Atmospheric gravity waves (GW) are small-scale propagating disturbances that arise due to the vertical forcing of air parcels by topography, convection, wind shear, jet streams, frontal systems and other tropospheric sources. GWs can be trapped in the stable boundary layer, propagate horizontally and cause a strong modulation of wind, temperature and humidity [1]. When passing a wind farm those AGWs cause sudden sharp increases and decreases in wind speed at rotor height and might affect the power generation.

Here we present GW observations from short-range wind lidars that are deployed on platforms within offshore wind farms in the Dutch North Sea for operational wind profile measurements [2]. The GWs are most easily recognized in the vertical velocity profiles, while the simultaneously measured horizontal wind reveals the potential impact on the wind farm performance. Typical GW periods are about 5 to 10 minutes, meaning that those GWs are easily missed when considering only the standard 10-minute averaged wind lidar data. Collocated automatic ceilometer lidars can provide additional information on the GWs.

Our North Sea wind lidar network currently consists of six platforms within four Dutch offshore wind farms (Borssele, Hollandse Kust Zuid, Hollandse Kust Noord and Hollandse Kust West) and grows together with the Dutch offshore wind energy development. We have collected about 20 GWs cases in the last five years, of which a few examples will be shown. Special emphasis will be made on GWs events that are observed throughout the whole network, including our onshore Cabauw atmospheric research site, highlighting the mesoscale nature of those GWs. Our observations offer the possibility to study offshore GWs, while the particular siting of our observations allows to directly relate the GWs to wind farm performance.

[1] Knoop, S., Assink, J., Tijm, S., and Leijnse, H.: High-resolution observations of a gravity wave event over the Netherlands, EMS 2024, https://doi.org/10.5194/ems2024-445

[2] Knoop, S. and de Jong, M.: Wind lidars within Dutch offshore wind farms, EMS 2023, https://doi.org/10.5194/ems2023-271

How to cite: Knoop, S., de Jong, M. D. J., and Assink, J.: Doppler lidar gravity wave observations within North Sea wind farms, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-107, https://doi.org/10.5194/ems2025-107, 2025.

Offshore wind capacity in the Belgian Offshore Zone (BOZ) is currently 2262 MW. The increasing reliance on wind energy highlights the need for accurate forecasting and effective energy dispatch. Rapid changes in wind power, known as wind power ramping events, pose particular challenges for grid management. To better support energy decision-making, in particular for the Belgian transmission system operator, the Royal Meteorological Institute of Belgium (RMI) has tested a Wind Farm Parameterization (WFP) in their numerical weather prediction (NWP) models to incorporate wake effects. Additionally, machine learning-based post-processing techniques, including a Multi Layer Perceptron and XGBoost, have been applied to enhance wind power forecast accuracy. While these efforts have led to noticeable decreases in overall power forecast errors, the improvements in power ramping predictability remain limited.

In many cases, models can capture the overall trend of power ramps but show timing shifts or slight under-/overestimation in ramp intensity. Rigid point-to-point verification may thus underestimate model skill. To apply a flexible verification framework, we investigated two approaches: the Buffer-Time approach, which allows a timing margin; and the Time-Window approach, which verifies event occurrences within a fixed interval. A power buffer is also introduced to account for small differences in ramp intensity. Using these approaches, we assess the predictability of 15 minute and hourly ramping events for a range of magnitudes of at least 15% of total BOZ capacity, for a 3-year period from June 2021 until June 2024. We compare various NWP models, including the operational RMI Alaro 4 km model, its WFP-enhanced version and the ECMWF HRES model (9 km). Results show that smaller and up power ramps are generally easier to forecast than larger and down ramps. Compared to the operational forecasts, improved modeling and machine learning-based post-processing helps reduce false alarms and better characterize the timing of ramping events. Meanwhile, the results suggest that precipitation has a notable impact on ramp forecast errors, given that many instances of false alarms correspond to precipitation events. In addition, the models, especially machine learning models, have difficulties in capturing extreme ramping events, particularly those caused by European windstorms.

How to cite: Meng, R., Smet, G., Van den Bergh, J., Van den Bleeken, D., Van Poecke, A., Tabari, H., Hellinckx, P., and Termonia, P.: Predictability of Wind Ramping Events in the Belgian Offshore Zone: Insights from NWP Models and Post-processing, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-490, https://doi.org/10.5194/ems2025-490, 2025.

Numerous programmes and initiatives have recently been created to make high-fidelity weather and climate prediction data more easily accessible to the public. In an increasingly digital world, these predictions can be directly used to create products and services that have an enormous impact on communities, businesses, and people’s lives in general. One of the most prominent efforts in this area is Destination Earth, the European Union’s flagship initiative to develop Digital Twin models of the planet Earth. The European Centre for Medium-Range Weather Forecasts (ECMWF) contributes to the Destination Earth initiative by co-developing the software infrastructure that allows data to flow efficiently from the weather and climate models to the downstream applications.

As part of this effort, ECMWF is developing a software called Plume that allows a weather model to extend its data processing functionalities through plugins. Plume plugins can read model data “on-the-fly“ (i.e. in memory) at each time step of the simulation. This is a key advantage as not all model data is available once the simulation has ended (data is in fact saved to disk at reduced frequency to avoid prohibitively expensive I/O operations and unmanageable data volumes). Plugins are also an effective way to deal with the complexity of large Weather Prediction models by allowing developers to implement data processing capabilities as well-defined, modular and more easily approachable software components. Therefore, plugins also offer great opportunities for collaborative development across institutions, third parties and scientific communities. In this context of collaborative development, ECMWF is also contributing to the EU Horizon project DTWO, that develops a Digital Twin for Wind Energy applications, making use of several data sources including Destination Earth.

This work presents the development of Plume plugins for wind energy in the DTWO project, and more specifically, a wind farm modelling plugin and a weather extreme events detection plugin. The wind farm plugin reads wind fields from memory at every time-step and implements an algorithm to model wind turbine wakes and their interactions, in a pre-defined geographic area. By operating directly on in-memory data, the plugin provides access to high-frequency wind fields and allows a more granular prediction of wind turbine wakes and energy yield. The extreme-event detection plugin scans selected model fields and applies user-defined extreme-event detection algorithms. When events of interest are found, the plugin can also notify a server with information relative to the events. This mechanism can be used to trigger automatic data processing workflows in response to selected notifications. Finally, the functionalities developed in these plugins will be available to weather prediction models, creating synergies across projects and maximising the impact of this development.

How to cite: Bonanni, A., Ducher, C., Sarmany, D., and Quintino, T.: Wind Energy Plugins for Weather Prediction Models, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-592, https://doi.org/10.5194/ems2025-592, 2025.

How to cite: Fischereit, J., Sahan, M. E., Imberger, M., and Larsén, X. G.: Benchmarking model coupling and wind farm parameterizations for wind energy applications , EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-495, https://doi.org/10.5194/ems2025-495, 2025.

A global decrease in near surface wind speed at mid latitudes since the 1980ies has been formalised as "The Global Terrestrial Stilling" theory[1]. Such continuing trend would be problematic for the wind power community and requires careful investigation.

A recent study[2] claimed that the stilling reversed since year 2010, leading to a recovery of global wind circulation. However the climate projections of the IPCC do not clearly show this recovery and instead predicts a global wind speed decrease over most land areas in the Northern hemisphere at mid latitudes.

The case of Poland seems particular as it displays the most important decrease in wind speed in climate projections from the IPCC.

In this presentation, we investigate the past and future wind speed and power resource close to the city of Bydgoszcz, Poland. Our study includes a set a various steps to go from the very broad resolution of climate change models to site specific tens of meters resolution features.

We first use multi-model climate projections and advanced statistical techniques to build a very high quality wind speed time serie. Second, we merge our result with high quality reanalysis data to enhance the quality of our time series in absolute value. Third, we perform a final downscaling to tens of meter precision using our CFD approach. And additionally, we extract the power output of a hypothetical wind farm.

Using these many steps, we can provide an advanced view of the wind resource in the future including meso-scale effects and site effects thanks to a hybrid approach including meso-scale, statistical and CFD tools.

[1] McVicar et al. (2012), Jour. of Hydrology, V416–417, p182-205, https://doi.org/10.1016/j.jhydrol.2011.10.024.
[2] Zeng et al. (2019), Nature Climate Change, 9, p979-985, https://doi.org/10.1038/s41558-019-0622-6

How to cite: Rozel, A., Herman, J., and Tromeur, E.: Is global terrestrial stilling reversal really happening? Climate change-related wind resource assessment in Poland., EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-144, https://doi.org/10.5194/ems2025-144, 2025.

In the offshore energy environment, accurate and timely metocean forecast data is essential for informed operational decision making. There is a growing demand from small and medium-sized enterprises (SMEs) for more detailed data that is freely available or at low cost. Improving near-shore forecasting can significantly improve decision making, particularly in marginal conditions, thereby expanding the safe operational window for offshore activities. This improvement depends on better data accuracy to improve forecasting and risk assessment.

Ensemble forecast models provide a comprehensive overview of metocean conditions by accounting for uncertainties and offering probabilistic insights. This makes them particularly useful for robust risk assessment and decision-making under uncertainty. However, ensemble forecasts are computationally demanding as they are run at frequent intervals with multiple perturbations. As a compromise, these models use lower resolution domains for computational efficiency, allowing probabilistic modelling with global coverage at the expense of deterministic accuracy.

This work presents a novel machine learning (ML) framework that has been developed to correlate and correct the deterministic error between low-resolution forecast models and high-resolution physics-based models or in-situ measurements. By integrating this ML framework into the forecast workflow, the model-based ensemble forecast results are adjusted at run time to improve deterministic accuracy. This approach enables the production of forecasts with downscaled accuracy, minimising production time and cost without compromising accuracy.

The work is based on an extensive database of calibrated high-resolution hindcast models and metocean measurements. The ML model is trained to learn non-linear downscaling functions that map low-resolution outputs to corresponding high-resolution wave models or observational data at predefined locations. The ML framework includes various models such as long short-term memory (LSTM), linear regression, random forest, gradient boosting, and dense neural networks. All the models share a standardised architecture optimised for metocean forecasting. Designed as a modular, plug-and-play solution, the framework enables rapid deployment, testing, and integration into the forecast workflow.

Results from a case study in the Baltic Sea are presented. With the ML integrated forecast approach a 65% reduction in RMSE for significant wave height is achieved compared to the unadjusted wave forecast data.

How to cite: Kistner, S., Santos, P., and Jacobsen, S.: Cost-Effective Downscaled Wave Forecasting Using Machine Learning, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-429, https://doi.org/10.5194/ems2025-429, 2025.

Clouds are a key component in weather and climate, influencing both incoming solar shortwave radiation and outgoing thermal radiation. During recent years, electricity production using solar photovoltaic (PV) panels has grown rapidly worldwide. As the number of PV installations continues to grow, it is apparent that the network of PV installations constitutes a highly interesting, potential new source of cloud information. From a meteorological perspective, there is a connection between solar electricity production (PV output), solar radiation and prevailing cloud conditions. When the meteorological conditions are known, the electricity production of a known PV system can be accurately modeled. Here, the cloud optical depth (COD), a parameter of the cloud optical properties, is of central importance, as it governs how incoming solar radiation attenuates due to clouds.

In this study, we have developed a new, fast, accurate and universally applicable physically-based approach for deriving COD directly from PV output measurements. In addition to these latter, the approach uses atmospheric variables such as wind speed, air temperature, and cloud-free solar radiation components altogether produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) as well as an overcast sky selection algorithm. The study is carried out at two stations of the Finnish Meteorological Institute providing relevant ground-based measurements. The approach exhibits a similar or better performance than an earlier developed and published state-of-the-art method when compared to ground-based and satellite-based COD retrievals serving as reference. It is intended to apply this approach over other locations in the world where PV output measurements are available.

How to cite: Wandji, W., Lindfors, A., Lipponen, A., and Arola, A.: Utilizing PV output for retrieving cloud information, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-2, https://doi.org/10.5194/ems2025-2, 2025.

Accurate estimation of Global Horizontal Irradiance (GHI) is essential for solar energy applications, climate modeling, and various geophysical processes.

Traditional satellite-based methods rely on the Independent Pixel Approximation (IPA), which treats each pixel as radiatively isolated from its neighbors, neglecting 3D cloud effects and horizontal photon transport. These limitations could be amplified by the higher spatial, temporal, and spectral resolutions of third-generation geostationary satellites. In this study, we evaluate deep learning models that explicitly incorporate spatial context from GOES-16 multispectral satellite imagery to improve satellite-based GHI estimation and address IPA limitations.

We compare two architectures—a Fully Connected Network (FCN) and a convolutional-based model—against a state-of-the-art physical retrieval method (PSM3), using in-situ GHI measurements from 31 U.S. stations.

Our results show that deep learning models leveraging spatial context outperform PSM3 across most metrics, especially under cloudy and partially clear conditions, yielding improved performance, stability, and reduced bias. The best-performing model achieves a 26.5% lower RMSE and a 21% lower MAE compared to PSM3 on a year-long test set. However, deep learning models still struggle to consistently outperform PSM3 in some scenarios in terms of bias, particularly under clear-sky conditions or on some specific test stations.

Qualitative analysis highlights specific weakness modes of PSM3, particularly when it misclassifies cloudy scenes as clear-sky, where deep learning models correctly capture cloud-induced variability.

We discuss the implications of these findings and potential directions for model improvements. This work underscores the potential of spatial-context-aware deep learning models to overcome IPA limitations for the next generation of satellite-based GHI retrieval methods, and improve GHI retrieval in heterogeneous atmospheric conditions. 

How to cite: Becquet, V., Blanc, P., Saint-Drenan, Y.-M., and Essia, Y.: Deep Learning and Spatial Context for Global Horizontal Irradiance Estimation: Addressing Independent Pixel Approximation Limitations with Satellite Imagery, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-294, https://doi.org/10.5194/ems2025-294, 2025.

Accurate forecasting of solar power generation is critical for grid stability and energy planning, particularly as photovoltaic (PV) systems expand across Europe. However, the inherently location-dependent nature of PV production, coupled with limited availability of site-specific data, presents a major challenge for generating reliable forecasts across spatial and temporal scales. This study presents a scalable and transferable machine learning framework that combines synthetic data and real-world observations to enhance solar PV forecasting in data-scarce regions 

We generate synthetic PV production time series across randomly selected locations in Europe using high-resolution hectometric numerical weather prediction (NWP) simulations. To increase realism and robustness, we integrate several additional data sources: ERA5 reanalysis for climatological consistency and gap filling, CAMS satellite-based radiation products for improved irradiance realism, and the high-resolution New European Wind Atlas (NEWA) for supplementary wind and solar surface fields. PV output is modelled using PVLib, using realistic metadata (e.g., panel tilt, azimuth, location) to simulate realistic production patterns.  

In addition to synthetic sites, a set of real PV locations is used to anchor the dataset and validate model behaviour. These real cases can also be perturbed or scaled to test robustness and generalization. A hybrid machine learning setup is then trained on this combined dataset, leveraging both foundation models and classical ML techniques. The training pipeline includes standardized preprocessing and feature engineering to ensure consistent input preparation across all sites and conditions. 

The trained models are evaluated on unseen PV sites and extreme weather cases to assess their generalization capacity and transferability. Our results show that synthetic data, especially when enhanced with multi-source auxiliary datasets, significantly improves forecast accuracy in previously unobserved or data-scarce areas. This approach lays the foundation for a transferable, pan-European PV forecasting system.

How to cite: Papazek, P., Schicker, I., and Gfäller, P.: Transferable Solar Power Forecasting Using Hectometric NWP and Foundation models , EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-383, https://doi.org/10.5194/ems2025-383, 2025.

As Germany accelerates its renewable energy transition aiming for 80% renewable electricity by 2030, the expansion of wind and solar capacity, the phase-out of fossil fuels, and improvements to grid infrastructure are crucial for ensuring a sustainable and secure energy system. In this context, seasonal forecasts of wind and solar radiation have the potential to support energy reserve management, planning for variable renewable supply, and improving the long-term resilience of the energy system. This study focuses on the development and evaluation of seasonal forecasts for 100m wind speed and solar radiation across Germany. We apply the statistical-dynamical downscaling method EPISODES (Kreienkamp et al., 2019) to hindcast data (1990–2020) from the German Climate Forecasting System Version 2.1 and investigate the predictability and forecast skill of 100m wind speed and solar radiation forecasts on lead times ranging from one to six months. The analysis focuses on the summer season, when solar energy production is highest, and the winter season, when wind energy production peaks. Despite the overall rather low forecast skill of seasonal forecasts for Germany, we find that skillful wind forecasts for the winter season are possible with a reasonable correlation to observations. Furthermore, the forecast model is able to predict solar radiation in summer over southern Germany, a region that contains most of the solar plants in Germany, relatively well. We further employ a statistically selected subsampling approach (Dalelane et al., 2020 and Dalelane et al. 2025, in preparation) to generate a smaller ensemble based on large-scale teleconnections in the North Atlantic and apply it to the forecasts. With this approach, we find a substantial increase in forecast skill for both wind and solar radiation in both summer and winter compared to the full ensemble. Our findings show that skillful seasonal forecasts in winter and summer are possible despite the limitations and challenges of seasonal prediction. In the future, we plan to use multi-model approaches and teleconnection indices to further explore potentials for more skillful seasonal prediction of wind and solar radiation and publish skillful forecasts on the DWD climate prediction webpage (http://www.dwd.de/climatepredictions). This user-oriented website consistently evaluates and displays subseasonal, seasonal and decadal climate predictions at high resolution for Germany.

Kreienkamp, F., Paxian, A., Früh, B., Lorenz, P., & Matulla, C. (2019). Evaluation of the empirical–statistical downscaling method EPISODES. Climate dynamics, 52, 991-1026.

Dalelane, C., Dobrynin, M., & Fröhlich, K. (2020). Seasonal forecasts of winter temperature improved by higher‐order modes of mean sea level pressure variability in the North Atlantic sector. Geophysical Research Letters, 47(16), e2020GL088717.

How to cite: Wandel, J., Paxian, A., Dalelane, C., Tyagi, A., Happ, A., and Siefert, M.: Seasonal forecasts of 100m Wind and solar radiation for Germany, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-218, https://doi.org/10.5194/ems2025-218, 2025.

Renewable energy production is directly influenced by weather conditions, making the energy sector highly sensitive to seasonal to decadal climate variations. Decadal climate predictions, which forecast climate variability over the next 1 to 10 years, are essential for optimising renewable energy deployment. For example, reliable long-term forecasts can help identify the most suitable locations for wind farms and solar plants, ensuring stable energy production and reducing risks associated with climate variability and change.

This study calculates climate impact indicators for the energy sector based on decadal climate predictions. Climate indicators are used to quantify the impact of climate variability on energy production, which is ultimately the most useful information for the energy industry. 

To calculate the indicators, different variables and temporal resolutions are required for each energy source. For solar energy, daily mean values of near-surface air temperature (TAS) and solar radiation (RSDS) are required. We generated the indicators using multi-model ensembles that combine predictions from climate forecast systems participating in the Decadal Climate Prediction Project (DCPP) component of Coupled Model Intercomparison Project Phase 6 (CMIP6). The ensemble includes 13 systems for solar and 4 for wind, depending on variable availability. To assess the forecast quality for the indicators, we use the ERA5 reanalysis as the reference dataset. The evaluation is carried out using both deterministic and probabilistic metrics.

For renewable energy, one important indicator is the capacity factor (CF), which measures the ratio of actual energy production to the maximum potential energy production if the system operates at full capacity. For solar energy, the CF is calculated based on RSDS and TAS. For wind energy, the CF depends on sfcWind and the turbine type, as turbine efficiency varies depending on their weight or height.

Additionally, we define an indicator of the number of effective days, which refers to the number of days when RSDS exceeds the threshold of 208 W/m², the minimum radiation necessary for effective solar energy production. Furthermore, we calculate the number of days when TAS surpasses 45°C, a threshold beyond which solar panels lose efficiency. Similarly, we define minimum and maximum wind speed thresholds for each turbine, within which energy production is possible. The number of days when wind speeds fall within these thresholds will indicate the number of days available for energy production.

The potential benefit of the decadal predictions of tailored indicators will be assessed through a co-evaluation process involving multiple stakeholders from the renewable energy sector. This co-evaluation process will be conducted within the framework of the BOREAS project and it will enable the quantification of the added value provided by this new source of climate information and its potential to ensure the resilience of the Spanish renewable sector.

How to cite: Moreno-Montes, S., Delgado-Torres, C., Torralba, V., Olmo, M., and Soret, A.: Decadal predictions of wind and solar power indicators to support the renewable energy sector, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-395, https://doi.org/10.5194/ems2025-395, 2025.

The energy strategy of Switzerland for the next 25 years contains a substantial increase of renewable energy sources in its electricity mix. By 2050, 36 TWh per year is planned to come from solar power (4 TWh today) and 4 TWh from wind power (approximately zero today). As for many other countries on similar pathways, this expansion of renewables will make electricity production and prices substantially more weather-dependent and hence volatile. It has thus become an important duty of Switzerland’s national weather service MeteoSwiss to provide the Swiss energy sector with high-quality weather and climate data and information for planning and operating such a future energy system. In this presentation, we provide an overview of some operational as well as currently developed weather and climate products at MeteoSwiss that are relevant for the energy industry. The first product, which has been operational since some years, seamlessly combines station observations, medium-range and subseasonal ECMWF forecasts, and statistical outlooks to monitor and predict heating degree days during the winter half-year on a local level as a widely used meteorological proxy for heating demand. The second product is an operational hourly satellite-based solar radiation climate dataset on a 2-km grid, which is based on EUMETSAT’s Climate Monitoring Satellite Application Facility (CM SAF) GeoSatClim algorithm with improved radiation in mountainous and potentially snow-covered terrain. This dataset will be crucial for planning the envisioned expansion of photovoltaic infrastructure and nowcasting its power production. The third product is a prototype of an hourly wind climate dataset on a 250-m grid, which is based on a new machine-learning algorithm that uses data from surface stations, numerical model simulations, and static topographic models as predictors. This prototype will improve the quality of gridded observational wind datasets particularly in mountainous terrain, which is essential for planning wind power infrastructure in Switzerland. All these products advance our endeavors in providing the energy sector and energy-targeting weather companies in Switzerland with a seamless operational monitoring, nowcasting, and forecasting suite of energy-relevant meteorological parameters.

How to cite: Büeler, D., Begert, M., Isotta, F., Lloréns Jover, I., Sharma, V., Tetzlaff, A., Zanetta, F., Schwierz, C., and Grams, C.: Energy meteorology at MeteoSwiss: overview of some current activities, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-220, https://doi.org/10.5194/ems2025-220, 2025.

Achieving Germany's national climate targets requires efforts to reduce emissions in all sectors. The German Ministry for Digital and Transport (BMDV) established the ‘Network of Experts’ (BMDV-Expertennetzwerk), a network of German government agencies focused on the future-oriented transformation of transport in Germany. The focus of the topic area “Renewable energies” is the assessment of renewable energy potential along the transportation infrastructure. In this topic area, Germany’s national meteorological service DWD (Deutscher Wetterdienst), the Federal Highway and Transport Research Institute (BASt) and the German Centre for Rail Traffic Research at the Federal Railway Authority (DZSF/EBA) work closely together.

The transport infrastructure offers significant potential for renewable energy production. To optimize energy management, it is crucial to analyze the variability of renewable energy in Germany. Earlier studies of DWD showed that solar radiation and wind speed complement each other well throughout the year, but extraordinary weather events can strain the energy system. An example is the so-called “Dunkelflaute”, a period of very low wind and solar energy production. Specific weather patterns can cause regional conditions of minimal wind and low solar radiation. Key factors when defining a Dunkelflaute are the renewable energy production, demand, the area of interest, duration and critical thresholds of the capacity factors.

The new analysis is based on capacity factors for wind and solar energy. Solar radiation is obtained from the SARAH-3 satellite dataset from CM SAF, as it provides high temporal and spatial resolution. Additionally, the 10 m wind speed and the 2 m temperature from the new regional reanalysis COSMO-R6G2, the follow-up product of DWD’s COSMO-REA6, are used to estimate the temperature-dependent efficiency of the PV modules. For the wind capacity factors, wind speeds near hub-height are required and are taken from COSMO-R6G2 as well as from the other new reanalysis ICON-DREAM.

Unlike previous studies of DWD, this analysis incorporates precise power plant location data from the German ‘Marktstammdatenregister’ to derive energy production values from the capacity factors. This allows for assessing the frequency and timing of low solar and wind energy production and estimating the occurrence of Dunkelflauten. Additionally, other extraordinary weather events for the energy sector are collected.

How to cite: Dost, V. and Bär, F.: Assessing ‘Dunkelflaute’ as an extraordinary weather event for the energy sector in Germany using precise power plant data, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-647, https://doi.org/10.5194/ems2025-647, 2025.