Assessment and added value estimation of an ensemble approach with a focus on global radiation forecasts

TL;DR

This study evaluates the COSMO-DE-EPS ensemble weather prediction system's effectiveness for solar energy applications, focusing on global radiation forecasts and introducing a new metric for ensemble added value.

Contribution

It introduces the ensemble added value metric and assesses the ensemble system's performance in predicting global radiation for renewable energy use.

Findings

Ensemble forecasts better capture observation variability.

Added value increases with forecast horizon and varies seasonally.

Ensemble approach outperforms single forecasts in key metrics.

Abstract

The assessment of the high-resolution ensemble weather prediction system COSMO-DE-EPS is achieved with the perspective of using it for renewable energy applications. The performance of the ensemble forecast is explored focusing on global radiation, the main weather variable affecting solar power production, and on quantile forecasts, key probabilistic products for the energy sector. First, the ability of the ensemble system to capture and resolve the observation variability is assessed. Secondly, the potential benefit of the ensemble forecasting strategy compared to a single forecast approach is quantitatively estimated. A new metric called ensemble added value is proposed, aiming at a fair comparison of an ensemble forecast with a single forecast, when optimized to the users' needs. Hourly mean forecasts are verified against pyranometer measurements over verification periods covering 2013. The results show in particular that the added value of the ensemble approach is season-dependent and increases with the forecast horizon.