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

Arash Khatibi and Stefan Krauter
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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

Energies, 2021, vol. 14, issue 4, 1-17

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.

Keywords: power potential estimation; photovoltaics; solar irradiance; temperature measurement; wind energy; wind speed; wind direction; satellite data; Meteonorm; MERRA-2; DWD (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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