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Retrieval of multi-wavelength aerosol lidar ratio profiles using Raman scattering and Mie backscattering signals
Atmospheric Environment 79 (2013) 36e40
Contents lists available at SciVerse ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Technical note
Retrieval of multi-wavelength aerosol lidar ratio profiles using Raman scattering and Mie backscattering signals Jia Su a, Zhaoyan Liu b, Yonghua Wu c, M. Patrick McCormick a, *, Liqiao Lei a a
Center for Atmospheric Sciences, Department of Atmospheric and Planetary Sciences, Hampton University, Hampton, VA 23668, USA National Institute of Aerospace, Hampton, VA 23666-6147, USA c Optical Remote Sensing Laboratory, The City College of New York, New York 10031, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 December 2012 Received in revised form 7 June 2013 Accepted 11 June 2013
We advance a novel retrieval technique that combines a Raman and multi-wavelength elastic backscattered signals to retrieve multi-wavelength lidar ratio profiles of aerosol. With profile of backscatter coefficients at 355 nm retrieved from elastic backscatter signal at 355 nm and Raman scattering signal at 387 nm, lidar ratio profiles can be calculated at 532 nm and 1064 nm from the elastic backscatter signals at these wavelengths, taking advantage that the 532 nm/355 nm and 1064 nm/355 nm backscatter ratios are generally approximately equal for two neighboring range bins. This technique has been tested using numerical simulations and applied to lidar measurements at the Hampton University, Hampton, Virginia. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Aerosol Lidar ratio Multi-wavelength
1. Introduction Atmospheric aerosols have a significant impact on climate change through the scattering and absorption of incoming solar and outgoing thermal radiation (Charlson et al., 1992; Liou, 1986). They also influence the environment and our ecology. An accurate measurement of the optical parameters of aerosols can improve our understanding of the climate system and its potential change. With the simple setup and suitability to operate on a routine basis, elastic backscatter lidars have been proven to be a very useful remote sensing tool. Systems have been developed to make measurements of aerosols and clouds from the ground, on a boat, air plane and even onboard a satellite (Gerrit and Gerard, 1992; McCormick, 1995; Strawbridgea and Snyderb, 2004; Winker et al., 2007; Müller et al., 2007; Vaughan et al., 2010; Su et al., 2012). Because the elastic lidar equation includes two unknown variables (backscatter and extinction), an assumption on a constant extinction-to-backscatter ratio (or lidar ratio) has to be made to solve the lidar equation in the data processing of single wavelength backscatter measurements (Fernald et al., 1972; Kovalev et al., 2007). Lidar ratio is related to the particle size distribution, shape, and chemical composition of different aerosols and its value can
* Corresponding author. Tel.: þ1 757 728 6867; fax: þ1 757 727 5090. E-mail addresses: [email protected] (J. Su), [email protected] (M.P. McCormick). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.06.027
vary for a large range (Müller et al., 2007; Josset et al., 2011; Burton et al., 2012). In the practice of backscatter lidar data processing, the lidar ratio is usually selected based on the best knowledge of the aerosol type gained from the lidar measurement and other information such as geolocation (Omar et al., 2005). However, a selected lidar ratio may not be representative when the aerosol composition is not uniform throughout the layer or the layer is a mixture of several types of aerosol. Techniques to retrieve aerosol extinction and backscatter as well as the lidar ratio have been proposed for multi-wavelength backscatter lidar measurements. Sasano and Browell (1989), Liu et al. (2000) and Vaughan (2004) suggest methods that can estimate a single average value of the lidar ratio for an aerosol layer, with an assumption that the backscatter profiles at different wavelengths are similar shapes, i.e., the aerosol composition and particle size distribution are invariant within the aerosol layer. The backscatter similarity is used as an extra constraint in the lidar retrieval. In practice, however, this assumption may not be satisfied and therefore errors may be resulted in. On the other hand, efforts have been made to enhance the capability of lidar systems. A high-spectral-resolution lidar (HSRL) (Grund and Eloranta, 1990; Liu et al., 1999) or a combined elasticRaman lidar (Ansmann et al., 1990) makes measurement of molecular backscattering or Raman scattering, in addition to the total aerosol and molecular backscattering that a conventional backscatter lidar measures. From these two channel measurements, both aerosol extinction and backscatter coefficients and lidar ratio can be directly determined as a function of range. At Hampton University
J. Su et al. / Atmospheric Environment 79 (2013) 36e40
37
(HU), Hampton, Virginia, we developed a multiple channels lidar that can measure the Raman scattering signal at 387 nm and elastic backscattering signals at 355 nm, 532 nm, and 1064 nm. In this paper we introduce a retrieval method, developed for the HU lidar measurement that takes advantages of both the combined elastic-Raman lidar and the multi-wavelength retrieval techniques. This method first derives profiles of backscatter and extinction coefficients as well as lidar ratio at 355 nm from the Raman signal at 387 nm and elastic backscattering signal at 355 nm using the elastic-Raman lidar technique as described by Ansmann et al. (1990). The backscatter profile derived at 355 nm is then used as a reference to retrieve profiles of the aerosol backscatter, extinction and lidar ratio at other elastic wavelengths of 532 nm and 1064 nm. We demonstrate the feasibility of this method through numerical simulations. We also apply this method to the HU lidar measurement data.
From Eq. (2), the real ratio of aerosol backscatter coefficients of the two adjacent points at wavelength of l approaches the ratio of aerosol backscatter coefficients of the two adjacent points at 355 nm, which can be used to constrain the unknown lidar ratio at the wavelength l (i.e., 532 nm or 1064 nm). Trial values of Sa (from 10 to 80 sr with an increment of 1 sr) are applied to a Fernald solution of the lidar equation to derive aerosol backscattering coefficient profile at wavelength l. For each trial value of Sa, values of ba,l(Sa, zn) and ba,l(Sa, zn þ 1) are calculated. We define the performance function P(Sa, zn þ 1) expressed by Eq. (3), then the lidar ratio at altitude of zn þ 1 can be determined when P(Sa, zn þ 1) is minimized.
2. Method
One can see that the lidar ratio retrieved using Eq. (3) only requires a similarity in the multiple wavelength backscatters between two neighboring range bins, which is easily met for the real atmosphere. This is unlike the previously proposed methods (Sasano and Browell, 1989; Liu et al., 2000; Vaughan, 2004) that require a backscatter similarity for the entire aerosol layer. Any
The details of the elastic-Raman lidar retrieval of aerosol extinction and backscatter coefficient and lidar ratio can be found in Ansmann et al. (1990). The aerosol backscatter at 355 nm can be derived using:
ba;355 ðzÞ ¼
X355 ðzÞ X ðzc Þ Rc R XR ðzÞ X355 ðzc Þ
Zzc aa;355 ðz0 Þ þ am;355 ðz0 Þ dz0 exp z
Zzc aa;R ðz0 Þ þ am;R ðz0 Þ dz0 exp
b ðS ; z a;l a nþ1 Þ ba;355 ðznþ1 Þ PðSa ; znþ1 Þ ¼ ba;l ðSa ; zn Þ ba;355 ðzn Þ
(3)
bR ðzÞ b ðz Þ bm;355 ðzÞ bR ðzc Þ m;355 c
(1)
z
Where b and a are the backscattering and extinction coefficients; the subscripts 355 and R represent the wavelength of 355 nm and the N2 Raman scattering at 387 nm, and a and m represent aerosol and molecular scatterings; X is the range-corrected lidar return signal; z is the altitude and zc is a reference altitude above the aerosol layer where the aerosol load is the smallest; Rc is the aerosol scattering ratio at zc (a nonzero value of 1.001 is assumed in the paper). We note that both the molecular backscattering coefficient bm,355(z) and Raman scattering coefficient bR(z) in the atmosphere are proportional to the air molecular number density and hence the term bR(z)/bR(zc) bm,355(zc) ¼ bm,355(z) in Eq. (1). aa,R can be derived from the Raman channel signal as described in Ansmann et al. (1990). The Angstrom exponent varies with different types of aerosol. From an analytic calculation, the error in the derived aerosol extinction at 355 nm is approximately 5% if Angstrom exponent varies between 0 and 2 when an assumed value of 1 is used (Tomasi et al., 2003). In the paper, an Angstrom exponent of 1 is assumed and aa,355 ¼ (lR/355) aa,R with lR ¼ 387 nm. ba,355(z) can then be calculated using Eq. (1) and Sa,355(z) (as a function of z) is derived directly from the ratio of aa,355(z)/ba,355(z). For two adjacent range bins zn and zn þ 1 (spaced by 7.5 m for the HU lidar), the aerosol properties at the two bins can be reasonably considered to be highly correlated. Therefore, we can assume
ba;355 ðznþ1 Þ ba;355 ðzn Þ z ba;l ðznþ1 Þ ba;l ðzn Þ
(2)
Where l is the wavelength of other elastic scattering signal (i.e., 532 nm or 1064 nm).
dissimilarity between two neighboring range bins (such as at the interface of two layers of different types of aerosol) only impacts the lidar ratio retrieval locally. As we know, Fernald method assumes that the aerosol lidar ratio is constant. So the lidar ratio obtained from Eq. (3) is not aerosol true lidar ratio. However, according to Eq. (3), aerosol true backscatter coefficients can be obtained using the lidar ratio. If Sfa(zn) and Sfa(zn þ 1) are the lidar ratios at altitude of zn and zn þ 1 retrieved using Eq. (3), aerosol true backscatter coefficients at altitude of zn and zn þ 1 can be expressed as following:
h
i
h
bra;l ðzn Þ ¼ ba;l Sfa ðzn Þ; zn ; bra;l ðznþ1 Þ ¼ ba;l Sfa ðznþ1 Þ; znþ1 Aerosol true extinction coefficient at altitude of zn obtained using Eq. (5):
ara;l ðznþ1 Þ ¼
ln
i
þ 1
(4) can be
Xl ðznþ1 Þ ba;l ðzn Þþbm;l ðzn Þ Xl ðzn Þ bra;l ðznþ1 Þþbm;l ðznþ1 Þ r
2dz
2am;l ðznþ1 Þdz
(5)
Where Xl is the range-corrected lidar return signal at the length of l. Aerosol true lidar ratio at altitude of zn þ 1 can be obtained using Eq. (6):
Sra;l ðznþ1 Þ ¼
ara;l ðznþ1 Þ
bra;l ðznþ1 Þ
(6)
38
J. Su et al. / Atmospheric Environment 79 (2013) 36e40
3. Simulation and experimental results Simulations are performed to test the method proposed in this paper with a modeled aerosol layer as shown in Fig. 1(a) and (b). The aerosol layer is distributed below 4 km and its backscatter and extinction coefficients at all the three wavelengths decrease with altitude. Atmospheric aerosols are with different size distribution,
shape, and chemical composition. As we know, lidar ratio is related to the particle size distribution, shape, and chemical composition of different aerosols. To approach to real atmospheric aerosol, both the lidar ratio and backscatter ratio for this aerosol layer are assumed to be varying with altitude and wavelength as shown in Fig. 1(c) and (d). Simulated range-corrected lidar elastic signals at 532 nm and 1064 nm are presented in Fig. 1(e). Range dependent
Fig. 1. Modeled (a) aerosol backscatter, (b) extinction coefficients, (c) lidar ratios at 355 nm, 532 nm and 1064 nm, and (d) 1064/355 and 532/355 backscatter ratios; (e) simulated rang-corrected lidar elastic signals at 532 nm and 1064 nm with noise; (f) simulated aerosol backscatter coefficients at 355 nm with noise and retrieved aerosol lidar ratio profile and the error at (g) 532 nm and (h) 1064 nm.
J. Su et al. / Atmospheric Environment 79 (2013) 36e40
39
Fig. 2. (a) Range-corrected lidar elastic signals at 355 nm, 532 nm, 1064 nm and N2 Raman signal at 387 nm measured at 17:54 on Nov. 03, 2008, retrieved (b) aerosol extinction and backscatter coefficient profiles at 355 nm using Eq. (1) and (c) lidar ratio profiles at 355 nm, 532 nm and 1064 nm.
Gaussian random noise is added to the simulated lidar signals and the signal-to-noise ratio (SNR) at 3 km is 12. Range dependent Gaussian random noise (SNR at 3 km is 8) is also added to simulated aerosol backscatter coefficients at 355 nm shown in Fig. 1(f). The aerosol backscatter coefficient at 355 nm is assumed to be known in this simulation because it is retrieval using the elastics-Raman retrieval technique. Eq. (5) is then applied to the simulated signals. The retrieved lidar ratios at 532 nm and 1064 nm and the corresponding errors are presented in Fig. 1(g) and (h). Good agreement is seen between the retrieved and modeled lidar ratio values even for the places where the backscatter ratio is not constant (between 0.75 km and 3 km). The lidar ratios at 532 and 1064 nm calculated using Sasano and Browell method are about 43 sr and 32 sr (Sasano and Browell method only can obtain an average aerosol lidar ratio.) Comparing the input and retrieved results, it indicates that the method proposed in this paper is applicable to the aerosol layers whose lidar ratio and backscatter ratio are varying with range. We have applied this new method to the HU lidar measurements. An example of measurement made on Nov. 03, 2008 is presented in Fig. 2. Shown in Fig. 2(a) are the rang-corrected lidar elastic backscatter signals at 355 nm, 532 nm and 1064 nm, and the N2 Raman signal at 387 nm from 1 km to 6 km (HU lidar signals below 1 km are affected by lidar’s overlap function and removed.). The data acquired from 17:54 to 18:14 (local time, about 20 min average) is averaged to improve the SNR (is about 10 at 3 km) of the Raman signal which is several others smaller than the backscatter signals. The aerosol extinction and backscatter coefficient profiles at 355 nm retrieved from the elastic (355 nm) and Raman (387 nm) signals are presented in Fig. 2(b). The retrieved aerosol lidar ratio profiles at 355 nm, 532 nm and 1064 nm are shown in Fig. 2(c). In
addition, the computed values of the standard deviation correspond to the error bars shown in Fig. 2(b) and (c). The proposed technique applies complicated and nonlinear data-handling procedure, so errors estimated uses a numerical procedure based on Monte Carlo technique (Pappalardo et al., 2004). This method is based on a random extraction of new lidar signals, each bin of which is considered as sample element of given Gaussian probability distribution with the experimentally observed mean value and standard deviation. The extracted lidar signals are then processed with the same retrieval algorithm to produce a set of solutions from which the standard deviation is calculated as a function of height. It is seen from the two figures that the values of the standard deviation depend on the height and are all not over 20%. These values fall in a reasonable range for aerosol. The values are agreed well with the results measured from simultaneous measurements made by the HU lidar and the satellite-borne CALIPSO lidar (Tao et al., 2008). 4. Conclusion In conclusion, we described a lidar retrieval method of multiwavelength lidar ratio profiles that combines the elastic-Raman lidar technique and the multi-wavelength retrieval technique. The method determines the aerosol backscatter and extinction coefficients and lidar ratio at 355 nm from the elastic lidar signal at 355 nm and Raman signal at 387 nm. The method further retrieves lidar ratio profiles at 532 nm and 1064 nm assuming that 532 nm/ 355 nm and 1064 nm/355 nm backscatter ratios are approximately equal for two neighboring range bins which can be easily met in the real atmosphere. The method has been tested with simulated lidar signals. The results showed that the method can be applied to the
40
J. Su et al. / Atmospheric Environment 79 (2013) 36e40
aerosol lidar ratio retrieval even when the aerosol backscatter ratio is varying. The method has also been applied to the aerosol measurements by the HU lidar located at Hampton, Virginia. Acknowledgments This study was supported by the PIRT project funded by US Army Research, Development and Engineering Command (AQC) Center (DOD) under HU PIRT Award # 551150-211150 and the National Oceanic and Atmospheric Administration (NOAA) under Grant e CREST Grant # NA06OAR4810162. References Ansmann, A., Riebesell, M., Weitkamp, C., 1990. Measurement of atmospheric aerosol extinction profiles with a Raman lidar. Optics Letters 15, 746e751. Burton, S.P., Ferrare, R.A., Hostetler, C.A., Hair, J.W., Rogers, R.R., Obland, M.D., Butler, C.F., Cook, A.L., Harper, D.B., Froyd, K.D., 2012. Aerosol classification using airborne high spectral resolution lidar measurements e methodology and examples. Atmospheric Measurement Techniques 5, 73e98. Charlson, R.J., Schwartz, S.E., Hales, J.M., Cess, R.D., Coakley, J.A., Hansen, J.E., Hofmann, D.J., 1992. Climate forcing by anthropogenic aerosols. Science 256, 598e603. Fernald, F.G., Herman, B.M., Reagan, J.A., 1972. Determination of aerosol height distributions by lidar. Journal of Applied Meteorology 11, 482e489. Gerrit, D.L., Gerard, J.K., 1992. NOVAM evaluation from aerosol and lidar measurements in a tropical marine environment. Proceedings of Atmospheric Propagation and Remote Sensing 14, 1672e1688. Grund, C.J., Eloranta, E.W., 1990. The 27e28 October 1986 fire ifo cirrus case study: cloud optical properties determined by high spectral resolution lidar. Monthly Weather Review 118, 2344e2349. Josset, D., Rogers, R., Pelon, J., Hu, Y., Liu, Z., Omar, A., Zhai, P.W., 2011. CALIPSO lidar ratio retrieval over the ocean. Optics Express 19, 18696e18706. Kovalev, V.A., Hao, W.M., Wold, C., 2007. Determination of the particulate extinction-coefficient profile and the column-integrated lidar ratios using the backscatter-coefficient and optical-depth profiles. Applied Optics 46, 8627e 8634. Liou, K.N., 1986. Influence of cirrus clouds on weather and climate processes: a global perspective. Monthly Weather Review 114, 1167e1172.
Liu, Z., Matsui, I., Sugimoto, N., 1999. High-spectral-resolution lidar using an iodine absorption filter for atmospheric measurements. Optical Engineering 38, 1661e 1667. Liu, Z., Voelger, P., Sugimoto, N., 2000. Simulations of the observation of clouds and aerosols with the experimental lidar in space equipment (elise). Applied Optics 39, 3120e3128. McCormick, M.P., 1995. Spaceborne lidars. The Review of Laser Engineering 23, 89e98. Müller, D., Ansmann, A., Mattis, I., Tesche, M., Wandinger, U., Althausen, D., Pisani, G., 2007. Aerosol-type-dependent lidar ratio observed with Raman lidar. Journal of Geophysical Research 112, D16202. Omar, A., Won, J., Winker, D., Yoon, S., Dubovik, O., McCormick, M.P., 2005. Development of global aerosol models using cluster analysis of aerosol robotic network (aeronet) measurements. Journal of Geophysical Research 110, 14e20. Pappalardo, G., Amodeo, A., Pandolfi, M., Wandinger, U., Ansmann, A., Bösenberg, J., Matthias, V., Amiridis, V., Tomasi, F.D., Frioud, M., Iarlori, M., Komguem, L., Papayannis, A., Rocadenbosch, F., Wang, X., 2004. Aerosol lidar intercomparison in the framework of the EARLINET project: 3. Raman lidar algorithm for aerosol extinction, backscatter, and lidar ratio. Applied Optics 43, 5370e5385. Sasano, Y., Browell, E.V., 1989. Light scattering characteristics of various aerosol types derived from multiple wavelength lidar observations. Applied Optics 28, 1670e1676. Strawbridgea, K.B., Snyderb, B.J., 2004. Daytime and nighttime aircraft lidar measurements showing evidence of particulate matter transport into the Northeastern valleys of the Lower Fraser Valley, BC. Atmospheric Environment 38, 5873e5886. Su, J., McCormick, M.P., Liu, Z., Lee, R.B., Leavor, K.R., Lei, L., 2012. Transmittance ratio constrained retrieval technique for lidar cirrus measurements. Optics Letters 37, 1595e1597. Tao, Z., Liu, Z., Wu, D., McCormick, M.P., Su, J., 2008. Determination of aerosol extinction-to-backscatter ratios from simultaneous ground-based and spaceborne lidar measurements. Optics Letters 33, 2986e2988. Tomasi, F.D., Blanco, A., Perrone, M., 2003. Raman lidar monitoring of extinction and backscattering of African dust layers and dust characterization. Applied Optics 20, 699e709. Vaughan, M.A., Liu, Z., McGill, M.J., Hu, Y., Obland, M.D., 2010. On the spectral dependence of backscatter from cirrus clouds: assessing CALIPO’S 1064 nm calibration assumptions using cloud physics lidar measurements. Journal of Geophysical Research 115, 1160e1166. Vaughan, M., 2004. Algorithm for retrieving lidar ratios at 1064 nm from spacebased lidar backscatter data. SPIE Proceedings 5240, 104e109. Winker, D., Hunt, W., McGill, M., 2007. Initial performance assessment of CALIPO. Geophysical Research Letters 34, 1e5.
Contents lists available at SciVerse ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Technical note
Retrieval of multi-wavelength aerosol lidar ratio profiles using Raman scattering and Mie backscattering signals Jia Su a, Zhaoyan Liu b, Yonghua Wu c, M. Patrick McCormick a, *, Liqiao Lei a a
Center for Atmospheric Sciences, Department of Atmospheric and Planetary Sciences, Hampton University, Hampton, VA 23668, USA National Institute of Aerospace, Hampton, VA 23666-6147, USA c Optical Remote Sensing Laboratory, The City College of New York, New York 10031, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 December 2012 Received in revised form 7 June 2013 Accepted 11 June 2013
We advance a novel retrieval technique that combines a Raman and multi-wavelength elastic backscattered signals to retrieve multi-wavelength lidar ratio profiles of aerosol. With profile of backscatter coefficients at 355 nm retrieved from elastic backscatter signal at 355 nm and Raman scattering signal at 387 nm, lidar ratio profiles can be calculated at 532 nm and 1064 nm from the elastic backscatter signals at these wavelengths, taking advantage that the 532 nm/355 nm and 1064 nm/355 nm backscatter ratios are generally approximately equal for two neighboring range bins. This technique has been tested using numerical simulations and applied to lidar measurements at the Hampton University, Hampton, Virginia. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Aerosol Lidar ratio Multi-wavelength
1. Introduction Atmospheric aerosols have a significant impact on climate change through the scattering and absorption of incoming solar and outgoing thermal radiation (Charlson et al., 1992; Liou, 1986). They also influence the environment and our ecology. An accurate measurement of the optical parameters of aerosols can improve our understanding of the climate system and its potential change. With the simple setup and suitability to operate on a routine basis, elastic backscatter lidars have been proven to be a very useful remote sensing tool. Systems have been developed to make measurements of aerosols and clouds from the ground, on a boat, air plane and even onboard a satellite (Gerrit and Gerard, 1992; McCormick, 1995; Strawbridgea and Snyderb, 2004; Winker et al., 2007; Müller et al., 2007; Vaughan et al., 2010; Su et al., 2012). Because the elastic lidar equation includes two unknown variables (backscatter and extinction), an assumption on a constant extinction-to-backscatter ratio (or lidar ratio) has to be made to solve the lidar equation in the data processing of single wavelength backscatter measurements (Fernald et al., 1972; Kovalev et al., 2007). Lidar ratio is related to the particle size distribution, shape, and chemical composition of different aerosols and its value can
* Corresponding author. Tel.: þ1 757 728 6867; fax: þ1 757 727 5090. E-mail addresses: [email protected] (J. Su), [email protected] (M.P. McCormick). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.06.027
vary for a large range (Müller et al., 2007; Josset et al., 2011; Burton et al., 2012). In the practice of backscatter lidar data processing, the lidar ratio is usually selected based on the best knowledge of the aerosol type gained from the lidar measurement and other information such as geolocation (Omar et al., 2005). However, a selected lidar ratio may not be representative when the aerosol composition is not uniform throughout the layer or the layer is a mixture of several types of aerosol. Techniques to retrieve aerosol extinction and backscatter as well as the lidar ratio have been proposed for multi-wavelength backscatter lidar measurements. Sasano and Browell (1989), Liu et al. (2000) and Vaughan (2004) suggest methods that can estimate a single average value of the lidar ratio for an aerosol layer, with an assumption that the backscatter profiles at different wavelengths are similar shapes, i.e., the aerosol composition and particle size distribution are invariant within the aerosol layer. The backscatter similarity is used as an extra constraint in the lidar retrieval. In practice, however, this assumption may not be satisfied and therefore errors may be resulted in. On the other hand, efforts have been made to enhance the capability of lidar systems. A high-spectral-resolution lidar (HSRL) (Grund and Eloranta, 1990; Liu et al., 1999) or a combined elasticRaman lidar (Ansmann et al., 1990) makes measurement of molecular backscattering or Raman scattering, in addition to the total aerosol and molecular backscattering that a conventional backscatter lidar measures. From these two channel measurements, both aerosol extinction and backscatter coefficients and lidar ratio can be directly determined as a function of range. At Hampton University
J. Su et al. / Atmospheric Environment 79 (2013) 36e40
37
(HU), Hampton, Virginia, we developed a multiple channels lidar that can measure the Raman scattering signal at 387 nm and elastic backscattering signals at 355 nm, 532 nm, and 1064 nm. In this paper we introduce a retrieval method, developed for the HU lidar measurement that takes advantages of both the combined elastic-Raman lidar and the multi-wavelength retrieval techniques. This method first derives profiles of backscatter and extinction coefficients as well as lidar ratio at 355 nm from the Raman signal at 387 nm and elastic backscattering signal at 355 nm using the elastic-Raman lidar technique as described by Ansmann et al. (1990). The backscatter profile derived at 355 nm is then used as a reference to retrieve profiles of the aerosol backscatter, extinction and lidar ratio at other elastic wavelengths of 532 nm and 1064 nm. We demonstrate the feasibility of this method through numerical simulations. We also apply this method to the HU lidar measurement data.
From Eq. (2), the real ratio of aerosol backscatter coefficients of the two adjacent points at wavelength of l approaches the ratio of aerosol backscatter coefficients of the two adjacent points at 355 nm, which can be used to constrain the unknown lidar ratio at the wavelength l (i.e., 532 nm or 1064 nm). Trial values of Sa (from 10 to 80 sr with an increment of 1 sr) are applied to a Fernald solution of the lidar equation to derive aerosol backscattering coefficient profile at wavelength l. For each trial value of Sa, values of ba,l(Sa, zn) and ba,l(Sa, zn þ 1) are calculated. We define the performance function P(Sa, zn þ 1) expressed by Eq. (3), then the lidar ratio at altitude of zn þ 1 can be determined when P(Sa, zn þ 1) is minimized.
2. Method
One can see that the lidar ratio retrieved using Eq. (3) only requires a similarity in the multiple wavelength backscatters between two neighboring range bins, which is easily met for the real atmosphere. This is unlike the previously proposed methods (Sasano and Browell, 1989; Liu et al., 2000; Vaughan, 2004) that require a backscatter similarity for the entire aerosol layer. Any
The details of the elastic-Raman lidar retrieval of aerosol extinction and backscatter coefficient and lidar ratio can be found in Ansmann et al. (1990). The aerosol backscatter at 355 nm can be derived using:
ba;355 ðzÞ ¼
X355 ðzÞ X ðzc Þ Rc R XR ðzÞ X355 ðzc Þ
Zzc aa;355 ðz0 Þ þ am;355 ðz0 Þ dz0 exp z
Zzc aa;R ðz0 Þ þ am;R ðz0 Þ dz0 exp
b ðS ; z a;l a nþ1 Þ ba;355 ðznþ1 Þ PðSa ; znþ1 Þ ¼ ba;l ðSa ; zn Þ ba;355 ðzn Þ
(3)
bR ðzÞ b ðz Þ bm;355 ðzÞ bR ðzc Þ m;355 c
(1)
z
Where b and a are the backscattering and extinction coefficients; the subscripts 355 and R represent the wavelength of 355 nm and the N2 Raman scattering at 387 nm, and a and m represent aerosol and molecular scatterings; X is the range-corrected lidar return signal; z is the altitude and zc is a reference altitude above the aerosol layer where the aerosol load is the smallest; Rc is the aerosol scattering ratio at zc (a nonzero value of 1.001 is assumed in the paper). We note that both the molecular backscattering coefficient bm,355(z) and Raman scattering coefficient bR(z) in the atmosphere are proportional to the air molecular number density and hence the term bR(z)/bR(zc) bm,355(zc) ¼ bm,355(z) in Eq. (1). aa,R can be derived from the Raman channel signal as described in Ansmann et al. (1990). The Angstrom exponent varies with different types of aerosol. From an analytic calculation, the error in the derived aerosol extinction at 355 nm is approximately 5% if Angstrom exponent varies between 0 and 2 when an assumed value of 1 is used (Tomasi et al., 2003). In the paper, an Angstrom exponent of 1 is assumed and aa,355 ¼ (lR/355) aa,R with lR ¼ 387 nm. ba,355(z) can then be calculated using Eq. (1) and Sa,355(z) (as a function of z) is derived directly from the ratio of aa,355(z)/ba,355(z). For two adjacent range bins zn and zn þ 1 (spaced by 7.5 m for the HU lidar), the aerosol properties at the two bins can be reasonably considered to be highly correlated. Therefore, we can assume
ba;355 ðznþ1 Þ ba;355 ðzn Þ z ba;l ðznþ1 Þ ba;l ðzn Þ
(2)
Where l is the wavelength of other elastic scattering signal (i.e., 532 nm or 1064 nm).
dissimilarity between two neighboring range bins (such as at the interface of two layers of different types of aerosol) only impacts the lidar ratio retrieval locally. As we know, Fernald method assumes that the aerosol lidar ratio is constant. So the lidar ratio obtained from Eq. (3) is not aerosol true lidar ratio. However, according to Eq. (3), aerosol true backscatter coefficients can be obtained using the lidar ratio. If Sfa(zn) and Sfa(zn þ 1) are the lidar ratios at altitude of zn and zn þ 1 retrieved using Eq. (3), aerosol true backscatter coefficients at altitude of zn and zn þ 1 can be expressed as following:
h
i
h
bra;l ðzn Þ ¼ ba;l Sfa ðzn Þ; zn ; bra;l ðznþ1 Þ ¼ ba;l Sfa ðznþ1 Þ; znþ1 Aerosol true extinction coefficient at altitude of zn obtained using Eq. (5):
ara;l ðznþ1 Þ ¼
ln
i
þ 1
(4) can be
Xl ðznþ1 Þ ba;l ðzn Þþbm;l ðzn Þ Xl ðzn Þ bra;l ðznþ1 Þþbm;l ðznþ1 Þ r
2dz
2am;l ðznþ1 Þdz
(5)
Where Xl is the range-corrected lidar return signal at the length of l. Aerosol true lidar ratio at altitude of zn þ 1 can be obtained using Eq. (6):
Sra;l ðznþ1 Þ ¼
ara;l ðznþ1 Þ
bra;l ðznþ1 Þ
(6)
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J. Su et al. / Atmospheric Environment 79 (2013) 36e40
3. Simulation and experimental results Simulations are performed to test the method proposed in this paper with a modeled aerosol layer as shown in Fig. 1(a) and (b). The aerosol layer is distributed below 4 km and its backscatter and extinction coefficients at all the three wavelengths decrease with altitude. Atmospheric aerosols are with different size distribution,
shape, and chemical composition. As we know, lidar ratio is related to the particle size distribution, shape, and chemical composition of different aerosols. To approach to real atmospheric aerosol, both the lidar ratio and backscatter ratio for this aerosol layer are assumed to be varying with altitude and wavelength as shown in Fig. 1(c) and (d). Simulated range-corrected lidar elastic signals at 532 nm and 1064 nm are presented in Fig. 1(e). Range dependent
Fig. 1. Modeled (a) aerosol backscatter, (b) extinction coefficients, (c) lidar ratios at 355 nm, 532 nm and 1064 nm, and (d) 1064/355 and 532/355 backscatter ratios; (e) simulated rang-corrected lidar elastic signals at 532 nm and 1064 nm with noise; (f) simulated aerosol backscatter coefficients at 355 nm with noise and retrieved aerosol lidar ratio profile and the error at (g) 532 nm and (h) 1064 nm.
J. Su et al. / Atmospheric Environment 79 (2013) 36e40
39
Fig. 2. (a) Range-corrected lidar elastic signals at 355 nm, 532 nm, 1064 nm and N2 Raman signal at 387 nm measured at 17:54 on Nov. 03, 2008, retrieved (b) aerosol extinction and backscatter coefficient profiles at 355 nm using Eq. (1) and (c) lidar ratio profiles at 355 nm, 532 nm and 1064 nm.
Gaussian random noise is added to the simulated lidar signals and the signal-to-noise ratio (SNR) at 3 km is 12. Range dependent Gaussian random noise (SNR at 3 km is 8) is also added to simulated aerosol backscatter coefficients at 355 nm shown in Fig. 1(f). The aerosol backscatter coefficient at 355 nm is assumed to be known in this simulation because it is retrieval using the elastics-Raman retrieval technique. Eq. (5) is then applied to the simulated signals. The retrieved lidar ratios at 532 nm and 1064 nm and the corresponding errors are presented in Fig. 1(g) and (h). Good agreement is seen between the retrieved and modeled lidar ratio values even for the places where the backscatter ratio is not constant (between 0.75 km and 3 km). The lidar ratios at 532 and 1064 nm calculated using Sasano and Browell method are about 43 sr and 32 sr (Sasano and Browell method only can obtain an average aerosol lidar ratio.) Comparing the input and retrieved results, it indicates that the method proposed in this paper is applicable to the aerosol layers whose lidar ratio and backscatter ratio are varying with range. We have applied this new method to the HU lidar measurements. An example of measurement made on Nov. 03, 2008 is presented in Fig. 2. Shown in Fig. 2(a) are the rang-corrected lidar elastic backscatter signals at 355 nm, 532 nm and 1064 nm, and the N2 Raman signal at 387 nm from 1 km to 6 km (HU lidar signals below 1 km are affected by lidar’s overlap function and removed.). The data acquired from 17:54 to 18:14 (local time, about 20 min average) is averaged to improve the SNR (is about 10 at 3 km) of the Raman signal which is several others smaller than the backscatter signals. The aerosol extinction and backscatter coefficient profiles at 355 nm retrieved from the elastic (355 nm) and Raman (387 nm) signals are presented in Fig. 2(b). The retrieved aerosol lidar ratio profiles at 355 nm, 532 nm and 1064 nm are shown in Fig. 2(c). In
addition, the computed values of the standard deviation correspond to the error bars shown in Fig. 2(b) and (c). The proposed technique applies complicated and nonlinear data-handling procedure, so errors estimated uses a numerical procedure based on Monte Carlo technique (Pappalardo et al., 2004). This method is based on a random extraction of new lidar signals, each bin of which is considered as sample element of given Gaussian probability distribution with the experimentally observed mean value and standard deviation. The extracted lidar signals are then processed with the same retrieval algorithm to produce a set of solutions from which the standard deviation is calculated as a function of height. It is seen from the two figures that the values of the standard deviation depend on the height and are all not over 20%. These values fall in a reasonable range for aerosol. The values are agreed well with the results measured from simultaneous measurements made by the HU lidar and the satellite-borne CALIPSO lidar (Tao et al., 2008). 4. Conclusion In conclusion, we described a lidar retrieval method of multiwavelength lidar ratio profiles that combines the elastic-Raman lidar technique and the multi-wavelength retrieval technique. The method determines the aerosol backscatter and extinction coefficients and lidar ratio at 355 nm from the elastic lidar signal at 355 nm and Raman signal at 387 nm. The method further retrieves lidar ratio profiles at 532 nm and 1064 nm assuming that 532 nm/ 355 nm and 1064 nm/355 nm backscatter ratios are approximately equal for two neighboring range bins which can be easily met in the real atmosphere. The method has been tested with simulated lidar signals. The results showed that the method can be applied to the
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