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Adjustment of the Angstrom-Prescott equation from Campbell-Stokes and Kipp-Zonen sunshine measures at different timescales in Spain

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  • Almorox, Javier
  • Arnaldo, J.A.
  • Bailek, Nadjem
  • Martí, Pau

Abstract

The sunshine-based Ångström–Prescott model and the temperature-based is widely used in the scientific literature, and, in general, it is accepted that the sunshine-based methods are more accurate. Sunshine duration is registered in many climatological stations, and it is highly correlated with solar radiation. However, in many applications of the Ångström–Prescott equation, no attention is payed to the sensor used for recording sunshine duration. In Spain, the Campbell-Stokes sensors are being replaced by Kipp and Zonen sensors. This study assessed the Ångström–Prescott goodness-of-fit and adjusted coefficients for Campbell-Stokes and Kipp and Zonen daily records at 10 stations in Spain. The analysis considered three different temporary windows, namely global, seasonal and monthly scales based. The results show differences in the sunshine hours measured by the two instruments, mainly because the Campbell-Stokes recorder is prone to over-burning. The Kipp and Zonen sensors can be considered as suitable as the Campbell-Stokes sensor, and provides a significant reduction in the statistical errors. The results also highlight the importance calibrating the Ångström–Prescott equation using seasonal and monthly coefficients, instead of using a unique pair of values. Further, the equation performance shows a clear seasonal pattern, with a higher accuracy during the summer.

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  • Almorox, Javier & Arnaldo, J.A. & Bailek, Nadjem & Martí, Pau, 2020. "Adjustment of the Angstrom-Prescott equation from Campbell-Stokes and Kipp-Zonen sunshine measures at different timescales in Spain," Renewable Energy, Elsevier, vol. 154(C), pages 337-350.
    • Handle: RePEc:eee:renene:v:154:y:2020:i:c:p:337-350
    DOI: 10.1016/j.renene.2020.03.023
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    as
    1. Jahani, Babak & Dinpashoh, Y. & Raisi Nafchi, Atefeh, 2017. "Evaluation and development of empirical models for estimating daily solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 878-891.
    2. Bouchouicha, Kada & Hassan, Muhammed A. & Bailek, Nadjem & Aoun, Nouar, 2019. "Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate," Renewable Energy, Elsevier, vol. 139(C), pages 844-858.
    3. Chen, Ji-Long & He, Lei & Yang, Hong & Ma, Maohua & Chen, Qiao & Wu, Sheng-Jun & Xiao, Zuo-lin, 2019. "Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 91-111.
    4. Paulescu, M. & Stefu, N. & Calinoiu, D. & Paulescu, E. & Pop, N. & Boata, R. & Mares, O., 2016. "Ångström–Prescott equation: Physical basis, empirical models and sensitivity analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 495-506.
    5. Jiandong Liu & Tao Pan & Deliang Chen & Xiuji Zhou & Qiang Yu & Gerald N. Flerchinger & De Li Liu & Xintong Zou & Hans W. Linderholm & Jun Du & Dingrong Wu & Yanbo Shen, 2017. "An Improved Ångström-Type Model for Estimating Solar Radiation over the Tibetan Plateau," Energies, MDPI, vol. 10(7), pages 1-28, July.
    6. Wan, Kevin K.W. & Tang, H.L. & Yang, Liu & Lam, Joseph C., 2008. "An analysis of thermal and solar zone radiation models using an Angstrom–Prescott equation and artificial neural networks," Energy, Elsevier, vol. 33(7), pages 1115-1127.
    7. Akarslan, Emre & Hocaoglu, Fatih Onur & Edizkan, Rifat, 2018. "Novel short term solar irradiance forecasting models," Renewable Energy, Elsevier, vol. 123(C), pages 58-66.
    8. Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    9. Paweł Bogawski & Ewa Bednorz, 2014. "Comparison and Validation of Selected Evapotranspiration Models for Conditions in Poland (Central Europe)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(14), pages 5021-5038, November.
    10. Almorox, J. & Benito, M. & Hontoria, C., 2005. "Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain," Renewable Energy, Elsevier, vol. 30(6), pages 931-936.
    11. Bayrakçı, Hilmi Cenk & Demircan, Cihan & Keçebaş, Ali, 2018. "The development of empirical models for estimating global solar radiation on horizontal surface: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2771-2782.
    12. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
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