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The evaluation of reanalysis and analysis products of solar radiation for Sindh province, Pakistan

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  • Tahir, Zia ul Rehman
  • Azhar, Muhammad
  • Blanc, Philippe
  • Asim, Muhammad
  • Imran, Shahid
  • Hayat, Nasir
  • Shahid, Hamza
  • Ali, Hasnain

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 (R2). The rMBE, rRMSE and R2 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.

Suggested Citation

  • Tahir, Zia ul Rehman & Azhar, Muhammad & Blanc, Philippe & Asim, Muhammad & Imran, Shahid & Hayat, Nasir & Shahid, Hamza & Ali, Hasnain, 2020. "The evaluation of reanalysis and analysis products of solar radiation for Sindh province, Pakistan," Renewable Energy, Elsevier, vol. 145(C), pages 347-362.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:347-362
    DOI: 10.1016/j.renene.2019.04.107
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    Citations

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    Cited by:

    1. Muhammad Asim & Jassinnee Milano & Hassan Izhar Khan & Muhammad Hanzla Tahir & M. A. Mujtaba & Abd Halim Shamsuddin & Muhammad Abdullah & M. A. Kalam, 2022. "Investigation of Mono-Crystalline Photovoltaic Active Cooling Thermal System for Hot Climate of Pakistan," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    2. Mehmood, Faiza & Ghani, Muhammad Usman & Asim, Muhammad Nabeel & Shahzadi, Rehab & Mehmood, Aamir & Mahmood, Waqar, 2021. "MPF-Net: A computational multi-regional solar power forecasting framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    3. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    4. Cao, Qimeng & Liu, Yan & Sun, Xue & Yang, Liu, 2022. "Country-level evaluation of solar radiation data sets using ground measurements in China," Energy, Elsevier, vol. 241(C).
    5. Muhammad Asim & Muhammad Hanzla Tahir & Ammara Kanwal & Fahid Riaz & Muhammad Amjad & Aamna Khalid & Muhammad Mujtaba Abbas & Ashfaq Ahmad & Mohammad Abul Kalam, 2023. "Effects of Varying Volume Fractions of SiO 2 and Al 2 O 3 on the Performance of Concentrated Photovoltaic System," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    6. Faisal Nawab & Ag Sufiyan Abd Hamid & Ali Alwaeli & Muhammad Arif & Mohd Faizal Fauzan & Adnan Ibrahim, 2022. "Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    7. Muhammad Asim & Adnan Qamar & Ammara Kanwal & Ghulam Moeen Uddin & Muhammad Mujtaba Abbas & Muhammad Farooq & M. A. Kalam & Mohamed Mousa & Kiran Shahapurkar, 2022. "Opportunities and Challenges for Renewable Energy Utilization in Pakistan," Sustainability, MDPI, vol. 14(17), pages 1-15, September.

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