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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 15, Iss. 11 — May. 28, 2007
  • pp: 6734–6743

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Retrieval of atmospheric particles optical properties by combining ground-based and spaceborne lidar elastic scattering profiles

Xuan wang, Maria Grazia Frontoso, Gianluca Pisani, and Nicola Spinelli  »View Author Affiliations


Optics Express, Vol. 15, Issue 11, pp. 6734-6743 (2007)
http://dx.doi.org/10.1364/OE.15.006734


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Abstract

A simple algorithm is derived to retrieve the aerosol backscattering and extinction vertical profiles from simultaneously detected ground and space elastic lidar signals, without any a priori hypothesis on aerosol particles properties. This technique can be applied at any wavelength whenever two “counter looking” lidars are available and the atmosphere can be considered horizontally homogeneous in a spatial scale of the order of the distance between the two lidar beams. To test the accuracy of the algorithm a numerical simulation has been performed. Moreover, it has been applied in a real case to level 1 products from CALIPSO.

© 2007 Optical Society of America

1. Introduction

Determining and quantifying the spatial and temporal distribution of the atmospheric aerosol is critical for climate change studies and to understand the tropospheric chemistry. Because of their short lifetime and the corresponding strong spatial variations synergy of satellite and ground-based measurements can be very helpful in characterizing the properties of aerosol on large time and space scales as required by global models [1–3

R. J. Charlson, et al., “Climate forcing of anthropogenic aerosols,“ Science, 255, 423–430 (1992). [CrossRef]

].

The LITE (Lidar In-space Technology Experiment) mission [4

D. M. Winker, R. H. Couch, and M. P. McCormick, “An overview of LITE: NASA’s Lidar In-space Technology Experiment,“ Proc. IEEE 84, 164–180 (1996). [CrossRef]

], launched in September 1994, was the first attempt to validate key enabling technologies required for operational spaceborne lidars and to explore the applications of spaceborne lidar to collect information on the dynamic range and variability of cloud and aerosol.

Another lidar equipped mission, the ICESat/GLAS (Ice, Cloud and land Elevation Satellite/Geoscience Laser Altimeter System) experiment [5

J. D. Spinhirne, et al., “Cloud and aerosol measurements from GLAS: Overview and initial results,“ J. Geophys. Res 32, L22S03, doi:10.1029/2005GL023507 (2005).

], has been launched in January 2003. GLAS is a laser altimeter designed to measure ice-sheet topography and associated temporal changes, as well as cloud and atmospheric properties.

The CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) mission started in April 2006, it is a collaborative effort to evaluate direct and indirect aerosol radiative forcing, to measure long-wave surface and atmospheric surface fluxes, and to estimate clouds radiative effects on climate system [6–8

D. M. Winker, J. R. Pelon, and M. P. Cormick, “The CALIPSO mission: spaceborn lidar for observation of aerosols and clouds,“ Proc. SPIE 4893, 1–11 (2003). [CrossRef]

]. To this purpose CALIPSO payload is furnished by three coaxial nadir viewing instruments: a two-wavelength polarization-sensitive lidar (CALIOP, i.e., Cloud-Aerosol LIdar with Orthogonal Polarization) operating at 532 nm and 1064 nm, a Wide Field Camera operating in the 670 nm region, and a three channel Imaging Infrared Radiometer operating in the thermal region at 8.7 μm, 10.5 μm, and 12.0 μm [9

M. A. Vaughan, et al., “CALIOP Algorithm Theoretical Basis Document,“ PS-SCI-202 Part 2.

]. Being CALIOP, as LITE and GLAS, a backscatter lidar, it cannot provide direct measurements of backscatter and extinction profiles. Aerosol optical parameters from elastic lidar data are usually retrieved using Klett [10

J. D. Klett, “Lidar inversion with variable backscatter/extinction ratios,“ Appl. Opt. 24, 1638–1643 (1985). [CrossRef] [PubMed]

] and Fernald [11

F. G. Fernald, B. J. Herman, and J. A. Reagan, “Determination of aerosol height distributions by Lidar,“ Journal of Applied Meteorology 11, 482–489 (1972). [CrossRef]

], or iterative [12

L. Elterman, “Aerosol measurements in the troposphere and stratosphere,“ Appl. Opt. 5, 1769–1776 (1966). [CrossRef] [PubMed]

,13

C. M. R. Platt, “Lidar and Radiometric Observations of Cirrus Clouds,“ J. Atmos. Sci. 30, 1191–1204 (1973). [CrossRef]

] algorithms that relies heavily upon an a priori estimation of the extinction to backscattering ratio, namely the lidar ratio (LR) which is strictly correlated to chemical and physical properties of aerosol particles [14

U. Wandinger, D. Müller, C. Böckmann, D. Althausen, V. Matthias, J. Bösenberg, V. Weiβ, M. Fiebig, M. Wendisch, A. Stohl, and A. Ansmann, “Optical and microphysical characterization of biomass-burning and industrial-pollution aerosols from multiwavelength lidar and aircraft measurements,“ J. Geophys. Res. 107, 10.1029/2 000JD000202 (2002). [CrossRef]

,15

K. Franke, A. Ansmann, D. Müller, D. Althausen, C. Venkataraman, M. Shekar Reddy, F. Wagner, and R. Scheele, “Optical properties of the Indo-Asian haze layer over the tropical Indian Ocean,“ J. Geophys. Res. 108, 10.I029/2002JD002473 (2003). [CrossRef]

]. This can introduce uncertainties of about 30%, though it can be estimated by collecting a variety of ancillary information [16

M. Vaughan, et al., “Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products,“ Proc. SPIE 5575, 16–30 (2004). [CrossRef]

]. Furthermore, simulations by Ansmann [17

A. Ansmann, “Ground-truth aerosol lidar observations: can the Klett solution obtained from ground and space be equal for the same aerosol case?,“ Appl. Opt. 45, 3367–3371 (2006) [CrossRef] [PubMed]

] show that systematic deviations up to 20% with respect to input true parameters could be found when the Klett solution for backscattering and extinction coefficient profiles are retrieved from both spaceborne and ground-based lidars. As a consequence, the possibility of determining the LR without any assumption seems to be important. Raman lidars are believed to be really advantageous because they provide unambiguous profile measurements of backscatter, extinction and hence LR over a wide range of altitude. However in practice the Raman technique has not yet been applied in the spaceborne lidars, neither it is applied to IR laser beams. Therefore LR at 1064 nm can only be derived from indirect estimations [18].

For these reasons the CALIPSO validation plan is a central issue to test the confidence level of the satellite data and to assure a high quality dataset. To this purpose validation activities are currently running in cooperation with existing instrument networks (AERONET, ADNET, ARM, CMDL, EARLINET, MFRSR, MPLNET, NDSC, REALM, SurfRad, USDA) and individual sites [19

Kovacs, et al., “Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Quid Pro Quo Validation,“ 12th ARM Science Team Meeting Proceedings (2002). [PubMed]

, 20

Hampton University website: http://calipsovalidation.hamptonu.edu/

].

The Napoli lidar station (40.838°N, 14.183°E, 118 m asl, Italy) is part of EARLINET (the European Aerosol Research LIdar NETwork) [21–23

J. Bösenberg, et al., “EARLINET: A European Aerosol Research Lidar Network,“ in Advances in Laser Remote Sensing, Eds: A. Dabas, Claude Loth and Jacques Pelon, ISBN 2-7302-0798-8, 155–158, (2001).

], which is a consortium of 25 lidar stations having as main goal to provide a quantitative comprehensive statistical and well-sampled database of the horizontal, vertical, and temporal aerosol distribution on a continental scale.

Direct comparison of ground-based and spaceborne aerosol lidar products is a difficult task for several reasons: i) different atmospheric attenuation has to be taken into account for ground-based and satellite lidars; ii) Raman measurements are not available for satellite lidars up to now; iii) vertical and horizontal resolutions can be rather different; iv) horizontally inhomogeneous aerosol conditions may lead to significant differences in the aerosol profiles obtained from ground and space. Furthermore, considering that the interesting altitude range is from ground up to 30–40 km, ground based systems must handle a very high dynamic range of the signals.

In this paper, we propose an algorithm to retrieve backscatter and extinction coefficient vertical profiles by combining simultaneous elastic signals from ground and space, without Raman measurements nor an a priori hypothesis on lidar ratio. Some considerations for the applicability of this algorithm are also presented.

In what follows we will indicate this algorithm as Counter-propagating Elastic Signals Combination (CESC).

2. The CESC Algorithm description

The precondition of this algorithm is that two “counter looking” lidars, a ground-based and a spaceborne lidar, measure the same atmosphere column which is stable within the measurement time. An additional hypothesis is related to the assumption that multiple scattering influence is negligible on both signals.

In what follows, we indicate with RCSs (z) and RCSg (z) the range corrected total elastic backscatter signal at the same wavelength as available from spaceborne and ground-based lidar respectively. If the above mentioned hypotheses are verified, the two signals can be written as follows:

RCSs (z)=k ⋅β (z)⋅exp ( − 2τzc)
(1)
RCSg (z)= k′⋅A (z)⋅β (z)⋅exp ( − 2τ 0z)
(2)

In Eqs. (1) and (2) z is the altitude along the vertical axis (z = 0 is the ground level), k , k′ include all instrumental constants of the two lidars, β(z) is the total backscattering and A(z) the overlap function of the ground-based lidar, respectively. Indicating with α(z)the total extinction coefficient at the altitude z, the terms τ zc = ∫ zc z α(ζ)dζ and τ0z = ∫ z 0(ζ)dζ represent the optical depth in the range from z to the altitude zc of spaceborne lidar and from the ground level to z, respectively.

Taking into account that zc is of the order of several hundreds km and the altitude range in which the validation occurs is not greater than 50 km above ground, a unitary overlap function has been considered in Eq. (1).

In the case of the ground-based lidar, the overlap function is strongly variable at low altitude but it asymptotically converges to unitary solution as a function of z. For that reason we will limit our considerations to altitudes where A(z) can be considered approximately constant or known from other ways.

Therefore we can write the product of the two signals as:

P (z)= RCSs (z)⋅ RCSg (z)= k ″⋅ β2⋅ (z)⋅exp [ −2( τzc+ τ 0z)]
(3)

where k′=k∙k′ is a constant.

Indicating with τ0c the optical depth from the ground level to zc, we can write: τ0z + τ zc = τ0c and the backscattering coefficient can be derived from Eq. (3) as:

β (z)= P(z)K
(4)

In Eq. (4) the quantity exp(-2τ0c ) has been included in the constant K, which can be determined by considering an altitude level z* at which the total backscattering β(z *) is known. If the altitude z* is chosen within an aerosol free region the total backscattering coefficient is determined from a pure molecular atmospheric model, β(z *) = βmol (z *), and the backscattering at z is obtained as:

β (z)= βmol ( z*)⋅ P (z) P ( z*).
(5)

Thus the particle backscattering βp (z) is given by:

βp (z)=β (z) − βmol (z).
(6)

In Eq. (5), P(z*) is evaluated by fitting -√P(z) to βmol (z) in the aerosol free range around z*. In this way the uncertainty in the retrieval can be kept as low as 2–5% even if the z* is chosen at high altitude where the signal from ground-based lidar is weak. Let us consider the ratio of Eqs. (1) and (2):

R (z)= RCSs (z) RCSg (z)= k k′⋅exp [ − ( τzc− τ 0z)]= k k′⋅exp ( −2 τ 0c)⋅exp (4 τ 0z).
(7)

From Eq. (7) the optical depth between ground and z level is obtained as:

τ 0z (z)= 14 [ ln R(z)−C]
(8)

where C=ln ( k k′)− 2τ 0c include all terms not depending on z. Than the optical depth between two levels z 1 and z 2 is given by:

τ ( z2− z1)= 14 [ln R ( z2)−ln R ( z1)]
(9)

and the particle extinction coefficient αp (z) is evaluated by differentiating Eq. (7) with respect to z and subtracting the molecule extinction αmol (z):

αp (z)= 14 d dz [ln R (z)] − αmol (z).
(10)

For Raman ground-based lidar the CESC algorithm allows also to determine the overlap function provided that the atmospheric properties along the two lidar paths are the same also at the lowest heights. In fact in this case the application of Eq. (5) furnishes the quantity G(z)=β(z)· A(z) , thus the overlap function is determined through the expression:

A (z)= ( G (z) βR (z))2
(11)

where βR (z) represents the total backscattering coefficient derived from the Raman technique.

3. Simulated case

Both ground-based and spaceborne lidar signals have been simulated in order to test the algorithm. Air density and the Rayleigh scattering coefficient are simulated from a standard atmospheric model (U.S. Standard Atmosphere) fitted to standard temperature and pressure values (20°C, 1 bar) at ground level. Below 1.5 km of altitude, a typical planetary boundary layer (PBL) aerosol (β = 1–5×10-6sr-1m-1) with LR = 70–80 sr [24

C. Cattrall, J. Reagan, K. Thome, and O. Dubovik, “Variability of aerosol and spectral lidar and backscatter and extinction ratios of key aerosol types derived from selected Aerosol Robotic Network locations,“ J. Geophys. Research 110, D10S11, 10.1029/2004JD005124 (2005). [CrossRef]

] was simulated. From 3 to 5.5 km two aerosol layers, separated by 0.5 km, are included in order to simulate lofted layers of Saharan desert mineral dust (LR = 40 sr) [25

L. Mona, A. Amodeo, M. Pandolfi, and G. Pappalardo, “Saharan dust intrusion in the Mediterranean area: three years of Raman lidar mesurements,“ J. Geophys. Res. 111, D16203 doi: 10.1029/2005JD006569 (2006). [CrossRef]

] and to verify the spatial resolution of the algorithm. A cirrus cloud (β = 8×10-6 sr-1 m-1, LR = 30 sr) [26

W. Chen, C. Chiang, and J. Nel, “Lidar ratio and depolarization for cirrus clouds,“ Appl. Opt. 30, 6470–6476 (2002). [CrossRef]

] is also simulated in the 9-10 km altitude range. In the simulated signals, only the statistic error is considered. The laser energy is chosen in order that the simulated lidar signals have a comparable signal-to-noise ratio with real signals integrated on 10 minutes for ground-based and 10 seconds for spaceborne lidars, respectively, having as reference the CALIPSO lidar. In fact, it can be reasonably assumed that during 10 minutes the atmosphere is stable and that it is homogeneous along about 60 km of the CALIPSO ground-track covered in 10 seconds.

In the simulation a vertical resolution of 60 m is considered for both ground-based and spaceborne lidars.

Fig. 1. Simulated RCS from ground-based lidar (solid line) and spaceborne lidar (dash line) signals (a); retrieved aerosol backscatter coefficient (open circle) and its solution (thick line) (b); retrieved aerosol extinction coefficient (open square) and its solution (thick line) (c). <LR> denotes the averaged lidar ratio for different layers. The vertical resolution of two RCS is 60 m.

Laser wavelength of 532 nm was used in the simulation even though the algorithm is wavelength independent. Here, only single Rayleigh and Mie scattering from pure molecules and aerosol, or clouds, are considered. In the retrieval of both backscattering and extinction profiles no data filtering is applied. The numerical derivative in Eq. (10) is obtained through the application of a linear fit on 5 data points up to 2 km and on 9 data points above this altitude. This corresponds to a vertical resolution of the extinction profile of about 200 m and 350 m up to 2 km and above this altitude, respectively.

Simulated RCS from ground-based and spaceborne lidars are shown in Fig. 1(a). Due to the very high dynamic range of the RCSg with respect to RCSs, the signal-to-noise ratio of ground-based lidar RCSg varies very much from low to high altitude while spaceborne lidar RCSs fluctuations remain of the same order. This is related to the fact that it is very difficult to improve the RCS signal-to-noise ratio by increasing the laser energy avoiding signal saturation in the near range.

In Fig. 1(b) the backscatter coefficient profile retrieved from Eq. (6) by considering a reference height z* in the range 10–12 km is shown; it perfectly overlaps the exact solution. Also the extinction coefficient profile from Eq. (10) [reported in Fig. 1(c)], agrees quite well with the solution, even if it has more uncertainties and worse spatial resolution. The averaged LR for both aerosol and cloud layer agree with the solution quite well with a standard deviation ranging from 10% for PBL aerosol to 15% for cirrus cloud.

4. A case study

We tested the CESC algorithm in a real case of measurements performed at Napoli in the framework of CALIPSO validation campaign.

The ground-based lidar is located in Napoli, Italy. An overview of the Raman lidar operating in Napoli is given in [27

L. Mona, et al., “Characterization of the variability of the humidity and cloud fields as observed from a cluster of ground-based lidar systems,“ Q. J. R. Meteorol. Soc. (Submitted).

]. The system works at 355 nm and 532 nm with coaxial geometry with respect to telescope. The elastic and Raman echoes are spectrally selected by a system of dichroic beam splitters and narrow band interferential filters. Ancillary instrumentation is used to measure atmospheric parameters like temperature, pressure, relative humidity, wind velocity and direction at ground level.

On 20 August 2006 EARLINET correlative measurements for CALIPSO were performed in Napoli. Lidar measurements started at 22:28 GMT of 19 August and ended at 02:40 GMT of the next day. The CALIPSO overpass happened around 01:17:24 GMT and the distance from the Napoli lidar station and the CALIPSO footprint track was about 50 km. During this measurement a wide soil dust layer was detected from lidar measurements. The layer base was situated at 1.5 km height extending up to 8.5 km. Analytical back-trajectories from HYSPLIT model [28

U. Draxler and G.D. v Rolph NOAA ARL HYSPLIT model. NOAA/Air Resource Laboratory, Silver Spring, MD, http://www.arl.noaa.gov/ready/hysplit4.html (2003).

] show that the origin of incoming air masses at these altitudes was located above the Sahara desert.

As shown in section 3, the CESC algorithm does not require normalization or calibration of range corrected signals for its application, nevertheless, in the analysis of the case study we have considered the total attenuated backscatter signal (ABS) at 532 nm (range-scaled, energy and gain normalized) as available from CALIPSO level 1 products, and the range corrected signal at the same wavelength from the ground-based lidar in Napoli. Because the CALIPSO lidar had a very narrow footprint, some considerations have to be done in order to consider spatial variability of aerosol and/or cloud layers. In this case the distance between the CALIPSO ground-track and the Napoli lidar station is about 50 km. This distance is lower than the typical aerosol air masses correlation scales (50–100 km). However, it is larger than the clouds correlation scales (a few kilometres to tens of kilometres). Furthermore, the lifetimes of a cloud could be as short as a few minutes [29

T.L. Anderson, R. J. Charlson, D. M. Winker, J. A. Ogren, and K. Holmen, “Mesoscale variations of tropospheric aerosols,“ J. Atmos. Sci. 60, 119–136 (2003). [CrossRef]

].

For the selected case we observed clouds at about 8 km above Napoli for almost the entire run, with the exception of the time period between 00:58 and 01:31 GMT. Moreover, range corrected signal time series [Fig. 2(a)] at 532 nm show that the main hypothesis of atmospheric stability in the altitude range affected by dust layer is rather well verified close to the spacecraft overpass (pink line). Horizontal homogeneity of aerosol layer was also checked by observing the Sea WiFS images [30

Sea WiFS website http://oceancolor.gsfc.nasa.gov/SeaWiFS/

], which clearly show the Saharan dust layer covering all the Mediterranean Sea. Beside, the ABS plot [Fig. 2(b)] showed that close to Napoli, CALIPSO was looking to a cloud free region as well, while the dust layer thickness was almost stable during the time interval considered in our analysis.

In the application of CESC algorithm an integration of signals was performed in order to increase the signal to noise ratio. The choice of the number of CALIPSO profiles to integrate has been done by taking into account the closeness of the spacecraft ground track to the lidar station. We averaged 326 profiles (∼ 16 seconds) corresponding to footprint path the order of the distance between CALIPSO ground track and the position of Napoli lidar, ∼ 60 km.

Signals from ground based lidar are acquired with a time resolution of 1 min. To increase the signal to noise ratio, we integrate ten profiles, centered on the overpass time.

Fig. 2. Time series of Napoli RCS profile: pink lines indicate the limits of time integration, and dash line is the overpass time (a); CALIPSO total ABS (km-1sr-1), measured from 20 August 2006, 01:10:15.4662 GMT to 01:23:34.4432 GMT. The thick pink line marks the overpass of CALIPSO spacecraft (b).

Figure 3(a) shows the averaged total attenuated backscatter and range corrected signal profiles. Appling Eq. (6) to these two elastic signals, we have retrieved the backscattering coefficient shown in Fig. 3(b). For this calculation the reference height was chosen in the free aerosol region between 9 and 11 km. Errors were calculated on the basis of error propagation and assuming the error on molecular profile to be negligible. For CALIPSO total attenuated backscatter the standard error on the averaged profile was considered. This takes into account both the statistical error on single profile signal and fluctuations due to different atmospheric conditions sounded along the considered path of CALIPSO. Figure 3(b) also shows the backscatter coefficient profile determined by the conventional Raman method [31

A. Ansmann and U. Wandinger “Combined Raman Elastic Backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter and lidar ratio,“ Appl. Phys. B 55, 18–28 (1992). [CrossRef]

]. The agreement of the two profiles is quite good. Discrepancies at the lowest heights are mainly due to atmospheric differences inside the planetary boundary layer, where orography and aerosol local sources play a significant role. Figure 3(c) shows the extinction coefficient profiles retrieved by CESC and Raman [32

A. Ansmann, M. Riebesell, and C. Weitkamp, “Measurement of atmospheric aerosol extinction profiles with a Raman lidar,“ Opt. Lett. 15, 746–748 (1990). [CrossRef] [PubMed]

] algorithms by using the same molecular profile. Since the two methods make use of different signals, the retrieved profiles are completely independent. Results are comparable starting from low altitudes. Moreover, the extinction profile retrieved by CESC algorithm is less noisy than that retrieved by Raman method.

The error bars reported in Figs. 3(b) and 3(c) account for statistical errors only and do not include the uncertainty on the pure molecular profile, which is about 2–5%.

Fig. 3. CALIPSO ABS as obtained by averaging of 326 profiles (thick line) and RCS from Napoli lidar (dash line) as obtained by averaging 10 min acquisition profiles (a); Backscatter coefficient as obtained from the CESC algorithm (thick line) and from Raman method (dash line) (b); Extinction profile from CESC (thick line) and from nitrogen Raman method (c)

5. Summary

In this paper we have introduced an algorithm which allows direct retrieving of aerosol backscatter and extinction profiles from simultaneous measurements from ground-based and spaceborne lidars by using only elastic signals, provided that the two lidars have sounded atmospheric columns with the same properties. This algorithm is wavelength independent and it allows evaluating the lidar ratio without any assumption on particles properties. This appears extremely interesting, in particular whenever the application of Raman method is difficult or impossible, e.g. for infrared lidar.

Results of numerical simulations and the application of the algorithm to real signals from ground-based and CALIPSO spaceborne lidar have been reported. The agreement between backscatter and extinction profiles obtained from Raman method and by application of CESC algorithm to two elastic signals at 532 nm from ground-based and spaceborne lidar is very good. Errors on final results from CESC algorithm are smaller than those of the Raman measurements.

The principal features of CESC algorithm are:

  • only two “counter looking” elastic backscatter signals are needed (one from groundbased and one from spaceborne or airborne lidar);
  • calibration or normalization of the signals are not required and only the presence of an aerosol-free layer is needed;
  • direct retrieval of backscattering, extinction and lidar ratio profiles in the infrared region is also possible;
  • taking into account that the CESC algorithm uses only elastic signals, its performances are expected to be very high also in day-time measurements.
  • if ground based lidar and footprint of spaceborne lidar are very well aligned it is possible to evaluate the overlap function of ground-based Raman lidar.

As a consequence, the CESC algorithm seems to be very pioneering and useful for retrieving aerosol optical properties both from space and airborne measurements campaign. Therefore an extensive application of CESC algorithm by ground-based lidar networks (as EARLINET) can be extremely useful for a direct retrieval of the aerosol optical properties and for spaceborne lidar data validation.

Acknowledgments

The spaceborne lidar data used were obtained from the NASA Langley Research Center (LaRC) Atmospheric Science Data Center.

The support of this work by the European Commission under contract 025991 EARLINET-ASOS is gratefully acknowledged.

Authors would like to express gratitude to Prof. R. Bruzzese for helpful discussions and encouragement during our work.

References and links

1.

R. J. Charlson, et al., “Climate forcing of anthropogenic aerosols,“ Science, 255, 423–430 (1992). [CrossRef]

2.

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3.

A. H. Omar, J.-G. Won, D. M. Winker, S.-C. Yoon, O. Dubovik, and M. P. McCormick, “Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements,“ J. Geophys. Res, 110, D10S14, doi:10.1029/2004JD004874,(2005). [CrossRef]

4.

D. M. Winker, R. H. Couch, and M. P. McCormick, “An overview of LITE: NASA’s Lidar In-space Technology Experiment,“ Proc. IEEE 84, 164–180 (1996). [CrossRef]

5.

J. D. Spinhirne, et al., “Cloud and aerosol measurements from GLAS: Overview and initial results,“ J. Geophys. Res 32, L22S03, doi:10.1029/2005GL023507 (2005).

6.

D. M. Winker, J. R. Pelon, and M. P. Cormick, “The CALIPSO mission: spaceborn lidar for observation of aerosols and clouds,“ Proc. SPIE 4893, 1–11 (2003). [CrossRef]

7.

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8.

CALIPSO mission on NASA website: http://www.nasa.gov/mission_pages/calipso/main/index.html

9.

M. A. Vaughan, et al., “CALIOP Algorithm Theoretical Basis Document,“ PS-SCI-202 Part 2.

10.

J. D. Klett, “Lidar inversion with variable backscatter/extinction ratios,“ Appl. Opt. 24, 1638–1643 (1985). [CrossRef] [PubMed]

11.

F. G. Fernald, B. J. Herman, and J. A. Reagan, “Determination of aerosol height distributions by Lidar,“ Journal of Applied Meteorology 11, 482–489 (1972). [CrossRef]

12.

L. Elterman, “Aerosol measurements in the troposphere and stratosphere,“ Appl. Opt. 5, 1769–1776 (1966). [CrossRef] [PubMed]

13.

C. M. R. Platt, “Lidar and Radiometric Observations of Cirrus Clouds,“ J. Atmos. Sci. 30, 1191–1204 (1973). [CrossRef]

14.

U. Wandinger, D. Müller, C. Böckmann, D. Althausen, V. Matthias, J. Bösenberg, V. Weiβ, M. Fiebig, M. Wendisch, A. Stohl, and A. Ansmann, “Optical and microphysical characterization of biomass-burning and industrial-pollution aerosols from multiwavelength lidar and aircraft measurements,“ J. Geophys. Res. 107, 10.1029/2 000JD000202 (2002). [CrossRef]

15.

K. Franke, A. Ansmann, D. Müller, D. Althausen, C. Venkataraman, M. Shekar Reddy, F. Wagner, and R. Scheele, “Optical properties of the Indo-Asian haze layer over the tropical Indian Ocean,“ J. Geophys. Res. 108, 10.I029/2002JD002473 (2003). [CrossRef]

16.

M. Vaughan, et al., “Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products,“ Proc. SPIE 5575, 16–30 (2004). [CrossRef]

17.

A. Ansmann, “Ground-truth aerosol lidar observations: can the Klett solution obtained from ground and space be equal for the same aerosol case?,“ Appl. Opt. 45, 3367–3371 (2006) [CrossRef] [PubMed]

18.

M. Vaughan, “Algorithm for retrieving lidar ratio at 1064 nm from space-based lidar backscatter,“ Proc. SPIE 5240, 104–115 (2004). [CrossRef]

19.

Kovacs, et al., “Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Quid Pro Quo Validation,“ 12th ARM Science Team Meeting Proceedings (2002). [PubMed]

20.

Hampton University website: http://calipsovalidation.hamptonu.edu/

21.

J. Bösenberg, et al., “EARLINET: A European Aerosol Research Lidar Network,“ in Advances in Laser Remote Sensing, Eds: A. Dabas, Claude Loth and Jacques Pelon, ISBN 2-7302-0798-8, 155–158, (2001).

22.

C. Böckmann, et al., “EARLINET-Lidar Algorithm Intercomparison,“ J. Aerosol Science 32, 433–434 (2001).

23.

EARLINET website: http://www.earlinet.org/

24.

C. Cattrall, J. Reagan, K. Thome, and O. Dubovik, “Variability of aerosol and spectral lidar and backscatter and extinction ratios of key aerosol types derived from selected Aerosol Robotic Network locations,“ J. Geophys. Research 110, D10S11, 10.1029/2004JD005124 (2005). [CrossRef]

25.

L. Mona, A. Amodeo, M. Pandolfi, and G. Pappalardo, “Saharan dust intrusion in the Mediterranean area: three years of Raman lidar mesurements,“ J. Geophys. Res. 111, D16203 doi: 10.1029/2005JD006569 (2006). [CrossRef]

26.

W. Chen, C. Chiang, and J. Nel, “Lidar ratio and depolarization for cirrus clouds,“ Appl. Opt. 30, 6470–6476 (2002). [CrossRef]

27.

L. Mona, et al., “Characterization of the variability of the humidity and cloud fields as observed from a cluster of ground-based lidar systems,“ Q. J. R. Meteorol. Soc. (Submitted).

28.

U. Draxler and G.D. v Rolph NOAA ARL HYSPLIT model. NOAA/Air Resource Laboratory, Silver Spring, MD, http://www.arl.noaa.gov/ready/hysplit4.html (2003).

29.

T.L. Anderson, R. J. Charlson, D. M. Winker, J. A. Ogren, and K. Holmen, “Mesoscale variations of tropospheric aerosols,“ J. Atmos. Sci. 60, 119–136 (2003). [CrossRef]

30.

Sea WiFS website http://oceancolor.gsfc.nasa.gov/SeaWiFS/

31.

A. Ansmann and U. Wandinger “Combined Raman Elastic Backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter and lidar ratio,“ Appl. Phys. B 55, 18–28 (1992). [CrossRef]

32.

A. Ansmann, M. Riebesell, and C. Weitkamp, “Measurement of atmospheric aerosol extinction profiles with a Raman lidar,“ Opt. Lett. 15, 746–748 (1990). [CrossRef] [PubMed]

OCIS Codes
(280.1100) Remote sensing and sensors : Aerosol detection
(280.3640) Remote sensing and sensors : Lidar

ToC Category:
Remote Sensing

History
Original Manuscript: April 3, 2007
Revised Manuscript: May 7, 2007
Manuscript Accepted: May 10, 2007
Published: May 16, 2007

Citation
Xuan wang, Maria Grazia Frontoso, Gianluca Pisani, and Nicola Spinelli, "Retrieval of atmospheric particles optical properties by combining ground-based and spaceborne lidar elastic scattering profiles," Opt. Express 15, 6734-6743 (2007)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-15-11-6734


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References

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  23. EARLINET website: http://www.earlinet.org/
  24. C. Cattrall, J. Reagan, K. Thome, O. Dubovik, "Variability of aerosol and spectral lidar and backscatter and extinction ratios of key aerosol types derived from selected Aerosol Robotic Network locations," J. Geophys. Research 110, D10S11, 10.1029/2004JD005124 (2005). [CrossRef]
  25. L. Mona, A. Amodeo, M. Pandolfi, G. Pappalardo, "Saharan dust intrusion in the Mediterranean area: three years of Raman lidar mesurements," J. Geophys. Res. 111, D16203 doi: 10.1029/2005JD006569 (2006). [CrossRef]
  26. W. Chen, C. Chiang, and J. Nel, "Lidar ratio and depolarization for cirrus clouds," Appl. Opt. 30, 6470-6476 (2002). [CrossRef]
  27. L. Mona,  et al., "Characterization of the variability of the humidity and cloud fields as observed from a cluster of ground-based lidar systems," Q. J. R. Meteorol. Soc. (Submitted).
  28. U. Draxler, G.D.v Rolph NOAA ARL HYSPLIT model. NOAA/Air Resource Laboratory, Silver Spring, MD, http://www.arl.noaa.gov/ready/hysplit4.html (2003).
  29. T. L. Anderson, R. J. Charlson, D. M. Winker, J. A. Ogren, and K. Holmen, "Mesoscale variations of tropospheric aerosols," J. Atmos. Sci. 60, 119-136 (2003). [CrossRef]
  30. Sea WiFS website http://oceancolor.gsfc.nasa.gov/SeaWiFS/
  31. A. Ansmann, U. Wandinger "Combined Raman Elastic Backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter and lidar ratio," Appl. Phys. B 55, 18-28 (1992). [CrossRef]
  32. A. Ansmann, M. Riebesell, C. Weitkamp, "Measurement of atmospheric aerosol extinction profiles with a Raman lidar," Opt. Lett. 15, 746-748 (1990). [CrossRef] [PubMed]

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