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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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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, JapanH. OkamotoKyushu Univ., Fukuoka, Japan

IGARSS 2011, 29/Jul/2011


Igarss 2011 29 jul 2011

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)


Nies lidar network

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


Igarss 2011 29 jul 2011

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


Application to shipborne lidar data i

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


Retrieved aerosol component data

Total

AOT (532)

Angstrom

Air-pollution aerosols

Sea-salt

Retrieved aerosol component data

Dust

Agreement within 5%


Application to shipborne lidar data ii

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


Igarss 2011 29 jul 2011

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


Igarss 2011 29 jul 2011

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.


Application to satellite borne 2 1 lidar caliop nasa 2006

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]


Mie lidar and high spectral resolution lidar hsrl measurements 1 2 1

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


Aerosol classification algorithms using 1 2 1 data

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)


1 1 1 algorithm

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


Application to ground based mie hsrl data nies tsukuba japan

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.)


Atlid earthcare 2015

355 nm High Spectral Resolution lidar (HSRL)

ATLID / EarthCARE (2015 ~)

3 channels: 1α+1+1

 Extinction coefficient ()

 Backscattering coefficient ()

 Depolarization ratio ()


Summary

  • 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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