350 likes | 489 Views
APAN Conference, Fukuoka Jan 21-23, 2003. Observations and Model Analysis of Recent Asian Dust Events. Nobuo Sugimoto (National Institute for Environmental Studies) Itsushi Uno (Research Institute for Applied Mechanics, Kyushu University)
E N D
APAN Conference, Fukuoka Jan 21-23, 2003 Observations and Model Analysis of Recent Asian Dust Events Nobuo Sugimoto (National Institute for Environmental Studies) Itsushi Uno (Research Institute for Applied Mechanics, Kyushu University) Atsushi Shimizu, Ichiro Matsui (National Institute for Environmental Studies) Kimio Arao (Nagasaki University) Hao Quan, Yan Cheng (CJFCEP, China) Jun Zhou (AIOFM, China) C-H Lee (Kyung Hee University, Korea)
Dust Project in the Global Environment Research Program of the Ministry of the Environment Observation of distribution and movement of Asian dust using lidars (2) Chemical analysis of Asian dust (3) Modeling study
NIES lidar observation network Tsukuba(36.05N, 140.12E) 1996-- Nagasaki (32.78N, 129.86E) Mar. 2001-- Beijing, China (39.9N, 116.3E) Mar. 2001-- Sri Samrong, Thailand (17.15N, 99.95E) Oct. 2001-- Suwon, Korea (37.14N, 127.04E) 2002-- Amami-Ohshima (28.44N, 129.70E) 2002-- Miyakojima (24.7N, 125.3E) 2002-- Fukue (32.63N, 128.83E) Oct. 2002-- Hefei, China (31.90N, 117.16E) Oct. 2002-- Research Vessel “Mirai” 1999--
Purpose of the lidar network observations • Climatology of aerosols and clouds • To understand aerosol phenomena including effects of Asian dust and anthropogenic aerosols on the environment and climate • To validate chemical transport models • Monitoring of Asian dust and anthropogenic aerosols in the regional and global scales
NIES Lidar Network for Asian Dust Observation Beijing Nagasaki Tsukuba
Lidar data Beijing 2002
Target classification method spherical aerosol dust Scattering intensity Laser ice cloud Laser water cloud dust P⊥ P// P// Depolarization ratio spherical aerosols Depolarization ratio d = P⊥/P//
Target classification 2 rain ice cloud water cl. dust aerosols unknown no obs. April 2001 Target classification using the signal intensity and the depolarization ratio.
The Chemical Forecast System (CFORS), (I. Uno)(A RAMS based regional model including chemistry) Comparison with Models
Which parameter shall we compare? Lidar signal intensity (depolarization) S1 extinction coefficient distribution and characteristics of other aerosols assumption on external mixing dust extinction coefficient mass/extinction conversion factor optical characteristics of dust dust density Chemical Transport Model
Ratio of dust is estimated by the following equations when we consider external mixture of dust and other spherical aerosols. R={(1-d2’)d-d2’}/{(d1’-d2’)(1+d)}……………………..(1)d1’=d1/(1+d1) ………………………(2)d2’=d2/(1+d2) ………………………(3)where d1 is depolarization ratio of dust, and d2 is depolarization ratio of other aerosols. Empirically, d1~0.35, d2~0.05.
Distributions of dust and spherical (air-pollution) aerosols estimated from the signal intensity and depolarization ratio dust air pollution aerosols dust Beijing March 2001 Day (UTC)
Summary We conducted continuous observations in Beijing, Nagasaki, and Tsukuba with automated polarization lidars since March 2001. A statistical analysis showed that the frequency of dust events in 2002 and 2001 was not very different in Beijing, but the frequency was much higher in 2002 in Tsukuba. We studied the dust source regions and transport paths using the regional chemical transport model CFORS. The results showed that most major dust events originated in Inner Mongolia and/or Mongolia. The dust was transported rapidly with the strong westerly of the storm, and the main part was transported northeast near the coast of China. In 2002, the location of dust streams were shifted slightly to the east, and this caused heavy dust events in Korea and northern Japan. This is probably related with the climate change.
RIAM-NIES CFORS Dust event on November 12, 2002
Lidar-CFORS1 Suwon Beijing
Lidar-CFORS2 Fukue Tsukuba
Lidar-CFORS3 Hefei Miyako-jima
Perspective Understanding dust phenomena Dust forecast Constructing dust monitoring network Ground based observation network Chemical transport model Satellite data (surface, dust)