DNI satellite data truth?

How to handle the difference in values derived from different satellite data providers?

Technical Top Tip courtesy of Solar Resource Assessment


When it comes to CSP project evaluation, one of the biggest uncertainty factors for the performance is the available Direct Normal Irradiance (DNI) at the site.

1. DNI vs. GHI
DNI distinguishes significantly from GHI (Global Horizontal Irradiance), which is more applicable for Photovoltaic projects. GHI includes direct and diffuse radiation, while DNI only considers the direct beam, which can be concentrated by mirrors. DNI intensity is strongly diminished by clouds, water vapor and aerosols such as dust, pollen, soot particles etc., while GHI is less influenced. Therefore variability of DNI in space and time is much higher than for GHI and data are also more complex to obtain. DNI can be measured on the ground by means of specific measurement devices or derived from satellite data. While clouds are relatively good to recognize from the satellites perspective, it is difficult to distinguish for example a sandstorm from the desert underneath. That said, obviously measurements at the ground should deliver the better data. But the sensor must be reliably calibrated and carefully operated. Typically long-term data from DNI ground measurements are not available in CSP project locations, thus satellite data are the first choice to evaluate the solar potential of one site.

2. Data from several sources increase accuracy of estimate.
Long-term satellite-derived DNI data are available from several independent providers. When comparing data from various providers for the exact same location, one finds immediately that values from different providers come to different results. To demonstrate the variability, we analyzed eight (8) satellite data sets from different providers for one specific location in Spain.

3. What is the basis for uncertainty of the various sources?
DNI is highly variable in space and time; small changes in the state of the atmosphere have a strong influence on attenuation of direct irradiance. Satellite-derived data usually have a higher uncertainty and differ by a certain degree from the real irradiation. Different processing algorithms and input data lead to differing results. Ground-measured data from pyrheliometers or pyranometers might be affected from bad calibration, wrong sun tracking, soiling of the instruments, etc. Many years of historical data leverage out the effect of inter-annual variability and lead to reduced long-term uncertainty

4. Conclusion: How to handle the differences in values?
Instead of focusing on one single source, even the most reliable looking one, several sources should be used. These data should be combined with ground-measured data, as far as available. This is the most reliable method to calculate long-term best estimates. Credibility of the final result will be strengthened with every additional data set of sufficient quality. A quality weight applied to each data set generates a best estimate out of the different sources.This weight needs to be based on the experience of an expert, who can also reflect the length of temporal coverage and the representativeness of a satellite data set for a site.

5. Rule of Thumb
As a rule of thumb regarding uncertainty of DNI, the following can be stated:
Up to +/- 30% when using a single source of satellite data
Up to +/- 15% when applying a quality weighted average of multiple sources
Up to +/- 5% when combining ground-measured data and site-specific
long-term satellite data

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The full Technical Top Tip with images and graphs is available at: http://www.csptoday.com/india/pdf/Suntrace2.pdf

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