When planning solar plants project developers and financers consider the long-term average of solar radiation and its uncertainty as the most important site selection criteria. Accurate time series of at least 10 years length are needed to calculate realistic estimates of such long-term averages. Ground-based measurements usually are not available for long time periods, but satellite-derived data may show significant differences compared to more accurate ground-measured values with respect to instantaneous values and frequency distribution. To overcome these shortcomings a new method for adaption of satellite-derived solar radiation values to ground measured time-series is developed. One year of overlapping time period is used for training the adaption. A weighted polynomial fit with additional constraints is then applied to the remaining satellite-derived solar radiation values. The method is tested at two sites for four satellite data models. The results show improvements for all data sets. After applying the adaption also during periods where no ground-based measurements are available the bias on average is nearing 0 % for most models. This requires that the satellite-derived data realistically represent the interannual variability. Frequency distributions are also matching better. Especially this is the case for maxima, which can be critical for design purposes.
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