# Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

## TL;DR

This study compares seven statistical post-processing methods to improve probabilistic solar radiation forecasts from NWP models, demonstrating enhanced accuracy and economic value, especially under clear sky conditions.

## Contribution

It provides a comprehensive comparison of parametric and non-parametric post-processing techniques for solar radiation forecasting, highlighting the superior performance of quantile regression and generalized random forests.

## Key findings

- All methods improved raw forecast accuracy.
- Quantile regression and generalized random forests performed best.
- Non-parametric methods excel under clear sky conditions.

## Abstract

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.