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A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products

Author

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  • Xu, Xiaojun
  • Du, Huaqiang
  • Zhou, Guomo
  • Mao, Fangjie
  • Li, Pingheng
  • Fan, Weiliang
  • Zhu, Dien

Abstract

Accurate information on the temporal and spatial distributions of solar radiation is very important in many scientific fields. In this study, instantaneous solar irradiances on a horizontal surface at 10:30 and 13:30 local time (LT) were calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric data products with relatively high spatial resolution using a solar radiation model. These solar irradiances were combined to derive half-hourly averages of solar irradiance (HASI) and daily global solar radiation (GSR) on a horizontal surface using linear interpolation, piecewise linear regression, and quadratic polynomial regression. Compared with field observations, the HASI were estimated accurately when the total cloud fraction (TCF) was <0.6. The accuracy of the estimates of the HASI was determined mainly by the quality of TCF. For TCF values <0.6, linear interpolation performed best, whereas quadratic polynomial regression was superior to the other methods for TCF values >0.6. Overall, the daily GSR estimated in this study was better than that estimated by the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis of NASA. The daily GSR estimated in this study was underestimated, whereas it was overestimated by MERRA. The combination of the daily GSR estimates of this study and MERRA offers a simple and feasible technique for reducing uncertainty in daily GSR estimates.

Suggested Citation

  • Xu, Xiaojun & Du, Huaqiang & Zhou, Guomo & Mao, Fangjie & Li, Pingheng & Fan, Weiliang & Zhu, Dien, 2016. "A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products," Energy, Elsevier, vol. 111(C), pages 117-125.
    • Handle: RePEc:eee:energy:v:111:y:2016:i:c:p:117-125
    DOI: 10.1016/j.energy.2016.05.095
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    4. WenminQin, & Wang, Lunche & Gueymard, Christian A. & Bilal, Muhammad & Lin, Aiwen & Wei, Jing & Zhang, Ming & Yang, Xuefang, 2020. "Constructing a gridded direct normal irradiance dataset in China during 1981–2014," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. 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.
    6. Fan, Junliang & Chen, Baiquan & Wu, Lifeng & Zhang, Fucang & Lu, Xianghui & Xiang, Youzhen, 2018. "Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions," Energy, Elsevier, vol. 144(C), pages 903-914.
    7. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
    8. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.

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