Comparative Analysis of Global Solar Radiation Models in Different Regions of China. (30th April 2018)

• Record Type: Journal Article
• Authors: Zhang, Qingwen; Cui, Ningbo; Feng, Yu; Jia, Yue; Li, Zhuo; Gong, Daozhi
• Other Names: Bonafoni Stefania Academic Editor.
• Is Part Of: Advances in meteorology. Volume 2018(2018)
• Publication Date: 2018-04-30
• Subjects: Meteorology -- Periodicals; Meteorology; Periodicals; 551.505
• DOI: 10.1155/2018/3894831
• Languages: English
• ISSNs: 1687-9309

Abstract

Abstract : Complete and accurate global solar radiation (R s ) data at a specific region are crucial for regional climate assessment and crop growth modeling. The objective of this paper was to evaluate the capability of 12 solar radiation models based on meteorological data obtained from 21 meteorological stations in China. The results showed that the estimated and measured daily R s had statistically significant correlations (P < 0 . 01 ) for all the 12 models in 7 subzones of China. The Bahel model showed the best performance for daily R s estimation among the sunshine-based models, with average R 2 of 0.910, average RMSE of 2.306 MJ m −2 d −1, average RRMSE of 17.3%, average MAE of 1.724 MJ m −2 d −1, and average NS of 0.895, respectively. The Bristow-Campbell (BC) model showed the best performance among the temperature-based models, with average R 2 of 0.710, average RMSE of 3.952 MJ m −2 d −1, average RRMSE of 29.5%, average MAE of 2.958 MJ m −2 d −1, and average NS of 0.696, respectively. On monthly scale, Ögelman model showed the best performance among the sunshine-based models, with average RE of 5.66%. The BC model showed the best performance among the temperature-based models, with average RE of 8.26%. Generally, the sunshine-based models were more accurate than the temperature-based models. Overall, the Bahel model is recommended to estimate daily R s, Ögelman model is recommended to estimate monthly average daily R s in China when the sunshine duration is available, and the BC model is recommended to estimate both daily R s and monthly average daily R s when only temperature data are available.