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

Germán Salazar, Christian Gueymard, Janis Bezerra Galdino, Olga de Castro Vilela and Naum Fraidenraich

Renewable and Sustainable Energy Reviews, 2020, vol. 117, issue C

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.

Keywords: Solar irradiance; Validation; Data quality control; Data gaps; Solar resource assessment (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.rser.2019.109478

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