The question of how to best evaluate and communicate the value of weather warnings was addressed by a survey. This included questions with respect to the relevance of various aspects of a warning, the potential benefit of providing information on warning quality, the type of useful information and suitable ways of providing such information. Apart from the core information of warnings (time, place and severity) impact information and behavioural advice are seen to add value to a warning.

In this study, the quality of 10 and 100 m wind speeds from three different reanalyses (global and regional) are evaluated along the different coasts of France. The evaluation show that Copernicus Regional Reanalysis for Europe (CERRA) has a high skill for surface wind speed on the three French seafronts, as well as for offshore wind speed at 100 m. Thus, CERRA appears to be the optimal reanalysis to use as a reference for offshore wind studies over the French maritime zone.

To better predict offshore wind energy in Belgium, we improved the Royal Meteorological Institute (RMI) weather model by directly incorporating the effects of wind turbines. We also used AI to account for wind farm wake effects, where turbines slow down wind for other turbines. By combining physics-based models with a neural network trained on observations from the Belgian Offshore Zone, we achieved more accurate forecasts. This helps ensure a stable power grid and supports the growing role of offshore wind in our energy mix.

Renewable energy is becoming more common, but its variability creates challenges in keeping electricity supply stable. This study examines how changes in energy generation, known as ramps, occur, how they can be modelled and the associated skill. By analyzing wind and solar power in Ireland, we find that using a mix of sources reduces extreme fluctuations and helps supply stability. Our results also stress the benefits of energy diversity and the challenges of modelling changes at a local scale.