The evaluation of reanalysis and analysis products of solar radiation for Sindh province, Pakistan. (January 2020)

• Record Type: Journal Article
• Authors: Tahir, Zia ul Rehman; Azhar, Muhammad; Blanc, Philippe; Asim, Muhammad; Imran, Shahid; Hayat, Nasir; Shahid, Hamza; Ali, Hasnain
• Is Part Of: Renewable energy. Volume 145(2020)
• Page Start: 347
• Page End: 362
• Publication Date: 2020-01
• Subjects: Solar radiation -- NCEP-NCAR -- NCEP-DOE -- NCEP-FNL -- NCEP-GFS -- JRA-55; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.04.107
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The quality of the surface solar irradiance (SSI) data from three numerical meteorological reanalysis products (NCEP-NCAR, NCEP-DOE and JRA-55) and two analysis products (NCEP-FNL and NCEP-GFS) are analysed by comparing with in-situ high-quality SSI measurement. The validation of estimates of SSI against measured data is done based on mean bias error (MBE), root mean squared error (RMSE), relative MBE, relative RMSE and coefficient of determination ( R 2 ). The rMBE, rRMSE and R 2 for five estimated SSI datasets for both stations range from −10.5–28.0%, 19.2–41.4% and 0.870 to 0.969 respectively. The measured clearness index shows that the cloud fraction is not accurately incorporated while estimating SSI from datasets, which is the main reason for errors, especially higher errors in the summer season because of Monsoon. The NCEP-FNL and NCEP-GFS predict cloudy conditions where the actual conditions are clear-sky, whereas the NCEP-NCAR predicts clear-sky conditions where the actual conditions are cloudy. The overestimation of clear-sky conditions leads to the overestimation of the SSI and the clearness index and vice versa. The overall results show that the estimates from NCEP-NCAR are the worst among all the datasets whereas NCEP-FNL and NCEP-DOE predict well for Karachi and Hyderabad respectively. Highlights: The quality of surface solar irradiance (SSI) data from five numerical meteorological products has been assessed. The validation of SSI products from datasets has been performed on the basis of statistical analysis. Seasonal variation pattern of solar energy was observed. Sky clearness index has been calculated from measured data and compared with datasets. The datasets that agree well with site climatic conditions has been proposed for the assessments of solar potential.