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Validation and Performance of Satellite Meteorological Dataset MERRA-2 for Solar and Wind Applications

Author

Listed:
  • Arash Khatibi

    (Faculty of Computer Science, Electrical Engineering and Mathematics, Electrical Energy Technology-Sustainable Energy Concepts (EET-NEK), Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany)

  • Stefan Krauter

    (Faculty of Computer Science, Electrical Engineering and Mathematics, Electrical Energy Technology-Sustainable Energy Concepts (EET-NEK), Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany)

Abstract

Fast-growing energy demand of the world makes the researchers focus on finding new energy sources or optimizing already-developed approaches. For an efficient use of solar and wind energy in an energy system, correct design and sizing of a power system is of high importance and improving or optimizing the process of data obtaining for this purpose leads to higher performance and lower cost per unit of energy. It is essential to have the most precise possible estimation of solar and wind energy potential and other local weather parameters in order to fully feed the demand and avoid extra costs. There are various methods for obtaining local data, such as local measurements, official organizational data, satellite obtained, and reanalysis data. In this paper, the Modern-Era Retrospective analysis for Research and Applications dataset version 2 (MERRA-2) dataset provided by NASA is introduced and its performance is evaluated by comparison to various locally measured datasets offered by meteorological institutions such as Meteonorm and Deutscher Wetterdienst (DWD, or Germany’s National Meteorological Service) around the world. After comparison, correlation coefficients from 0.95 to 0.99 are observed for monthly global horizontal irradiance values. In the case of air temperature, correlation coefficients of 0.99 and for wind speed from 0.81 to 0.99 are observed. High correlation with ground measurements and relatively low errors are confirmed, especially for irradiance and temperature values, that makes MERRA-2 a valuable dataset, considering its world coverage and availability.

Suggested Citation

  • Arash Khatibi & Stefan Krauter, 2021. "Validation and Performance of Satellite Meteorological Dataset MERRA-2 for Solar and Wind Applications," Energies, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:882-:d:495809
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    References listed on IDEAS

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    1. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
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    1. Mathews, Duncan & Ó Gallachóir, Brian & Deane, Paul, 2023. "Systematic bias in reanalysis-derived solar power profiles & the potential for error propagation in long duration energy storage studies," Applied Energy, Elsevier, vol. 336(C).
    2. Emily Cowin & Changlong Wang & Stuart D. C. Walsh, 2023. "Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets," Energies, MDPI, vol. 16(8), pages 1-21, April.
    3. de Aquino Ferreira, Saulo Custodio & Cyrino Oliveira, Fernando Luiz & Maçaira, Paula Medina, 2022. "Validation of the representativeness of wind speed time series obtained from reanalysis data for Brazilian territory," Energy, Elsevier, vol. 258(C).
    4. Hamza S. Abdalla Lagili & Aşkın Kiraz & Youssef Kassem & Hüseyin Gökçekuş, 2023. "Wind and Solar Energy for Sustainable Energy Production for Family Farms in Coastal Agricultural Regions of Libya Using Measured and Multiple Satellite Datasets," Energies, MDPI, vol. 16(18), pages 1-53, September.

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