Abstract

We present an alternative method for determining the total offset in lidar signal created by a daytime background-illumination component and electrical or digital offset. Unlike existing techniques, here the signal square-range-correction procedure is initially performed using the total signal recorded by lidar, without subtraction of the offset component. While performing the square-range correction, the lidar-signal monotonic change due to the molecular component of the atmosphere is simultaneously compensated. After these corrections, the total offset is found by determining the slope of the above transformed signal versus a function that is defined as a ratio of the squared range and two molecular scattering components, the backscatter and transmittance. The slope is determined over a far end of the measurement range where aerosol loading is zero or, at least, minimum. An important aspect of this method is that the presence of a moderate aerosol loading over the far end does not increase dramatically the error in determining the lidar-signal offset. The comparison of the new technique with a conventional technique of the total-offset estimation is made using simulated and experimental data. The one-directional and multiangle measurements are analyzed and specifics in the estimate of the uncertainty limits due to remaining shifts in the inverted lidar signals are discussed. The use of the new technique allows a more accurate estimate of the signal constant offset, and accordingly, yields more accurate lidar-signal inversion results.

© 2009 Optical Society of America

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References

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  1. V. A. Kovalev, “Distortion of the extinction coefficient profile caused by systematic errors in lidar data,” Appl. Opt. 43, 3191-3198 (2004).
    [CrossRef] [PubMed]
  2. S. R. Ahmad and E. M. Bulliet, “Performance evaluation of a laboratory-based Raman lidar in atmospheric pollution measurement,” Opt. Laser Technol. 26, 323-331 (1994).
    [CrossRef]
  3. H. Shimizu, Y. Sasano, H. Nakane, N. Sugimoto, I. Matsui, and N. Takeuchi, “Large-scale laser radar for measuring aerosol distribution over a wide area,” Appl. Opt. 24, 617-626(1985).
    [CrossRef] [PubMed]
  4. Y. Zhao, “Signal-induced fluorescence in photomultipliers in differential absorption lidar systems,” Appl. Opt. 38, 4639-4648 (1999).
    [CrossRef]
  5. J. A. Sunesson, A. Apituley, and D. P. J. Swart, “Differential absorption lidar system for routine monitoring of tropospheric ozone,” Appl. Opt. 33, 7045-7058 (1994).
    [CrossRef] [PubMed]
  6. H. S. Lee, G. K. Schwemmer, C. L. Korb, M. Dombrowski, and C. Prasad, “Gated photomultiplier response characterization for DIAL measurements,” Appl. Opt. 29, 3303-3315 (1990).
    [CrossRef] [PubMed]
  7. M. Bristow, “Suppression of afterpulsing in photomultipliers by gating the photocathode,” Appl. Opt. 41, 4975-4987 (2002).
    [CrossRef] [PubMed]
  8. V. A. Kovalev, W. M. Hao, C. Wold, and M. Adam, “Experimental method for the examination of systematic distortions in lidar data,” Appl. Opt. 46, 6710-6718 (2007).
    [CrossRef] [PubMed]
  9. M. Adam, V. A. Kovalev, C. Wold, J. Newton, M. Pahlow, Wei M. Hao, and M. B. Parlange, “Application of the Kano-Hamilton multiangle inversion method in clear atmospheres,” J. Atmos. Ocean. Technol. 24, 2014-2028 (2007).
    [CrossRef]
  10. O. Uchino and I. Tabata, “Mobile lidar for simultaneous measurements of ozone, aerosols, and temperature in the stratosphere,” Appl. Opt. 30, 2005-2012 (1991).
    [CrossRef] [PubMed]
  11. J. R. Taylor, An Introduction to Error Analysis. the Study of Uncertainties in Physical Measurements (University Science Books, 1997).
  12. A. Comeron, F. Rocadenbosch, M. A. Lopez, A. Rodriguez, C. Munoz, D. Garcia-Vizcaino, and M. Sicard, “Effects of noise on lidar data inversion with the backward algorithm,” Appl. Opt. 43, 2572-2577 (2004).
    [CrossRef] [PubMed]
  13. V. A. Kovalev, W. M. Hao, and C. Wold, “Determination of the particulate extinction-coefficient profile and the column-integrated lidar ratios using the backscatter-coefficient and optical-depth profiles,” Appl. Opt. 46, 8627-8634 (2007).
    [CrossRef] [PubMed]

2007 (3)

2004 (2)

2002 (1)

1999 (1)

1994 (2)

J. A. Sunesson, A. Apituley, and D. P. J. Swart, “Differential absorption lidar system for routine monitoring of tropospheric ozone,” Appl. Opt. 33, 7045-7058 (1994).
[CrossRef] [PubMed]

S. R. Ahmad and E. M. Bulliet, “Performance evaluation of a laboratory-based Raman lidar in atmospheric pollution measurement,” Opt. Laser Technol. 26, 323-331 (1994).
[CrossRef]

1991 (1)

1990 (1)

1985 (1)

Adam, M.

V. A. Kovalev, W. M. Hao, C. Wold, and M. Adam, “Experimental method for the examination of systematic distortions in lidar data,” Appl. Opt. 46, 6710-6718 (2007).
[CrossRef] [PubMed]

M. Adam, V. A. Kovalev, C. Wold, J. Newton, M. Pahlow, Wei M. Hao, and M. B. Parlange, “Application of the Kano-Hamilton multiangle inversion method in clear atmospheres,” J. Atmos. Ocean. Technol. 24, 2014-2028 (2007).
[CrossRef]

Ahmad, S. R.

S. R. Ahmad and E. M. Bulliet, “Performance evaluation of a laboratory-based Raman lidar in atmospheric pollution measurement,” Opt. Laser Technol. 26, 323-331 (1994).
[CrossRef]

Apituley, A.

Bristow, M.

Bulliet, E. M.

S. R. Ahmad and E. M. Bulliet, “Performance evaluation of a laboratory-based Raman lidar in atmospheric pollution measurement,” Opt. Laser Technol. 26, 323-331 (1994).
[CrossRef]

Comeron, A.

Dombrowski, M.

Garcia-Vizcaino, D.

Hao, W. M.

Hao, Wei M.

M. Adam, V. A. Kovalev, C. Wold, J. Newton, M. Pahlow, Wei M. Hao, and M. B. Parlange, “Application of the Kano-Hamilton multiangle inversion method in clear atmospheres,” J. Atmos. Ocean. Technol. 24, 2014-2028 (2007).
[CrossRef]

Korb, C. L.

Kovalev, V. A.

Lee, H. S.

Lopez, M. A.

Matsui, I.

Munoz, C.

Nakane, H.

Newton, J.

M. Adam, V. A. Kovalev, C. Wold, J. Newton, M. Pahlow, Wei M. Hao, and M. B. Parlange, “Application of the Kano-Hamilton multiangle inversion method in clear atmospheres,” J. Atmos. Ocean. Technol. 24, 2014-2028 (2007).
[CrossRef]

Pahlow, M.

M. Adam, V. A. Kovalev, C. Wold, J. Newton, M. Pahlow, Wei M. Hao, and M. B. Parlange, “Application of the Kano-Hamilton multiangle inversion method in clear atmospheres,” J. Atmos. Ocean. Technol. 24, 2014-2028 (2007).
[CrossRef]

Parlange, M. B.

M. Adam, V. A. Kovalev, C. Wold, J. Newton, M. Pahlow, Wei M. Hao, and M. B. Parlange, “Application of the Kano-Hamilton multiangle inversion method in clear atmospheres,” J. Atmos. Ocean. Technol. 24, 2014-2028 (2007).
[CrossRef]

Prasad, C.

Rocadenbosch, F.

Rodriguez, A.

Sasano, Y.

Schwemmer, G. K.

Shimizu, H.

Sicard, M.

Sugimoto, N.

Sunesson, J. A.

Swart, D. P. J.

Tabata, I.

Takeuchi, N.

Taylor, J. R.

J. R. Taylor, An Introduction to Error Analysis. the Study of Uncertainties in Physical Measurements (University Science Books, 1997).

Uchino, O.

Wold, C.

Zhao, Y.

Appl. Opt. (10)

H. Shimizu, Y. Sasano, H. Nakane, N. Sugimoto, I. Matsui, and N. Takeuchi, “Large-scale laser radar for measuring aerosol distribution over a wide area,” Appl. Opt. 24, 617-626(1985).
[CrossRef] [PubMed]

Y. Zhao, “Signal-induced fluorescence in photomultipliers in differential absorption lidar systems,” Appl. Opt. 38, 4639-4648 (1999).
[CrossRef]

J. A. Sunesson, A. Apituley, and D. P. J. Swart, “Differential absorption lidar system for routine monitoring of tropospheric ozone,” Appl. Opt. 33, 7045-7058 (1994).
[CrossRef] [PubMed]

H. S. Lee, G. K. Schwemmer, C. L. Korb, M. Dombrowski, and C. Prasad, “Gated photomultiplier response characterization for DIAL measurements,” Appl. Opt. 29, 3303-3315 (1990).
[CrossRef] [PubMed]

M. Bristow, “Suppression of afterpulsing in photomultipliers by gating the photocathode,” Appl. Opt. 41, 4975-4987 (2002).
[CrossRef] [PubMed]

V. A. Kovalev, W. M. Hao, C. Wold, and M. Adam, “Experimental method for the examination of systematic distortions in lidar data,” Appl. Opt. 46, 6710-6718 (2007).
[CrossRef] [PubMed]

V. A. Kovalev, “Distortion of the extinction coefficient profile caused by systematic errors in lidar data,” Appl. Opt. 43, 3191-3198 (2004).
[CrossRef] [PubMed]

O. Uchino and I. Tabata, “Mobile lidar for simultaneous measurements of ozone, aerosols, and temperature in the stratosphere,” Appl. Opt. 30, 2005-2012 (1991).
[CrossRef] [PubMed]

A. Comeron, F. Rocadenbosch, M. A. Lopez, A. Rodriguez, C. Munoz, D. Garcia-Vizcaino, and M. Sicard, “Effects of noise on lidar data inversion with the backward algorithm,” Appl. Opt. 43, 2572-2577 (2004).
[CrossRef] [PubMed]

V. A. Kovalev, W. M. Hao, and C. Wold, “Determination of the particulate extinction-coefficient profile and the column-integrated lidar ratios using the backscatter-coefficient and optical-depth profiles,” Appl. Opt. 46, 8627-8634 (2007).
[CrossRef] [PubMed]

J. Atmos. Ocean. Technol. (1)

M. Adam, V. A. Kovalev, C. Wold, J. Newton, M. Pahlow, Wei M. Hao, and M. B. Parlange, “Application of the Kano-Hamilton multiangle inversion method in clear atmospheres,” J. Atmos. Ocean. Technol. 24, 2014-2028 (2007).
[CrossRef]

Opt. Laser Technol. (1)

S. R. Ahmad and E. M. Bulliet, “Performance evaluation of a laboratory-based Raman lidar in atmospheric pollution measurement,” Opt. Laser Technol. 26, 323-331 (1994).
[CrossRef]

Other (1)

J. R. Taylor, An Introduction to Error Analysis. the Study of Uncertainties in Physical Measurements (University Science Books, 1997).

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Figures (8)

Fig. 1
Fig. 1

Model profiles of the vertical particulate extinction coefficient (solid curve) and the molecular extinction coefficient (dotted curve) used for simulations.

Fig. 2
Fig. 2

Noise-corrupted profile of the square-range-corrected signal of a ground-based lidar versus height calculated for the model atmosphere in Fig. 1.

Fig. 3
Fig. 3

Range-resolved profiles of d Y / d x obtained using the numerical differentiation of Eq. (7) with the running mean. The gray cross symbols show the derivative profile obtained with s 1 = 200 m and the thick curve shows that obtained with s 2 = 2000 m . The doted horizontal line shows the actual signal offset B = 300 a . u .

Fig. 4
Fig. 4

Height-corrected signals P ( h ) h 2 determined from the noise-corrupted signal P Σ ( h ) = P ( h ) + B using the estimates B ( s 1 ) , B ( s 2 ) , and B ( P Σ ) (the curves 1, 2, and 3, respectively). The model range-corrected signal, not corrupted by noise, is shown as curve 4.

Fig. 5
Fig. 5

Vertical profiles of particulate extinction coefficient retrieved with the far-end solution using the estimates B ( s 1 ) and B ( P Σ ) (the thin and thick solid curves, respectively). The dotted curve shows the “actual” (model) profile.

Fig. 6
Fig. 6

Derivative of Y ( x ) versus x calculated for the lidar signal P Σ ( r ) azimuthally averaged over the elevation 32 ° (solid curve). The offset, B ( s ) , determined as the average of the function over the range from 4000 to 5500 m , is shown as the horizontal dotted line.

Fig. 7
Fig. 7

Square-range-corrected signals versus range for the elevation 32 ° . Black dots show the signal calculated using the estimate B ( s ) = 420.61 a . u . and the gray squares show that obtained with B ( P Σ ) = 421.26 a . u .

Fig. 8
Fig. 8

Dependence of particulate optical depth on the height calculated using different methods for the estimation of the total signal offsets. The dotted curve shows the optical depth obtained from signals for which the estimate B ( s ) was used for the azimuthally averaged signals, the dashed curve shows that obtained with the estimate B ( P Σ ) , and the solid curve shows the profile obtained when using an average of B ( s ) and B ( P Σ ) .

Equations (12)

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P Σ ( h ) = P ( h ) + B ,
P ( h ) = 1 h 2 C 0 β π ( h ) [ T t ( 0 , h ) ] 2 ,
[ T t ( 0 , h ) ] 2 = [ T p ( 0 , h ) ] 2 [ T m ( 0 , h ) ] 2 = exp [ 2 0 h κ p ( ξ ) d ξ ] exp [ 2 0 h κ m ( ξ ) d ξ ] ,
P ( h ) h 2 = [ P Σ ( h ) B ] h 2 .
Y ( h ) = P Σ ( h ) h 2 β π , m ( h ) [ T m ( 0 , h ) ] 2 = C 0 [ 1 + R β ( h ) ] [ T p ( 0 , h ) ] 2 + B h 2 β π , m ( h ) [ T m ( 0 , h ) ] 2 ,
C 0 [ 1 + R β ( h ) ] [ T p ( 0 , h ) ] 2 = A ,
Y ( x ) = A + B x ,
x = h 2 β π , m ( h ) [ T m ( 0 , h ) ] 2 .
B = Y ( x 2 ) Y ( x 1 ) x 2 x 1 = B + C 0 { [ 1 + R β ( h 2 ) ] [ T p ( 0 , h 2 ] 2 [ 1 + R β ( h 1 ) ] [ T p ( 0 , h 1 ] 2 } x 2 x 1 .
Δ B = [ x 2 x 2 x 1 ] P ( h 2 ) [ x 1 x 2 x 1 ] P ( h 1 ) .
Δ B = P ( h 1 ) ( x 1 x 2 x 1 ) { ( 1 + R β ( h 2 ) 1 + R β ( h 1 ) ) exp [ 2 τ p ( h 1 , h 2 ) ] 1 } ,
P ( h 2 ) P ( h 1 ) < x 1 x 2 .

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