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 periods of 2001-2020. Comparisons with longer-term observations show that deviations in average global radiation caused by the selected time periods are typically less than 1–3% (RMSE) across all weather stations.
Model Accuracy
Meteonorm uses models to compute irradiance on tilted surfaces and to estimate additional meteorological parameters. Depending on the available data, one or more models may be applied.
The hourly model in Meteonorm tends to slightly overestimate total radiation on inclined surfaces by 0–3%, depending on the specific model used. The discrepancy between modeled and measured values is typically within ±10% for individual months and ±6% for annual totals.
Meteonorm uses models to compute the different irradiance components (GHI, DNI, DHI) and to estimate additional meteorological parameters. Depending on the available data, one or more models may be applied.
Comparisons of radiation components calculated with the Meteonorm chain of algorithm and radiation components measured at ground measurement stations (see Validation) show excellent agreement. The modelled hourly global horizontal radiation in Meteonorm matches very well with measured hourly global horizontal radiation. 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.
The direct normal radiation component also shows good agreement with a slight positive bias: There is a slight positive bias in the DNI modelled by Meteonorm 9 compared to the measured values with an average relative difference of 3.4% (RMSE of 7.3%) in yearly means.
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 the modeling inaccuracies, making Meteonorm a reliable and robust tool for climate-based simulations.