While satellite data bias for a site can be confidently guaranteed only between +/-3%-6% of daytime mean irradiance (depending on climate and terrain), a documented strength of satellite models is their ability to capture relative interannual variability for a given site (Ineichen 2011). In other words, the models may exhibit a bias for a particular location as a result of, for example, the difficult nature of the terrain and low resolution of the aerosol data; however, this bias will tend to persist over the long term. Therefore, if the satellite model can be calibrated against a short-term measurement campaign, its long-term accu – racy—that is, its ability to predict irradiance before and after the measurement period—should be substantially improved. A calibration campaign of 6-12 mo will typically reduce the MBE confidence interval by half. This calibration process is often referred to as site adaptation or measure-correlate-predict.

Site adaptation methodologies range in complexity from a simple bias correction, such as applying the same calibration factor to all predicted data points, to more sophisticated and generally more effective techniques con­sisting of matching measured and modeled frequency distributions by reducing the KSI error metric, whereby a different correction factor is applied to the model depending on its value. In arid areas with sparse cloud cover, MBE is usually driven by problems with aerosol parameterization; therefore, methods based on adaptation of aerosols to local conditions may be very effective. Figure 2.17 is an example of site adaptation.

Updated: August 4, 2015 — 12:09 pm