IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v145y2020icp347-362.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119305919
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.04.107?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    7. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:145:y:2020:i:c:p:347-362. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.