1. CHARACTERIZATION OF AEROSOLS BASED ON THE SIMULTANEOUS MEASUREMENTS M. Nakata, T. Yokomae, T. Fujito, I. Sano & Sonoyo Mukai Kinki University, Higashi-Osaka, Japan

2. Introduction Studying aerosol characteristics is an important subject especially in urban areas. In this work, we classify aerosol properties by utilizing the ground observations and investigate characterization of aerosols over Higashi- Osaka, Japan. Then the obtained results are examined for aerosol retrieval with Aqua/MODIS. aerosol properties size composition amount shape m = n-ki dV / dlnr AOT refractive index size dist function optical thickness ～ sphere

3. 1. Classification of aerosol types 2. Correlation between AOT and PM 3. Aerosol retrieval from Aqua/MODIS 4. Summary Contents

4. Clustering of global aerosols Omar et al. 2005 present work The 26 parameters Complex refractive index (8) Mean radius (2) (fine and coarse) Standard deviation (2) (fine and coarse) Mode total volumes (2) (fine and coarse) Single scattering albedo (4) (441, 673, 873 and 1022 nm) Asymmetry factor (4) (441, 673, 873 and 1022 nm) Extinction/backscatter ratio (4) (441, 673, 873 and 1022 nm) Parameters: The 5 parameters Aerosol optical thickness(3) (440, 675 and 870 nm) Angstrom exponent (2) (440/870 and 440/675)  Fewer essential parameters can make the interpretation of resultant clusters easier. Method: Aerosols are classified into 6 categories by k-Means clustering method with AERONET data. Our results coincide with Omar's

5. Desert dustBiomass burning Continental pollution Polluted marine Dirty pollution Rural (background) size distribution for 6 aerosol categories: bi-modal (fine & coarse) lognormal fn. locations size distribution

6. Size fn. available for 6 aerosol categories is demanded in practice. r r   An approximate size distribution (the parameter to characterize aerosol size is "f" alone, where f is the fraction of fine ptl.):

7. 1. Classification of aerosol types 2. Correlation between AOT and PM 3. Aerosol retrieval from Aqua/MODIS 4. Summary Contents

8. Map of AERONET site in NASA/AERONET web page Kyoto Kobe Osaka Higashi -Osaka Nara Kinki University Campus, Higashi-Osaka, Japan 34.65°N, 135.59°E Ground measurements at Higashi-Osaka Photometry :AERONET sun/sky radiometer PM sampling:PM 2.5 & PM 10 &OBC SPM-613D NIES/LIDAR Location Ground measurements at Higashi-Osaka

9. AOT (0.675 µm) at Higashi-Osaka from 2004 to 2010 Photometry AERONET sun/sky radiometer AERONET/Osaka site

10. PM 2.5 and PM C at Higashi-Osaka from 2004 to 2010 PM C = PM 10 - PM 2.5 PM sampling PM 2.5 & PM 10 &OBC SPM-613D

11. Classification results of AERONET/Osaka Cluster-A: Large AOT & small   Asian dust Cluster-C: Small AOT & large   Clear atmosphere is not too often Cluster-B & F: Small AOT & large  but slightly dirtier than clear (Cluster-C)  Background at Osaka Cluster-D: Large AOT & Large  Cluster-E: Small AOT & small   Typical aerosol event involving small aerosols Classification results for global as AOT (0.675  m) against  (0.44/0.87  m)

12. Scatter diagrams as AOT (0.675  m) against  (0.44/0.87  m) for three clusters of aerosols at Higashi-Osaka. Cluster-2: Large AOT & Large  Cluster-3: Large AOT & Small  Cluster-1: Small AOT Aerosol properties at Higashi-Osaka site are roughly reclassifies into 3 clusters

13. 1) Cluster-1,-2 (Anthropogenic) & 2) Cluster-3 (Asian dust) The correlation between AOT and PM 2.5 is improved for 2-clusters as: PM 2.5 = 62.4 AOT + 12.4 PM 2.5 = 52.8 AOT + 9.68 2hours time shift : PM 2.5 = 95.1 AOT - 18.6 Estimation of PM 2.5 from AOT ad vice versa

14. 1. Classification of aerosol types 2. Correlation between AOT and PM 3. Aerosol retrieval from Aqua/MODIS 4. Summary Contents

15. {r m,  } : {0.14,1.86} {r m,  } : {3.42,2.34} 0.1 0.2 0.3 0.4 0.5 【 1 】 size distribution ： represented by f 【 Retrieval Flow for dust storm 】 R sim ( ) : R obs ( ) f & m = n( ) – k( ) i f*, n*( ), k*( ) R( ) ←New Radiative Transfer code (successive scattering method*) 【 2 】 refractive index: m = n( ) – i ･ k( ) 【 aerosol model 】 * available for semi-infinite atmosphere model i.e. for optically thick heavy aerosol events

16. c ) ex. Yellow dust storm on April 10 in 2006 over the Badain Jaran Desert Aqua/MODIS image AOT 4.0 Dust aerosol mass concentration with SPRINTARS

17. Retrieval of dust aerosols the Badain Jaran Desert refractive index the heavy yellow dust storm can be interpreted by the large sized aerosol model with f=0.094 and refractive index (m) derived from AERONET data at Dalanzadgad in the Gobi Desert (41N, 105E) (43N, 104E)

18. 1.Aerosol properties are classified with a clustering method by utilizing the ground measurements by AERONET. 2. The size distribution available for every aerosol category is proposed. 3. The cluster information can be used to improve estimation of PM 2.5 from AOT. 4. New algorithms for aerosol retrieval based on the proposed aerosol models and the semi-infinite radiative transfer simulations are available for the yellow dust storm with Aqua/MODIS. Summary