Abstract

A multiple-field-of-view (MFOV) lidar is used to characterize size and optical depth of low concentration of bioaerosol clouds. The concept relies on the measurement of the forward scattered light by using the background aerosols at various distances at the back of a subvisible cloud. It also relies on the subtraction of the background aerosol forward scattering contribution and on the partial attenuation of the first-order backscattering. The validity of the concept developed to retrieve the effective diameter and the optical depth of low concentration bioaerosol clouds with good precision is demonstrated using simulation results and experimental MFOV lidar measurements. Calculations are also done to show that the method presented can be extended to small optical depth cloud retrieval.

© 2008 Optical Society of America

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References

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  1. J.-R. Simard, G. Roy, P. Mathieu, V. Larochelle, J. McFee, and J. Ho, “Standoff sensing of bioaerosols using intensified range-gated spectral analysis of laser-induced fluorescence,” IEEE Trans. Geosci. Remote Sens. 42, 865-874 (2004).
    [CrossRef]
  2. E. W. Eloranta, “Practical model for the calculation of multiply scattered lidar returns,” Appl. Opt. 37, 2464-2472 (1998).
    [CrossRef]
  3. I. Veselovskii, M. Korenskii, V. Griaznov, D. N. Whiteman, M. McGill, G. Roy, and L. Bissonnette, “Information contain of data measured with multiple-field-of-view lidar,” Appl. Opt. 45, 6839-6848 (2006).
    [CrossRef] [PubMed]
  4. L. R. Bissonnette, “Lidar and multiple scattering,” in Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, Springer Series in Optical Sciences, C.Weikamp, ed. (Springer, 2005).
  5. N. Roy, G. Roy, L. R. Bissonnette, and J.-R. Simard, “Measurement of the azimuthal dependence of cross-polarized lidar returns and its relation to optical depth,” Appl. Opt. 43, 2777-2785 (2004).
    [CrossRef] [PubMed]
  6. G. Roy, L. R. Bissonnette, C. Bastille, and G. Vallée, “Retrieval of droplet-size density distribution from multiple field-of-view cross-polarized lidar signals,” Appl. Opt. 38, 5202-5211(1999).
    [CrossRef]
  7. L. Katsev, E. P. Zege, A. S. Prikhach, and I. N. Polonsky, “Efficient technique to determine backscattered light power for various atmospheric and oceanic sounding and imaging systems,” J. Opt. Soc. Am. A 14, 1338-1346 (1997).
    [CrossRef]
  8. M. Born and E. Wolf, Principles of Optics (Pergamon, 1975), Chap. 8.
  9. E. P. Shettle and R. W. Fenn, “Models for the aerosols of the lower atmosphere and the effects of humidity variations on their optical properties,” Air Force Geophysics Laboratory Tech. Rep. AFGL-TR-79-0214, (Air Force Geophysics Laboratory, 1979).
  10. L. R. Bissonnette, “Multiple-scattering lidar equation,” Appl. Opt. 33, 6449-6465 (1995).
  11. G. Roy, L. R. Bissonnette, C. Bastille, and G. Vallée, “Estimation of cloud droplet size density distribution from multiple-fields-of-view lidar,” Opt. Eng. 36, 3404-3415 (1997).
    [CrossRef]
  12. D. S. Goldman, “Light box for taking flats,” http://www.astrodon.com/oldsite/LearningCurve.html.
  13. E. E. Eloranta, “High spectral resolution lidar,” in Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, C. Weikamp, ed. Springer Series in Optical Sciences (Springer, 2005), 455 pp.
  14. E. W. Eloranta, “Lidar multiple scattering models for use in cirrus clouds,” in Twenty-First International Laser Radar Conference Proceedings, L. Bissonnette, G. Roy, and G. Vallée, eds. (2002), pp. 519-522.
  15. G. Roy, L. R. Bissonnette, and C. Bastille, “Efficient field-of-view control for multiple-field-of-view lidar receivers,” in Nineteenth International Laser Radar Conference Proceedings, U. N. Sing, S. Ismail, and G. K. Schwemmer, eds. (NASA/CP-1998-207671/PT2, 1998), pp. 767-770.
  16. J. Swithenbank, J. M. Beer, D. S. Taylor, D. Abbot, and G. C. McCreath, “A laser diagnostic technique for the measurement of droplet and particle size distribution,” in AIAA 14th Aerospace Sciences Meeting (1976), pp. 76-69.

2006 (1)

2004 (2)

N. Roy, G. Roy, L. R. Bissonnette, and J.-R. Simard, “Measurement of the azimuthal dependence of cross-polarized lidar returns and its relation to optical depth,” Appl. Opt. 43, 2777-2785 (2004).
[CrossRef] [PubMed]

J.-R. Simard, G. Roy, P. Mathieu, V. Larochelle, J. McFee, and J. Ho, “Standoff sensing of bioaerosols using intensified range-gated spectral analysis of laser-induced fluorescence,” IEEE Trans. Geosci. Remote Sens. 42, 865-874 (2004).
[CrossRef]

1999 (1)

1998 (1)

1997 (2)

G. Roy, L. R. Bissonnette, C. Bastille, and G. Vallée, “Estimation of cloud droplet size density distribution from multiple-fields-of-view lidar,” Opt. Eng. 36, 3404-3415 (1997).
[CrossRef]

L. Katsev, E. P. Zege, A. S. Prikhach, and I. N. Polonsky, “Efficient technique to determine backscattered light power for various atmospheric and oceanic sounding and imaging systems,” J. Opt. Soc. Am. A 14, 1338-1346 (1997).
[CrossRef]

1995 (1)

L. R. Bissonnette, “Multiple-scattering lidar equation,” Appl. Opt. 33, 6449-6465 (1995).

Appl. Opt. (5)

IEEE Trans. Geosci. Remote Sens. (1)

J.-R. Simard, G. Roy, P. Mathieu, V. Larochelle, J. McFee, and J. Ho, “Standoff sensing of bioaerosols using intensified range-gated spectral analysis of laser-induced fluorescence,” IEEE Trans. Geosci. Remote Sens. 42, 865-874 (2004).
[CrossRef]

J. Opt. Soc. Am. A (1)

Opt. Eng. (1)

G. Roy, L. R. Bissonnette, C. Bastille, and G. Vallée, “Estimation of cloud droplet size density distribution from multiple-fields-of-view lidar,” Opt. Eng. 36, 3404-3415 (1997).
[CrossRef]

Other (8)

D. S. Goldman, “Light box for taking flats,” http://www.astrodon.com/oldsite/LearningCurve.html.

E. E. Eloranta, “High spectral resolution lidar,” in Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, C. Weikamp, ed. Springer Series in Optical Sciences (Springer, 2005), 455 pp.

E. W. Eloranta, “Lidar multiple scattering models for use in cirrus clouds,” in Twenty-First International Laser Radar Conference Proceedings, L. Bissonnette, G. Roy, and G. Vallée, eds. (2002), pp. 519-522.

G. Roy, L. R. Bissonnette, and C. Bastille, “Efficient field-of-view control for multiple-field-of-view lidar receivers,” in Nineteenth International Laser Radar Conference Proceedings, U. N. Sing, S. Ismail, and G. K. Schwemmer, eds. (NASA/CP-1998-207671/PT2, 1998), pp. 767-770.

J. Swithenbank, J. M. Beer, D. S. Taylor, D. Abbot, and G. C. McCreath, “A laser diagnostic technique for the measurement of droplet and particle size distribution,” in AIAA 14th Aerospace Sciences Meeting (1976), pp. 76-69.

L. R. Bissonnette, “Lidar and multiple scattering,” in Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, Springer Series in Optical Sciences, C.Weikamp, ed. (Springer, 2005).

M. Born and E. Wolf, Principles of Optics (Pergamon, 1975), Chap. 8.

E. P. Shettle and R. W. Fenn, “Models for the aerosols of the lower atmosphere and the effects of humidity variations on their optical properties,” Air Force Geophysics Laboratory Tech. Rep. AFGL-TR-79-0214, (Air Force Geophysics Laboratory, 1979).

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Figures (14)

Fig. 1
Fig. 1

Second-order scattering detected at an angle θ by the MFOV lidar; the ellipses represent the phase functions.

Fig. 2
Fig. 2

Number and volume density distribution for trimodal lognormal distributions.

Fig. 3
Fig. 3

Phase function as a function of the scattering angle.

Fig. 4
Fig. 4

Single scattering lidar return as a function of distance from the lidar system.

Fig. 5
Fig. 5

Lidar encircled energy return as a function of FOV at a sounding distance of 140 m .

Fig. 6
Fig. 6

Δ P norm ( z c , θ i + 1 θ i ) as a function of 0.5 ( θ i + 1 + θ i ) for a sounding distance of 140 m for the 10, 20, and 35 μm particles.

Fig. 7
Fig. 7

Δ P norm ( z c , θ i + 1 θ i ) as a function of 0.5 ( θ i + 1 + θ i ) for a sounding distance of 178 m for the 10, 20, and 35 μm particles.

Fig. 8
Fig. 8

Δ P norm ( z c , θ i + 1 θ i ) as a function of 0.5 ( θ i + 1 + θ i ) for a sounding distance of 1025 m for the 10, 20, and 35 μm particles.

Fig. 9
Fig. 9

Δ P norm ( z c , θ i + 1 θ i ) as a function of 0.5 ( θ i + 1 + θ i ) for a sounding distance of 1075 m for the 10, 20, and 35 μm particles.

Fig. 10
Fig. 10

MFOV imaging lidar.

Fig. 11
Fig. 11

Encircled energy as a function of FOVs for the Elm and Timothy pollens for sounding measurement distances of 158 and 178 m , respectively.

Fig. 12
Fig. 12

Measured normalized Δ P norm ( z c , θ i + 1 θ i ) for the Elm and Timothy pollens for sounding measurement distances of 158 and 178 m respectively.

Fig. 13
Fig. 13

Δ P norm ( z c , θ i + 1 θ i ) as a function of 0.5 ( θ i + 1 + θ i ) for sounding distances of 4010 m for 0.2 and 1 μm particles.

Fig. 14
Fig. 14

Δ L ( β i + 1 β i ) as a function of scattering angle 0.5 ( β i + 1 β i ) for linear and logarithmic spacing of the interval.

Tables (4)

Tables Icon

Table 1 Trimodal Lognormal Size Density Distribution Parameters

Tables Icon

Table 2 Retrieved Effective Diameters and Optical Depths and Their Relative Errors for a 10 m Depth Cloud Center at a Distance of 125 m from the Lidar System a

Tables Icon

Table 3 Retrieved Effective Diameters and Optical Depths and Their Relative Errors for a 10 m Depth Cloud Center at a Distance of 1000 m from the Lidar System a

Tables Icon

Table 4 Comparison of the Mean Diameter Measured with a Microscope and the MFOV Lidar

Equations (35)

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P s ( z c ) = P 0 c τ 2 A z c 2 α s ( z c ) p ( r , z c , β = π ) exp [ 2 0 z c α ( z ) d z ] ,
P D ( z c , Δ θ i ) = P 0 exp [ 2 0 z c α ( z ) d z ] c τ 2 A z c 2 2 0 z c 0 2 π β i β i + 1 [ α s ( z ) p ( r , z , β ) ] [ α s ( z c ) p ( r , z c , β back ) ] sin β d β d ϕ d z .
Δ L ( r , z , β i + 1 β i ) β i β i + 1 0 2 π p ( r , z , β ) sin β d β d φ
P D ( z c , θ i + 1 θ i ) = 2 P s ( z c ) 0 z c α s Δ L ( r , z , β i + 1 β i ) d z .
P s s ( z c , θ i + 1 θ i ) = P s ( z c ) [ ( 1 f ) { exp [ 1 2 ( θ i ψ 0 ) 2 ] exp [ 1 2 ( θ i + 1 ψ 0 ) 2 ] } + f { exp [ 1 2 ( θ i ψ 1 ) 2 ] exp [ 1 2 ( θ i + 1 ψ 1 ) 2 ] } ] .
P ( z c , θ i + 1 θ i ) = P s s ( z c , θ i + 1 θ i ) + P D ( z c , θ i + 1 θ i ) ,
P ( z c , θ i + 1 ) = i = 1 i + 1 P ( z c , θ i + 1 θ i ) .
Δ P Norm ( θ i + 1 θ i ) = P D ( z c , Δ θ i , α b > 0 ) + P s s ( z c , Δ θ i , α b > 0 ) P s ( z c , θ s , α b > 0 ) P D ( z c , Δ θ i , α b = 0 ) + P s s ( z c , Δ θ i , α b = 0 ) P s ( z c , θ s , α b = 0 ) ,
d N ( r ) d r = i = 1 3 ( N i ln ( 10 ) * r * s i * 2 π ) exp [ ( log r log r i ) 2 2 s i 2 ] ,
P D ( z c , θ i + 1 θ i ) P s ( z c , θ s ) = 2 0 z c α s ( z ) Δ L ( r , z , β i + 1 β i ) d z .
P D ( z c , θ i + 1 θ i ) P S ( z c , θ s ) = 2 0 z a α s ( z ) Δ L ( r , z , β i + 1 β i ) d z + 2 z a z b α s ( z ) Δ L ( r , z , β i + 1 β i ) d z + 2 z b z c α s ( z ) Δ L ( r , z , β i + 1 β i ) d z .
α s ( z ) p ( r , z , β ) = α a ( z ) ω 0 a p a ( r , β ) + α R p R ( r , β ) ,
α s ( z ) p ( r , z , β ) = α a ( z ) ω 0 a p a ( r , β ) + α b ( z ) ω 0 b p b ( r , β ) + α R p R ( r , β ) ,
Δ P norm ( θ i + 1 θ i ) = P D ( z c , θ i + 1 θ i , α b > 0 ) P s ( z c , θ s , α b > 0 ) P D ( z c , θ i + 1 θ i , α b = 0 ) P s ( z c , θ s , α b = 0 ) = 2 ϖ 0 b z a z b α b Δ L ( r , z , β i + 1 β i ) d z .
p ( β ) = A 2 π ω 0 b y 2 exp ( A 1 2 y 2 β 2 ) ,
y = π d eff λ , A 1 = 0.544 , A 2 = 0.139 ,
Δ L ( β i + 1 β i ) = 2 A 2 y 2 ω 0 b β i β i + 1 exp ( A 1 2 y 2 β 2 ) β d β .
Δ P norm ( θ i + 1 θ i ) = 2 τ A 2 A 1 2 { exp [ A 1 2 y 2 ( z c z c z ¯ ) 2 θ i 2 ] exp [ A 1 2 y 2 ( z c z c z ¯ ) 2 θ i + 1 2 ] } ,
d eff = 1.30 2 λ π θ max , Log [ ln ( 1 + C t e ) 2 [ ( 1 + C t e ) 2 1 ] ] 0.5 z c z ¯ z c ,
τ = ln ( P ( z c , θ min ) P ref ( z c , θ min ) ) 1 / 2 ,
τ = Δ P Norm ( θ i + 1 θ i ) T mask 2 A 2 A 1 2 { exp [ A 1 2 y 2 ( z c z c z ¯ ) 2 θ i 2 ] exp [ A 1 2 y 2 ( z c z c z ¯ ) 2 θ i + 1 2 ] } .
0.58 λ θ FOV   max [ ln ( 1 + C t e ) 2 [ ( 1 + C t e ) 2 1 ] ] 0.5 z c z ¯ z c < d eff < 0.58 λ θ FOV   min [ ln ( 1 + C t e ) 2 [ ( 1 + C t e ) 2 1 ] ] 0.5 z c z ¯ z c ,
P s s ( z c , θ i + 1 θ i ) = P 0 e 2 0 z c α ( z ) d z A z c 2 c τ 2 [ α s ( z c ) ] p ( π ) [ 1 f 2 π ψ 0 2 0 2 π θ i θ i + 1 e 1 / 2 ( θ / ψ 0 ) 2 θ d θ d ϕ + f 2 π ψ 1 2 0 2 π θ i θ i + 1 e 1 / 2 ( θ / ψ 1 ) 2 θ d θ d ϕ ] .
P s s ( z c , θ i + 1 θ i ) = P 0 exp [ 2 0 z c α ( z ) d z ] A z c 2 c τ 2 [ α s ( z c ) ] p ( π ) ( ( 1 f ) { exp [ 1 2 ( θ i ψ 0 ) 2 ] exp [ 1 2 ( θ i + 1 ψ 0 ) 2 ] } + f { exp [ 1 2 ( θ i ψ 1 ) 2 ] exp [ 1 2 ( θ i + 1 ψ 1 ) 2 ] } ) ,
P s , 1 f ( z c ) = ( 1 f ) P 0 A z c 2 c τ 2 [ α a ( z c ) ] p ( π ) e 2 0 z c α ( z ) z d z = ( 1 f ) P s ( z c ) .
P s s ( z c , θ i + 1 θ i ) = P s ( z c ) ( ( 1 f ) { exp [ 1 2 ( θ i ψ 0 ) 2 ] exp [ 1 2 ( θ i + 1 ψ 0 ) 2 ] } + f { exp [ 1 2 ( θ i ψ 1 ) 2 ] exp [ 1 2 ( θ i + 1 ψ 1 ) 2 ] } ) .
Δ P norm ( θ i + 1 θ i ) = 2 τ A 2 A 1 2 [ exp ( A 1 2 y 2 z c θ i 2 z c z ¯ ) exp ( A 1 2 y 2 z c θ i + 1 2 z c z ¯ ) ] .
θ i = 10 log θ 1 + ( log θ n log θ 1 ) [ ( i 1 ) ( n 1 ) ] ,
θ i + 1 = θ i + C t e θ i ,
Δ P ( θ i + 1 θ i ) = 2 τ A 2 A 2 2 { exp [ A 1 2 y 2 ( z c θ i z c z ¯ ) 2 ] exp [ A 1 2 y 2 ( z c θ i z c z ¯ ) 2 2 A 1 2 y 2 C t e ( z c θ i z c z ¯ ) 2 A 1 2 y 2 C t e 2 ( z c θ i z c z ¯ ) 2 ] } .
θ max , log = λ d eff 1 π A 1 [ ln ( 1 + C t e ) 2 [ ( 1 + c t e ) 2 1 ] ] 0.5 z c z ¯ z c .
d L ( β ) d β = 2 A 2 y 2 ω 0 b e A 1 2 y 2 β 2 β .
β max = λ d eff π A 1 2 = 1.30 λ π d eff .
θ max , lin = 1.30 λ π d eff z c z ¯ z c .
θ max , log = 2 θ max , lin .

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