Information content and sensitivity of the 3<i>β</i> + 2<i>α</i> lidar measurement system for aerosol microphysical retrievals

Atmospheric Measurement Techniques - Tập 9 Số 11 - Trang 5555-5574
S. P. Burton1, Eduard Chemyakin2, Xu Liu1, Kirk Knobelspiesse3, Snorre Stamnes1, Patrícia Sawamura4, Richard H. Moore1, C. A. Hostetler1, R. A. Ferrare1
1NASA Langley Research Center, Hampton, VA, USA
2Science Systems and Applications Inc., Hampton, VA, USA
3NASA Goddard Space Flight Center, Greenbelt, MD, USA
4Universities Space Research Association, Columbia, MD, USA

Tóm tắt

Abstract. There is considerable interest in retrieving profiles of aerosol effective radius, total number concentration, and complex refractive index from lidar measurements of extinction and backscatter at several wavelengths. The combination of three backscatter channels plus two extinction channels (3β + 2α) is particularly important since it is believed to be the minimum configuration necessary for the retrieval of aerosol microphysical properties and because the technological readiness of lidar systems permits this configuration on both an airborne and future spaceborne instrument. The second-generation NASA Langley airborne High Spectral Resolution Lidar (HSRL-2) has been making 3β + 2α measurements since 2012. The planned NASA Aerosol/Clouds/Ecosystems (ACE) satellite mission also recommends the 3β + 2α combination.Here we develop a deeper understanding of the information content and sensitivities of the 3β + 2α system in terms of aerosol microphysical parameters of interest. We use a retrieval-free methodology to determine the basic sensitivities of the measurements independent of retrieval assumptions and constraints. We calculate information content and uncertainty metrics using tools borrowed from the optimal estimation methodology based on Bayes' theorem, using a simplified forward model look-up table, with no explicit inversion. The forward model is simplified to represent spherical particles, monomodal log-normal size distributions, and wavelength-independent refractive indices. Since we only use the forward model with no retrieval, the given simplified aerosol scenario is applicable as a best case for all existing retrievals in the absence of additional constraints. Retrieval-dependent errors due to mismatch between retrieval assumptions and true atmospheric aerosols are not included in this sensitivity study, and neither are retrieval errors that may be introduced in the inversion process. The choice of a simplified model adds clarity to the understanding of the uncertainties in such retrievals, since it allows for separately assessing the sensitivities and uncertainties of the measurements alone that cannot be corrected by any potential or theoretical improvements to retrieval methodology but must instead be addressed by adding information content.The sensitivity metrics allow for identifying (1) information content of the measurements vs. a priori information; (2) error bars on the retrieved parameters; and (3) potential sources of cross-talk or "compensating" errors wherein different retrieval parameters are not independently captured by the measurements. The results suggest that the 3β + 2α measurement system is underdetermined with respect to the full suite of microphysical parameters considered in this study and that additional information is required, in the form of additional coincident measurements (e.g., sun-photometer or polarimeter) or a priori retrieval constraints. A specific recommendation is given for addressing cross-talk between effective radius and total number concentration.

Từ khóa


Tài liệu tham khảo

ACE Science Working Group: Aerosol, Cloud and Ecosystems (ACE) Proposed Satellite Mission, NASA, Study, 2010.

Ångström, A.: On the Atmospheric Transmission of Sun Radiation and on Dust in the Air, Geogr. Ann., 11, 156–166, https://doi.org/10.2307/519399, 1929.

Berg, L. K., Fast, J. D., Barnard, J. C., Burton, S. P., Cairns, B., Chand, D., Comstock, J. M., Dunagan, S., Ferrare, R. A., Flynn, C. J., Hair, J. W., Hostetler, C. A., Hubbe, J., Jefferson, A., Johnson, R., Kassianov, E. I., Kluzek, C. D., Kollias, P., Lamer, K., Lantz, K., Mei, F., Miller, M. A., Michalsky, J., Ortega, I., Pekour, M., Rogers, R. R., Russell, P. B., Redemann, J., Sedlacek, A. J., Segal-Rosenheimer, M., Schmid, B., Shilling, J. E., Shinozuka, Y., Springston, S. R., Tomlinson, J. M., Tyrrell, M., Wilson, J. M., Volkamer, R., Zelenyuk, A., and Berkowitz, C. M.: The Two-Column Aerosol Project: Phase I – Overview and impact of elevated aerosol layers on aerosol optical depth, J. Geophys. Res.-Atmos., 90, 336–361, https://doi.org/10.1002/2015JD023848, 2015.

Bockmann, C.: Hybrid regularization method for the ill-posed inversion of multiwavelength lidar data in the retrieval of aerosol size distributions, Appl. Optics, 40, 1329–1342, 2001.

Bockmann, C., Miranova, C., Muller, D., Scheidenbach, L., and Nessler, R.: Microphysical aerosol parameters from multiwavelength lidar, J. Opt. Soc. Am.-A., 22, 518–528, https://doi.org/10.1364/JOSAA.22.000518, 2005.

Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small Particles, John Wiley, Hoboken, NJ, USA, p. 530, 1983.

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., and Froyd, K. D.: Aerosol classification using airborne High Spectral Resolution Lidar measurements – methodology and examples, Atmos. Meas. Tech., 5, 73–98, https://doi.org/10.5194/amt-5-73-2012, 2012.

Burton, S. P., Hair, J. W., Kahnert, M., Ferrare, R. A., Hostetler, C. A., Cook, A. L., Harper, D. B., Berkoff, T. A., Seaman, S. T., Collins, J. E., Fenn, M. A., and Rogers, R. R.: Observations of the spectral dependence of linear particle depolarization ratio of aerosols using NASA Langley airborne High Spectral Resolution Lidar, Atmos. Chem. Phys., 15, 13453–13473, https://doi.org/10.5194/acp-15-13453-2015, 2015.

Chemyakin, E., Müller, D., Burton, S., Kolgotin, A., Hostetler, C., and Ferrare, R.: Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data, Appl. Optics, 53, 7252–7266, https://doi.org/10.1364/AO.53.007252, 2014.

Chemyakin, E., Burton, S., Kolgotin, A., Müller, D., Hostetler, C., and Ferrare, R.: Retrieval of aerosol parameters from multiwavelength lidar: investigation of the underlying inverse mathematical problem, Appl. Optics, 55, 2188–2202, https://doi.org/10.1364/AO.55.002188, 2016.

Cheung, H. C., Chou, C. C.-K., Huang, W.-R., and Tsai, C.-Y.: Characterization of ultrafine particle number concentration and new particle formation in an urban environment of Taipei, Taiwan, Atmos. Chem. Phys., 13, 8935–8946, https://doi.org/10.5194/acp-13-8935-2013, 2013.

Costabile, F., Barnaba, F., Angelini, F., and Gobbi, G. P.: Identification of key aerosol populations through their size and composition resolved spectral scattering and absorption, Atmos. Chem. Phys., 13, 2455–2470, https://doi.org/10.5194/acp-13-2455-2013, 2013.

Donovan, D. P. and Carswell, A. I.: Principal component analysis applied to multiwavelength lidar aerosol backscatter and extinction measurements, Appl. Optics, 36, 9406–9424, https://doi.org/10.1364/AO.36.009406, 1997.

Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011.

Gasteiger, J. and Freudenthaler, V.: Benefit of depolarization ratio at λ  =  1064 nm for the retrieval of the aerosol microphysics from lidar measurements, Atmos. Meas. Tech., 7, 3773–3781, https://doi.org/10.5194/amt-7-3773-2014, 2014.

Hair, J. W., Hostetler, C. A., Cook, A. L., Harper, D. B., Ferrare, R. A., Mack, T. L., Welch, W., Izquierdo, L. R., and Hovis, F. E.: Airborne High Spectral Resolution Lidar for profiling aerosol optical properties, Appl. Optics, 47, 6734–6752, https://doi.org/10.1364/AO.47.006734, 2008.

Hasekamp, O. P., Litvinov, P., and Butz, A.: Aerosol properties over the ocean from PARASOL multiangle photopolarimetric measurements, J. Geophys. Res.-Atmos., 116, D14204, https://doi.org/10.1029/2010JD015469, 2011.

Hoppel, W., Frick, G., and Larson, R.: Effect of nonprecipitating clouds on the aerosol size distribution in the marine boundary layer, Geophys. Res. Lett., 13, 125–128, 1986.

Kaufman, Y. J., Gitelson, A., Karnieli, A., Ganor, E., Fraser, R. S., Nakajima, T., Mattoo, S., and Holben, B. N.: Size distribution and scattering phase function of aerosol particles retrieved from sky brightness measurements, J. Geophys. Res.-Atmos., 99, 10341–10356, https://doi.org/10.1029/94JD00229, 1994.

Knobelspiesse, K., Cairns, B., Mishchenko, M., Chowdhary, J., Tsigaridis, K., van Diedenhoven, B., Martin, W., Ottaviani, M., and Alexandrov, M.: Analysis of fine-mode aerosol retrieval capabilities by different passive remote sensing instrument designs, Opt. Express, 20, 21457–21484, https://doi.org/10.1364/OE.20.021457, 2012.

Kolgotin, A. and Müller, D.: Theory of inversion with two-dimensional regularization: profiles of microphysical particle properties derived from multiwavelength lidar measurements, Appl. Optics, 47, 4472–4490, https://doi.org/10.1364/AO.47.004472, 2008.

Mozurkewich, M., Chan, T.-W., Aklilu, Y.-A., and Verheggen, B.: Aerosol particle size distributions in the lower Fraser Valley: evidence for particle nucleation and growth, Atmos. Chem. Phys., 4, 1047–1062, https://doi.org/10.5194/acp-4-1047-2004, 2004.

Müller, D., Wandinger, U., and Ansmann, A.: Microphysical particle parameters from extinction and backscatter lidar data by inversion with regularization: theory, Appl. Optics, 38, 2346–2357, https://doi.org/10.1364/AO.38.002346, 1999.

Müller, D., Hostetler, C. A., Ferrare, R. A., Burton, S. P., Chemyakin, E., Kolgotin, A., Hair, J. W., Cook, A. L., Harper, D. B., Rogers, R. R., Hare, R. W., Cleckner, C. S., Obland, M. D., Tomlinson, J., Berg, L. K., and Schmid, B.: Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US, Atmos. Meas. Tech., 7, 3487–3496, https://doi.org/10.5194/amt-7-3487-2014, 2014.

National Research Council: Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, The National Academies Press, Washington, D.C., USA, 400 pp., 2007.

Pérez-Ramírez, D., Whiteman, D. N., Veselovskii, I., Kolgotin, A., Korenskiy, M., and Alados-Arboledas, L.: Effects of systematic and random errors on the retrieval of particle microphysical properties from multiwavelength lidar measurements using inversion with regularization, Atmos. Meas. Tech., 6, 3039–3054, https://doi.org/10.5194/amt-6-3039-2013, 2013.

Posselt, D. J. and Mace, G. G.: MCMC-Based Assessment of the Error Characteristics of a Surface-Based Combined Radar–Passive Microwave Cloud Property Retrieval, J. Appl. Meteorol. Clim., 53, 2034–2057, https://doi.org/10.1175/JAMC-D-13-0237.1, 2014.

Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, Series on Atmospheric, Oceanic and Planetary Physics, 2, edited by: Taylor, F. W., World Scientific, New Jersey, USA, 2000.

Schuster, G. L., Dubovik, O., and Holben, B. N.: Angstrom exponent and bimodal aerosol size distributions, J. Geophys. Res., 111, D07207, https://doi.org/10.1029/2005jd006328, 2006.

Seinfeld, J. and Pandis, S.: Atmospheric Chemistry and Physics, A Wiley-Inter Science Publication, John Wiley & Sons Inc, New York, USA, 2006.

Twomey, S.: Introduction to the mathematics of inversion in remote sensing and interative measurements, Elsevier Scientific Publishing Company, Amsterdam, the Netherlands, 1977.

Veselovskii, I., Kolgotin, A., Griaznov, V., Müller, D., Wandinger, U., and Whiteman, D. N.: Inversion with regularization for the retrieval of tropospheric aerosol parameters from multiwavelength lidar sounding, Appl. Optics, 41, 3685–3699, https://doi.org/10.1364/AO.41.003685, 2002.

Veselovskii, I., Kolgotin, A., Griaznov, V., Müller, D., Franke, K., and Whiteman, D. N.: Inversion of multiwavelength Raman lidar data for retrieval of bimodal aerosol size distribution, Appl. Optics, 43, 1180–1195, 2004.

Veselovskii, I., Kolgotin, A., Müller, D., and Whiteman, D. N.: Information content of multiwavelength lidar data with respect to microphysical particle properties derived from eigenvalue analysis, Appl. Optics, 44, 5292–5303, https://doi.org/10.1364/AO.44.005292, 2005.

Veselovskii, I., Dubovik, O., Kolgotin, A., Lapyonok, T., Di Girolamo, P., Summa, D., Whiteman, D. N., Mishchenko, M., and Tanré, D.: Application of randomly oriented spheroids for retrieval of dust particle parameters from multiwavelength lidar measurements, J. Geophys. Res.-Atmos., 115, D21203, https://doi.org/10.1029/2010JD014139, 2010.

Veselovskii, I., Dubovik, O., Kolgotin, A., Korenskiy, M., Whiteman, D. N., Allakhverdiev, K., and Huseyinoglu, F.: Linear estimation of particle bulk parameters from multi-wavelength lidar measurements, Atmos. Meas. Tech., 5, 1135–1145, https://doi.org/10.5194/amt-5-1135-2012, 2012.

Veselovskii, I., Goloub, P., Podvin, T., Bovchaliuk, V., Derimian, Y., Augustin, P., Fourmentin, M., Tanre, D., Korenskiy, M., Whiteman, D. N., Diallo, A., Ndiaye, T., Kolgotin, A., and Dubovik, O.: Retrieval of optical and physical properties of African dust from multiwavelength Raman lidar measurements during the SHADOW campaign in Senegal, Atmos. Chem. Phys., 16, 7013–7028, https://doi.org/10.5194/acp-16-7013-2016, 2016.

Xu, X. and Wang, J.: Retrieval of aerosol microphysical properties from AERONET photopolarimetric measurements: 1. Information content analysis, J. Geophys. Res.-Atmos., 120, 7059–7078, https://doi.org/10.1002/2015JD023108, 2015.

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Atmospheric Measurement Techniques

Tập 9 Số 11

5555-5574

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