nisizawa_IGARSS_HSRL_FR2.T07.ppt

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Nishizawa, T., N. Sugimoto, I Matsui, (National Institute for Environmental Studies (NIES), Japan) IGARSS 2011, 29/Jul/2011 DUAL-WAVELENGTH HIGH-SPECTRAL-RESOLUTION LIDAR FOR ADVANCED CLASSIFICATION AND RETRIEVAL OF AEROSOLS FR2T07

Transcript of nisizawa_IGARSS_HSRL_FR2.T07.ppt

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Nishizawa, T., N. Sugimoto, I Matsui, (National Institute for Environmental Studies (NIES), Japan)

IGARSS 2011, 29/Jul/2011

DUAL-WAVELENGTH HIGH-SPECTRAL-RESOLUTION LIDAR

FOR ADVANCED CLASSIFICATION AND RETRIEVAL OF AEROSOLS

FR2T07

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NIES Lidar Network

20 observation sites in East-Asia using 2+1 Mie lidar 532nm attenuated Backscatter (532)

532nm total depolarization (532) 1064nm attenuated backscatter (1064)

Measured data

APD(1064nm)

PMTs(532nm)

2+1 Mie lidar

China

Japan

Thai

Mongol

Korea

NIES Lidar network

Lidar at “Hedo” site

The lidars measure aerosols (& clouds) 24-hour-automatically and we provide 2+1 data in semi-real-time (http://www-lidar.nies.go.jp/)

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NIES Lidar Network

ObservationCompact 2 (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]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

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NIES Lidar Network EvolutionIndependent extinction measurement (i.e., HSRL and Raman lidar) is useful

to classify weak/strong light absorption particles [Nishizawa et al., 2008]

Data analysisCombined use of HSRL(532) and 2(532,1064) Mie lidar

1α+2 data SF-NT-OC (Weak / Small) + BC (Strong / Small) +Dust (Weak / Large) [Nishizawa et al., 2008]

Ground-based network measurement 2+1 Mie lidar + 1α N2 Raman (607) channel

with automatically measurement capability [1α+2+1 lidar](6 sites in the NIES Lidar Network) [Xie et al., 2008]

Dual wavelength (355, 532nm) HSRL + 1(1064)+2(1064,532) Mie lidar with automatically measurement capability [2α+3+2 lidar](under development) [Nishizawa et al., 2008,2010]

Light absorption+ Spectral

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HSRL techniques

Narrow-band filter

Narrow-band laser: injection-seeded, Nd:YAG laser (1064, 532, 355nm)

Iodine filter (532nm)[Liu et al. 1999]

λ

Etalon (355nm)[Imaki et al. 2005]

Backscattered light spectrum

ParticleMolecule

(Doppler broadening)

λ

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2+3+2 HSRL systemWhole picture

Laser

532nm HSRL + 1064nm receiver

Iodine filter

APD(1064nm)

PMT(532nm)

355nm HSRL receiver

EtalonPMT(355nm)

Container

Transmitter

Laser type Nd:YAG, Q-switched, injection-seeded, linearly polarized (Continuum Surelite I)

Wavelength 1064, 532, 355nm

Line width 0.005 cm-1

Pulse energy 100mJ (for each wavelengths)

Repetition rate

10Hz

Divergence 0.1mrad (using a 5x expander)

Receiver

Telescope Cassegrain, D=21cm, F=2.63m

FOV 0.5mrad

Detectors Licel APD for 1064nmLicel PMT for 532, 355nmLicel PMT for 387nm (N2 Raman channel)

Wavelength separators

Optical filter 1nm-FWHM interference filter for each wavelength

532nm HSRL 40cm iodine cell (Rayleigh)

355nm HSRL Fabry-Perot etalon (Finesse=10, FSR=5GHz) (Mie)

Data acquisition

Analog measurement for 1064, 532, 355nm (25MHz, 12bit A/D)Photon counting measurement for 387nm (40MHz, 12bit)

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Iodine filter

355nm

532nm1064nm

Laser

Tel

esco

pe

Etalon

To realize automatically, long-term continuous network measurement…..

Automatically tune Laser wavelength to Iodine absorption wavelength,and Automatically tune etalon transmittance wavelength to the tuned

laser wavelength

Tu

ne th

e wavelen

gth

sAutomatically wavelength tuning system

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Laser wavelength tuning system

LaserPinhole

PC ADC

Photodiode

AOM

AOM

I2cell(L=10cm)

NDFilter

Wavelength shift [pm]

Tra

nsm

ittan

ce

Pinhole

AOM

I2 cell

Photodiode

Measured Iodine absorption spectrumλo+δλ

λo−δλ

Ratio of P(λo+δλ) to P(λo−δλ)

Center wavelength of Iodine absorption line used in this study ( line number:1111 )

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Etalon wavelength tuning system I

Etalon

1m(Focused light dia. =

4mm)

PM

T355,M

ie,ch1

PMT355,Mie,ch2Pinhole mirror(Pinhole dia. = 3mm)

Lens

Finness = 10FSR = 5GHz

Simulated interference fringes

P=+1.6hPa

P=-1.6hPaP=-3.2hPa

P=+3.2hPa

Measured signalsSimulated signals

Maximum transmittance for Mie scatter

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Example of observation and analysisHalf day measurement (17LT Aug. 20 ~ 9LT Aug. 21)

at Tsukuba (140.12E, 36.05N), JapanMeasured signals

P532,Mie+Ray

P1064

δ532

Derived particle opt. prop.

Backscatter [/km/sr]

Extinction [/km]

Extinction / Backscatter [sr]

Particle depolarization ratio

Cloud

P532,Ray

S : ~20sr for water-clouds, 30~80sr for aerosols

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532

53 210

64

532

Algorithm using 2+3+2 data

Sea-salt

Dust

[Depolarization]

SF-NT-OC

[Bac

ksca

tter

]

[Exti

nctio

n]

BC

355

1064355

● Extinction coefficients for 4 aerosol components at each layer● Mode radii for 3 aerosol components at each layer

(SF-NT-OC, sea-salt, dust)

Assumption:●External mixture of 4 components●Lognormal size distribution●Fix refractive index●Spheroid for dust

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Conclusions and future plans● We constructed 532/355nm dual-wavelength HSRL system (2+3+2 Mie-HSRL lidar) and developed automatically wavelength tuning system.=> We conduct continuous measurement by the developed HSRL system=> We conduct more advanced aerosol component classification analysis.

● We are currently constructing 1+2+2 Mie-HSRL lidar for shipborn measurement, applying the developed laser wavelength tuning system to this

lidar.

Optical/microphysical data provided from these observations and aerosol component analysis are useful for validation of satellite measurements and numerical models and assimilation of numerical models, improving our understandings about impacts of aerosols and clouds in global and regional scales.

This work was supported by “Environment Technology Development Fund by the Ministry of the Environment Japan’’ B-0803.

Acknowledgements