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The role of aerosol models of the SMARTS code in predicting the spectral direct-beam irradiance in an urban area

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  • Kaskaoutis, D.G.
  • Kambezidis, H.D.

Abstract

Measurements of the ground-level spectral distribution of the direct-beam solar irradiance between 300 and 1000nm were made in Athens, Greece, in May 1995. Results obtained using simple model for the atmospheric radiative transfer of sunshine (SMARTS) (version 2.9.2) parametric model for the urban atmosphere of Athens are analyzed and compared to the ground-level experimental spectral solar irradiance measurements obtained by the passive pyrheliometric scanner (PPS) in three discrete bands, UV (320–400nm), VIS (400–700nm) and NIR (700–1000nm). The study uses two different input parameters for the aerosol characterization: the aerosol optical depth at 500nm, tα0.5, and the Ångström turbidity coefficient, β. The results clearly show that the nine aerosol models implemented in the SMARTS code lead to quite different predictions of the direct-beam spectrum, strongly depended on the input parameter. In all cases the inadequacies between the measured and the modeled direct-beam spectra are lower and higher when the urban and maritime aerosol models are used, respectively.

Suggested Citation

  • Kaskaoutis, D.G. & Kambezidis, H.D., 2008. "The role of aerosol models of the SMARTS code in predicting the spectral direct-beam irradiance in an urban area," Renewable Energy, Elsevier, vol. 33(7), pages 1532-1543.
    • Handle: RePEc:eee:renene:v:33:y:2008:i:7:p:1532-1543
    DOI: 10.1016/j.renene.2007.09.006
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    1. Heras, Carlos & Salinas, Iñigo & Andres, Salvador & Sevilla, Marina & Villafranca, Asier & Martinez, David & Sanchez, Marcelino, 2024. "High-resolution spectral atmospheric attenuation measurement for solar power plants," Renewable Energy, Elsevier, vol. 225(C).
    2. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    3. Nonnenmacher, Lukas & Kaur, Amanpreet & Coimbra, Carlos F.M., 2016. "Day-ahead resource forecasting for concentrated solar power integration," Renewable Energy, Elsevier, vol. 86(C), pages 866-876.
    4. Del Hoyo, Mirko & Rondanelli, Roberto & Escobar, Rodrigo, 2020. "Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile," Renewable Energy, Elsevier, vol. 148(C), pages 1137-1149.
    5. Benkaciali, Saïd & Haddadi, Mourad & Khellaf, Abdellah, 2018. "Evaluation of direct solar irradiance from 18 broadband parametric models: Case of Algeria," Renewable Energy, Elsevier, vol. 125(C), pages 694-711.
    6. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2016. "Recent improvements of the Meteorological Radiation Model for solar irradiance estimates under all-sky conditions," Renewable Energy, Elsevier, vol. 93(C), pages 142-158.
    7. Psiloglou, B.E. & Kambezidis, H.D. & Kaskaoutis, D.G. & Karagiannis, D. & Polo, J.M., 2020. "Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece," Renewable Energy, Elsevier, vol. 146(C), pages 1372-1391.
    8. del Campo-Ávila, J. & Piliougine, M. & Morales-Bueno, R. & Mora-López, L., 2019. "A data mining system for predicting solar global spectral irradiance. Performance assessment in the spectral response ranges of thin-film photovoltaic modules," Renewable Energy, Elsevier, vol. 133(C), pages 828-839.

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