Accuracy of SolarGIS data is sufficient for many application in solar industry. However, when it is necessary to lower uncertainity to the minimum level and when ground measurements of GHI or DNI are available at least for a short time (e.g. 1 year), quality enhancement of SolarGIS time-series or TMY data can be performed. The resulting data has a lower bias, minimum RMSD and balanced probability distribution function.
This is particularly relevant for CSP and CPV projects, and for large PV projects, esspecially when entering new territories.
Adaptation of time-series DNI data for site Tamanrasset (Algeria).
Scatterplot on the left represents the original DNI ground–satellite data, with bias -4.2%.
Scatterplot on the right shows the correction of bias and frequency distribution after adaptation.
The fundamental difference between a satellite observation and a ground measurement is that signal received by the satellite radiometer integrates an area (e.g. MSG represents an area of about 3x3 km in a sub-satellite position) while a ground station represents a pinpoint measurement. The satellite pixel is not capable to describe the inter-pixel variability in complex regions, where within one pixel diverse natural conditions mix-up (e.g. fog in narrow valleys or along the coast) or frequent occurence of intermittent cloudy weather. Other factor is the influence of coarse spatial resolution of atmospheric databases (such as aerosols or water vapour). This results in a mismatch when comparing instantaneous values from these two observation instruments.
- The SolarGIS satellite-derived data are correlated with ground measurement data with two objectives:
- Improvement of the overall bias
- Fit of the frequency distribution function.
Optimally, high-quality ground measurements should be available for a period of at least one year, so that all seasons are included to remove systematic deviations from the satellite data.
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