New technique for retrieval of atmospheric temperature profiles from Rayleigh-scatter lidar measurements using nonlinear inversion

doi:10.1364/AO.51.007945

New technique for retrieval of atmospheric temperature profiles from Rayleigh-scatter lidar measurements using nonlinear inversion

Jaya Khanna, Justin Bandoro, R. J. Sica, and C. Thomas McElroy »View Author Affiliations

Applied Optics, Vol. 51, Issue 33, pp. 7945-7952 (2012)
http://dx.doi.org/10.1364/AO.51.007945

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Abstract

The conventional method of calculating atmospheric temperature profiles using Rayleigh-scattering lidar measurements has limitations that necessitate abandoning temperatures retrieved at the greatest heights, due to the assumption of a pressure value required to initialize the integration at the highest altitude. An inversion approach is used to develop an alternative way of retrieving nightly atmospheric temperature profiles from the lidar measurements. Measurements obtained by the Purple Crow lidar facility located near The University of Western Ontario are used to develop and test this new technique. Our results show temperatures can be reliably retrieved at all heights where measurements with adequate signal-to-noise ratio exist. A Monte Carlo technique was developed to provide accurate estimates of both the systematic and random uncertainties for the retrieved nightly average temperature profile. An advantage of this new method is the ability to seed the temperature integration from the lowest rather than the greatest height, where the variability of the pressure is smaller than in the mesosphere or lower thermosphere and may in practice be routinely measured by a radiosonde, rather than requiring a rocket or satellite-borne measurement. Thus, this new technique extends the altitude range of existing Rayleigh-scatter lidars 10–15 km, producing the equivalent of four times the power-aperture product.

OCIS Codes
(000.2170) General : Equipment and techniques
(010.3640) Atmospheric and oceanic optics : Lidar
(280.3640) Remote sensing and sensors : Lidar
(280.6780) Remote sensing and sensors : Temperature

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: July 10, 2012
Revised Manuscript: October 11, 2012
Manuscript Accepted: October 17, 2012
Published: November 19, 2012

Citation
Jaya Khanna, Justin Bandoro, R. J. Sica, and C. Thomas McElroy, "New technique for retrieval of atmospheric temperature profiles from Rayleigh-scatter lidar measurements using nonlinear inversion," Appl. Opt. 51, 7945-7952 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-33-7945

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References

A. Hauchecorne and M. L. Chanin, “Density and temperature profiles obtained by lidar between 35 and 70 km,” Geophys. Res. Lett. 7, 565–568 (1980). [CrossRef]
P. R. Bevington and D. K. Robinson, Data Reduction and Error Analysis for the Physical Sciences, 2nd ed. (McGraw-Hill, 1992).
P. C. Mahalanobis, “On the generalized distance in statistics,” Proc. Natl. Inst. Sci. India 2, 49–55 (1936).
V. A. Kovalev and W. E. Eichinger, Elastic Lidar: Theory, Practice and Analysis Method (Wiley, 2004).
P. B. Russell and B. M. Morley, “Orbiting lidar simulations. 2: density, temperature, aerosol, and cloud measurements by a wavelength combining technique,” Appl. Opt. 21, 1554–1563 (1982). [CrossRef]
J. P. Thayer, N. B. Nielsen, R. E. Warren, C. J. Heinselman, and J. Sohn, “Rayleigh lidar system for middle atmosphere research in the arctic,” Opt. Eng. 36, 2045–2061 (1997). [CrossRef]
R. J. Sica, S. Sargoytchev, P. S. Argall, E. F. Borra, L. Girard, C. T. Sparrow, and S. Flatt, “Lidar measurements taken with a large-aperture liquid mirror. 1. Rayleigh-scatter system,” Appl. Opt. 34, 6925–6936 (1995). [CrossRef]
E. L. Fleming, S. Chandra, J. J. Barnett, and M. Corney, “Zonal mean temperature, pressure, zonal wind and geopotential height as functions of latitude,” Adv. Space Res. 12, 11–59 (1990).
P. S. Argall and R. J. Sica, “A comparison of Rayleigh and sodium lidar temperature climatologies,” Ann. Geophys. 25, 27–35 (2007). [CrossRef]
JCGM, “Evaluation of measurement data: guide to the expression of uncertainty in measurement,” Tech. Rep. (Joint Committee for Guides in Meteorology, 2008).
JCGM, “Evaluation of measurement data,” supplement 1 to the “Guide to the expression of uncertainty in measurement propagation of distributions using a Monte Marlo method,” Tech. Rep. (Joint Committee for Guides in Meteorology, 2008).
R. W. Hamming, Digital Filters (Prentice-Hall, 1977).

Adv. Space Res.

E. L. Fleming, S. Chandra, J. J. Barnett, and M. Corney, “Zonal mean temperature, pressure, zonal wind and geopotential height as functions of latitude,” Adv. Space Res. 12, 11–59 (1990).

Ann. Geophys.

P. S. Argall and R. J. Sica, “A comparison of Rayleigh and sodium lidar temperature climatologies,” Ann. Geophys. 25, 27–35 (2007). [CrossRef]

Appl. Opt.

Geophys. Res. Lett.

A. Hauchecorne and M. L. Chanin, “Density and temperature profiles obtained by lidar between 35 and 70 km,” Geophys. Res. Lett. 7, 565–568 (1980). [CrossRef]

Opt. Eng.

J. P. Thayer, N. B. Nielsen, R. E. Warren, C. J. Heinselman, and J. Sohn, “Rayleigh lidar system for middle atmosphere research in the arctic,” Opt. Eng. 36, 2045–2061 (1997). [CrossRef]

Proc. Natl. Inst. Sci. India

P. C. Mahalanobis, “On the generalized distance in statistics,” Proc. Natl. Inst. Sci. India 2, 49–55 (1936).

Other

V. A. Kovalev and W. E. Eichinger, Elastic Lidar: Theory, Practice and Analysis Method (Wiley, 2004).
P. R. Bevington and D. K. Robinson, Data Reduction and Error Analysis for the Physical Sciences, 2nd ed. (McGraw-Hill, 1992).
JCGM, “Evaluation of measurement data: guide to the expression of uncertainty in measurement,” Tech. Rep. (Joint Committee for Guides in Meteorology, 2008).
JCGM, “Evaluation of measurement data,” supplement 1 to the “Guide to the expression of uncertainty in measurement propagation of distributions using a Monte Marlo method,” Tech. Rep. (Joint Committee for Guides in Meteorology, 2008).
R. W. Hamming, Digital Filters (Prentice-Hall, 1977).

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[1] A. Hauchecorne and M. L. Chanin, “Density and temperature profiles obtained by lidar between 35 and 70 km,” Geophys. Res. Lett. 7, 565–568 (1980). [CrossRef]

[2] P. R. Bevington and D. K. Robinson, Data Reduction and Error Analysis for the Physical Sciences, 2nd ed. (McGraw-Hill, 1992).

[3] P. C. Mahalanobis, “On the generalized distance in statistics,” Proc. Natl. Inst. Sci. India 2, 49–55 (1936).

[4] V. A. Kovalev and W. E. Eichinger, Elastic Lidar: Theory, Practice and Analysis Method (Wiley, 2004).

[5] P. B. Russell and B. M. Morley, “Orbiting lidar simulations. 2: density, temperature, aerosol, and cloud measurements by a wavelength combining technique,” Appl. Opt. 21, 1554–1563 (1982). [CrossRef]

[6] J. P. Thayer, N. B. Nielsen, R. E. Warren, C. J. Heinselman, and J. Sohn, “Rayleigh lidar system for middle atmosphere research in the arctic,” Opt. Eng. 36, 2045–2061 (1997). [CrossRef]

[7] R. J. Sica, S. Sargoytchev, P. S. Argall, E. F. Borra, L. Girard, C. T. Sparrow, and S. Flatt, “Lidar measurements taken with a large-aperture liquid mirror. 1. Rayleigh-scatter system,” Appl. Opt. 34, 6925–6936 (1995). [CrossRef]

[8] E. L. Fleming, S. Chandra, J. J. Barnett, and M. Corney, “Zonal mean temperature, pressure, zonal wind and geopotential height as functions of latitude,” Adv. Space Res. 12, 11–59 (1990).

[9] P. S. Argall and R. J. Sica, “A comparison of Rayleigh and sodium lidar temperature climatologies,” Ann. Geophys. 25, 27–35 (2007). [CrossRef]

[10] JCGM, “Evaluation of measurement data: guide to the expression of uncertainty in measurement,” Tech. Rep. (Joint Committee for Guides in Meteorology, 2008).

[11] JCGM, “Evaluation of measurement data,” supplement 1 to the “Guide to the expression of uncertainty in measurement propagation of distributions using a Monte Marlo method,” Tech. Rep. (Joint Committee for Guides in Meteorology, 2008).

[12] R. W. Hamming, Digital Filters (Prentice-Hall, 1977).

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New technique for retrieval of atmospheric temperature profiles from Rayleigh-scatter lidar measurements using nonlinear inversion