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Estimation of daily average values of the Ångström turbidity coefficient β using a Corrected Yang Hybrid Model

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  • Salazar, Germán
  • Utrillas, Pilar
  • Esteve, Anna
  • Martínez-Lozano, José
  • Aristizabal, Mariana

Abstract

This paper aims to test a method for estimating daily values of atmospheric turbidity from non-specialized data, such as those obtained from agro-meteorological stations. This method allows estimating the spatial and temporal evolution of aerosols concentration in more places than just those in which direct measurements are available. The method is based on the Corrected Yang Hybrid Model (CYHM). The data used in the determination of errors between measured and estimated values of the daily Ångström turbidity coefficient β were recorded in Valencia, Spain, during 2009 and 2011. These data were global solar irradiance, direct solar irradiance, temperature, relative humidity and Aerosol Optical Depth (AOD) measured at an AERONET station. The errors are shown as a function of daily clearness index Kt, observing that as Kt decreased, the error of estimate β increased. Taking into account that the nominal error of the apparatus used to measure AOD has the same order of magnitude as the calculated errors and that most of the terms involved in the measurement of atmospheric transmittance phenomena have been estimated, the method produces results that are acceptable for general purposes. The method was applied to historical meteorological data recorded in Bogotá, Colombia. The daily values of atmospheric turbidity were estimated for 1983 and 1997, showing little changes in atmospheric turbidity between those years.

Suggested Citation

  • Salazar, Germán & Utrillas, Pilar & Esteve, Anna & Martínez-Lozano, José & Aristizabal, Mariana, 2013. "Estimation of daily average values of the Ångström turbidity coefficient β using a Corrected Yang Hybrid Model," Renewable Energy, Elsevier, vol. 51(C), pages 182-188.
    • Handle: RePEc:eee:renene:v:51:y:2013:i:c:p:182-188
    DOI: 10.1016/j.renene.2012.09.023
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    References listed on IDEAS

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    1. Boland, John & Ridley, Barbara & Brown, Bruce, 2008. "Models of diffuse solar radiation," Renewable Energy, Elsevier, vol. 33(4), pages 575-584.
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    1. Zou, Ling & Wang, Lunche & Xia, Li & Lin, Aiwen & Hu, Bo & Zhu, Hongji, 2017. "Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems," Renewable Energy, Elsevier, vol. 106(C), pages 343-353.
    2. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction," Renewable Energy, Elsevier, vol. 190(C), pages 408-424.
    3. Lin, Aiwen & Zou, Ling & Wang, Lunche & Gong, Wei & Zhu, Hongji & Salazar, Germán Ariel, 2016. "Estimation of atmospheric turbidity coefficient β over Zhengzhou, China during 1961–2013 using an improved hybrid model," Renewable Energy, Elsevier, vol. 86(C), pages 1134-1144.
    4. Wang, Lunche & Salazar, Germán Ariel & Gong, Wei & Peng, Simao & Zou, Ling & Lin, Aiwen, 2015. "An improved method for estimating the Ångström turbidity coefficient β in Central China during 1961–2010," Energy, Elsevier, vol. 81(C), pages 67-73.
    5. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.

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