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Calibrating the Ångström–Prescott Model with Solar Radiation Data Collected over Long and Short Periods of Time over the Tibetan Plateau

Author

Listed:
  • Jiandong Liu

    (State Key Laboratory of Severe Weather, Institute of Agro-Meteorology and Ecology, Chinese Academy of Meteorological Sciences, Beijing 100081, China)

  • Yanbo Shen

    (Public Meteorological Service Centre, China Meteorological Administration, Beijing 100081, China)

  • Guangsheng Zhou

    (State Key Laboratory of Severe Weather, Institute of Agro-Meteorology and Ecology, Chinese Academy of Meteorological Sciences, Beijing 100081, China)

  • De-Li Liu

    (NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, PMB, Wagga Wagga, NSW 2650, Australia)

  • Qiang Yu

    (State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Northwest A&F University, Yangling 712100, China)

  • Jun Du

    (Tibet Institute of Plateau Atmospheric and Environmental Research, Tibet Autonomous Meteorological Administration, Lhasa 850001, China)

Abstract

As the coefficients of the Ångström–Prescott model is site-dependent, the sparsity of radiation stations in regions like the Tibetan Plateau (TP) presents challenges for model calibration. Due to the unique climate and the clean air conditions over the TP, it might be feasible to calibrate the Ångström–Prescott model with short-term observations from scientific expeditions. To test this hypothesis, we used various datasets with different lengths at four stations, together with 435 daily radiations measured during a scientific expedition at Banga in the central TP from 2014 to 2015, to calibrate the Ångström–Prescott model. We found that calibration with a 1-year data length resulted in model performances comparable to those with a 20-year data length. Analysis of the expedition observations showed that the monthly average daily radiation ranged from 15.2 MJ/m 2 d in December 2014 to 26.5 MJ/m 2 d in July 2015, with an average value of 20.6 MJ/m 2 d. When this set of expedition data was used for calibration, the Ångström–Prescott model performed well with an NSE (Nash–Sutcliffe efficiency) of 0.820. If no data were available for calibration, the coefficients of the Ångström–Prescott model could also be directly calculated by parameterization methods established with calibrations at the other radiation stations. In this situation, the LiuJD method performed the best with the highest NSE of 0.792, followed by the LiuXY method with an NSE of 0.764. The FAO method performed poorly with an NSE of 0.578, while the Gopinathan method performed the worst with the lowest NSE of 0.218. Thus, the best strategy to calibrate the Ångström–Prescott model in the Tibetan Plateau is to use data from local observations, even if collected over short periods. When these are not available, the coefficients of the Ångström–Prescott model should be calculated using the parameterization method established with calibrations over the Tibetan Plateau.

Suggested Citation

  • Jiandong Liu & Yanbo Shen & Guangsheng Zhou & De-Li Liu & Qiang Yu & Jun Du, 2023. "Calibrating the Ångström–Prescott Model with Solar Radiation Data Collected over Long and Short Periods of Time over the Tibetan Plateau," Energies, MDPI, vol. 16(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7093-:d:1259765
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    References listed on IDEAS

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    1. Luis Eduardo Ordoñez Palacios & Víctor Bucheli Guerrero & Hugo Ordoñez, 2022. "Machine Learning for Solar Resource Assessment Using Satellite Images," Energies, MDPI, vol. 15(11), pages 1-13, May.
    2. Chang, Kai & Zhang, Qingyuan, 2019. "Improvement of the hourly global solar model and solar radiation for air-conditioning design in China," Renewable Energy, Elsevier, vol. 138(C), pages 1232-1238.
    3. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    4. Liu, Jiandong & Linderholm, Hans & Chen, Deliang & Zhou, Xiuji & Flerchinger, Gerald N. & Yu, Qiang & Du, Jun & Wu, Dingrong & Shen, Yanbo & Yang, Zhenbin, 2015. "Changes in the relationship between solar radiation and sunshine duration in large cities of China," Energy, Elsevier, vol. 82(C), pages 589-600.
    5. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong & Zhao, Liang, 2011. "Global solar radiation estimation with sunshine duration in Tibet, China," Renewable Energy, Elsevier, vol. 36(11), pages 3141-3145.
    6. Xiaoyang Song & Yaohuan Huang & Chuanpeng Zhao & Yuxin Liu & Yanguo Lu & Yongguo Chang & Jie Yang, 2018. "An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images," Energies, MDPI, vol. 11(11), pages 1-14, November.
    7. Adaramola, Muyiwa S., 2012. "Estimating global solar radiation using common meteorological data in Akure, Nigeria," Renewable Energy, Elsevier, vol. 47(C), pages 38-44.
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