirnov, Y. J.
Kaufman, M. D.King, D. Tanre, and I. Slutsker (2002a),
Variability of absorption and optical properties of key
aerosol types observed in worldwide locations, J. Atmos.
Sci., 59, 590–608.

－ 131 －Aerosol-cloud interactions derived from remote sensing and in-situ aircraft measurements
G. Pandithurai1, S. Dipu1 and T. Takamura2
1
Indian Institute of Tropical Meteorology, Pune, India
2
CEReS, Chiba University, Chiba, Japan
1. Introduction
The effect of aerosols on cloud microphysical andradiative properties has the greatest uncertainty of all known
climate forcing mechanisms. For a constant amount ofcondensed water, an increase in the number of CCN will
generate a cloud that consists of smaller drops and reflects
more energy to space, which is commonly referred to as the‘‘albedo effect’’ [Twomey, 1977]. However, because thedroplets are smaller they may inhibit collision-coalescencein the cloud, suppressing droplet growth that stops drizzle
and extends cloud lifetime [Albrecht, 1989]. The indirect
effect of aerosols is continuing to be an enormous challenge
from both the observational and modeling perspectives, andprogress is crucial in order to improve our ability to predictclimate change. Current estimates of global average aerosol
indirect forcing based on satellite data ranges from -0.6 to-1.7 Wm-2. There is greater realization of the importance ofincluding the indirect effect because direct effect is thought
to be insufficient to allow proper simulation of observed
temperature changes. Satellite remote sensors have been
employed to provide a regional and even global view ofaerosol effects on clouds [Nakajima et al., 2001; Breon et al.,2002]. More recently, surface-based remote sensing has also
been applied to address aerosol effects on cloud
microphysics [Feingold et al., 2006; Pandithurai et al.,2009], as these surface stations yield high temporal
resolution data and because they sample aerosol below,
rather than adjacent to clouds and hence they do not sufferfrom cloud contamination. The main advantage of thesurface-based remote sensing is that the effect of aerosol on
cloud can be examined in a single column of air at the scaleof cloud droplet formation, and at high temporal resolution.
Long term observations of aerosol and cloud properties andsensitivity of cloud properties to aerosols over different
climatic regions are required for better parameterization ofaerosol-cloud interactions. The primary objective of thisstudy is to examine the response of cloud radiative
properties to changes in aerosol using surface-based remotesensing systems over Eastern China Sea region that is
influenced by the transport of aerosols from the Asian
continent. Recent aircraft measurements over India arealso used to examine the aerosol indirect effect and the effectof droplet dispersion on indirect effect estimates.

2. Data and Methods
Spring-2008 observational campaign was conducted atCape Hedo (26.87 N, 128.25 E), Okinawa Island, Japan to
study the aerosols and their radiative effects transported from
East Asia especially from China. In addition to otherobservational facilities, this campaign included an operationof Frequency Modulated Continuous Wave (FMCW)
Millimeter wave Cloud radar (MMCR) from February 17 to
May 04, 2008. The experimental systems used in this study
includes a i) Dual frequency microwave radiometer (MWR),ii) Polarization capable dual-wavelength lidar [Shimizu et al.,2004], iii) Millimeter wave cloud radar [Takano et al., 2005],iv) CCN counter, v) i-skyradiometer (Prede Co., ProductName POM-02), vi) Whole sky imager, vii) Nephelometerand viii) Optical particle counter.
Cloud droplet effective radius (Reff) is retrieved from thereflectivity profiles of FMCW cloud radar operating at 95
GHz and column liquid water path (LWP) derived from
MWR. Technical details of the FMCW radar and its
comparison with a monostatic 95 GHz cloud radar SPIDER
can be found elsewhere [Takano et al., 2005]. Liquid water
path is an important parameter for studying aerosol-cloudinteractions and is not widely measured. The Radiometrics
WVR-1100 microwave radiometer is a passive instrument
which measures column integrated water vapor and liquidwater based on the microwave emissions of atmosphericwater vapor and liquid water molecules at 23.8 GHz and31.4 GHz. Typical retrieval uncertainties in the column
integrated liquid water are 25–30 gm-2. The datasets used inthis study were obtained from the SKYNET archive(http://atmos.cr.chiba-u.ac.jp), which archives and distributes
data collected from most of these instruments.
3. Aerosol effects on cloud droplet size
To investigate the aerosol indirect effect, collocatedmeasurements of different experimental systems wereanalyzed. As suggested by Feingold et al. [2006], daytime
well-mixed boundary layer conditions are considered foraerosol indirect effect analysis to ensure that the surface
observations are representative of those that that are－ 133 －

influencing the cloud droplet nucleation properties. Here weused height-averaged cloud droplet effective radius (Reff) andexamined changes in Reff as a function of surface aerosol
scattering coefficient, CCN number and OPC aerosol size
distribution. Aerosol indirect effect estimates are based onrelative changes in droplet radius or cloud optical thickness
for changes in aerosol loading and is less sensitive toabsolute values. As CCN measurements are not available on
all the days considered in this analysis, we use βsca as CCN
proxy. Analysis shows that about 30% increase inaccumulation mode particles led to 15% decrease in droplet
effective radius. Wind fields and back trajectory analysis forthe experimental days considered in the analysis show the
trajectories are of northwesterlies and westerlies, whichindicate transport of airmasses from Asian continent towards
the Okinawa Island. Data sets of selected days consideredin the analysis were sorted for different LWP ranges viz.
50–100, 100–150, 150–200, 200–250 and 250–300 gm-2.
The scatter plot between Reff and βsca for different ranges of
LWPs shown in Figure 1 and the slope of the linear fit for
the above plots give aerosol indirect effect (IE).

Guwahati (26.1 N, 91.6 E) and Pune (18.5 N, 73.8 E).
During some of the flights observations were collected overcoastal Arabian Sea and Bay of Bengal to study the cloud
microphysics of marine clouds and their transformation tocontinental clouds during the flights. A detailed descriptionof the above CAIPEEX project can be found in the web
page: http://www.tropmet.res.in/~caipeex/.
Flight average data sets of warm cloud samples were usedin the analysis. The threshold cloud droplet diameter (DL)of modal liquid water content is used as cut-off and cloud
samples less than DL values of 24 µm only are considered inthis analysis. Measurements of aerosol accumulation mode
number concentrations (Nacc) and CCN supersaturation
spectrum were obtained from sub-cloud flights before
climbing into cloud profiling. First AIE (Twomey

effect) is estimated from the ratio of the relativechange in cloud droplet number concentration (Nc)with respect to relative change in aerosol number
concentration (Nacc).
N c  a a  N acc  ab..........(1)where first AIE or Twomey effect is given as I0=1/3ba. It isclear that as aerosol concentration increases as cloud droplet
number concentration increases. According to equation (1),Twomey effect estimated over Indian region during
CAIPEEX campaign is 0.096 which is 1/3 ba (ba=0.288).

Figure 1. Values of Reff as measured by MMCR versus
aerosol scattering coefficient asmeasured by nephelometer.

4. Aerosol indirect effect over India using CAIPEEX
Datasets
CAIPEEX (Cloud Aerosol Interaction and Precipitation
Enhancement Experiment) phase I is devoted for intensive
cloud and aerosol observations using an instrumented
aircraft N361JC, over different regions in India, during theperiod May to September 2009 which includes pre-monsoon,
active and break monsoon conditions. Aircraft missions
were conducted from different locations in India viz.
Pathankot (32.2 N, 75.6 E), Hyderabad (17.5 N, 78.4 E),
Bangalore (13N, 77.5E), Bareilly (28.4 N, 79.4 E),

5. Effect of cloud droplet dispersion on Twomey effect
In most of the studies, aerosol-cloud interaction is
explained by considering only changes in the cloud droplet
concentration, neglecting changes in the spectral shape ofthe cloud droplet size distribution. However, it has beenshown that the relative dispersion ‘ε’ has a significant role indetermining cloud radiative properties. It is already reported
that enhanced ε leads to warming effect, which can offset
Twomey effect by 10 to 80% (Liu and Daum, 2002). Allexisting estimates of dispersion effect were based on some
empirical β(ε)-N relationship, which has a large uncertainty.
In order to reduce the uncertainty or for better estimationof dispersion effect, Liu et al (2008) examined thedependence of β on (L/N) (i.e., water per droplet) anddescribed the dependence of β on (L/N) by the following
expression,

L  a  
N

 b

..........(2)where L is cloud liquid water content, N is cloud droplet
number concentration and β is effective radius ratio.

－ 134 －

2.2
2.0

=0.196(L/N)

Penner (2001), A possible correlation between
satellite-derived cloud and aerosol microphysicalparameters, Geophys. Res. Lett., 28, 1171–1174.

-0.0871.8

Effectve Radius Ratio

1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
1E-11

1E-10

1E-9

1E-8

Water per Droplet L/N (g)Figure 2. Relation between effective radius ratio andwater per droplet obtained fromCAIPEEX flight averagedata sets

Acknowledgments
We thank SKYNET and CHAMMPS, Cape Hedo for
maintaining the experimental facilities at the site. We also
thank CAIPEEX team for aircraft measurements and the
project was fully funded by Ministry of Earth Sciences,
India. Thanks are also due to Global Earth Observation
System of Systems (GEOSS), Ministry of Education,
Culture, Sports, Science and Technology (MEXT), Japan.

Pandithurai, G., T. Takamura, J. Yamaguchi, K. Miyagi, T.
Takano, Y. Ishizaka, A. Shimizu (2009), Aerosol effect on
cloud droplet size as monitored from surface remotesensing over east china sea region, Geophys. Res. Lett.,
36, L13805, doi:10.1029/2009GL038451.
Shimizu, A., N. Sugimoto, I. Matsui, K. Arao, I. Uno, T.
Murayama, N. Kagawa, K. Aoki, A. Uchiyama, and A.
Yamazaki (2004), Continuous observations of Asian dust
and other aerosols by polarization lidars in China andJapan during ACE-Asia, J. Geophys. Res., 109, D19S17,
doi: 10.1029/2002JD003253.
Takano, T., K. –I. Akita, H. Kubo, Y. Kawamura, H.
Kumagai, T. Takamura, Y. Nakanishi, T. Nakajima (2005),
Observations of clouds with the newly developed cloudprofiling FM-CW radar at 95 GHz, Proc. SPIE 5979 art
no 597907.
Twomey, S. (1977), The influence of pollution on the shortwave albedo of clouds, J. Atmos. Sci., 34, 1149-1152.

References
Albrecht, B. A. (1989), Aerosols, cloud microphysics, andfractional cloudiness, Science, 245, 1227-1230.
Breon, F.-M., D. Tanre, and S. Generoso (2002), Aerosol
effect on cloud droplet size monitored from satellite,
Science, 295, 834-838.
Feingold, G., W. Eberhard, D.E. Lane, and M. Previdi (2003),
First measurements of the Twomey effect using
ground-based remote sensors, Geophys. Res. Lett., 30(6),1287, doi:10.1029/2002GL016633.
Feingold, G., R. Furrer, P. Pilewskie, L. A. Remer, Q. Min,and H. Jonsson (2006), Aerosol indirect effect studies at
Southern Great Plains during the May 2003 Intensive
Operations Period, J. Geophys. Res., 111, D05S14,
doi:10.1029/2004JD005648.
Liu, Y. and P. H. Daum (2002), Indirect warming effect fromdispersion forcing, Nature, 419, 580-581.
Liu, Y., P.H. Daum, H. Guo and Y. Peng (2008), Dispersion
bias, dispersion effect and the aerosol-cloud conundrum,
Envrion. Res. Lett., doi:10.1088/1748-9326/3/4/045021.

Nakajima, T., A. Higurashi, K. Kawamoto, and J. E.
－ 135 －Aerosol optical and radiative properties: synergy between sky radiometer and lidar
Sang-Woo Kim
School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea
1. Introduction
Anthropogenic and natural aerosols are important
atmospheric constituents that significantly contribute to
Earth’s radiation budget through a variety of pathways suchas direct effects on scattering and absorption of solar
radiation, indirect effects on cloud microphysics, andsemi-direct effects (Kaufman et al., 2002; Ramanathan et al.,2007; Yoon et al., 2005). In the past 30 ~ 40 years, North
East Asia has been experiencing a considerable increase inanthropogenic pollution aerosols. In addition, North East
Asia is one of the major source regions of wind-blown
mineral dust aerosols. These aerosols significantly influence
the solar and terrestrial radiation budgets (Huebert et al.,
2003; Kim et al., 2004, 2005, 2008; Nakajima et al., 2007;
Ramanathan et al., 2007; Won et al., 2004; Yoon et al., 2005).
The aim of this study is (1) to investigate the columnar
optical properties and evaluate the resulting aerosol direct
radiative forcing and atmospheric heating at Gosan (Korea)
for a seven-year period, based on sun/sky radiometer andmicro-pulse lidar observations and radiative transfer
calculations, (2) to evaluate aerosol lidar ratio(extinction-to-backscatter ratio) using 4-year measurementsof elastic-backscatter lidar and sky radiometer at Seoul
National University of Seoul, Korea.
2. Aerosol Direct Radiative Forcing (ADRF)
2.1 ADRF calculation
The aerosol direct radiative forcing (F) and forcing
efficiency (β, defined as F per unit τ) for the predominantly
cloud-free days are calculated using the column radiativemodel (CRM-2.1.2), which is a stand-alone version of theradiative transfer model implemented in the NCAR Climate
Community Model (CCM-3.6). The CRM uses a
δ-Eddington approximation with 19 spectral intervals
spanning from 0.2 to 5 μm and accounts for several gas
absorption spectra; seven spectral bands for O3 and H2O andthree for CO2, and the absorption of N2O, CH4, CFC-11,
CFC-12 are included. The followings are replaced in theoriginal version of the CRM so that it can be used in thepresent study (Won et al., 2004):(a) The number of computational layers is extended from18 to 54; from surface to approximately 63 km altitude. The
vertical resolution is about 23.7 hPa (i.e., 0.2 km interval

near the surface and gradually broadened with increasing
altitude, about 2.85 km at top layer).
(b) The aerosol module of the CRM is modified to ingest
the AERONET measured variables (τ, ω, and g), andsubsequently calculate the parameters at the 19 bands
from the information obtained at the four AERONET
wavelengths. We used a linear regression method in the log– log plane (where  is the wavelength and  denotes τ, ω,and g) to determine the aerosol parameters in 19
wavelength-bands.
(c) The AOD obtained from AERONET sky radiometer
was apportioned with altitude using MPL-derived extinction
profiles. The constant values of SSA and g were applied to
all altitudes in the calculation of atmospheric heating rate.
In addition, we estimate the effect of aerosols on theradiation budget by considering their vertical distribution(i.e., atmospheric heating rate) on the basis of the aerosol
profiles measured by the co-located MPL system.
2.2 ADRF and atmospheric heating rate at Gosan
Figure 1(a) shows the monthly mean temporal variationsin the optical properties of aerosols and precipitable watercontent obtained from AERONET observations. It should be
mentioned that almucantar retrievals from AERONET
instrument are not sufficient for radiative transfer
calculations in November due to routine instrument
maintenance and calibration. At the Gosan site, the monthly
mean AOD at 675 nm lies between 0.12 and 0.36 (annual
mean of 0.29  0.19), with significantly high values (>0.33)
observed from April to June, while low-to-moderatemonthly mean AODs were apparent during the remaining
months. The monthly mean Ångström exponent (Å) range
remained fairly low (<1.0) from February to May, indicating
the relative dominance of coarse particles to the solar
extinction, whereas it was relatively high in the remaining
months (>1.2), suggesting the important role played by fineaerosols on radiation. The high AODs observed from March
to May can be attributed to the frequent outbreak of Asian
dust storms (as indicated by the low Å). Accumulation of
locally and regionally emitted fine pollution aerosols (asindicated by the relatively high Å) due to the prevailing
stationary anti-cyclonic circulation also leads to an increase
in AODs in May and June. Monthly values of SSA are－ 137 －

675

0.6
0.4
0.2

440/870

1.8

1.2

0.6
1.00

675

Month

(b)

0.8

Aerosol Direct Radiative Forcing (Wm-2)

(a)

0.95

3

6

7

8

9

10 11 12 13
1 14
2

MAM JJA SON DJF

-10

-20

-30

-40
TOA
SFC
-50
3

4

5

6

7

8

9

10 11 12 13
1 14
2

MAM JJA SON DJF

Aerosol Forcing Efficiency (Wm-2 -1)

0

0.70

g 675

5

0

0.90

0.65
0.60
6

TPW (cm)

4

4
2
0
3

4

5

6

7

8

9

Month

10

11

12

13
1

14
2

MAM JJA SON DJF

-30

-60

-90

-120
TOA
SFC
-150

SeasonFigure 1. (a) Monthly (left panel) and seasonal (right panel) means of AOD (), Ångström exponent (Å), SSA (ω),
asymmetric parameter (g), and total amount of precipitable water (TPW) determined by the AERONET Sun/sky
radiometer at Gosan from April 2001 to March 2008. (b) The monthly (left panel) and seasonal (right panel)variations of aerosol direct radiative forcing (upper panel; W m–2) and forcing efficiency (lower panel; W m–2 –1)
at Gosan.
relatively low from February to May (<0.93) due to thetransports of dust as well as fine pollution aerosols (Huebert
et al., 2003), whereas high values of SSA (>0.93) are
observed during the rest of the period. The annual mean of
SSA is 0.93  0.04. The asymmetric parameter (g) shows
almost constant values (0.65 < g < 0.67), except during
autumn (~0.63).
Figure 1(b) shows the monthly and seasonal variations inaerosol direct radiative forcing (F; W m–2) and forcing
efficiency (β; F/τ; W m–2 τ–1) at Gosan. The monthly meanFs are calculated using the monthly mean aerosolparameters. Similar to the case of AOD variations, strong
intra-seasonal and seasonal variations were observed in theaerosol radiative forcings, with quite high values: annual
mean of –27.55  9.21 (surface), –15.79  4.44 (TOA), and11.76  5.82 W m–2 (atmosphere). The mean β is evaluatedto be –91.85  11.12 W m–2 –1 at the surface and –53.76 
6.70 W m–2 –1 at the TOA. From March to June, the surfaceF (β) was between –27.29 (–85.33) and –34.76 W m–2(–97.19 W m–2 τ–1), that at the TOA was between –16.84(–51.82) and –19.10 W m–2 (–56.05 W m–2 τ–1), and theatmospheric forcing was between 10.45 and 16.41 W m–2.
Significant aerosol forcings from March to May are
consistent with high AODs and can be attributed to thewind-blown mineral dust aerosols transported from the
Asian continent by the westerlies (Kim et al., 2005, 2007,2008; Nakajima et al., 2007). Enhanced aerosol direct
forcings in May and June are due to an increase in the finepollution aerosol loads, which is caused by stationary
regional meteorological conditions and the associatedtransport of anthropogenic fine pollution aerosols from
China (Kim et al., 2007). The continental air mass becomes
weak and the clean marine air mass has not expanded over
China/Korea in May and June, thereby mid-latitude westerly
zonal flow (i.e., west-to-east flow) is relatively stagnant. The
large discrepancy in F between the surface and the TOA,
i.e., atmospheric forcing, suggests the presence oflight-absorbing aerosols, which is consistent with relatively
low SSA. During these periods, atmospheric radiative
forcing also increases. These results indicate that aerosols
over East Asia can significantly heat the atmosphere. On theother hand, the relatively low monthly mean aerosol direct
forcings from July to August and in autumn/winter are
directly related to the frequent rainfalls and very high windspeed conditions, respectively.
Monthly and seasonal mean profiles of atmosphericheating rate are shown in Figure 2. The evaluation of F
is usually dependent on the total amount of aerosols in the－ 138 －

Altitude (km)(a) 4

[K/day]

3

0.25

0.25
0.50

2

0.75
0.50

1.00
1.25
1.50
1.75
2.25

1

0.75
1.00
1.25
1.50

2.00

2.50

1.75

2.75

3

4

5

2.25

6

7

8

2.00 2.25

9

10

11

12

1
13

0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25

2
14

Month
Figure 2. Monthly variations in atmospheric shortwave heating rates (K day–1) at Gosan, Korea.
atmospheric column. However, the radiative heating rate,
defined as the rate of temperature change in a layer due toradiative heating and cooling, at a shortwave regime can be
influenced by aerosol vertical distributions and the amountof light-absorbing aerosols (i.e., AOD and SSA). The
radiative heating of the atmosphere is apparent below analtitude of approximately 1.5 km because most of theaerosols are confined to the planetary boundary layer. The
strongest radiative heating occurs in spring and summer due
to of the increase in dust aerosols and light-absorbing
aerosols (May and June). The monthly mean radiative
heating rate in May exceeds 2.7 K day–1, which is almost
twice that in autumn and winter. In addition, compared toautumn and winter, the frequent passage of springtime Asianmineral dust aerosols in free troposphere and the seasonalincrease of boundary layer mixing height in summer causesignificant atmospheric radiative heating above 1 km.

3. Characteristics of the lidar ratio determined fromlidar and sky radiometer measurements in Seoul
Figure 3 shows the flow of lidar ratio calculation processby comparing the aerosol optical depth calculated from lidar(lidar) and sun/sky radiometer (sky) measurements. If thedifference between lidar and sky is less than 0.5%, we finallydetermine lidar ratio and then extract aerosol extinction
profile.
Figure 4 shows seasonal variations of lidar ratio,Ångström exponent, and depolarization ratio from 4-year
measurements of elastic-backscatter lidar and sky radiometerat Seoul National University of Seoul, Korea. The mean
lidar ratio (with standard deviation) based on 4 years ofmeasurements is found to be 61.69±16.46 sr, and weakseasonal variations are noted with a maximum in JJA(68.06±16.78 sr) and a minimum in DJF (57.18±17.94 sr).

Figure. 3. Flow chart of the algorithm for determination of the lidar ratio and extinction profile.
－ 139 －

Figure. 4. Seasonal variation of (a) lidar ratio, (b)Ångström exponent, and (c) depolarization ratio at
Seoul, Korea. Top and bottom of Boxes andwhiskers represent 75th, 25th, 95th and 5th
percentile, respectively. Lines in the boxes are
median values and dots represent mean values.
Lidar ratio and depolarization ratio are at 532 nm,
and Ångström exponent is determined from AOT at
five wave length (400, 500, 675, 870, 1020 nm).

Table 1. Lidar ratio at 532 nm for different aerosol types.
Aerosol type
Clean

Dust

Pollution

1)

Lidar ratio (Sr)

Location

Reference

32 ± 6AfricaVoss et al., 2001

35

EuropeAnsmann et al., 2001

45.0 ± 9.5

Seoul, Korea

This Study

41 ± 8

SaharaVoss et al., 2001

42 - 55

Tokyo, Japan

Liu et al., 2002

50.4 ± 9.4

Tokyo, Japan

Murayama et al., 2003

1)

42 ± 4

Except Asia

Cattrall et al., 2005

45.5 ± 8.6

Anmyeon, Korea

Noh et al., 2007

51.7 ± 13.7

Seoul, Korea

This Study

50 - 70

Portugal

Ansmann et al., 2001

49 - 70

India

Franke et al., 2001

71 ± 101)

Urban

62.2 ± 13.2
Seoul, Korea
Lidar ratios retrieved from 26 AERONET sites across the globe.

Table 1 shows the lidar ratios for for clean, dust, andpolluted conditions in Seoul, Korea. The lidar ratios forclean, dust, and polluted conditions in this study are
estimated to be 45.01±9.48 sr, 51.68±13.66 sr, and62.17±13.17 sr, respectively. While the lidar ratio for thepolluted condition is appeared to be consistent with previous
studies, clean and dust conditions tend to have larger ratios,compared to previous estimates. This discrepancy is thought
to be mainly due to the anthropogenic aerosols existing
throughout the year around Seoul, which may cause
increased lidar ratios even for clean and dust conditions.

Cattrall et al., 2005
This Study

4. Summary
We investigate the columnar optical properties ofaerosols advected from the Asian continent to Gosan(Korea) and evaluate the resulting direct radiative
forcing and atmospheric heating for a seven year period,
based on AERONET observations and radiative transfer
calculations. We also evaluate aerosol lidar ratio(extinction-to-backscatter ratio) using 4-year measurementsof elastic-backscatter lidar and sky radiometer at Seoul
National University of Seoul, Korea. The major conclusionsof this study are as follows:－ 140 －

1. At the Gosan site, the monthly-mean AOD at 675 nm
ranged between 0.12 and 0.36. High AODs wereobserved from April to June (>0.33), while
low-to-moderate monthly mean AODs were apparent
during the remaining months. A fairly low Å (<1.0)
prevailed from February to May due to Asian dusts.
2. Annual average clear-sky direct forcing (forcing
efficiency) at the surface was –27.55  9.21 W m–2(–91.85  11.12 W m–2 –1) and at TOA was –15.79 
4.44 W m–2 (–53.76  6.70 W m–2 –1), thereby leading
to an atmospheric absorption of 11.76  5.82 W m–2.
From March to June, the surface aerosol radiative
forcing (β) ranged between –27.29 (–85.33) and –34.76
W m–2 (–97.19 W m–2 τ–1), that at TOA was between–16.84 (–51.82) and –19.10 W m–2 (–56.05 W m–2 τ–1),
and the atmospheric forcing was between 10.45 and16.41 W m–2.
3. The atmospheric absorption translated to an atmosphericheating of 1.5 to 3.0 K day–1. The strongest radiative
heating was estimated to occur from April to June (>2.5
K day–1).
4. The mean lidar ratio (with standard deviation) based on
4 years of measurements in Seoul is found to be
61.69±16.46 sr, and weak seasonal variations are notedwith a maximum in JJA (68.06±16.78 sr) and aminimum in DJF (57.18±17.94 sr).
5. The lidar ratios for clean, dust, and polluted conditions inSeoul are estimated to be 45.01±9.48 sr, 51.68±13.66 sr,and 62.17±13.17 sr, respectively.

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Island, Korea. Atmospheric Research, 86, 76-87,
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－ 142 －

Lidar Network Observations of Tropospheric Aerosols in East Asia
Nobuo Sugimoto1, Ichiro Matsui1, Atsushi Shimizu1, Tomoaki Nishizawa1, Boyan Tatarov1,
Soonchang Yoon2, Dulam Jugder3, and Tamio Takamura4
1
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Japan, nsugimot@nies.go.jp
2Seoul National University, Seoul, Korea
3
Institute of Meteorology and Hydrology, Ulaanbaatar, Mongolia
4
CEReS, Chiba University, Chiba, Japan
Abstract
Observations of tropospheric aerosols using a ground-based network of two-wavelength (532nm, 1064nm)
polarization (532nm) lidars (SKYNET-lidar < NIES Lidar Network < AD-Net < GALION) are reported. Currently,
the lidars are continuously operated at 18 locations in East Asia, including 3 stations in Mongolia, Seoul, Korea,
and Phimai, Tailand. The data from the lidars in the network are transferred to the National Institute forEnvironmental Studies (NIES) in real time and processed to derive the attenuated backscattering coefficients atthe two wavelengths and the volume depolarization ratio. The extinction coefficient estimates for non-sphericalaerosols (dust) and spherical aerosols are also derived automatically with the real-time data processing system.
The data from the network are used in various studies on atmospheric environment and climate change. In thispaper, we report on the lidar instrument and the network, the data analysis methods for estimating aerosol
components, and the results of recent studies on Asian dust, air pollution aerosols, and forest fire smokes. The roleof the lidar network in the GAW Aerosol Observation Lidar Network (GALION) and the role of GALION inclimate change studies will be also discussed.
Keywords : lidar, aerosol, mineral dust, air pollution, SKYNET, data assimilation

1. Introduction
Various kinds of aerosols such as mineral dust,
air-pollution aerosols, biomass burning smoke, and forest firesmoke coexist in the East Asian region, and it is important tounderstand the effects of these aerosols on climate and theenvironment. We started continuous observation with acompact automatic Mie scattering lidar in 1996 at NIES in
Tsukuba in a research project on the effect aerosols in the
global warming. In 2001, we constructed lidars in Beijing
and Nagasaki and started network observations with thelidars at the three locations. The network has been expandedin the research programs and international cooperation.
Some of the lidars were constructed in the Asian-dustmonitoring program with the Ministry of the Environmentof Japan. Currently, we are operating the lidars at 17
locations in Japan, Korea, Mongolia, and Thailand (theNIES Lidar Network).1-3) We also have cooperative stations
in Korea and China. Some of the lidars are collocated with
the SKYNET radiometers and operated as SKYNET-lidar.4)
The NIES Lidar Network is a part the Asian dust research
network (AD-NET) and the GAW Aerosol Lidar
Observation Network (GALION), which is a global lidar
network combining existing regional lidar networks.5-6)

2. Lidar System and the Network
The lidar used in the network is two wavelength (1064nm,
532nm) Mie-scattering lidar with a depolarization ratio
measurement function at 532nm. Figure 1 shows a block
diagram of the lidar system. The lidar uses a commercial
flashlamp pumped Nd:YAG laser (Quantel Ultra) as a lightsource. The output power at 1064nm and 532nm is 20mJ
and 20mJ. The receiver telescope diameter is 20cm.
Polarization components of received light at 532 nm areseparated with a polarization prism and detected with two
photomultiplier tubes (PMTs) (Hamamatsu). Received lightat 1064nm is detected with an avalanche photodiode (APD)(Licel). Signals from the PMTs and APD are digitized with
12-bit analogue-to-digital converters and recorded on a harddisk of the data acquisition PC. The lidars are operated
continuously regardless of weather. In the continuous
observation, 5-min averaged lidar profiles are measured
every 15 minutes. Consequently, 96 sets of profiles are
obtained per day. Recently, a detection system for nitrogen
Raman scattering (607nm) was added to the lidar at the
primary observation sites. Although the Raman scattering
signals are useful only in the nighttime, the lidar ratio is
obtained for aerosols in the lower troposphere.

－ 143 －

Fig. 1. Block diagram of the lidar system for the network.
The locations of the lidars in the NIES lidar network arelisted in Table 1. The lidars in Fukue, Hedo, Chiba, Seoul,
Phimai are operated in GEOSS/SKYNET. The lidars inToyama, Nagasaki, Matsue, Niigata, and Tokyo wereconstructed by the Ministry of the Environment of Japan in
the Asian dust monitoring program. All of the lidars in NIES

lidar network, except for cooperative stations, participate in
GALION.
A real-time data processing system was developed for thelidar network.8) It collects data from the lidar stations every
hour through the internet and processes the data to derive theattenuated backscattering coefficients at 532nm and 1064nm,
the total depolarization ratio at 532nm, and the extinction
coefficient estimates for non-spherical dust and sphericalaerosols.9-10) The method used for estimating contributionsof dust and spherical aerosols in an aerosol mixture is basedon the assumption that the observed aerosols are simple
external mixtures of dust and spherical aerosols withdifferent aerosol depolarization ratios. The automaticallyprocessed data and quick-look indications are posted on the
www page of NIES at http://www-lidar.nies.go.jp/.
A data analysis system was also developed for the Raman
signals to derive the extinction coefficient, the backscatter
coefficient, and the lidar ratio. Also an algorithm wasdeveloped for deriving black carbon concentration as well as
dust and spherical aerosols.11)

Table 1 NIES Lidar Network Stations－ 144 －

3. Studies Using the Lidar Network Data
3-1. Study of Asian dust
Almost all of the Asian dust events since 2001 where dust
was transported to Japan were observed with the lidar
network. The observed data were compared with thechemical transport model CFORS, and dust emission andtransport were studied.
A 4D-Var data assimilation system using the dust
extinction coefficient data from the lidar network wasdeveloped for Asian dust. It was demonstrated that theassimilation system was able to reproduce the surface
PM10, the MODIS aerosol optical depth, and the
CALIPSO/CALIOP data.12-15) 4D-Var data assimilation also
proved useful for better estimating the emission of dust in
the source region. The change in dust emission withvegetation growth in the Gobi desert in Mongolia wasstudied with the data assimilation for the dust events in thespring of 2007.16)
Studies of long-range transport of Asian dust are also
being conducted using the lidar network data, CALIPSO/CALIOP, and global chemical transport models.17-18)
3-2. Study of forest fire smoke
Smoke plumes from forest fires in Russia and Mongolia
are often observed in the spring. In June 2007, dense smoke
plumes originating from a forest fire in northern Mongolia
were observed in the upper troposphere with the lidars in
Nagasaki and Fukue. The smoke from the same origin was
also observed with an HSRL in Tsukuba. The opticalcharacteristics of the smoke were studied with thetwo-wavelength method and the HSRL. The result indicatedthe lidar ratio and the Angstrom exponent were comparable
to those in the previously reported forest fire smoke cases,
however the depolarization ratio was two to three times high(0.15). This suggests mixing with solid particles such as ash
and/or mineral dust in the strong convection in
pyrocumulonimbus. This raised a problem in the lidar data
analysis method to derive aerosol components and also inmodeling forest fires.19)
3-3. Climatology of aerosols
Climatological studies are also being done for both dust
and spherical aerosols using long-term lidar network data,
CALIPSO/CALIOP data, and chemical transport models.
The result of a study on the seasonal variation of sphericalaerosols in Beijing, Guangzhou, and Cape Hedo showedthat the vertical profiles of aerosols exhibited different
seasonal variation that can be explained by transport of air

pollution with the Asian monsoon system. It was also
demonstrated the aerosol profiles derived from theground-based lidar and CALIPSO agreed well, and theCommunity Multi-scale Air Quality Modeling System(CMAQ) reasonably reproduced the aerosol distribution andthe seasonal variation. The fraction of aerosol components(carbonaceous, sulfate, etc.) at different locations and thecontributions of source regions were studied using
CMAQ.20)

3-4. Data assimilation of aerosol climate model
One of the most important subjects in the applications of
the lidar network data to climate change studies is data
assimilation of aerosol climate models. Currently, studies ofdata assimilation including lidar data are being conducted atthree research groups in Japan. It would be useful to collect
all available ground-based lidar profiles (globally, including
past data) and provide them for data assimilation studies.
Acknowledgements
We thank Y. Fujiyoshi, T. Hayasaka, T. Takamura, S.
Mukai, I. Sano, S-W. Kim, N-H. Kim, C-H. Lee, J. Zhou, C.
Xie, Z. Wang, Y. Zhang, D. Batdorj, M. Hashizume, A.
Chabangborn, the Ministry of the Environment, Japan, andthe Sino-Japan Friendship Center for Environmental
Protection in Beijing for cooperation in operating the lidar
network. The SKYNET part of this work is supported by theObservational Research Project for Atmospheric Change in
Troposphere (GEOSS program) of the Ministry of
Education, Culture, Sports, Science and Technology, Japan.
The Raman scattering measurements and data analysis study
is supported by a Grant-in-Aid for Scientific Research in
Innovative Areas under Grant No. 4003 from the Ministry of
Education, Culture, Sports, Science and Technology, Japan.
The Asian dust study is supported by the Global
Environment Research Fund of the Ministry of theEnvironment, Japan (C-0901).
References
1) Sugimoto, N., Matsui, I., Shimizu, A., Nishizawa, T., Hara,
Y., Xie, C., Uno, I., Yumimoto, K., Wang, Z., Yoon, S-C.,“Lidar Network Observations of Troposheric Aerosols,”
Proc. SPIE 7153, doi: 10.1117/12.806540 (2008).
2) Shimizu, A., Sugimoto, N., Matsui, I., Tatarov, B., Xie, C.,
Nishizawa, T., and Hara, Y., “NIES Lidar Network;
Strategies and Applications,” 24th International Laser
Radar Conference 23-27 June 2008, Boulder (ISBN978-0-615-21489-4) 707-710 (2008).

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3) Sugimoto, N., Shimizu, A., Matsui, I., Itsushi, U., Arao,
K., Chen, Y., Zhao, S., Zhou, J. and Lee, C-H., “Study ofDust Transport Using a Network of Continuously
Operated Polarization Lidars,” Water, Air, and Soil
Pollution: Focus, 5, 145-157 (2005).
4) SKYNET www page. http://atmos.cr.chiba-u.ac.jp/
5) Bösenberg, J., et al., “Plan for the implementation of the
GAW Aerosol Lidar Observation Network GALION,”GAW Report No. 178 (2007).
6) Hoff, R. M., Bösenberg, J., and Pappalardo, G., “TheGAW Aerosol Lidar Observation Network (GALION),“the 24th International Laser Radar Conference 23-27
June 2008, Boulder, Colorado (ISBN 978-0-615-21489-4)
719-721 (2008).
8) Shimizu, A., Sugimoto, N., Matsui, I., Detailed
Description of Data Processing System for Lidar Network
in East Asia, 25th International Laser Radar Conference,
911-913 (2010).
9) Sugimoto, N., Uno, I., Nishikawa, M., Shimizu, A.,
Matsui, I., Dong, X., Chen, Y. and Quan, H., “Record
Heavy Asian Dust in Beijing in 2002: Observations andModel Analysis of Recent Events,” Geophys. Res. Lett.,
30, 1640, doi:10.1029/2002GL016349 (2003).
10) Shimizu, A., Sugimoto, N., Matsui, I., Arao, K., Uno, I.,
Murayama, T., Kagawa, N., Aoki, K., Uchiyama, A. andYamazaki, A., Continuous observations of Asian dust andother aerosols by polarization lidar in China and Japan
during ACE-Asia, J. Geophys. Res., 109, D19S17,
doi:10.1029/2002JD003253 (2004).
11) Nishizawa, T., Sugimoto, N., Matsui, I., Shimizu, A.,
Tatarov, B., Okamoto, H., “Algorithm to retrieve aerosol
optical properties from high spectral resolution lidar andpolarization Mie-scattering lidar measurements,” IEEE
Trans. Geos. Rem. Sens., 46, 4094-4103 (2008).
12) Yumimoto, K., Uno, I., Sugimoto, N., Shimizu, A.,
and Satake, S., “Adjoint Inverse Modeling of Dust
Emission and Transport over East Asia,”
2006GL028551R, Geophys. Res. Lett., 34, L08806,
doi:10.1029/2006GL028551 (2007).
13) Yumimoto, Y., Uno, I., Sugimoto, N., Shimizu, A., Liu,
Z. and Winker, D. M., “Adjoint inversion modeling of
Asian dust emission using lidar observations,” Atmos.
Chem. Phys. Atmos. Chem. Phys., 8, 2869-2884 (2008).
14) Uno, I., Yumimoto, K., Shimizu, A., Hara, Y., Sugimoto,N., Wang, Z., Liu, Z. and Winker, D. M., “3D Structure of
Asian Dust Transport revealed by CALIPSO Lidar and a
4D VAR Dust Model,” Geophys. Res. Lett., 35, L06803,
doi:10.1029/2007GL032329 (2008).

15) Hara, Y., Yumimoto, K., Uno, I., Shimizu, A., Sugimoto,N., Liu, Z. and Winker, D. M., “Asian dust outflow in the
PBL and free atmosphere retrieved by NASA CALIPSO
and an assimilated dust transport model,” Atmos. Chem.
Phys., 9, 1227–1239, (2009).
16) Sugimoto, N., Hara, Y., Yumimoto, K., Uno, I.,
Nishikawa, M., and Dulam, J., “Dust emission estimatedwith an assimilated dust transport model using
lidar network data and vegetation growth in theGobi desert in Mongolia, “SOLA, Vol. 6, 125-128,
doi:10.2151/sola.2010-032 (2010).
17) Uno, I., Eguchi, K., Yumimoto, K., Takemura, T.,
Shimizu, A., Uematsu, M, Liu, Z., Wang, Z., Hara Y., and
Sugimoto, N., “Asian dust transported one full circuit
around the globe,” Nature Geoscience, Published Online:
20 July 2009, DOI: 10.1038/NGEO583 (2009).
18) Yumimoto, K., Eguchi, K., Uno, I., Takemura, T., Liu, Z.,
Shimizu, A., and Sugimoto, N., “An elevated large-scale
dust veil from the Taklimakan Desert: Intercontinental
transport and three-dimensional structure as captured by
CALIPSO and regional and global models,” Atmos.
Chem. Phys., 9, 8545–8558 (2009).
19) Sugimoto, N., Tatarov, B., Shimizu, A., Matsui, I., andNishizawa, T., “Optical Characteristics of Forest-Fire
Smoke Observed with Two-Wavelength Mie-Scattering
Lidars and a High-Spectral-Resolution Lidar over Japan,”
SOLA, Vol. 6, 093-096, doi:10.2151/sola.2010-024(2010).
20) Hara, Y., Uno, I., Shimizu, A., Sugimoto, N., Matsui, I.,
Kurokawa, J., Ohara T., Liu, Z., Zhang, Y., Liu, X., Wang,
Z., “An Integrated Analysis of Spherical Aerosol
Distribution in Eastern Asia Based on Ground/Spaced-Based Lidar and a Chemical Transport Model,”25th International Laser Radar Conference, 449-452(2010).

－ 146 －

Shortwave versus longwave aerosol radiative forcing over an urban Environment.
A.S. Panicker1, G. Pandithurai2, T.Takamura3, Dong-In Lee1
1
Global Research Laboratory, Pukyong National University, 599/1 Daeyeon 3 Dong, Nam Gu,
Busan 608737, South Korea.
2
Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Pune, India
3
Centre for Environmental Remote Sensing, Chiba University, Japan
Abstract
Collocated observations of aerosol optical parameters, radiative fluxes in the longwave (0.4-50µm) andshortwave (0.3-3 µm) spectral regions were used to quantify aerosol radiative forcing in the longwave (LW) andshortwave (SW) spectral regions over an Indian urban site, Pune during October 2004 to May 2005. Aerosol
optical parameters derived from a Prede Sun/Sky radiometer such as aerosol optical depth (AOD), single
scattering albedo, asymmetry parameter, TOMS column ozone and MODIS water vapor were used in Santa
Barbara Discrete ordinate Rdaitive Transfer model (SBDART) to derive short and long wave radiative fluxes.
Short wave fluxes were found to be comparing well with the real time observed fluxes from a Short wave
Pyranometer. The under estimation of modeled long wave fluxes got improved and found to be comparable withobserved fluxes from a Precision infrared radiometer, on the inclusion of six hourly NCEP profiles of temperatureand humidity in SBDART model. ‘No aerosol’ fluxes were also derived using SBDART in both the spectral region.
The differences in fluxes with and without aerosol conditions were calculated to derive radiative forcing. The
short wave radiative forcing during different seasons viz. Post monsoon (October, November), winter (Dec-Feb)
and Pre-monsoon (March- May) respectively found to be -36, -33 and -44 Wm-2 at the surface and +0.5,-0.55,+0.4 Wm-2 at the Top of the Atmosphere (TOA) . The corresponding long wave enhancement found to be +8.5, +8,+10.5 Wm-2 at the surface and, +3.5, +3 and +4.7 Wm-2 at TOA. The study suggests that around 23-25% of
aerosol cooling in the short wave region is being compensated by long wave enhancement at the surface.
1. Introduction
Aerosols, which are microscopic particles suspended in
the atmosphere are key components of the climate system.
Apart from their crucial role in air pollution, aerosols
influence the climate directly and indirectly throughradiative forcing. Direct radiative forcing includes thescattering and absorption of the incoming solar radiation by
aerosols, which in turn cools the earth’s surface (Badrinathand latha, 2006; Takamura et al., 2007; Panicker et al., 2008).
Aerosols influence the climate indirectly by altering cloud
microphysical properties and hence increasing albedo andcloud life time (Twomey, 1977). Several studies reportaerosol radiative forcing in the shortwave spectral region(Conant, 2000; Jayaraman et al., 1998). But it is shown thataerosol long-wave forcing is of comparable magnitude asthat of greenhouse gases in heating the earth’s surface and in
modifying earth’s energy balance (Vogelmann et al. 2003).
Direct observations of aerosol IR forcing are rare either at
the surface (e.g., Lubin and Simpson, 1994; Spankuch et al.
2000), in the atmosphere (Highwood et al., 2003), or at the
top of the atmosphere (Ackerman and Chung, 1992; Hsu etal., 2000). Thus, our understanding of IR aerosol forcing islargely model-based with few direct observations available
for validations (Vogelmann et al., 2003). In this scenario, weare trying to estimate aerosol radiative forcing in short andlong wave spectral regions, to find the exact modulation ofshort wave cooling by longwave enhancement at the surface.

2. Instrumentation and Data
The instruments used in this study are (i) Prede sun/sky
radiometer; (ii) Pyranometer and (iii) an Infrared radiometer(Pyrgeometer) are installed in the roof of Indian Institute ofTropical Meteorology [IITM], Pune [18º32’ N ,73º51’ E, 559
m AMSL]. The Prede sky radiometer is an automatic sun
tracking instrument, which is capable of measuring direct
solar and diffuse sky radiance at five spectral channels. The
filters are centered at wavelengths 400, 500, 675, 870 and
1020 nm and a detailed description of calibration
methodology and data reduction procedures of this
instrument are presented in Nakajima et al. (1996). Oncea month, sky radiometer is operated to collect disc sans toestimate solid view angles at different wavelengths as part ofthe calibration and used in data processing. Absolutecalibration is done by PREDE, Japan at the factory and the－ 147 －

the site, we incorporated six hourly NCEP profiles fromsurface to 300 mb level in SBDART and diurnal LW fluxes
were simulated for all the 21 selected days. The introductionof NCEP profiles reduced the mean bias from -4.6 to -2.2
Wm-2. A scatter plot between observed and modeled LW
fluxes with tropical model and realistic six hourly NCEP
profiles are shown in figure 1.

a 380
-2

-2

Modeled LW flux (Wm )last such calibration was done in December 2003. Also, onvery clear sky days, absolute calibration constant V0(λ) isestimated using the modified Langley plot technique andfound to be consistent with the manufacturer calibration(Pandithurai et al. 2004). A Kipp and Zonen ventilated
Pyranometer [Model CM21] and a Pyrgeometer [Eppley
Model PIR] are used to measure down-welling short- andlong-wave radiative fluxes respectively. Both sensors are
ventilated, inspected and cleaned daily to avoid datacontamination.

4. Results and Discussions
4.1. Comparison of Observed and Modeled fluxes
On comparing, modeled Short wave fluxes were found to
be matching well with the real time observed fluxes from a
Short wave Pyranometer. Longwave fluxes have been
estimated for 21 clear sky days during winter 2004-05 at halfhourly intervals with SBDART and were compared with
Pyrgeometer (PIR) observed fluxes. The fluxes derived withdefault tropical profile were compared with a mean bias of-4.6 Wm-2. The underestimation of fluxes may be due tofixed tropical model profiles of temperature and humidity
used for the entire day. In the longwave, aerosol volume
extinction depends more strongly on relative humidity thanin most of the shortwave, implying that realistic relative
humidity profiles must be taken in to account whilemodeling the longwave fluxes (Lubin et al., 2002). It is also
shown that simultaneous measurements of vertical
distribution of aerosols, surface temperature and water vapor
are critical to the understanding of LWARF (Zhang andChristopher, 2003). Hence, it is proposed to use temporalvariation of realistic vertical profiles of atmospherictemperature and humidity. As such observations on verticalprofiles of temperature and humidity were not available over

340

320

300

280
280

300

320

340

360

380

2

b

Observed LW flux(W/m )
380
-2

-2

Modeled LW flux (Wm )3. Methodology
Aerosol optical parameters derived from a Prede Sun/Sky
radiometer such as aerosol optical depth (AOD), single
scattering albedo, asymmetry parameter, TOMS column
ozone and MODIS water vapor were used in Santa Barbara
Discrete ordinate Rdaitive Transfer model (SBDART) to
derive short and long wave radiative fluxes. The derived
short-long wave fluxes were compared with the observed
fluxes from A Pyranometer and Pyrgeometer respectively.
‘No aerosol’ fluxes were also derived using SBDART in
both the spectral region. The differences in fluxes with andwithout aerosol conditions were calculated to deriveradiative forcing.

360

Mean Bias = -4.6Wm
Y= 0.65*x+119.2
R =0.88360

Mean Bias= -2.17Wm
y= 0.86*x+43.58
R=0.96

340

320

300

280
280

300

320

340

360

380

-2

Observed LW flux (Wm )Figure 1: Comparison of Observed fluxes with Modeled LW
fluxes (a) with Default model profile (b) with NCEP 6-hr
profiles during winter 2004-05.
4.2. Estimation of Aerosol short- Long wave radiative
forcing
Aerosol Short- long wave radiative forcing has been
estimated as described in section 3.
The short wave radiative forcing during different seasons
viz. Post monsoon (October, November), winter (Dec-Feb)
and Pre-monsoon (March- May) respectively found to be-36, -33 and -44 Wm-2 at the surface and +0.5,-0.55, +0.4

－ 148 －

Wm-2 at the Top of the Atmosphere (TOA), which are
directly correlated with the aerosol loading during respective
periods. The positive TOA forcing is attributed to the highabsorbing type aerosols present over the experimental station.
Aerosol longwave radiative forcing has been estimatedusing realistic NCEP profiles instead of model profile, alongwith other inputs specified in section 3. The long wave
enhancement found to be +8.5, +8, +10.5 Wm-2 at thesurface and, +3.5, +3 and +4.7 Wm-2 at TOA. The study
suggests that around 23-25% of aerosol cooling in the shortwave region is being compensated by long wave
enhancement at the surface.
Seasonsa

Post-Monsoon 04

WInter

Pre-Monsoon

0-5

Raiative forcing (Wm-2)

-10
-15
-20
-25

Acknowledgements
Authors acknowledge Prof. B.N. Goswami, director IITM,
Dr. R. Krishnan and Dr. P.C.S. Devara for theirencouragements. A.S.Panicker and Dong-In Lee gratefully
acknowledge theSupport from National Research
Foundation of Korea (NRF) through a grant provided by the
Korean Ministry of Education, Science & Technology(MEST) in 2010 (No. K20607010000).

-30
-35
-40

Surface forcing
TOA forcingb
10

LWARF_surface
LWARF_TOA

8-2

LW ARF(W m )spectral regions over an Indian urban site, Pune during
October 2004 to May 2005. The short wave radiative forcing
during different seasons viz. Post monsoon (October,
November), winter (Dec-Feb) and Pre-monsoon (MarchMay) respectively found to be -36, -33 and -44 Wm-2 at thesurface and +0.5,-0.55, +0.4 Wm-2 at the Top of the
Atmosphere (TOA). The corresponding long wave
enhancement found to be +8.5, +8, +10.5 Wm-2 at thesurface and, +3.5, +3 and +4.7 Wm-2 at TOA. The study
suggests that around 23-25% of aerosol cooling in the shortwave region is being compensated by long wave
enhancement at the surface.

6

4

2

0
post-monsoon2004

Winter2004-05

Pre-Monsoon

SeasonFigure 2: Estimated radiative forcing at surface and TOA (a)for shortwave fluxes, (b) For Longwave fluxes

5. Summary and Conclusions
Collocated observations of aerosol optical parameters,
radiative fluxes in the longwave (0.4-50µm) and shortwave(0.3-3 µm) spectral regions were used to quantify aerosol
radiative forcing in the longwave (LW) and shortwave (SW)

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Heymsfield (2002), Long–wave radiative forcing ofIndian Ocean tropospheric aerosol, J. Geophys. Res.,
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dispersions, Appl. Opt, 35, 2672-2686.
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Kewat (2008), Observations of enhanced aerosol
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Takenaka (2007), Aerosol radiative characteristics at
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Development of correction method for integrating nephelometer and recent trend of aerosol
optical properties based on ground-based measurement at Tsukuba
A. Uchiyama, A. Yamazaki, R.Kudo, and T. Sakami
Meteorological Research Institute, Japan Meteorological Agency,
1-1, Naganine, Tsukuba, Ibaraki, JAPAN 305-0052
uchiyama@mri-jma.go.jp
Abstract
Measurements of Integrating nephelometer has error due to the truncation of light detection area. We
developed a correction method using multi-wavelength nephelometer and absorption photometer. The accuracy ofthe method was evaluated using the simulation data. Applying this method to the data measured at Tsukuba, therecent trend of aerosol optical properties was investigated.
Keywords : Integrating Nephelometer, PSAP, Scattering Coefficient of Aerosol, Absorption coefficient of Aerosol,
Single scattering albedo of aerosol, asymmetry factor of aerosol
1. Introduction
The optical property of aerosol is one of the importantparameters for the earth radiation budget and the atmosphericenvironment. In order to investigate the optical property ofaerosol and to monitor the change of atmosphericenvironment, scattering and absorption coefficients ofaerosol are measured at ground-based observation sites. The
scattering coefficient is frequently measured using an
integrating nephelometer. The integrating nephelometercannot measure the light scattered to the extreme forward
direction (scattering angle is nearly 0 degree.) and theextreme backward direction (scattering angle is nearly 180
degree.). Therefore, even if the distribution of light source inthe nephelometer is correct, the scattering coefficientmeasured by the nephelometer is underestimated. In thisstudy, the method to correct the scattering coefficientsmeasured using the three wavelength nephelometer and three
wavelength absorption photometer is developed. The
accuracy of the method is evaluated by the simulation using
OPAC (Hess et al, 1998) model. The method is a statistical
retrieval one and the volume size distribution and thecomplex refractive index are simultaneously estimated.
Applying this method to the data measured at Tsukuba
during the period from 2002 to 2010, the trend of opticalproperties of aerosol was investigated.
2. Methods
We use statistically optimized estimation method. The
optimized estimate of the parameter is considered to be avalue that maximized the following likelihood function

L(xb , f o , x) = Pb ( xb ) Po (f o )=1(2π )( n + m ) / 2 B

1/ 2

R

1/ 2

1
⎡ 1
⎤
× exp ⎢ − (x − xb )T B −1 (x − xb ) − (f(x) − f o )T R −1 (f(x) − f o ) ⎥2⎣ 2⎦

When the following function J is minimum, the above
function is maximized.
1
J (x) = (x − xb )T B −1 (x − xb )2
1
+ (f (x) − f o )T R −1 (f (x) − f o )2, where x is parameter to be determined, xb is the initial
estimate, f0 is measurement, f(x) is measurement estimatedby forward model. B and R are covariance matrices.
In this study, scattering and backscattering coefficientsmeasured by TSI model 3563 at the wavelength of 450, 550
700nm and absorption coefficients measured by Particle
Soot Absorption Photometer (PSAP) at wavelength of 462,
526, 650nm are considered as measurement values.
Given the measurement values, we estimate the size
distribution of aerosol and complex refractive index using theabove method. Using the estimated parameters, we calculate
the single scattering properties (scattering coefficients, single
scattering albedo, and asymmetry factor) based on Mie
Theory. These values are regards as the corrected scattering
coefficients and single scattering albedo.

3. Simulation of measurement
The accuracy of the method was evaluated using simulation
data. The simulated data is calculated based on OPAC (Hesset al, 1998) model. OPAC model consist of 10 aerosol types.

－ 151 －

Each aerosol type is external mixture of basic models. The
single scattering properties of mineral dust are calculatedusing the database for spheroid by Dubovik et al. (2006).
The single scattering properties of the other aerosol arecalculated based on Mie theory. The truncation of integrating
nephelometer is taken into consideration. Each aerosol typeincludes hygroscopic aerosol. Therefore, scattering properties
for each aerosol type depend on relative humidity.

4．Parameter setting and initial value
The statistical optimized method needs the error
covariance for each data of measurements and the initial
estimates of parameters to be retrieved. We assume that theoff-diagonal elements of covariance matrices are zero anddiagonal elements are non zero. The following standard
deviations (σ) are used in our method.
Scattering and back scattering coefficients:
σ=0.5E-06 (1/m)
Absorption coefficient:
σ=0.5E-06 (1/m)
Index of refraction (real part):
σ=(log(1.6)-log(1.4))/2～0.029
Index of refraction（imaginary part）:
σ=(log(0.05)-log(0.005))/2～0.5
Volume size distribution :σ=Δlog(dV/dlogR)～2
Smoothing constrain is also treated as a measurement. It is
assumed that the second derivative is zero.
Wavelength dependence of index of refraction:difference between next points is 5%.
Volume size distribution spectrum:
difference between next points is factor 2.
The range of particle radius is R=0.00631 to 25.1 μm anddivided into 19 bins with ΔlogR＝0.2 width. The range of
wavelength is λ=0.316 to 1.0 μm and divided into 6
wavelength with Δlogλ＝0.1.
The power low distribution is assumed as the initial
estimate of number size distribution.
r < 0.05μ m

dN / dr = C= Cr− (α + 3)

0.05μ m ≤ r < 8μ m

= Cr

−6

8.0μ m ≤ r, where α is Ångström exponent.
It is assumed that the initial real part of index of refractionis 1.5. The imaginary part is determined as the estimatedsingle scattering albedo (SSA) at each wavelength is equal tomeasurement SSA. When we minimize J(x),Gauss-Newton method is used and the initial estimate is
replaced by the solution of the previous step.

5. Test results
In Fig. 1, the comparison among truth, measurement andcorrected SSA at the wavelength of 550nm is shown. In
every case, corrected values are closer to the truth one than
measurements. In Desert model (5), the truth is calculated
by non-spherical particle model. Even in this case, thecorrection by spherical model works well. The RMS
difference between truth and measurement values is 0.009 to
0.015. The RMS difference between truth and correctedvalues is 0.002 to 0.004; relative error is 0.3 to 0.5%.

(a)

(b)

Fig. 1. (a) Comparison among truth, measurement (opencircles) and corrected (open squares) single scattering
albedo (SSA) at 550nm. (b) difference of SSA
between truth and measurement (open circles) anddifference of SSA between truth and corrected one(open squares). Model no. = OPAC model no. x10 +humidity ID(1-8). Relative humidity values are 0, 50, 70,
80, 90, 95, 98, 99%.
We also examined accuracy of corrected (estimated)values in the cases that scattering and absorption coefficients
have error. In Fig. 2, the comparison among truth,
measurement and corrected SSA at 550nm is shown in thecase of absorption coefficient with +3% systematic errors.
When SSA is small, the error becomes large. But, in mostcases, the influence of error was small. We also examined－ 152 －

accuracy in the case of absorption coefficient with +5%
errors (Figures are not shown here). Even in this case, theinfluence of error was small in most cases, because thescattering coefficients are larger than absorption coefficientsby several times or one order.

(a)

(b)

Fig. 2 Same as Fig. 1 except results in the case ofabsorption coefficient with 3% error.
Though we do not show figures here, the following
additional results were obtained.
(1) The measured scattering and absorption coefficients arereconstructed within RMS 3E-07(1/m) and 8E-08(1/m),
respectively.
(2) The scattering coefficients are estimated within RMS
1.2E-03 (1/m). When particles are large (for example,
relative humidity is high), error is large.
(3) The extinction coefficient shows the same tendency as thescattering coefficients.
(4) The 1% and 3% error of scattering coefficient do notaffect to the SSA estimation. In the case of 5% error, the errorof the estimated SSA is the same order as the SSA calculatedusing measurement values.
(5) The RMS error of estimated asymmetry factor is 0.03.
(6) The small structure of size distribution cannot be
estimated. In many cases, the mono-modal size distribution

is estimated, even if the original size distribution is bi-modal
one.
(7) When smoothing constrain of size distribution is strict,there is a tendency that the size distribution in the region oflarger particle size becomes large as the particle size
increased.
(8) The correlation between the original and estimatedeffective radiuses is 0.85. But, the estimated effective radius
is 1.6 times as large as the original one.
(9) The volume weighted index of refraction is not close to
the estimated one. The wavelength dependence is similar
to the volume weighted one.

６．Aerosol optical properties at Tsukuba
Applying the above method to the data measured at
Tsukuba during the period from 2002 to 2010, the trend of
optical properties of aerosol was investigated.
The scattering coefficients are measured using TSI model
3563 at 3 wavelengths 450,550,700nm during the period
from January 2002 to present. The absorption coefficient ismeasured using PSAP at a wavelength 565nm during thesame period as TSI nephelometer.
The absorption
coefficients are measured using PSAP at 3 wavelengths 462,
526, 650nm during the period from March 2006 to present.
We only analyzed the data in the dry condition; the relative
humidity in nephelometer inlet is less than 50%. When thedata of one wavelength PSAP is used, it is assumed that the
wavelength dependence of absorption coefficient is λ−1.1. 1.1
is the mean value from 2006 to 2010.
In Fig. 3, scattering and absorption coefficients are shown.
After 2006, both scattering and absorption coefficients are
decreasing. The change of absorption coefficients (Cabs)
shows seasonal variation. In the winter season, Cabs is large
and in the summer season, Cabs is small.
In Fig. 4, SSA at the wavelength of 550nm is shown. The
large part of SSA (550nm) is between 0.8 and 0.9 and almost
constant. After 2007, SSA(550nm) is slightly increasing. Thechange of SSA shows seasonal variation. In the winterseason, SSA is small, and in the summer season, SSA is large.
This seasonal variation is consistent with the change ofabsorption coefficient.
In Fig. 5, asymmetry factor (g) at the wavelength of
550nm is shown. g(550nm) is between 0.5 and 0.7. After
2007, g(550nm) is slightly decreasing. The change ofasymmetry factor also shows seasonal variation. In thewinter season, g(550nm) is small, and in the summer season,g(550nm) is large.
Though figures are not shown here, the changes of－ 153 －

Ångström exponent and effective radius (Reff) show
seasonal variation. In the winter season, Ångström exponent
is high and in the summer season, Ångström exponent is low.
In winter season, Reff is small, and in the summer season,
Reff is large. After 2008, Ångström exponent is slightly
increasing and Reff is slightly decreasing. The trend ofg(550nm) is consistent with those of Ångström exponent and
Reff.

7．Summary
We examined accuracy of the method developed usingthat simulation data based on OPAC model. The measured
scattering and absorption coefficients can be reconstructedwithin RMS =2.4 to 3.1E-07(1/m), 0.4 to 0.8E-07(1/m).
SSA can be estimated with RMS =0.002～0.004 (relative
error 0.3～0.5%). In all cases, the estimated SSA is closer tothe truth than the non-corrected measurement one. The RMS
error of corrected scattering coefficients was
1.2E-05(1/m), and when the aerosol consists of the larger
size particles (for example, relative humidity is high case),
the accuracy of estimated scattering coefficient is not good.
The RMS error of asymmetry factor is 0.03.
In our method, volume size distribution and index ofrefraction are retrievel. The small structure of size
distribution cannot be estimated. The correlation between theoriginal and estimated effective radiuses is 0.85. But, theestimated effective radius is 1.6 times as large as the originalone. We compared between the estimated index of refraction
and the volume weighted one, but it is not found thequantitative coincidence between both indexes of refraction.
The wavelength dependence was qualitatively coincident.
Applying this method to the data measured during 2002 to2010 at Tsukuba, the trend of aerosol optical properties isinvestigated. After 2006, the scattering and absorption
coefficients are gradually decreasing. SSA is almost constant,but after 2007, SSA is slightly increasing. The asymmetry
factor and Ångström exponent of extinction coefficient also
has similar trend. After 2007, the asymmetry factor andÅngström exponent are slightly decreasing and increasing,respectively.

and I. Slutsker, 2006: Application of spheroid models toaccount for aerosol particle nonsphericity in remote sensingof desert dust, J. Geophys. Res., 111, D11208,
doi:10.1029/2005JD006619.

Fig. 3 monthly mean values and standard deviationof scattering coefficients at 550nm and absorption
coefficient at 526nm. Open symbols mean that thecorrection was made using 3wavelngth (3λ)
nephelometer and 1wavelength (1λ) PSAP. Closed
symbols mean that the correction was made using 3λnephelometer and 3λ PSAP.

References
1) Hess M., P. Koepke, and I. Schult, 1998: Optical
Properties of aerosols and clouds: The software package
OPAC, Bull. Amer. Meteor. Soc., 79, 831-844.
2)Dubovik, O., A. Sinyuk, T. Lapyonok, B. N. Holben, M.
Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Muñoz, B.
Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin,－ 154 －

Fig. 4 Same as Fig. 3 except SSA.

Fig.5 Same as Fig. 3 except asymmetry factor.

Use of spectral irradiances measured at surface to retrieve aerosol optical parameters
Pradeep Khatri1, Tamio Takamura1, Akihiro Yamazakai2, and Yutaka Kondo3
1
Center for Environmental Remote Sensing , Chiba University,Chiba, Japan
2
Meteorological Research Institute, Tsukuba, Japan
3
Research Center for Advanced Science and Technology, The University of Tokyo
Abstract
This study presents a method to retrieve key aerosol optical parameters such as aerosol optical thickness andsingle scattering albedo using spectral direct and diffuse irradiances measured at surface by taking into accountthe cosine error correction factors in detail. The proposed method is applied to observed spectral irradiance data at
Hedo observation site of SKYNET network. We found very good agreement for aerosol optical thickness andreasonable agreement for single scattering albedo with results from sky radiometer.
Keywords: Aerosol optical thickness, single scattering albedo, cosine error correction factor
1. Introduction
Aerosols are known to play important roles on
atmospheric heat budget and climate change through theirdirect and indirect effects. Due to the importance of aerosols
on climate change study, they have received considerable
interests in the recent years. As a result, aerosols are being
monitored by several space- and ground- based remotesensing approaches. Aerosol optical parameters obtained
from ground-based remote sensing methods are widely used
to validate results from several satellites as well as numericalmodel simulations. Among several ground-based remotesensing networks in the world, SKYNET network hasmonitoring sites in different parts of the Asia. Sky
radiometer, which measures aerosol optical and physicalcharacteristics, is the key instrument of this network. In orderto validate aerosol optical parameters obtained from sky
radiometer, a new radiometer that can measure spectral
diffuse, direct, and global irradiances is installed at some key
SKYNET sites. This study is dedicated to develop algorithm
for such newly developed radiometer.
2. Data
We used irradiances measured by spectral radiometer(MS-700) with automated shadow band (Manufacturer:
EKO Co., Ltd., Japan). The measurement sequence starts
with a measurement made at nadir, i.e., total horizontal
irradiances. The band is then rotated so that threemeasurements are made in seqence; the middle one blocks
the sun and other two block strips of sky 90 either side. Suchside measurements are used to correct diffuse irradiance bytaking into account the excess sky blocked by the band when
the sun-blocking measurements are made. Direct irradiances

can be obtained from measured direct and diffuse irradiances.
The spectral range of this instrument is from 301.1 nm to1147.7 nm with resolution of 3.3nm.Along with suchmeasured spectral irradiances, we used other data such asozone concentration from TOMS, spectral surface
reflectance from MODIS, precipitable water content (PWC)
from microwave radiometer in our algorithm. Aerosol
optical parameters obtained from sky radiometer were used
to compare our retrieved results.

3. Description of an algorithm
Fig. 1 shows a simple flow chart for the retrieval ofaerosol optical thickness and single scattering albedo using
spectral irradiances measured by MS-700. For our retrieval,
we carefully selected the wavelengths at which absorptions
by atmospheric constituents such as ozone, water vapor etc.
are negligible. The selected wavelengths are 400nm, 500nm,
675nm, 870nm, and1020nm.The cosine error correction
factors for direct irradiances at certain zenith and azimuth
angles, which were provided by the company, wereextrapolated/interpolated to zenith angle ranging from 0 to
900 and azimuth angle ranging from 0 to 3600 at the step of0.50. One can calculate the solar position from given latitude,
longitude, and observation time, and apply suchinterpolated/extrapolated data to correct spectral direct
irradiances measured by MS-700. After correcting MS-700
measured direct irradiances, Beer-Lambert law was used to
calculate total optical thickness. By subtracting Rayleighoptical thickness and ozone optical thickness from the total
optical thickness, aerosol optical thickness (AOT) was
calculated.

－ 155 －

Fig. 1. A simple flow chart showing the retrieval method of aerosol optical thickness and single scattering albedo
using spectral irradiances measured by MS-700.

－ 156 －

Single scattering albedo(SSA) is the key optical parameterof aerosol. We used radiative transfer (RT) model to estimate
SSA. In our method, at first AOT from MS-700, asymmetry
parameter from sky radiometer, PWC from microwave
radiometer, ozone concentration from TOMS, spectral
surface reflectances from MODIS and 31 SSA values
ranging from 0.7 to 1.0 at an interval of 0.01 were inputtedin RT model to estimate radiances at different directions(zenith angle: 0 to 900 and azimuth angle: 0 to 3600).
Therefore, we calculated 31 sets of radiance data at different
directions. Such modeled radiances were converted toMS-700 equivalent radiances by applying fine gridded
cosine error correction factors for direct irradiances as
described above. MS-700 equivalent radiances are definedas such radiances whose integration over the full zenith andazimuth angles should give diffuse irradance equivalent tothat measured by MS-700. By integrating MS-700
equivalent radiances at different directions for each value ofSSA, we obtained 31 values of MS-700 equivalent diffuse
irradiances. One can directly compare such 31 values of
MS-700 equivalent diffuse irradiances with directly
measured MS-700 diffuse irradianceto find the most plausible value of SSA. This method cangive correct SSA if measured diffuse irradiance by MS-700
is highly qualitative. On the other hand, one may estimate 31
values of MS-700 equivalent diffuse/global ratio or
diffuse/direct ratio and compare with respective measured
component to find the most plausible value of SSA. The
latter method is more suitable if the calibration constant of
the instrument is not very accurate.

4. Results and discussion
We applied the above-mentioned algorithm to datameasured by MS-700 at Hedo observatory of SKYNET
network. We used data observed in the spring season(March, April, and May) of 2009. As an example ofretrieved AOTs from MS-700 instrument, we show
comparison of MS-700 result with that of sky radiometerat 500nm in Fig. 2. For comparison, we selected Level
2.0 data of sky radiometer, which were analyzed using
SKYRAD.pack software1) and cloud screening algorithm
2). As shown in Fig. 2, the values of AOTs retrieved
from MS-700 agreed quite well with sky radiometer at
500nm by falling data around 1:1 line. The RMSE fordata shown in Fig. 2 is 0.024. Though the results for otherwavelengths are not shown here, the values of RMSE forthe wavelengths of 400nm, 675nm, 870nm, and 1020nm
were 0.055, 0.021, 0.020 and 0.026, respectively. Thoseretrieved results with will be further improved by
performing sensitivity studies with different solar
spectrum data and technique of picking out solar
spectrum data at the wavelengths of our interest.
We retrieved SSA values by applying the first method
described in section 3 i.e. we compared measured diffuse
irradiance with 31 values of modeled MS-700 equivalentdiffuse irradiances.
This is because we verified the calibration constant of
MS-700 provided by company by performing closure
experiment with standard grating sun photometer(GER-2600). Fig. 3 shows the Comparison of MS-700
measured diffuse irradiance and MS-700 equivalentmodeled diffuse irradiance at the wavelength of 500nm.

Fig. 2. Comparison of aerosol optical thickness (AOT) at
500nm obtained from MS-700 and sky radiometer.

Fig. 3. Comparison of MS-700 measured diffuse irradianceand MS-700 equivalent modeled diffuse irradiance at the
wavelength of 500nm.

－ 157 －

Fig. 4. Time series of SSA values at 500nm retrieved from MS-700 and sky radiometer
Fig. 4 shows the time series of SSA values at 500nm
retrieved from MS-700 and sky radiometer. As shown in Fig.
4, the values of SSA at 500nm agreed reasonably. For otherwavelengths (not shown here), SSA also agreed reasonably.
However, there were some large distinct differences at someobservation point at those wavelengths. Based on oursensitivity study, it was observed that retrieved SSA values
from data of both MS-700 as well as sky radiometer depend
on a number of input parameters. As a part of validating
retrieved SSAs from both MS-700 and sky radiometer,
spectral SSAs from each instrument as well as othernecessary parameters were inputted in a radiative transfer
model to estimate broad band diffuse fluxes(0.315-2.8µm),
which were then compared with directly measured data. For
MS-700 data, the relationship was Modeled
flux=1.05*Measured flux with R2=0.983. For sky
radiometer data , For sky radiometer data, the relationshipwas Modeled flux=1.08*Measured flux with R2=0.924.
Similarly, the values of RMSE between modeled andmeasured fluxes for MS-700 and sky radiometer data were
8.15Wm-2 and 14.47Wm-2, respectively.

Acknowledgements
This research is performed as a part of the SKYNET
activities by the Observational Research Project for
Atmospheric Change in the Troposphere (GEOSS program)of the Ministry of Education, Culture, Sports, Science andTechnology, Japan. This research is also partially supportedby the Global Environmental Research Fund (B-083) of the
Ministry of the Environment, Japan.
References
1) Nakajima et al.: Use of sky brightness measurementsfrom ground for remote sensing of particulate
polydispersions. Appl. Opt., 35, 2672-2686, 1996.
2) Khatri, P., and T. Takamura: An algorithm to screen
cloud-affected data for sky radiometer data analysis, J.
Meteor. Soc. Japan, 87, 189-204.

－ 158 －