In this paper we analyse the suitability of different reanalyses and meso-scale models for simulating wind energy in Germany. We found that all datasets overestimate energy production, with errors ranging from 5 % to 45 %. This suggests that the underlying models may not accurately reflect average wind conditions. CERRA and ERA5 performed the best, but they also require regional adjustment. Understanding the cause of these differences is crucial for improving weather and wind energy modelling.

This paper explores advances in weather intelligence as an integral part of the digital transformation of society, enabling everyone to make better informed decisions to avoid harm and enhance economic productivity. 

Climate services comprise of climate model or observational data that is provided by scientists or authorities to stakeholders who use them in decision-making processes. However, providers and users typically have diverging expectations regarding the data. We here describe various aspects of the gap between needs and offers that were identified in two workshops in the Main River catchment (central Germany). To bridge the gap, we propose a framework that can guide future stakeholder dialogues.