Accuracy

Differences between the various data sources and computational methods can be summarized as follows:

Quality of the Database

Global irradiance and air temperature datasets have undergone extensive validation. The RMSE (root mean square error) for interpolated yearly irradiance values is approximately 6%, while for temperature it is 0.9°C.

Climatic Variability

The Meteonorm Climate database is based on the 20-year measurement period 2001–2020. Comparisons with longer-term observations show that deviations in average global irradiance caused by the selected time periods are typically less than 1–3% (RMSE) across all weather stations.

Model Accuracy

Meteonorm uses computational models to compute irradiance on tilted surfaces and to estimate additional meteorological parameters.

The hourly model in Meteonorm tends to slightly overestimate total irradiance on inclined surfaces by 0–3%. The discrepancy between modelled and measured values is typically within ±10% for individual months and within ±6% for annual totals.

The modelled hourly global horizontal irradiance in Meteonorm matches very well with measured hourly global horizontal irradiance. The Kolmogorov-Smirnov test Integral (KSI) as described in Espinar et al. 2008 shows good agreement with KSI OVER percent (KSI %) values between 6.4 and 27.7 for different stations.

Direct normal irradiance also shows good agreement with measured values, with a slightly positive bias. Yearly averages have an average relative difference of 3.4% (RMSE of 7.3%).

General Remark

It is important for users to recognize that the underlying data and computational models represent approximations of real-world conditions. Nevertheless, the natural year-to-year variability in measured global irradiance is often greater than model uncertainty, making Meteonorm a reliable and robust tool for climate-based simulations.