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GALION WS, 20-23 Sep 2010, Geneva
Nobuo SUGIMOTO National Institute for Environmental Studies
Atsushi Shimizu, Ichiro Matsui, Tomoaki Nishizawa, Boyan Tatarov, Yukari Hara, Tamio Takamura, Soon-chang Yoon, Zifa Wang, Itsushi Uno, …
NIES lidar network (1)AD Net stations
Ryori
Gwangju
Taipei
Mineral dust
Forest fire
Industrial
Biomass burning
NIES lidar network (2)
Two-wavelength (1064nm, 532nm) Mie-scattering lidar with polarization channels at 532nm. (Raman receivers (607nm) are being added at several observation sites.)
Realtime data processing system
Extinction coefficient estimates of dust (left) and spherical aerosols (right) for primary locations (April 2009).
Dust event
(NIES Lidar Network)
Research programs and international cooperation
-Research on Asian dust in the Research Program of Ministry of the Environment of Japan
-Research on regional air pollution in the Research Program of Ministry of the Environment of Japan
-Research on the effects of aerosols on plants and human health with Ministry of Education Science and Technology of Japan
International cooperation-Working group on Dust and Sand Storm under Japan-China-Korea Tripartite Environment Ministers Meeting (TEMM) (Data sharing and model inter-comparison)
-WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) (Realtime data sharing and model intercomparison in Asian node)
-Plan to cooperate with the Seven Southeast Asian Studies (7SEAS)
-GALION
(NIES Lidar Network)
NIES lidar network (3)
4D-Var data assimilation system for Asian dust
4DVAR data assimilation of Asian dust using the NIES lidar network data (Yumimoto et al. 2007, 2008)
Comparison of the assimilated dust transport model with CALIPSO data (Hara et al. 2009)Please see the publication list athttp://www-lidar.nies.go.jp/~cml/English/PublicationsE.html
2.2 Monthly (non-dust) AOT variation at NIES lidar sites2006 2007 2008 Summertime peak
Autumn peak
Summertime trough (clean)
Guangzhou
Beijing
Okinawa/Hedo
・ Space and ground-based lidar AOT values show relatively good agreement.・ CMAQ shows similar seasonal variation.
(7/15)
(Hara et al.)
2.3 Seasonal variation of vertical profiles
・ The seasonal variation in the aerosol scale height at Beijing is largest (about 1 km) among these sites which is correlated to the large seasonal variation of the mixing layer depth .
・ We can classify typical seasonal variations of spherical AOT at the three lidar sites into two types: the ‘summertime peak’ type and the ‘summertime trough’ type.
Beijing
Guangzhou Okinawa/HedoSummertime trough type
Summertime peak type
(8/15)AOT(ZH)=AOT(6km)(1-e-1) 0.63 AOT(6km)ZH=aerosol scale height
Time
Time Time
富山の例
Near-surface (120m-1km) dust and spherical-aerosol extinction coefficient used in
the epidemiological study.
Desert-dust is associated with increased risk of asthma
hospitalization in children
Figure 2 Meteorologically adjusted Odds Ratios (ORs) for the relations between asthma hospitalizations and heavy dust exposure (daily average level above 0.1mg/m3) with various cumulative lags
Figure * Adjusted OR for the relations between asthma hospitalizations and heavy sphere particle exposure (daily average level above 0.1mg/m3) for various cumulative lag periods
Figure ** Adjusted OR for the relations between asthma hospitalizations and heavy SPM (suspended particulate matter) exposure (daily average level above 0.1mg/m3) for various cumulative lag periods
K. Kanatani, et. al. American Journal of Respiratory and Critical Care Medicine, 2010.
cutoff
Figure 3 Association between meteorologically adjusted OR and cut-off values for dust particle level.
Mass(SPM)/extinction ~ 1000 (g/m3)/(km-1)
Mass/extinction conversion factor (MEF)PM10/(Lidar extinction) and assimilated CFORS model was compared for mineral dust.
MEF for PM10 shows temporal (and spatial variation)Variation in MEF for PM2.5 is much smaller. PM2.5 is less dependent on particle size distribution. We can better quantify “dust PM2.5” from the dust extinction coefficient.
CFORS has 12 size bins for dust
(Sugimoto et al., 2010)
Mongolian forest fire in June 2007
(Sugimoto et al., SOLA 2010)
S1(532nm)= 65±5sr, PDR= 0.14±0.03, BAE(532,1064)=1.1±0.2
Ongoing projects
1) Climatology and case studies using lidar network data (and CALIPSO)
2) Real time data assimilation for dust forecasting.
3) Assimilation of regional chemical transport models including spherical aerosols
4) Assimilation of global aerosol climate models including the lidar network data
-Meteorological Research Institute (T. Sekiyama) -Kyushu University (T. Takemura, K. Yumimoto) -University of Tokyo (T. Nakajima, N. Schutgens) 4) Development of a multi-wavelength high-spectral-resolution lidar
(2+3+2) and a data analysis method
2+3+2 HSRL system
532nm HSRL + 1064nm receiver
Iodine filter
APD(1064nm)
PMT(532nm)
355nm HSRL receiver
EtalonPMT(355nm)
Laser wavelength tuning system
LaserPinhole
PC ADC
Photodiode
AOM
AOM
I2cell(L=10cm)
NDFilter
Wavelength shift [pm]
Tran
smitt
ance
Pinhole
AOM
I2 cell
Photodiode
Measured Iodine absorption spectrumλo+δλ
λo−δλ
Ratio of P(λo+δλ) to P(λo−δλ)Center wavelength of Iodine absorption line used in this study ( line number:1111 )
Preliminary measurement17LT Aug. 20 ~ 9LT Aug. 21 at Tsukuba (140.12E, 36.05N),
JapanMeasured signals
P532,paericle+molecule
P532,molecule
P1064, particle+molecule
δ532
Derived particle opt. prop.
Backscatter [/km/sr]
Extinction [/km]
Extinction / Backscatter [sr]
Particle depolarization ratio
Cloud
Etalon wavelength tuning system
Etalon
1m(Focused light dia. =
4mm)
PM
T355,M
ie,ch1
PMT355,Mie,ch2Pinhole mirror(Pinhole dia. = 3mm)
Lens
Finness = 10FSR = 5GHz
Simulated interference fringes
P=+1.6hPa
P=-1.6hPaP=-3.2hPa
P=+3.2hPa
Measured signalsSimulated signals
Maximum transmittance for Mie scatter