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IGARSS 2011, 29/Jul/2011

FR2T03. AEROSOL CLASSIFICATION RETRIEVAL ALGORITHMS FOR EARTHCARE/ATLID, CALIPSO/CALIOP, AND GROUND-BASED LIDARS. Sugimoto, N., T. Nishizawa, I. Matsui, National Institute for Environmental Studies (NIES), Tsukuba, Japan H. Okamoto Kyushu Univ., Fukuoka, Japan. IGARSS 2011, 29/Jul/2011.

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IGARSS 2011, 29/Jul/2011

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  1. FR2T03 AEROSOL CLASSIFICATION RETRIEVAL ALGORITHMS FOR EARTHCARE/ATLID, CALIPSO/CALIOP, AND GROUND-BASED LIDARS Sugimoto, N., T. Nishizawa, I. Matsui, National Institute for Environmental Studies (NIES), Tsukuba, JapanH. OkamotoKyushu Univ., Fukuoka, Japan IGARSS 2011, 29/Jul/2011

  2. NIES Lidar Network The lidars measure aerosols (& clouds) 24-hour-automatically and we provide 2+1 data in semi-real-time (http://www-lidar.nies.go.jp/) NIES Lidar network 2+1 Mie lidar Lidar at “Hedo” site Mongol Korea Japan China Thai APD (1064nm) PMTs (532nm) Measured data 20 observation sites in East-Asia using 2+1 Mie lidar 532nm attenuated Backscatter (532) 532nm total depolarization (532) 1064nm attenuated backscatter (1064)

  3. Observation Compact2 (532, 1064nm) + 1 (532nm) Mie lidar with automatically measurement capability  20 sites ground based network observation in East Asia (2001~)  Ship-borne measurements (1999~, vessel “MIRAI” (JAMSTEC)) [Sugimoto et al., 2001; 2005] NIES Lidar Network • Data analysis • Classify aerosol components and Retrieve their extinctions at each layer • (assuming external mixture of each aerosol component) • 1(532)+1 data Dust (nonSpherical) + non-Dust (Spherical) [Sugimoto et al., 2003; Shimizu et al., 2004] • 2 data Air-pollution aerosol*(Small) + Sea-salt or Dust(Large) • [Nishizawa et al., 2007; 2008] •  2+1 dataAir-pollution aerosol* (Spherical / Small) + • Sea-salt (Spherical / Large) + • Dust (nonSpherical / Large) [Nishizawa et al., 2010] Polarization Spectral Polarization +Spectral *Air-pollution aerosol is defined as mixture of Sulfate, Nitrate, Organic carbon, and Black carbon

  4. DS 532,  1064 532, || SS AP 2+1 algorithm Assumptions Log-normal size distribution Mode radius, standard deviation, refractive indexes Spheroidal for dust(Spherical forthe other components) 3 components in each layer AP : Air-pollution SS : Sea-salt DS : Dust rm: Mode radius S : Lidar ratio (Extinction-to-Backscatter ratio) δ : Particle depolarization ratio

  5. 6 km 532 0 km 6 km 1064 0 km 6 km 532 0 km 14 days Observed data (2+1 Mie lidar) Pacific Ocean near Japan Application to shipborne lidar data I MIRAI/JAMSTEC Tohoku Univ. HP, http://caos-a.geophys.tohoku.ac.jp

  6. Total AOT (532) Angstrom Air-pollution aerosols Sea-salt Retrieved aerosol component data Dust Agreement within 5%

  7. Tropical Pacific Ocean Application to shipborne lidar data II Mirai Cruises MR01K05: 9.21 ~ 12.17, 2001 MR04K07: 11.18 ~ 12.9, 2004 MR04K08: 12.16 ~ 2.17, 2005 MR06K05: 10.16 ~ 11.25, 2006 7-month data in total

  8. Horizontal distribution (Optical thickness) The total optical thicknesses were larger from the Japan to the New Guinea and in the western region off Sumatra Island than in the other regions.  AP was the major contributor to the total optical thickness of aerosols. Total Air-Pollution SS DS 12-hour average

  9. Comparison with a global aerosol transport model “SPRINTARS” [Nishizawa et al. JGR 2008] AP 532 Lidar Lidar SPRINTARS SPRINTARS AP SS 532 1064 Mean values (Obs.)=0.0027 km-1sr -1 (Sim.)=0.0017 km-1sr -1 Mean values (Obs.)=0.0006 km-1sr -1 (Sim.)=0.0003 km-1sr -1 Mean values (Obs.)=0.044 km-1 (Sim.)=0.009 km-1 Mean values (Obs.)=0.005 km-1 (Sim.)=0.014 km-1 *SPRINTARS is a global, three-dimensional aerosol transport model [Takemura et al. 2005]. The simulation data by the SPRINTARS was provided by Takemura of Kyusyu Univ.

  10. Saharan Dust transport to the Atlantic Ocean 2006.8/1, 2:36UTC Application to satellite-borne 2+1 lidar[CALIOP/NASA 2006~] β532 Altitude [km] β1064 Cited from NASA/CALIOP website δ532 Aerosol Mask Scheme ●Remove cloud area CloudSat + CALIOP [Hagihara et al. 2009] ●Remove molecule scat. area CALIOP (β1064) ＊β1064 was re-calibrated by using water-cloud signals Air pollution Altitude [km] Sea-salt Dust Latitude [deg]

  11. 532 HSRL Mie lidar Observation Site NIES, Tsukuba HSRL Extinction(532nm） Backscatter  (532nm） Mie lidar Backscatter  (1064nm） Depolarization  (532nm) Observed data April 8 2005, 0~10 UTC 532 Mie-lidar and High-Spectral-Resolution-Lidar (HSRL) measurements (1α+2β+1δ) S532= 532 / 532 1064

  12. Classify aerosol components and Retrieve their extinctions at each layer (assuming external mixture of each aerosol component)  1α+2 dataSF-NT-OC (Weak / Small) + BC (Strong / Small) + Dust (Weak / Large) [Nishizawa et al., 2008]  1α+1+1 dataSF-NT-OC (Weak / Spherical) + BC (Strong / Spherical) + Dust (Weak / Non-spherical) Aerosol classification algorithmsusing 1α+2β+1δ data Light absorption +Spectral Light absorption + Polarization *Air-pollution aerosol is defined as mixture of Sulfate (SF), Nitrate (NT), Organic carbon (OC), and Black carbon (BC)

  13. DS 532,  532, || α532 SF-NT-OC BC Assumptions Log-normal size distribution Mode radius, standard deviation, refractive indexes Spheroidal for dust(Spherical forthe other components) 1 + 1 + 1algorithm rm: Mode radius S : Lidar ratio (Extinction-to-Backscatter ratio) δ : Particle depolarization ratio Dust + BC + SF-NT-OC

  14. Dust Sulfate BC+OC Observation site Estimates SPRINTARS SF-NT-OC Application to ground-based Mie/HSRL data(NIES, Tsukuba, Japan) Dust BC Sulfate originated from Coastal area of China Dust originated from Gobi desert BC+OC originated from Coastal area of China and Indochina peninsula Provided by Dr. Takemura (Kyusyu Univ.)

  15. 355 nm High Spectral Resolution lidar (HSRL) ATLID / EarthCARE (2015 ~) 3 channels: 1α+1+1  Extinction coefficient ()  Backscattering coefficient ()  Depolarization ratio ()

  16. We developed several aerosol classification and retrieval algorithms. • => The algorithms can be used to understand aerosol component distributions • in regional and global scales by applying to the network lidar data and the satellite-borne lidar data. • We are going on developing (or improving) aerosol classification and retrieval algorithms using more channels. •  NIES 2+1 Mie lidar + Raman (or HSRL) • 1α+2+1 dataSF-NT-OC (Weak / Small / Spherical) + • BC (Strong / Small / Spherical) + • Dust (Weak / Large / Non-spherical) + • Sea-salt (Weak / Large / Spherical) • NIES 2α+3+2 HSRL (Under development : Nishizawa et al. FR2T07) • 2α+3+2 dataSF-NT-OC (Weak / Small / Spherical) + • BC (Strong / Small / Spherical) + • Dust (Weak / Large / Non-spherical) + • Sea-salt (Weak / Large / Spherical) + • Size information for SF-NT-OC, Dust, Sea-salt Summary Light absorption +Spectral +Polarization Light absorption +Spectral +Polarization

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