IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v117y2020ics1364032119306860.html
   My bibliography  Save this article

Solar irradiance time series derived from high-quality measurements, satellite-based models, and reanalyses at a near-equatorial site in Brazil

Author

Listed:
  • Salazar, Germán
  • Gueymard, Christian
  • Galdino, Janis Bezerra
  • de Castro Vilela, Olga
  • Fraidenraich, Naum

Abstract

This study analyzes five years of 1-min solar global horizontal irradiance (GHI) and direct normal irradiance (DNI) observations obtained at Petrolina (northeast Brazil). Quality-assured hourly and daily averages are obtained after applying filters and methodologies based on a Baseline Solar Radiation Network (BSRN) quality-control procedure. To calculate correct hourly averages, a minimum fraction of 20% of valid GHI or DNI minutely data is needed, as well as at least 60% of valid days to calculate correct daily-mean monthly values. An asymmetric diurnal pattern is found in GHI and DNI during all months, attributed to consistently higher cloudiness in the morning. The quality-assured hourly and monthly-mean GHI and DNI time series are compared to estimates from 11 solar databases regularly used in solar resource assessment studies: CAMS, CERES, ERA5, INPE, MERRA-2, Meteonorm, NASA-POWER, NSRDB, SARAH, SWERA-BR, and SWERA-US. For hourly GHI values, a range of RMS differences is found between the best (CAMS, 17.3%) and the worst (MERRA-2, 38.9%) results. The latter database is also affected by a larger bias (18.7%) than CAMS (4%). Larger RMS differences are found with hourly DNI, in a range extending from 37% (CAMS) to 63.4% (ERA5). Biases are all above 12%, except for CERES (−1%). For long-term mean-monthly GHI results, low biases of less than 1% are obtained with CAMS, CERES and NASA-POWER, whereas MERRA-2 overestimates (13%). Larger biases are found for mean-monthly DNI, spanning between CAMS (3%) and Meteonorm (−18.4%). Overall, CAMS appears the most consistent solar database for long-term irradiance time series at Petrolina. The significant differences found here between modeled databases are larger than expected, and underline the importance of regional validation studies like this one to decrease the incidence of uncertainties in solar resource assessments on the design and performance of solar energy projects.

Suggested Citation

  • Salazar, Germán & Gueymard, Christian & Galdino, Janis Bezerra & de Castro Vilela, Olga & Fraidenraich, Naum, 2020. "Solar irradiance time series derived from high-quality measurements, satellite-based models, and reanalyses at a near-equatorial site in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    • Handle: RePEc:eee:rensus:v:117:y:2020:i:c:s1364032119306860
    DOI: 10.1016/j.rser.2019.109478
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032119306860
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2019.109478?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.

    References listed on IDEAS

    as
    1. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    2. Yang, Dazhi, 2018. "A correct validation of the National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 152-155.
    3. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9781107005198, September.
    4. Salazar, Germán & Checura Diaz, Miguel S. & Denegri, María J. & Tiba, Chigueru, 2015. "Identification of potential areas to achieve stable energy production using the SWERA database: A case study of northern Chile," Renewable Energy, Elsevier, vol. 77(C), pages 208-216.
    5. Martins, F.R. & Pereira, E.B. & Silva, S.A.B. & Abreu, S.L. & Colle, Sergio, 2008. "Solar energy scenarios in Brazil, Part one: Resource assessment," Energy Policy, Elsevier, vol. 36(8), pages 2843-2854, August.
    6. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9780521182935, September.
    7. Salazar, Germán A. & Fraidenraich, Naum & de Oliveira, Carlos Antonio Alves & de Castro Vilela, Olga & Hongn, Marcos & Gordon, Jeffrey M., 2017. "Analytic modeling of parabolic trough solar thermal power plants," Energy, Elsevier, vol. 138(C), pages 1148-1156.
    8. Gueymard, Christian A., 2014. "A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1024-1034.
    9. Soria, Rafael & Lucena, André F.P. & Tomaschek, Jan & Fichter, Tobias & Haasz, Thomas & Szklo, Alexandre & Schaeffer, Roberto & Rochedo, Pedro & Fahl, Ulrich & Kern, Jürgen, 2016. "Modelling concentrated solar power (CSP) in the Brazilian energy system: A soft-linked model coupling approach," Energy, Elsevier, vol. 116(P1), pages 265-280.
    10. Polo, J. & Téllez, F.M. & Tapia, C., 2016. "Comparative analysis of long-term solar resource and CSP production for bankability," Renewable Energy, Elsevier, vol. 90(C), pages 38-45.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Sawadogo, Windmanagda & Bliefernicht, Jan & Fersch, Benjamin & Salack, Seyni & Guug, Samuel & Diallo, Belko & Ogunjobi, Kehinde.O. & Nakoulma, Guillaume & Tanu, Michael & Meilinger, Stefanie & Kunstma, 2023. "Hourly global horizontal irradiance over West Africa: A case study of one-year satellite- and reanalysis-derived estimates vs. in situ measurements," Renewable Energy, Elsevier, vol. 216(C).
    2. Barbón, A. & Bayón-Cueli, C. & Bayón, L. & Rodríguez-Suanzes, C., 2022. "Analysis of the tilt and azimuth angles of photovoltaic systems in non-ideal positions for urban applications," Applied Energy, Elsevier, vol. 305(C).
    3. Yang, Dazhi, 2022. "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    4. Arsenio Barbón & Luis Bayón & Guzmán Díaz & Carlos A. Silva, 2022. "Investigation of the Effect of Albedo in Photovoltaic Systems for Urban Applications: Case Study for Spain," Energies, MDPI, vol. 15(21), pages 1-20, October.
    5. Paulescu, Marius & Badescu, Viorel & Budea, Sanda & Dumitrescu, Alexandru, 2022. "Empirical sunshine-based models vs online estimators for solar resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Chen, Shanlin & Li, Chengxi & Xie, Yuying & Li, Mengying, 2023. "Global and direct solar irradiance estimation using deep learning and selected spectral satellite images," Applied Energy, Elsevier, vol. 352(C).
    7. Barbón, A. & Carreira-Fontao, V. & Bayón, L. & Silva, C.A., 2023. "Optimal design and cost analysis of single-axis tracking photovoltaic power plants," Renewable Energy, Elsevier, vol. 211(C), pages 626-646.
    8. Barbón, A. & Bayón-Cueli, C. & Bayón, L. & Carreira-Fontao, V., 2022. "A methodology for an optimal design of ground-mounted photovoltaic power plants," Applied Energy, Elsevier, vol. 314(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Dazhi & Gueymard, Christian A., 2019. "Producing high-quality solar resource maps by integrating high- and low-accuracy measurements using Gaussian processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    2. Fichter, Tobias & Soria, Rafael & Szklo, Alexandre & Schaeffer, Roberto & Lucena, Andre F.P., 2017. "Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model," Energy, Elsevier, vol. 121(C), pages 695-715.
    3. Anne-Maree Dowd & Michelle Rodriguez & Talia Jeanneret, 2015. "Social Science Insights for the BioCCS Industry," Energies, MDPI, vol. 8(5), pages 1-19, May.
    4. Fankhauser, Samuel & Jotzo, Frank, 2017. "Economic growth and development with low-carbon energy," LSE Research Online Documents on Economics 86850, London School of Economics and Political Science, LSE Library.
    5. Tilmann Rave, 2013. "Innovationsindikatoren zum globalen Klimaschutz – FuE-Ausgaben und Patente," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(15), pages 34-41, August.
    6. Daniel Moran & Richard Wood, 2014. "Convergence Between The Eora, Wiod, Exiobase, And Openeu'S Consumption-Based Carbon Accounts," Economic Systems Research, Taylor & Francis Journals, vol. 26(3), pages 245-261, September.
    7. Lykke E. Andersen & Luis Carlos Jemio, 2016. "Decentralization and poverty reduction in Bolivia: Challenges and opportunities," Development Research Working Paper Series 01/2016, Institute for Advanced Development Studies.
    8. Gueymard, Christian A. & Bright, Jamie M. & Lingfors, David & Habte, Aron & Sengupta, Manajit, 2019. "A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 412-427.
    9. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    10. Inglesi-Lotz, Roula, 2017. "Social rate of return to R&D on various energy technologies: Where should we invest more? A study of G7 countries," Energy Policy, Elsevier, vol. 101(C), pages 521-525.
    11. Tom Mikunda & Tom Kober & Heleen de Coninck & Morgan Bazilian & Hilke R�sler & Bob van der Zwaan, 2014. "Designing policy for deployment of CCS in industry," Climate Policy, Taylor & Francis Journals, vol. 14(5), pages 665-676, September.
    12. Li, Yating & Fei, Yinxin & Zhang, Xiao-Bing & Qin, Ping, 2019. "Household appliance ownership and income inequality: Evidence from micro data in China," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    13. Xiaolun Wang & Xinlin Yao, 2020. "Fueling Pro-Environmental Behaviors with Gamification Design: Identifying Key Elements in Ant Forest with the Kano Model," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    14. Florian Knobloch & Hector Pollitt & Unnada Chewpreecha & Vassilis Daioglou & Jean-Francois Mercure, 2017. "Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5C," Papers 1710.11019, arXiv.org, revised May 2018.
    15. He, Gang & Victor, David G., 2017. "Experiences and lessons from China’s success in providing electricity for all," Resources, Conservation & Recycling, Elsevier, vol. 122(C), pages 335-338.
    16. Jun Nakatani & Tamon Maruyama & Kosuke Fukuchi & Yuichi Moriguchi, 2015. "A Practical Approach to Screening Potential Environmental Hotspots of Different Impact Categories in Supply Chains," Sustainability, MDPI, vol. 7(9), pages 1-15, August.
    17. van der Zwaan, Bob & Kober, Tom & Calderon, Silvia & Clarke, Leon & Daenzer, Katie & Kitous, Alban & Labriet, Maryse & Lucena, André F.P. & Octaviano, Claudia & Di Sbroiavacca, Nicolas, 2016. "Energy technology roll-out for climate change mitigation: A multi-model study for Latin America," Energy Economics, Elsevier, vol. 56(C), pages 526-542.
    18. Selosse, Sandrine & Ricci, Olivia & Maïzi, Nadia, 2013. "Fukushima's impact on the European power sector: The key role of CCS technologies," Energy Economics, Elsevier, vol. 39(C), pages 305-312.
    19. Kamjoo, Azadeh & Maheri, Alireza & Putrus, Ghanim A., 2014. "Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 66(C), pages 677-688.
    20. Mokri, Alaeddine & Aal Ali, Mona & Emziane, Mahieddine, 2013. "Solar energy in the United Arab Emirates: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 340-375.

    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:rensus:v:117:y:2020:i:c:s1364032119306860. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.