AD-Net status GALION WS, 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, …

AD-Net stations *NIES Lidar Network * * * ** **SKYNET

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)

Data distribution (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 at

March 25- April 3, 2007 May 21-30, 2007 without assimilation with assimilation dust emission factor

2.2 Monthly (non-dust) AOT variation at NIES lidar sites 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/Hedo Summertime trough type Summertime peak type (8/15) AOT(Z H )=AOT(6km)(1-e -1 )  0.63 AOT(6km) Z H =aerosol scale height 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/m 3 )/(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

Moscow forest fire smoke NRL NAAPS Russian forest fire smoke in Aug 2010

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 nm receiver Iodine filter APD (1064nm) PMT (532nm) 355nm HSRL receiver Etalon PMT (355nm)

Laser wavelength tuning system Laser Pinhole PCADC Photodiode AOMAOM AOMAOM I 2 cell (L=10cm) ND Filter Wavelength shift [pm] Transmittance Pinhole AOM I 2 cell Photo diode Measured Iodine absorption spectrum λ o +δλ λ o −δλ Ratio of P(λ o +δλ) to P(λ o −δλ) Center wavelength of Iodine absorption line used in this study （ line number:1111 ）

Preliminary measurement 17LT Aug. 20 ~ 9LT Aug. 21 at Tsukuba (140.12E, 36.05N), Japan Measured signals P 532,paericle+molecule P 532,molecule P 1064, particle+molecule δ 532 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) PMT 355,Mie,ch1 PMT 355,Mie,ch2 Pinhole mirror (Pinhole dia. = 3mm) Lens Finness = 10 FSR = 5GHz Simulated interference fringes P=+1.6hPa P=-1.6hPa P=-3.2hPa P=+3.2hPa Measured signalsSimulated signals Maximum transmittance for Mie scatter

NIES lidar network (1) NIES Lidar Network 7SEAS Network (J. Campbell et al.) China India