  The increased usage of solar energy places additional importance on forecasts
of solar radiation. Solar panel power production is primarily driven by the
amount of solar radiation and it is therefore important to have accurate
forecasts of solar radiation. Accurate forecasts that also give information on
the forecast uncertainties can help users of solar energy to make better solar
radiation based decisions related to the stability of the electrical grid. To
achieve this, we apply statistical post-processing techniques that determine
relationships between observations of global radiation (made within the KNMI
network of automatic weather stations in the Netherlands) and forecasts of
various meteorological variables from the numerical weather prediction (NWP)
model HARMONIE-AROME (HA) and the atmospheric composition model CAMS. Those
relationships are used to produce probabilistic forecasts of global radiation.
We compare 7 different statistical post-processing methods, consisting of two
parametric and five non-parametric methods. We find that all methods are able
to generate probabilistic forecasts that improve the raw global radiation
forecast from HA according to the root mean squared error (on the median) and
the potential economic value. Additionally, we show how important the
predictors are in the different regression methods. We also compare the
regression methods using various probabilistic scoring metrics, namely the
continuous ranked probability skill score, the Brier skill score and
reliability diagrams. We find that quantile regression and generalized random
forests generally perform best. In (near) clear sky conditions the
non-parametric methods have more skill than the parametric ones.
