Weather forecasts that include uncertainty can be difficult to interpret and apply to real decisions. This study presents simplified and user-friendly tools developed in collaboration with professionals to make probabilistic forecasts more accessible. Tested to heat stress during the Paris 2024 Olympic and Paralympic Games and late frost in vineyards, these tools help anticipate risks and support earlier, more informed, and more effective responses.

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.

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.

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.

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.

Long-term series of precipitation data are important for e.g. the precise quantification of climate change.

Such time series should be consistent, but changes in the measurement conditions can't always be avoided. Parallel measurements are carried out at German climate reference stations in order to analyse the effects of changes in the measuring systems, e.g. when switching the device to another.

In this study, the influence of funnel heating on precipitation measurement  was analysed.

We looked into how a numerical experiment represents the interactions between the air and the sea at scales of about 100–1000 km. We found that slight changes in sea surface temperature (SST) drive dramatic modifications in evaporation rates from the sea surface. Cold sea patches tend to cool down the air and to keep moisture trapped within the lower atmospheric layers. Warmer SST, instead, makes them more buoyant and inflate with drier air, thus diluting moisture over thicker air layers.

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. 

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.

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.

On 6 July 2023, a severe hailstorm hit Vitoria-Gasteiz, caused by a supercell. Active cells moved southwest to northeast, with some shifting west-east. The storm, with radar reflectivities up to 65 dBz, brought intense showers and hail up to 5 cm in diameter. It caused injuries, vehicle damage and urban flooding. This study analyzes the storm’s conditions and surface effects using different data.

COSMO-REA6 is widely used in energy applications but has known deficits. Its successor, COSMO-R6G2, is evaluated for Germany, focusing on Global Horizontal Irradiance (GHI) as well as wind speed. It shows slightly lower GHI and good wind quality in flat areas, but underestimates night-time wind in mountains. Overall, it is a useful successor, though users should consider remaining limitations.

Weather forecasts have greatly improved over recent decades, yet errors remain. We studied how people judge different types of forecast errors using two short surveys. People rated errors differently depending on their consequences, with forecasts that underestimated the intensity and persistence of events judged most negatively. These findings show what forecast users value and contribute to the development of verification methods that better reflect public needs.