ACPD Atmospheric Chemistry and Physics Discussions ACPD 1680-7375 Copernicus GmbH Göttingen, Germany 10.5194/acpd-8-19989-2008 Calibrated sky imager for aerosol optical properties determination Cazorla A. 1 2 Shields J. E. 3 Karr M. E. 3 Burden A. 3 Olmo F. J. 1 2 Alados-Arboledas L. 1 2 Departamento de Física Aplicada, Facultad de Ciencias, Universidad de Granada, Fuentenueva s/n, 18071, Granada, Spain Centro Andaluz de Medio Ambiente (CEAMA), Junta de Andalucía-Universidad de Granada, Avda. del Mediterraneo s/n. 18071, Granada, Spain Marine Physical Lab, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr., La Jolla CA 92093-0701, USA 28 11 2008 8 6 19989 20018 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys-discuss.net/8/19989/2008/acpd-8-19989-2008.html The full text article is available as a PDF file from http://www.atmos-chem-phys-discuss.net/8/19989/2008/acpd-8-19989-2008.pdf

The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested to determine optical properties of the atmospheric aerosol. Different neural network-based models calculate the aerosol optical depth (AOD) for three wavelengths using the radiance extracted from the principal plane of sky images from the WSI as input parameters. The models use data from a CIMEL CE318 photometer for training and validation and the wavelengths used correspond to the closest wavelengths in both instruments. The spectral dependency of the AOD, characterized by the Ångström exponent α in the interval 440–870, is also derived using the standard AERONET procedure and also with a neural network-based model using the values obtained with a CIMEL CE318. The deviations between the WSI derived AOD and the AOD retrieved by AERONET are within the nominal uncertainty assigned to the AERONET AOD calculation (±0.01), in 80% of the cases. The explanation of data variance by the model is over 92% in all cases. In the case of α, the deviation is within the uncertainty assigned to the AERONET α (±0.1) in 50% for the standard method and 84% for the neural network-based model. The explanation of data variance by the model is 63% for the standard method and 77% for the neural network-based model.

 References Alados, I., Mellado, J. A., Ramos, F., and Alados-Arboledas, L.: Estimating UV erythemal irradiance by means of neural networks, Photochem. Photobiol., 80(2), 351–358, 2004. Alados, I., Gomera, M. A., Foyo-Moreno, I., and Alados-Arboledas, L.: Neural network for the estimation of UV erythemal irradiance using solar broadband irradiance, Int. J. Climatol., 27, 1791–1799, 2007. Ångström, A.: The parameters of atmospheric turbidity, Tellus, 16, 64–75, 1964. Cazorla, A., Olmo, F. J., and Alados-Arboledas, L.: Determinación de la cubierta nubosa en imágenes de cielo mediante un perceptrón multicapa (Determination of cloud cover on sky images by means of Multilayer Perceptron), in: Actas del Simposio de Inteligencia Computacional, edited by: Rojas, I. and Pomares, H., SICO2005, Comput. Intell. Soc., Thomson, Spain, 27–30, 2005. Cazorla, A., Olmo, F. J., and Alados-Arboledas, L.: Development of a sky imager for cloud cover assessment, J. Opt. Soc. Am., A., 25(1), 29–39, 2008. Cazorla, A., Olmo, F. J., and Alados-Arboledas, L.: Using a sky imager for aerosol characterization, Atmo. Environ., 42(11), 2739–2745, 2008b. Cormen, T. H., Leiserson, C. E., and Rivest, R. L.: Introduction to algorithms, MIT Press, Cambridge, MA, 2001. Dubovik, O., Holben, B. N., Lapyonok, T., Sinyuk, A., Mishchenko, M. I., Yang, P., and Slutsker, I.: Non-spherical aerosol retrieval method employing light scattering by spheroids, Geophys. Res. Lett., 29(10), 1415, doi:10.1029/2001GL014506, 2002. Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B. N., Mishchenko, M., Yang, P., Eck, T. F., Volten, H., Muñoz, O., Veihelmann, B., van der Zande, W. J., Leon, J. F., Sorokin, M., and Slutsker, I.: Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust, J. Geophys. Res., 111, D11208, doi:10.1029/2005JD006619, 2006. Foster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., and Van Dorland, R.: Changes in Atmospheric Constituents and in Radiative Forcing, in: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M. , Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007. Gil, J. E., Cazorla, A., Olmo, F. J., and Alados-Arboledas, L.: Experimental set up to study the cloud radiative effects on UVB at Granada (Spain), edited by: Richter, A. K., European Geosciences Union, General Assembly, vol. 1, EGU05-A-00320, AS1.08.1WE2P-0059, 2005. Gutiérrez, J. M., Cano, R., Cofiño, A. S., and Sordo, C.: Redes Probabilísticas y Neuronales en las Ciencias Atmosféricas (Probabilistic and Neural Netwoks on Atmospheric Sciences), Series Monograf\'ias del Instituto Nacional de Meteorolog\'ia, Ministerio de Medio Ambiente, Madrid, 2004. Haykin, S.: Neural Networks, Comprehensive Foundation, MacMillan College Publishing Company, Inc., New York, 1994. Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET-A federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66(1), 1–16, 1998. The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M. , Chen, Z.,Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, UK and NY, NY, USA, 996~pp. IPCC, 2007, Climate Change, 2007. Johnson, R. W., Hering, W. S., Gordon, J. I., Fitch, B. W., and Shields, J. E.: Preliminary analysis and modeling based upon project OPAQUE profile and surface data. University of California, San Diego, Scripps Institution of Oceanography, Visibility Laboratory, SIO Ref. 80-5, AFGL-TR-0285, NTIS No. ADB-052-1721, 1980. Johnson, R. W., Hering, W. S., and Shields, J. E.: Automated visibility and cloud cover measurements with a solid state imaging system, University of California, San Diego, Scripps Institution of Oceanography, Marine Physical Laboratory, SIO 89-7, GL-TR-89-0061, NTIS No. ADA216906, 1989. Kaufman, Y. J.: Aerosol optical thickness and atmospheric path radiance, J. Geophys. Res.-Atmos., 98(D2), 2677–2692, 1993. Kaufman, Y. J., Tanré, D., Holben, B. N., Mattoo, S., Remer, L. A., and Eck, T. F.: Aerosol radiative impact on spectral solar flux at the surface, derived from principal plane sky measurements, J. Atmos. Sci., 59(3), 635–646, 2002. Long, C. N., Sabburg, J. M., Calbó, J., and Pagès, D.: Retrieving cloud characteristics from ground based daytime color all-sky images, J. Atmos. Ocean. Tech., 23(5), 633–652, 2006. Lyamani, H., Olmo, F. J., and Alados-Arboledas, L.: Saharan dust outbreak over south-eastern Spain as detected by sun photometer, Atmos. Environ., 39, 7276–7284, 2005. Lyamani, H., Olmo, F. J., Alcantara, A., and Alados-Arboledas, L.: Atmospheric aerosols during the 2003 heat wave in southeastern spain I: spectral optical depth, Atmos. Environ., 40, 6453–6464, 2006a. Lyamani, H., Olmo, F. J., Alcantara, A., and Alados-Arboledas, L.: Atmospheric aerosols during the 2003 heat wave in southeastern spain II: Microphysical columnar properties and radiative forcing, Atmos. Environ., 40, 6465–6476, 2006b. Olmo, F. J. and Alados-Arboledas, L.: Pinatubo eruption effects on solar radiation at Almería, Tellus, 47B, 602–606, 1995. Olmo, F. J., Quirantes, A., Lara, V., Lyamani, H., and Alados-Arboledas, L.: Aerosol optical properties assessed by an inversion method using the solar principal plane for non-spherical particles, Journal of Quantitative Spectroscopy and Radiative Transfer, 109(8), 1504–1516, 2008. Park, J. and Sandberg, J. W.: Universal approximation using radial basis functions network, Neural Computation, 3, 246–257, 1991. Poggio, T. and Girosi,F.: Networks for approximation and learning, Proc., IEEE, 78(9), 1481–1497, 1990. Sabburg, J.: Quantification of Cloud Around The Sun And Its Correlation With Global UV Measurement, PhD Thesis, Queensland University of Technology, Brisbane, Australia, 2000. Sánchez, C., Olmo Reyes, F. J., and Alados-Arboledas, L.: Determination of aerosol optical thickness from measurements of spectral sky radiance, J. Aerosol Sci., 29(10), 1199–1211, 1998. Shields, J. E., Johnson, R. W., and Koehler, T. L.: Automated whole sky imaging systems for cloud field assessment. Proceedings of the Fourth Symposium on Global Change Studies, American Meteorological Society, Boston, MA, 1993. Shields, J. E., Karr, M. E., Tooman, T. P., Sowle, D. H., and Moore, S. T.: The Whole Sky Imager – A year of progress, Proceedings of Eighth Atmospheric Radiation Measurement (ARM), Science Team Meeting, 1998a. Shields, J. E., Johnson, R. W., Karr, M. E., and Wertz, J. L.: Automated Day/Night Whole Sky Imager for field assessment of cloud cover distributions and radiance distributions, Proceedings of the 10th Symposium on Meteorological Observations and Instrumentations, Phoenix, AZ, American Meteorological Society, Boston, MA, 1998b. Shields, J. E., Johnson R. W., Karr, M. E., Burden, A. R., and Baker, J. G.: Whole Sky Imagers for real-time cloud assessment, cloud free line of sight determinations and potential tactical applications, The Battlespace Atmospheric and Cloud Impacts on Military Operations (BACIMO) Conference, Monterey, CA, 2003. Smirnov, A., Holben, B. N., Eck, T. F., Dubovik, O., and Slutsker, I.: Cloud-screening and quality control algorithms for the AERONET database, Remote Sens. Environ., 73(3), 337–349, 2000. Tou, J. T. and Gonzalez, R. C.: Pattern Recognition, Addison-Wesley Publishing Company, Reading, MA, 1974. Yee, P. V. and Haykin, S.: Regularized Radial Basis Function Networks: Theory and Applications, Wiley & Sons LTD, New York, 2001.