The success of wind power means that wind turbines are increasingly put in sites with complex terrain, forests, or coastal and offshore regions that are difficult to model and measure. Major challenges for solar power are notably accurate measurements and the short-term prediction of the spatiotemporal evolution of the effects of cloud field and aerosols. Planning and meteorology challenges in Smart Cities are common for both. For both solar and wind power, the integration of large amounts of renewable energy into the grid is another critical research problem due to the uncertainties linked to their forecast and to patterns of their spatio-temporal variabilities.
We invite contributions on all aspects of weather dependent renewable power generation, including, but not limited to:
• Wind conditions (both resources, siting conditions and loads) on short and long time scales for wind power development, in different environments (e.g. mountains, forests, coastal, offshore or urban).
• Offshore wind development: interaction between atmosphere, sea and wind turbine/wind farms, for both bottom-fixed and floating wind, and its impact on marine environment
• Long term analysis of inter-annual variability of solar and wind resource
• Typical Meteorological Year and probability of exceedance for wind and solar power development
• Wind and solar resource and atlases
• Wake effect models and measurements, especially for large wind farms and offshore
• Performance and uncertainties of forecasts of renewable power at different time horizons and in different external conditions.
• Forecast of extreme wind events and wind ramps
• Local, regional and global impacts of renewable energy power plants or of large-scale integration.
• Dedicated wind measurement techniques (SODARS, LIDARS, UAVs, Satellite etc.)
• Dedicated solar measurement techniques from ground-based and space-borne remote sensing
• Tools for urban area renewable energy supply strategic planning and control
• AI and Machine Learning approaches for weather forecasting and its applications
The atmospheric boundary layer above forest canopies is difficult to measure in practice, and our understanding of its flow physics, including the potential wind resource, is limited in part by observational constraints. Most available datasets come from tower point measurements, which do not generally reach into heights encountered by a turbine rotor, or from remote sensing measurements, which are usually located outside of the forest in a clearing and thus do not accurately represent flow conditions above the canopy. Here, we present a field campaign that deployed four Doppler lidars in a U.S. Appalachian Forest including installment on top of a 30 m tall tower. These lidars allow for wind measurements across tall turbine rotor heights to be made directly above forested regions. Nearby wind turbines in the wooded Appalachians have hub-heights approaching 90 m and rotor diameters of 127 m, with maximum and minimum blade heights of 152 m and 25 m, respectively. We describe the experimental set-up, lidar strategies, adjoining radiosonde and UAS IOPs, and novel use of AI to drive optimal lidar scans. These data are being collected as part of the DOE “Addressing Challenges in Wind Forecasting for Tall Turbines Across Regions with Terrain and Land Surface Heterogeneity” project and will be used for analysis of forest-atmosphere interactions and numerical model validation.
How to cite: Wharton, S., Puccioni, M., De Wekker, S., Arthur, R., and Fast, J.: Doppler Lidar Provide New Insights into the Wind Resource over Forests , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12864, https://doi.org/10.5194/egusphere-egu25-12864, 2025.
Low-level jets (LLJs) are fast-moving air streams in the lower part of the troposphere, characterized by wind maxima and wind shear typically occurring at the same level as wind turbine rotors. Technological advances have enabled the design of taller and more efficient wind turbines, making LLJs at higher altitudes potentially significant for their performance. Evaluating LLJ characteristics and understanding their formation mechanisms is essential for accurately assessing turbine loads and power production.
In this context, three years of wind profiles obtained every 15 minutes from two Doppler lidars installed in Dunkirk, a coastal city in northern France, were used to detect LLJs up to 1,500 m. The study focused on assessing the frequency and main characteristics of LLJs in the region and identifying their formation mechanisms. Additionally, the study aimed to evaluate the impact of these jets on wind turbines, especially given the rapid development of offshore installations.
Results indicate that LLJs are a common atmospheric phenomenon, occurring 15 % of the time, predominantly on the nights of the spring and summer seasons. This suggests that frictional decoupling due to radiative cooling is a key factor in LLJ formation. However, the city's coastal location induces additional formation mechanisms driven by the land-sea thermal gradient and the proximity of the English Channel.
The results demonstrated that these jets impact wind turbines since 38 % of the LLJ cores are located in the rotor layer of the most commonly installed offshore wind turbines. However, LLJs are not necessarily beneficial for their power production as the high wind speeds they imply are confined to a relatively thin layer, while the wind outside of this layer exhibits relatively lower velocities. Jet shear has a minimal impact on these turbines since it is similar to the shear observed in non-jet conditions. Indeed, these turbines are mainly located within the surface layer, where ground-induced shear is predominant. On the contrary, future wind turbines will be more impacted by LLJs due to larger rotor sizes and will experience greater negative shear, leading to significant loads on their blades.
How to cite: Haezebrouck, P., Dieudonné, E., Sokolov, A., Delbarre, H., Augustin, P., and Fourmentin, M.: Investigation of low-level jets and their impacts on wind turbine performance in the southern North Sea using Doppler lidars, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3567, https://doi.org/10.5194/egusphere-egu25-3567, 2025.
Wind energy is one pillar towards a decarbonized future energy system. A precondition for an efficient expansion and deployment of wind turbines is reliable and highly resolved information on wind energy potentials. Such detailed information is for example rare in many parts of Africa where it is crucially needed to explore large untapped renewable energy potentials. This study used a new high-resolution, kilometer-scale meteorological data set from dedicated ICON model atmospheric simulations in limited area mode over southern Africa (ICON-LAM). The wind speeds at hub height and wind energy potentials from ICON-LAM, the commonly used ERA5, and a statistical downscaling variant of ERA5 using the Global Wind Atlas (ERA5_GWA) were compared. The wind speed evaluation against weather mast measurements shows that ERA5 and ERA5_GWA underestimate hub-height wind speeds with a mean error (ME) of −1.8 m s−1 (−27%) and −0.3 m s−1 (−4.7%), respectively, while ICON-LAM has a ME of −0.1 m s−1 (−1.8%). Noteworthily, ICON-LAM especially outperforms ERA5 and ERA5_GWA by a large margin in simulating the most relevant range of wind speeds (from 11 m s−1 to 25 m s−1) for wind turbines. This leads to a 48% higher average wind energy potential derived from ICON-LAM compared to ERA5. Estimates based on the ERA5_GWA show a similar average wind energy potential to ERA5, resulting from the spatial heterogeneity of wind energy potential. Such an underestimation of wind energy potential may hinder local development and deployment of wind energy by undervaluing the economic payback, which again underlines the importance of using highly resolved atmospheric model simulations.
How to cite: Chen, S., Goergen, K., Hendricks Franssen, H.-J., Winkler, C., Wahabou, Y., Poll, S., Linssen, J., Vereecken, H., Stolten, D., and Heinrichs, H.: Kilometer-scale regional atmospheric modelling reveals underestimation of onshore wind energy potentials over southern Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6695, https://doi.org/10.5194/egusphere-egu25-6695, 2025.
Localised surface properties are essential in assessing wind resources for renewable energy development. Here, we estimate extreme winds using three convection-permitting models (CPMs) through a systematic surface-based categorisation for Central Europe. We developed a comprehensive classification framework integrating three fundamental surface parameters: climate regimes (Koppen-Geiger), aerodynamic roughness length (Z0), and slope variability. The methodology combines these parameters into distinctive surface categories, enabling a detailed analysis of wind extremes at 100m height across different surface configurations.
We analysed wind speed time series from the CPM ensemble for each resulting surface category, focusing on extreme events and their relationship with surface characteristics. The resulting classification has provided a sound basis for 67 unique surface combinations, allowing us to compare models over varying terrain and climate types and establish substantial differences in extreme wind behaviours.
This research contributes to improving wind energy planning by (1) identifying surface configurations that may influence extreme wind predictions, (2) providing a systematic approach to evaluate model performance across different surface conditions, and (3) giving an understanding of the relationship between surface characteristics and wind extremes at turbine height. The findings directly apply to wind farm siting and risk assessment in complex terrain regions.
Our methodology and results are particularly relevant for renewable energy applications. This work addresses critical needs in wind energy planning by improving our understanding of extreme wind behaviour across diverse surface conditions.
How to cite: Correa-Sánchez, N., Larsén, X., Dallan, E., Borga, M., and Marra, F.: Surface-driven categorisation of extreme wind events in convection-permitting models: Implications for wind energy planning in Central Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21854, https://doi.org/10.5194/egusphere-egu25-21854, 2025.
Wind energy production depends heavily on weather conditions, and the growing deployment of wind turbines in complex terrain and offshore locations presents considerable forecasting challenges. Current numerical weather prediction (NWP) models often struggle to provide accurate forecasts in these environments due to limited spatial and temporal resolutions, infrequent model updates, and the lack of representation of wind turbine induced wake effects on atmospheric flows. These limitations lead to inaccuracies in power production forecasts, impacting the efficiency and reliability of renewable energy systems.
To address these limitations, Meteomatics has developed an operational high-resolution NWP model featuring a horizontal grid spacing of 1 km and an hourly update frequency. This model integrates data from Meteomatics' proprietary network of Meteodrones, along with traditional data sources such as ground-based weather stations, radar, satellite observations, and radiosondes. Meteodrones are small unmanned aircraft systems capable of collecting vertical atmospheric profiles up to altitudes of 6000 m.
Here, the impact of recent enhancements implemented in to Meteomatics' high-resolution NWP model on wind power forecasting is evaluated. Key updates include an extension of the forecast lead time to 72 hours and an increase in temporal resolution to 15-minute intervals, aligning with the interval used in energy trading. Additionally, the model's domain, covering the pan-European region (EURO1k), has been expanded with the introduction of a new domain covering the North American continent (US1k). Importantly, the model now incorporates a parameterization of wind turbine effects, enabling accurate representation of wind wake phenomena. The findings highlight the critical role of state-of-the-art high-resolution numerical weather forecasting in improving the cost efficiency of wind energy production. These advancements facilitate greater integration of wind energy into the broader energy mix, thereby contributing to a reduction in CO2 emissions and supporting the transition to sustainable energy systems.
How to cite: Villinger, J. T., Rausch, J., Umek, L., Schluchter, C., Thaler, M., Schmoeckel, J., Hutchinson, R., and Fengler, M.: Improving Wind Power Forecasting with Meteomatics High-Resolution Model Resolving Wind Turbine Wake Effects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13278, https://doi.org/10.5194/egusphere-egu25-13278, 2025.
Numerical simulations, satellite observations, and field campaigns have demonstrated that wind turbine wakes can alter near-ground air temperature and humidity [Baidya Roy, 2004; Xie, 2017; Wu, 2023; Zhou, 2012; Smith, 2013; Rajewski, 2013; Takle, 2014; Armstrong, 2016; Archer, 2019]. In the wind turbine community, high-resolution large eddy simulations of wind turbine wakes often rely on idealized incident flows and surface conditions, which differ from real-world conditions. Since wind turbine wakes and near-ground air properties are highly sensitive to atmospheric and surface conditions, we employ an online coupling between a realistic atmospheric model, a soil–vegetation–atmosphere transfer model, and an aerodynamic technique based on body forces for the wake of wind turbine following the recommendations of Porté-Agel (2019). The ability of the multi-scale setup to reproduce realistic atmospheric conditions, as well as its capability to reproduce meteorological variations induced by wind turbines, has been validated (under review [Boumendil, 2025] and [Boumendil, 2024]) using measurements from the VERTEX campaign on a 2MW wind turbine turbine located on the East Coast of Delaware, USA [Archer, 2019; Wu, 2021]. Here, we extend this validated setup to investigate a highly stratified stable atmosphere, where wind turbine impacts are expected to be most pronounced.
We employed the atmospheric model Meso-NH [Lac, 2018], initialized and forced with analysis files. Using a grid-nesting configuration, we simulate scales ranging from the mesoscale, capturing diurnal cycles, to the microscale, resolving the flow behavior around wind turbines while accounting for realistic features such as orography, surface cover, clouds, and radiation.
An online coupling with the SURFEX [Masson, 2013] soil–vegetation transfer model is employed to finely model surface properties such as albedo, surface fluxes, ground roughness, or leaf area index depending on land cover. A high-resolution surface database, combining data from OpenStreetMap with the ECOCLIMAP nomenclature [Champeaux, 2005] is uses as inputs for the surface modelling platform SURFEX. Additionally, the effects of wakes from trees [Aumond, 2013] and urban buildings [Schoetter, 2020] were incorporated through added drag forces. The wake of the wind turbine is modeled using an Actuator Disk with Rotation, where rotation speed, blade pitch angle, and rotor direction are updated during the simulation by a controller.
In the highly stratified stable atmosphere, Meso-NH captures the strong near-ground temperature inversion and the wind veer within the rotor area. The interaction between the wake of the wind turbine and the stable atmosphere results in pronounced temperature variations, with warming in the lower rotor area and cooling above. This case study highlights the ability of the model to investigate wind turbine interactions with realistic atmospheric conditions, paving the way for further case studies.
How to cite: Boumendil, P., Joulin, P.-A., Rodier, Q., and Masson, V.: Near-ground meteorological variations induce by a single wind turbine in a realistic highly stratified stable atmosphere., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8831, https://doi.org/10.5194/egusphere-egu25-8831, 2025.
Over the sea, the atmospheric boundary layer is often capped by a shallow, thin, and stable layer, known as the capping inversion, beneath the stable free atmosphere. As offshore wind turbines grow taller, interactions with these stratified layers may become more frequent. Under specific atmospheric conditions, such interactions can generate gravity waves, potentially affecting wind farm performance and environmental impacts.
In his 2010 work, Smith notably highlighted the potential for wind farms to induce gravity waves. Since then, the need to better understand interactions between the atmospheric boundary layer and wind turbines has grown, driven by efforts to optimize the efficiency and design of wind farms. Numerical methods, particularly those employing mesoscale models, have become essential tools for addressing these challenges. Several studies have confirmed the ability of wind farms to excite gravity waves. However, most research has focused on entire wind farms, with limited attention to the specific dynamics of gravity waves generated by individual turbines. A finer-scale understanding of the generation, propagation, and interaction of waves emitted by single turbines within a farm would provide a more comprehensive basis for modeling and analysis at larger scales.
This study aims to improve the understanding and characterization of interfacial gravity waves induced by a single wind turbine operating within conventionally neutral boundary layers. Using the Meso-NH model—capable of decameter-scale atmospheric simulations within a Large Eddy Simulation framework—and its coupling with EOL, an actuator-based aerodynamic model, this work quantifies the properties of gravity waves under various atmospheric conditions and wind turbine configurations. These results will not only help to improve engineering models for estimating production, by better representing wind flow and wind turbine wakes on farms, but also to assess their potential impact on meteorology.
How to cite: Joulin, P.-A. and Masson, V.: Interfacial Gravity Waves from a Single Wind Turbine in a Conventionally Neutral Boundary Layer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16488, https://doi.org/10.5194/egusphere-egu25-16488, 2025.
There are very few studies in which the WRF model is used under Large Eddy Simulation (WRF-LES) mode for real time, offshore conditions. This study utilizes WRF-LES to investigate the wind characteristics during a real storm over the North Sea, west of the Danish Jutland coast. A WRF-based multiscale simulation was conducted to examine the storm, which is characterized by strong south-westerly winds, representing open ocean conditions. The simulation setup comprised four nested domains: three outer domains at mesoscale resolution (9.9 km to 1.1 km, domain 1 to 3) and an innermost domain running in LES mode (spatial resolution 100 m, domain 4). ERA5 reanalysis was used to drive the outermost mesoscale domain, while the other were one-way nested domains.
Results from the LES domain, domain 4, were compared to those from the finest mesoscale domain, domain 3. Lidar measurements of wind speed and direction from 40~m to about 250 m inside the studied domain are used to evaluate the simulations. Compared to the results from domain 3, the simulated vertical profiles of wind speed from the LES domain aligned more closely with the measurements up to a height of 150 m. At higher elevations, the profiles from the mesoscale and WRF-LES output converged, with both outputs overestimating the wind speeds. The wind directions were well simulated in both mesoscale and Les domains. The performance of the mesoscale and WRF-LES output in comparison with measurements is further explored using time series analysis at multiple heights.
How to cite: Hamzeloo, S., Guo Larsén, X., Peña, A., and Fischereit, J.: A Large Eddy Simulation using the WRF model over the sea: a real case study of a storm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11305, https://doi.org/10.5194/egusphere-egu25-11305, 2025.
Climate change triggered the necessity of moving to a greener energy generation which includes renewable energy sources, such as wind and solar. However, integrating renewable energy sources into the current energy network is a challenging task, as these are highly weather dependent. The main challenge is to balance energy demand and supply, as both are now weather dependent.
In previous work energy shortfall (difference between energy demand and renewable generation) across 28 European countries over the boreal winter was investigated from the perspective of weather regimes. In this work, it was shown that some weather regimes greatly favour the occurrence of periods of high energy demand and low renewable generation i.e. periods of high shortfall. Previous research has shown that subseasonal drivers can have a significant impact on weather regimes. Therefore, in this study, we aim to quantify the impact of subseasonal drivers on the occurrence of weather regimes and in turn, on energy. The focus is on the Madden-Julian Oscillation and the stratospheric polar vortex.
Results show that the Madden-Julian Oscillation, substantially impacts the occurrence of the negative phase of North Atlantic Oscillation and the Scandinavian Trough, but has limited influence on other weather regimes. Comparatively, the stratospheric polar vortex affects the occurrence of all weather regimes. Further on, we observe that both drivers impact the occurrence of energy days (days with extreme energy demand, shortfall or wind generation). This impact varies greatly between countries and depending on the phase of the S2S drivers. The lagged response suggests that there is great potential for these drivers to be predictors.
How to cite: Rouges, E., Kretschmer, M., and Shepherd, T.: High energy shortfall across 28 European countries during the winter: Investigation of the role of the Madden-Julian Oscillation and stratospheric polar vortex, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2479, https://doi.org/10.5194/egusphere-egu25-2479, 2025.
Solar energy plays a major role in climate change mitigation. With rising shares of solar power in the grid, short-term forecasts of surface solar irradiance (SSI) are becoming increasingly important for grid operators to enable cost-efficient supply and demand balancing. Solar nowcast models provide estimates of SSI from minutes to hours ahead. Accurate solar nowcasts are required across spatially extensive areas as most solar power is generated by decentralised photovoltaic systems. Such regional-scale SSI estimates can be derived from geostationary satellites, like Meteosat, that monitor Earth in visible and infrared bands. Existing regional-scale solar nowcast models are usually deterministic, lacking forecast uncertainty awareness, and require satellite Level-2 products of SSI as input obtained from radiation retrievals such as Heliosat. We present the first probabilistic regional-scale solar nowcast models, SolarSTEPS and SHADECast (Carpentieri et al., 2023, 2024), an autoregressive model and a generative diffusion model, that can be applied to regions ranging from tens to several thousand kilometers in extent. Our solar nowcast models improve forecast accuracy and reliability in all cloudiness conditions compared to existing models. SHADECast extends the forecast horizon of our state-of-the-art SolarSTEPS model by 26 minutes at lead times of 15 minutes to 2 hours. We also present a deep-learning-based emulator of Heliosat SARAH-3 (Pfeifroth et al., 2021) that estimates instantaneous SSI across Europe with similar accuracy as SARAH-3. We demonstrate that the emulator, a convolutional residual network, can even outperform SARAH-3 in SSI accuracy when a subsequent fine-tuning step is added in which the emulator is retrained on pyranometer stations, resulting in more accurate SSI initialisations for solar nowcast models. The emulator estimates SSI at kilometer-scale and 15-minute intervals based on visible and infrared images of Meteosat's Spinning Enhanced Visible and Infrared Imager. Pyranometers from BSRN, IEA-PVPS and European national weather services were employed for emulator fine-tuning and testing.
How to cite: Meyer, A., Schuurman, K., and Carpentieri, A.: Probabilistic solar radiation forecasting across Europe using deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5236, https://doi.org/10.5194/egusphere-egu25-5236, 2025.
The movement and dynamics of clouds significantly impact solar radiation and energy production from photovoltaic (PV) systems. Short-term solar irradiance forecasts, ranging from hours to days, are essential for reliable energy supply through PV plants. Forecasts using geostationary satellites outperform numerical weather prediction models for intraday forecasts. However, forecast accuracy depends heavily on prevailing weather conditions.
The North Atlantic Oscillation (NAO), a key teleconnection over the Euro-Atlantic region, significantly shapes weather patterns in western Europe and impacts the accuracy of satellite-based solar irradiance forecasts. The present study analyzes eight years (2016-2024) of Global Horizontal Irradiance (GHI) forecasts at the SIRTA Observatory in Palaiseau, near Paris (France). The forecasts are generated four hours ahead with 15-minute time step using a cloud motion vector (CMV) computation to extrapolate the cloud over. These forecasts are validated against pyranometer observations. GHI forecast errors are analyzed for two periods (2016-2020 and 2020-2024), focusing on seasonal variations and the impact of NAO teleconnection indices provided by the Climate Prediction Center of the National Centers for Environmental Prediction (NCEP CPC).
The GHI forecast error values were averaged across all forecast horizons (0 to 240 minutes). The results indicated that the relative root mean square mean error (RRMSE) is 32.7% for spring and autumn seasons from 2016 to 2024. NAO+ and NAO- teleconnection indices are respectively associated with lower (29.5%) and higher (36.2%) RRMSE values across spring and autumn seasons and both time periods (2016-2020 and 2020-2024). NAO+ events are characterized by anticyclonic circulations over the Atlantic Ocean, bring reduced precipitation and stable weather across Europe, resulting in clearer skies and lower forecast errors. Conversely, NAO- events lead to higher errors due to less stable conditions. These findings are particularly significant as North Atlantic weather regimes, typically reliable predictors of forecast errors, appear less effective during transitional seasons like spring and autumn.
In winter and summer seasons, distinct patterns in GHI forecast errors were observed. During the winter of 2016-2020, NAO+ and NAO- events yielded higher (44.5%) and lower RRMSE in GHI forecast (31%), respectively. This trend reversed during the winter of 2020-2024, with NAO+ and NAO- events respectively, showed lower (43%) and higher (49%) RRMSE values. These seasonal variations during winter align with changes in the frequency of NAO events from 2020-2024, when NAO- occurrences increased while NAO+ occurrences decreased. During summer, similar seasonal trends were observed, though with reversed magnitudes during both NAO+ and NAO- regimes for 2016-2020 and 2020-2024.
Changes in GHI forecast errors emphasize the importance of understanding large-scale atmospheric patterns for a better interpretation of GHI forecasts. Errors linked to NAO indices in winter and summer should be further studied, as they may also be influenced by other teleconnections and weather regimes. As a dominant teleconnection over Europe and the Atlantic, advanced knowledge of NAO indices and their interaction with other weather systems helps in anticipating forecast errors, offering critical insights for energy traders and grid operators to enhance smart grid management.
How to cite: Singh, S., Cros, S., and Badosa, J.: The role of North Atlantic Oscillation teleconnections in solar irradiance nowcasting error variability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9336, https://doi.org/10.5194/egusphere-egu25-9336, 2025.
The global transition to low or no carbon electricity grids requires the use of a large amount of renewable energy sources such as photovoltaic solar power. However, to integrate these intermittent energy sources within stable electricity grids requires accurate solar power generation forecasts.
Satellite imagery is highly valuable for making short-term forecasts of solar generation. The stream of satellite data is low latency, usually only minutes behind real-time, is measured frequently, and is a direct measurement of the atmosphere. This complements numerical weather predictions (NWPs) which take several hours to compute from initial conditions, generally produce forecasts at only hourly steps, and are simulated and so have an imperfect and limited expression of the atmosphere.
Including satellite data often makes for better solar forecasts than using NWPs alone. However, for solar forecasts at time horizons beyond a couple of hours, satellite imagery becomes less and less useful as the atmospheric conditions will continue to evolve beyond those captured in the most recently available satellite image.
In this work, we introduce a machine learning model to forecast upcoming satellite images from recent satellite images. This can be done using relatively simple neural network architectures designed for video prediction. We show that we can increase the accuracy of solar generation forecasts in Great Britain by using these forecasted satellite images instead of just using recent satellite images.
We find that using predicted future satellite images complements using NWPs alone in making accurate solar energy predictions. Additionally, we propose that the task of forecasting future satellite images is pertinent to renewable energy generation forecasts and is a task which could be uniquely suited to be tackled with machine learning architectures used for AI weather forecasting.
How to cite: Fulton, J., Efremova, N., Simpson, N., Fenton, I., Corcoran, E., Robinson, J., Asthana, M., Yatsyshin, P., and Pedrazzini, N.: Using AI forecast of satellite imagery to improve solar generation forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18719, https://doi.org/10.5194/egusphere-egu25-18719, 2025.
The Mediterranean Basin is one of the sunniest regions globally, offering high potential for solar energy production. This makes energy production from photovoltaics a cornerstone to the efforts of the Mediterranean countries for decarbonization. Under cloudless skies, dust aerosols are among the main attenuators of surface solar radiation in the Mediterranean. Over the sunniest regions the role of dust can be even more significant than that of clouds.
In this study we used various earth observation products (from IASI, MODIS, CALIPSO), lidar aerosol extinction profiles, and HYSPLIT trajectories to identify strong dust events in 2021 – 2022. Four events where dust originated from different areas in Africa and the Middle East, and travelled over many AERONET stations (in an area covering latitudes from 30° N to 45° N and longitudes from -10° E to 40° E) were identified. AERONET measurements have been used to study the optical (Optical Depth, Angstrom Exponent, Single Scattering Albedo) and microphysical (size distribution) properties of the aerosol mixture at the affected sites and to discuss the role of the mixing of dust with local pollutants.
Furthermore, AERONET products were used as inputs to the UVSPEC model of the libRadtran package to perform radiative transfer (RT) simulations, assuming cloud-free conditions during the days of the events. The Global Horizontal Irradiance (GHI) and the Direct Normal Irradiance (DNI) were simulated and were subsequently used as inputs to the Global Solar Energy Estimator (GSEE). Finally, the energy production from photovoltaics positioned at fixed tilt angles and on solar tracking systems was simulated. Energy production losses due to the presence of dust have been quantified by comparing the simulated energy production with the corresponding simulations for the same days, assuming aerosol-free conditions. Losses that exceed 80% have been observed over specific locations.
Acknowledgements: Authors would like to acknowledge the Action Harmonia CA21119 supported by COST (European Cooperation in Science and Technology).
How to cite: Aslanoğlu, S. Y., Chadoulis, R.-T., Charalampous, G., Herrero-Anta, S., Herrero del Barrio, C., Kouklaki, D., Moustaka, A., Mytilinaios, M., Papetta, A., Papadimitriou, N., Solomos, S., Gkikas, A., Spyrou, C., Vandenbussche, S., Proestakis, E., and Fountoulakis, I.: Intense dust events over the Mediterranean Basin and their impact on PV power potential, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20121, https://doi.org/10.5194/egusphere-egu25-20121, 2025.
The global target of net-zero emissions and carbon neutrality by the mid-21st century is accelerating the transition to clean energy. Accurately assessing solar energy potential requires high-quality hourly surface solar radiation (SSR) and direct radiation (Rd) datasets. This study evaluates hourly SSR and Rd data from two reanalysis products (ERA5 and MERRA-2) and three satellite-derived products (CERES, SARAH-E, and Solcast) against 22 years of homogeneous surface observations in China. This validation utilizes data from 96 stations for SSR and 17 stations for Rd, and includes both accuracy and stability tests:
- According to the accuracy test, SSR and Rdare often overestimated, with lower accuracy observed during sunrise and sunset. SSR exhibits larger seasonal variations in accuracy than Rd, with accuracy declining in the cold season. SARAH-E and ERA5 demonstrate the least overestimation of the diurnal cycle of SSR, indicating the highest accuracy. CERES and SARAH-E demonstrate the highest accuracy for Rd, with CERES underestimating and SARAH-E overestimating throughout the day.
- Decadal trends of SSR and Rdare also overestimated by most products. SSR stability is lower in the cold season compared to the warm season. Rd stability decreases notably in cloudy and polluted MERRA-2 and CERES exhibit the highest stability for SSR, while ERA5 demonstrates the highest stability for Rd.
In summary, as highlighted by the bold lines in Figure 1, ERA5 excels in capturing the diurnal cycle of SSR, and CERES demonstrates superior performance for Rd across China.
Figure 1. Overall comparison of products over China for hourly surface solar radiation (a) and direct radiation (b), regarding accuracy index (nMABD, the normalized mean absolute bias deviation, %) and stability index (absolute decadal trend bias, % decadal–1). The performance of each product correlates positively with the size of its hexagon.
How to cite: Wang, H. and Wang, Y.: Evaluation of Hourly Solar Radiation Products for Solar Energy Applications over China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8062, https://doi.org/10.5194/egusphere-egu25-8062, 2025.
The assessment of solar irradiance variability is relevant for evaluating irradiance-based models, resource assessment and forecasting applications in the solar energy field. One well-established irradiance-based model database for solar project development is the Copernicus Atmosphere Monitoring Service (CAMS) through its CAMS Radiation Service (CRS) that offers historical all-sky solar irradiance estimates. In this work, the accuracy of the CRS GHI product over France is evaluated under different irradiance variability conditions by applying a sky condition classification method based on 1-minute Global Horizontal Irradiance (GHI) observations. A dense network of GHI measurements over France with more than 230 ground stations in the year 2015 is used as a case study.
The classification method is based on a visual interpretation of GHI measurement patterns for the Baseline Surface Radiation Network (BSRN) station of Carpentras during the years 2012 and 2013, which forms a reference database. This reference database is composed of 280 manually classified hours in minute resolution for GHI into eight different classes (from clear sky to variable and overcast sky conditions). Ten variability indices (VIs) are applied in the classification scheme including the clear sky index (kc); the average, maximum and standard deviation of the absolute values for the first derivative of kc; the VIs proposed by Stein et al. (2012) and Coimbra et al. (2013); VIs based on envelopes curves obtained according to the local maxima and minima time-series; and three VIs that counts GHI values overpassing the clear sky irradiance in 3%, 5% and 10%. The classification model consists of three main steps: a discrimination filter, a probability classification approach and a median distance-based approach. The discrimination filter is a counting step that checks if the VIs are inside the Carpentras reference database domain for a particular class. The class with the most VIs will be the selected class. If the maximum number of VIs counted is the same for two or more classes, then a probability classification approach makes the class decision. This probability approach uses Kernel density estimation to calculate the neighborhood probability of a specific VI to be part of one of the eight classes. The class with the higher mean probability over all classes will be selected. Finally, for all the cases outside the domain of the reference database, the median distance-based approach with normalized VIs is applied as presented by Schroedter-Homscheidt et al. (2018).
The evaluation of the CRS GHI over France is shown in Figure 1. The highest values of the Root Mean Square Deviation (RMSD) are found in class 6, which is mostly dominated by broken clouds. Also classes 4, 7 and 8 present large RMSD. The identification of this broken cloud conditions cluster is useful for further developments of the CRS algorithm in these challenging situations.
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Figure 1 – CRS RMSD in different sky conditions over France (hourly resolution).
How to cite: Rodrigues de Miranda, D., Azam, F., Lezaca, J., Saint-Drenan, Y.-M., and Schroedter-Homscheidt, M.: Assessment of CAMS Radiation Service over France in different sky conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10807, https://doi.org/10.5194/egusphere-egu25-10807, 2025.
Renewable energy sources are gaining increasing importance due to the rising prices, depletion of fossil fuels, and the need to achieve climate protection goals. Solar energy has the advantage of being exploitable to some extent worldwide. In East-Central Europe, particularly in Hungary, the use of solar energy is growing rapidly, with the installed capacity of photovoltaic power plants increasing from 14 MW to ~4 GW between 2012 and 2022.
The estimation of photovoltaic power potential (PVpot) and its changes based on the outputs of general circulation models (GCMs) has become a popular research topic over the past decade, since GCM biases can lead to biases in regional climate models through the downscaling process. In general, previous studies have estimated an increase in PVpot for Central Europe during the 21st century. However, the effects of inter-model variability and internal variability of GCMs on PVpot in Europe, particularly in East-Central Europe, are less thoroughly examined.
This analysis seeks to assess the sensitivity of PVpot to inter-model variability and internal variability of GCMs in Europe, with a focus on East-Central Europe. For this purpose, the number of days with small (or large) PVpot will be calculated which – as it was pointed out by Feron et al. (2021) – may exhibit greater differences in the future compared to historical periods, unlike the PVpot itself. Different realizations of future outputs from CMIP6 GCMs for 2071-2100 (based on the SSP2-4.5 and SSP5-8.5 scenarios) will be compared to historical outputs for 1981-2010, focusing on seasonal changes. For comparison reasons, reanalyses (e.g., ERA5, CERRA) will also be applied for the historical period.
Our findings provide essential insights for energy planners to mitigate the impacts of future weather variability.
Feron et al. (2021). Nature Sustainability, 4(3), 270-276
The research was funded by the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014).
How to cite: Kalmár, T. and Kristóf, E.: Understanding future solar energy trends in Europe: The impact of the variability in CMIP6 GCMs on photovoltaic power potential, with a special focus on East-Central Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-976, https://doi.org/10.5194/egusphere-egu25-976, 2025.
To meet France’s CO2 emission reduction of 33 % by 2030 compared to 1990 and reach greenhouse gas neutrality in 2050, sustainable energy sources are key to clean power production and reduced emissions from the energy sector. However, non-dispatchable renewables such as wind and solar photovoltaic (PV) power require accurate forecasts to improve their grid stability, reliability, and penetration level not to mention supply-demand matching. Indeed, those sources are dependent on weather conditions such as solar radiation or wind speeds, making their load highly variable and challenging to balance for grid operators.
Despite the increase of data availability from both weather and energy fields, regional wind and PV supply forecasts are usually indirect. Either a bottom-up approach of plant-level forecasts or a time series prediction incorporating lagged values is used. The potential of spatially explicit data for direct prediction is still underestimated. In this work, we present a methodology for predicting solar and wind power production at the country scale in France using machine learning models trained with spatially resolved weather data combined with geospatial information about production sites’ capacity.
A dataset spanning from 2012 to 2023 is built, using daily power production data from the national grid operator as the target variable, with daily weather data from ERA5, the capacity and location of the production sites, and electricity prices as input features. Three modeling approaches are explored to handle spatially resolved weather data: spatial averaging over the country, dimension reduction through principal component analysis, and a convolutional neural network (CNN) architecture to exploit complex spatial patterns. We benchmarked state-of-the-art machine learning models such as tree-based architectures, additive models, and neural networks on daily power supply for the midterm horizon. Hyperparameter tuning procedures based on different cross-validation methods were also investigated to reach the lowest generalization error possible.
Despite the variance introduced by the model and the data, our cross-validation experiments showed that while using one-to-one models on the spatial average of weather data, the time-series dedicated procedures tend to estimate the generalization error better than standard methods like K-Fold. This allowed us to push the model calibration to reach the best performance on unseen test data. However, they fall short of the CNN ingesting entire weather maps which predicts twice as good. Indeed, CNN is the best model for both PV and wind, achieving errors of around 5 %. This is mainly due to its ability to exploit spatial weather patterns on production site locations to extrapolate the trend in renewable power supply as underlined by an interpretability method. In fact, one-to-one models utilized on both spatial average and principal components extracted from weather maps are struggling to grasp the increase in power supply due to the growth in installed capacity.
Our study highlighted the potential of spatially explicit data and dedicated models to improve the accuracy of direct regional renewable power supply. Such enhancements will lead to a better supply-demand balance while incorporating a growing part of sustainable energy into our electricity mix.
How to cite: Lindas, E., Goude, Y., and Ciais, P.: Leveraging Spatially Explicit Data for Accurate Renewable Energy Forecasting in France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11490, https://doi.org/10.5194/egusphere-egu25-11490, 2025.
Posters on site: Tue, 29 Apr, 16:15–18:00 | Hall X5
What kind of tools are needed for accurate and precise forecasting of offshore wind power, in an unprecedented fast development of offshore wind and market, now and near future, for the key stakeholders?
The DTWO project aims to be pioneering initiative in the digitalization of offshore wind energy by combining
- Federated digital twin architecture, allowing users to customize without sharing sensitive data;
- Seamless model integration of a wide array of existing models and data sources from regional weather model, to wind farm and turbine wakes, to marine environment conditions, to wind resource, energy yield and design parameters, to turbine performance and life time, and to grid balancing and energy market;
- Granular prediction capabilities by implementing latest scientific outcomes and technology;
- High-level cybersecurity, addressing concerns around data vulnerability in digital transformation efforts.
DTWO’s federated digital twin platform includes five modules for Earth, Wakes, Siting, Turbines and Grids. DTWO provides a data hub with FAIR and conditionally open data, and a tool hub featuring open and conditionally open tools. The digital twin modules are implemented using industrial use cases, tested through representative test scenarios.
DTWO brings together the expertise of the world’s leading offshore wind industries, alongside research centres, IT consulting and digital service providers, academic institutions, a science communication organisation, energy forecasting experts, and meteorological centres. The project website provides more information about the development of the European Horizon supported project https://dtwo-project.eu/.
Codes for organizations: 1. DTU; 2. VKI; 3. DHI; 4. ECMWF; 5. SSP; 6. ICONS; 7. PG; 8. Fraunhofer-IWES; 9. ENFOR; 10. KNMI; 11. SGRE
How to cite: Larsén, X. and the DTWO consortium: The Digital Twins for Winds-Offshore (DTWO) Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13201, https://doi.org/10.5194/egusphere-egu25-13201, 2025.
This work presents key findings from the first Austrian workshop of IEA Wind TCP Task 51 on "Forecasting for the Weather-Driven Energy System", which brought together 120 participants from over 50 organizations. Through structured stakeholder engagement, the workshop revealed critical priorities for advancing renewable energy forecasting in complex terrain.
Results highlight the continued dominance of day-ahead forecasting (56% of respondents), while identifying growing needs in extreme weather prediction (85% concerned) and artificial intelligence integration (rated 4.35/5 in importance). On the other hand, a number of gaps were identified related to the awareness of extremes and uncertainty and the knowledge and implementation status of such forecast tools. The Alpine context presents unique challenges, where complex terrain and cross-border power flows create specific forecasting requirements. Based on stakeholder feedback, two follow-up workshops will be organised focusing on extreme events and integrated forecasting solutions.
This study provides concrete guidance for developing next-generation forecasting systems and demonstrates the value of structured stakeholder engagement in shaping forecasting solutions for the energy transition.
How to cite: Tilg, A.-M., Schicker, I., Strauss, L., Mader, F., Niederl, A., Messner, J., and Möhrlen, C.: The IEA Wind TCP Task 51 Austria - Stakeholder interaction and priorities for forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15415, https://doi.org/10.5194/egusphere-egu25-15415, 2025.
Offshore wind farms, as a rapidly expanding component of the wind energy sector, play a critical role in advancing global carbon neutrality, a trend expected to persist. In this study, we leverage ERA5 reanalysis data to refine offshore wind speed trends projected by CMIP6 models. This methodology provides improved estimates for changes in offshore Wind Power Density (WPD) under four Shared Socioeconomic Pathways (SSP) scenarios. Our results indicate a consistent upward trend in global offshore WPD throughout the 21st century across all SSP scenarios. Among regions with significant existing offshore wind installations, Europe is projected to experience the most pronounced increase, with offshore WPD potentially rising by up to 26% under 4°C of global warming. These findings reveal a significant enhancement of global offshore WPD in a warming climate, offering critical insights for optimizing the strategic development of future wind energy systems worldwide.
How to cite: Shen, C. and Yuan, H.-S.: Enhanced Offshore Wind Potential in a Warming Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1827, https://doi.org/10.5194/egusphere-egu25-1827, 2025.
In Arctic countries, a large fraction of wintertime electricity consumption is used for heating spaces and, thus, the electricity consumption is highly sensitive to outside temperature variations. Also, the production of electricity by wind turbines depends directly on surface wind speed. Wintertime weather in Northern Europe is significantly influenced by the state of the stratospheric polar vortex, the westerly wind pattern circulating the polar region during winter. When the polar vortex is strong (weak), winter temperatures are more likely mild (cold) and surface wind speeds are higher (lower) in Northern Europe.
Sudden stratospheric warmings (SSWs) are, as the name implies, events where stratospheric temperature abruptly increases due to significant weakening or breaking of the polar vortex. This usually causes a sudden outbreak of cold and less windy weather in Northern Europe which can last for weeks. The occurrence probability of SSW events during the winter season is affected by several factors, e.g., the phase of the so called Quasi-Biennial Oscillation (QBO). During easterly QBO phase, characterized by equatorial stratospheric zonal winds flowing from east towards west, more planetary waves are guided to the polar stratosphere where they weaken the stratospheric polar vortex. As a result, the probability for SSW events is higher in easterly QBO phase compared to the westerly phase.
Here we study Finland’s electricity consumption and wind power generation separately in winters with and without an SSW in different QBO phases. We find that the electricity consumption is significantly higher in winters with SSWs compared to winters where an SSW does not happen, while the opposite is true for the wind power generation. We also evaluate the uncertainties of seasonal predictions of electricity consumption and wind power generation based on seasonal predictions of SSW probability.
How to cite: Juntunen, V., Asikainen, T., Salminen, A., and Vokhmianin, M.: The impact of sudden stratospheric warmings on electricity consumption and wind power generation in Finland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9925, https://doi.org/10.5194/egusphere-egu25-9925, 2025.
The decarbonization of the power sector is among the most challenging tasks in the effort to mitigate climate change and achieve the 7th United Nations Sustainable Development Goal (SDG-7) for Affordable and Clean Energy by 2030. The rapid growth in the installed capacity of solar photovoltaics (PV) in recent years, driven by their cost-effectiveness, highlights their potential as a promising technology for large-scale transitions. However, solar energy is a variable source, the availability of which depends strongly on atmospheric conditions, particularly clouds and aerosols. Therefore, assessing the expected power output is essential for planning sustainable investments, such as the installation and maintenance of solar farms, while reliable solar power forecasting is crucial for their integration into energy supply grids. The Copernicus Atmospheric Monitoring Service (CAMS) solar radiation time-series product provides historical data for the global horizontal irradiance, along with its components, including direct and diffuse, which renders it suitable for performing estimations of the produced energy from photovoltaics. We use the Global Solar Energy Estimator (GSEE), a widely used open-access model for simulating solar plants, aiming to evaluate the use of CAMS solar radiation time-series product for estimating the solar PV power potential. More precisely, we compare the CAMS-based solar power generation with the output from simulations derived using ground-based actinometric measurements of the direct and diffuse surface solar radiation components that were available at five BSRN sites in Europe and North Africa, obtained from stations with quite different prevailing aerosol and cloudiness conditions. The analysis has been performed for photovoltaics that are positioned at fixed tilt angles and on solar tracking systems. CAMS solar radiation product is widely used to simulate the PV power potential and thus the findings of this study provide valuable insights from the reliability of using it for such assessments.
How to cite: Papadimitriou, N., Fountoulakis, I., Gkikas, A., Papachristopoulou, K., Kapsomenakis, J., Kazadzis, S., Kazantzidis, A., and Zerefos, C. S.: Using the CAMS solar radiation time-series product to model solar PV power potential. Uncertainty evaluation under diverse atmospheric conditions using ground-based measurements., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13836, https://doi.org/10.5194/egusphere-egu25-13836, 2025.
The daily stability of solar irradiance significantly influences photovoltaic (PV) power generation; however, existing metrics for assessing it normally fail to robustly correlate with daily PV output. To address this gap, we introduce a new metric, the solar instability index (SII), formulated by applying the Wasserstein distance to assess the deviation of intra-day solar irradiance pattern from the anticipated diurnal cycle. In our case station, SII closely correlates with atmospheric moisture and available solar energy, suggesting its strong association with synoptic weather events that lead to solar resource loss. We further scrutinize the efficacy of SII alongside two existing metrics through two case studies. The results demonstrate that SII excels in capturing low-frequency variations in solar irradiance without relying on arbitrarily assigned parameters, thereby outperforming the other two metrics in establishing a robust correlation with PV power output. As such, in scenarios involving site selection for PV power plant, SII stands as a valuable metric for assessing the potential stability of daily PV power generation.
How to cite: Tian, Q., Li, J., Xie, Z., Li, P., Wang, Y., Chen, D., and Zheng, Y.: A Novel Metric for Quantifying Solar Irradiance Stability: Mapping Solar Irradiance Variability to Photovoltaic Power Generation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-26, https://doi.org/10.5194/egusphere-egu25-26, 2025.
Accurate forecasting of weather-dependent renewable energy production is vital for energy security and economic stability, especially in regions undergoing rapid photovoltaic (PV) energy expansion. This study investigates the impact of Saharan dust events (SDEs) on PV power generation forecasts in Hungary, a leading European country in terms of PV penetration. Utilising a comprehensive dataset comprising 46 identified SDEs from 2020 to 2023, the research quantifies forecast errors and production deviations under dusty and non-dusty conditions. The analysis reveals that current forecasting models fail to account for dust-related impacts, resulting in significant errors in day-ahead scheduling. During SDEs, PV generation deficits and surpluses were found to be 30.9% and 17.6% higher than during non-dusty periods, respectively. On deficit days, the primary factor reducing irradiance was found to be unforeseen cloud cover, particularly extensive cirrus clouds. Conversely, on days with surplus PV generation, reduced radiative forcing from cirrus clouds, along with the replacement of anticipated stratus and scattered radiation from dusty atmospheres, contributed to prolonged irradiance. These findings underscore the dual impact of atmospheric dust, directly decreasing irradiance and indirectly altering cloud formation mechanisms, which are not adequately captured in current PV production models.
The study emphasises the necessity to incorporate dust-specific atmospheric models and refine dust-cloud interaction parameterisations in energy forecasts. This is of particular relevance as Hungary and other regions increase their reliance on PV energy within their renewable energy portfolios. The research also has broader implications for grid stability, energy policy, and climate change mitigation, highlighting the necessity for accurate and adaptable forecasting systems to address the growing challenges posed by atmospheric variability.
The research was supported by the FFT NP FTA and NRDI projects FK138692, TKP2021-NKTA-21 and RRF-2.3.1-21-2021.
How to cite: Rostási, Á., Gresina, F., Gelencsér, A., Csávics, A., and Varga, G.: Saharan Dust and Solar Energy: Quantifying Forecasting Challenges in Hungary’s Rapidly Growing PV Sector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12210, https://doi.org/10.5194/egusphere-egu25-12210, 2025.
South America is one of the regions with the highest renewable power share in its energy matrix. However, it is heavily affected during drought driven by El Niño/Southern Oscillation (ENSO) and the positive phase of the Atlantic Meridional Mode (AMM), since hydropower is the main source. Wind and solar energy are soaring due to economic development and as an alternative/complement to hydropower and fossil fuels. Nonetheless, they can be affected by climate variability modes and it is thus essential to determine the impacts of ocean-atmospheric modes on these two renewable energies. Our research focuses on understanding the links between climate modes and the seasonal variability of potential wind and solar generation. The understanding of the physical mechanisms driving renewable energy variability might be useful for improving sub-seasonal to seasonal forecasts and, hence, properly managing energy production and storage for the following months.
The analysis is also centred around three energy hubs (regions with multi-annual high production capacity of renewable energy). They are located near or on the north Caribbean coast, the east and east coast of Brazil, and the west coast of Peru and the Bolivian Altiplano. The research mainly uses composites of physically consistent interpolations (i.e. reanalysis ERA5) and some satellite-based observations from CLARA cloud cover (1980 - 2020). The ocean-atmospheric modes are defined using Sea Surface Temperature indices. It analyses the anomalies of wind speed, its direction and wind power density (WPD), but also Sea Level Pressure anomalies when climate modes are active. For solar energy, a capacity factor (CF) is calculated using an empirical method that considers the irradiance and the temperature of the panel (based on 2 m air temperature and incident radiation). To study the mechanisms producing its variability, we also analyse the atmospheric moisture transport (VIMF) and cloud cover. The ocean-atmospheric modes’ activation times are defined with Sea Surface Temperature indices.
We analysed the mechanisms of ENSO and of two climate modes in the Atlantic Ocean (the AMM and the Atlantic El Niño equatorial mode), as we discovered these modes can alter regional atmospheric circulation. Cross-equatorial wind anomalies – driven by the AMM – increase or reduce WPD depending on the region while also creating anomalous VIMF, convergence, and clouds, hence affecting the solar CF, in the north Caribbean and east Brazilian hubs. Not only does ENSO affect solar energy through atmospheric subsidence and reduction of cloud cover, but it also affects WPD attracting and accelerating winds to the equatorial east Pacific. The Atlantic equatorial mode (Atl3) is an important source of climate variability, but we discovered that its effects over the continent and the energy hubs are not so strong and widespread compared to those from the other two modes. We also found that solar and wind are not very often complementary, but they can potentially complement hydropower because stronger winds and less cloud cover are present during droughts.
Future research could focus on evaluating the impacts of sub-seasonal phenomena on renewable energy and their influence on predictability.
How to cite: Duque-Gardeazabal, N., Brönnimann, S., Friedman, A. R., Dolores-Tesillos, E., and Martius, O.: Ocean-atmospheric drivers of wind and solar energy seasonal variability in tropical South America, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1947, https://doi.org/10.5194/egusphere-egu25-1947, 2025.
On 7-9 January 2005 Storm Erwin passed across northern Europe causing damage and interrupting power and transportation networks from Ireland to the eastern Baltic region. In northern England the storm was associated with severe river flooding in Carlisle region that cut transportation links into the city and necessitated evacuations. Across the Baltic region strong winds were reported, resulting in large scale forest damage and power outages. In Denmark, wind energy was impacted as wind speeds crossed the 25 m/s cutoff threshold for turbine operations, leading to a mass shut down of wind turbines and requiring electricity to be imported to make up the shortfall. In Sweden, there were widespread power outages as transmissions lines were blown down in the winds, and coastal nuclear power plants were shut down when sea spray caused short-circuiting problems in power transmission. The storm was associated with a notable coastal surge and flooding, particularly in Denmark and the eastern Baltic. The present contribution presents an overview of the societal impacts of the storm. A detailed analysis is carried out of offshore impacts around the North Sea using tide gauge and wave data recorded during the event, and shipping accidents from media reports.
How to cite: Kettle, A.: Storm Erwin: Societal and energy impacts in northern Europe on 7-9 January 2005, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2140, https://doi.org/10.5194/egusphere-egu25-2140, 2025.
Due to its limited natural resources, Taiwan has historically relied heavily on imported natural gas and coal for power generation. The government has recently emphasized shifting toward renewable energy sources to achieve energy independence. With global initiatives targeting net-zero carbon emissions by 2050 and the European Union planning to implement a carbon tax on heavy industries by 2026, the demand for renewable energy solutions has significantly increased. This research investigates optimal locations for deploying wind turbines and photovoltaic panels to maximize renewable energy output across inland and offshore regions of Taiwan (118°–123°E, 21°–26°N). The wind energy potential is assessed using Wind Power Density (WPD), calculated by the formula E = 0.5ρV³, where ρ represents air density and V denotes wind speed at 10 meters above sea level. Data from satellite-based sensors (GMI, SMAP, ASCAT, AMSR-2, SSMI) were validated against Copernicus reanalysis datasets and in-situ measurements from buoys operated by Taiwan’s Central Weather Administration (CWA). Results indicate that the Taiwan Strait, particularly offshore central Taiwan, is the most suitable area for offshore wind turbine installations, with monthly average wind speeds ranging from 13 to 16 m/s in December between 2015 and 2023. For solar energy assessment, Short Wave Radiation (SWR) data from JAXA’s Himawari geostationary satellites provided insights into the spatial distribution of solar radiation around Taiwan from 2015 to 2024. The analysis identified southwestern Taiwan as the most promising region for photovoltaic installations, with monthly average SWR values ranging from 230 to 280 W/m² in July. Topographic analysis using Earth Topography (ETOPO) data revealed that lower elevations (0–200 meters) are more suitable for photovoltaic systems than mountainous regions, further reinforcing the viability of the southwestern plains for large-scale solar energy projects. Validation of satellite-derived SWR values against ground-based Global Solar Radiation (GSR) measurements from the CWA indicated a consistent overestimation in the Himawari data, with an average difference of 37.2 MJ/m². Overall, this study provides valuable insights into the strategic siting of Taiwan's wind and solar energy infrastructure, supporting global decarbonization efforts and fostering the development of green energy.
How to cite: Wang, C.-Y. and Hsu, P.-C.: Utilizing Satellite and Meteorological Data to Evaluate Potential Wind Farm and Photovoltaic Panel Sites Inland and Offshore Taiwan , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2999, https://doi.org/10.5194/egusphere-egu25-2999, 2025.
Shandong Province, a critical hub for renewable energy in China, presents a diverse set of challenges and opportunities in wind power development. The region's wind farms span inland plains, coastal plains, and hilly terrains, with installed capacities ranging from 28,400 kW to 800,000 kW. While these diverse landscapes offer significant potential for wind power, several challenges persist, including grid integration issues, regulatory inconsistencies, and the need for advanced technologies to enhance energy efficiency. Additionally, social acceptance concerns related to environmental impacts further complicate the development of renewable energy projects. This study leverages wind and power data from multiple wind farms in Shandong Province to develop machine learning-based power forecasting models. Specifically, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks are employed to address spatiotemporal variability in wind power generation across diverse terrains. Results highlight the influence of geographic and meteorological factors on forecasting accuracy and underscore the potential of AI-driven approaches to mitigate uncertainties associated with wind power integration into the grid. Our findings demonstrate that terrain-specific modeling, coupled with advanced forecasting techniques, can significantly improve the reliability of wind power generation in complex environments. By addressing key challenges unique to Shandong Province, this research contributes valuable insights into sustainable energy planning and the broader integration of renewable energy into China's power grid.
How to cite: Song, G., Chinta, V., and Wu, K.: AI-Driven Power Forecasting for Renewable Energy: A Multi-Terrain Analysis from Shandong Province Wind Farms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14067, https://doi.org/10.5194/egusphere-egu25-14067, 2025.
In the process of tower photothermal power generation, solar radiation undergoes the attenuation of the whole atmosphere, reaches the heliostat and then is reflected to the heat collector. The transfer of solar radiation from the heliostat to the heat collector occurs at an altitude of 0 to 300m from the ground, which is defined to be the near-surface layer in this study and is concentrated with high aerosol loadings. Thus, the extinction effects of near-surface aerosols are crucial in the site selection of photothermal power generation and in the evaluation of photothermal power generation efficiency.
In this work, we first analyzed the vertical distribution of near-surface aerosol extinction over North China (NC) and its correlation with meteorological factors. CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) is a satellite-borne lidar instrument aboard CALIPSO satellite, which provide globally aerosol vertical profiles with unprecedented coverage and spatial resolution. To circumvent data scarcity of longer-term in situ surface measurement of aerosol vertical profiles over the NC region, here CALIOP Level 2 version 4.1 aerosol profile product at 532 nm from January 2019 to December 2019 were adopted. The screened daytime CALIOP L2 data over the NC region were assigned and aggregated into horizontal grids with a resolution of 0.5°×0.5°. The vertical distribution of aerosol extinction coefficient reveals that in winter, Spring and autumn, the aerosol extinction values from near surface to about 1.5km are significantly higher than that above 1.5km. Especially in winter, high aerosol extinction values are found below 1km, indicating weak vertical mixing in winter. The relatively constant aerosol extinction values from near surface to above 2km indicates a higher well-mixed planetary boundary layer (pbl) height in summer. Aerosol extinction between 0-300m accounts for 32%, 17%, 9% and 20% of the aerosol extinction of the whole atmosphere in winter, spring, summer and autumn separately. PM2.5 concentration and surface relative humidity are positively correlated with near-surface aerosol extinction (r=0.4 and 0.31 respectively). Meanwhile, surface visibility is negatively related to the near-surface aerosol extinction (r=-0.45).
Then the aerosol extinction coefficient in the near-surface layer was adopted in the SMARTS model and we simulated the radiation transfer between 0 to 300m under different weather conditions. Simulation results of SMARTS model considered near-surface aerosol extinction are closer to radiation observations of a 325m meteorological tower in Beijing than the results of the original SMARTS model under all typical weather conditions.
Knowledge of the attenuation of aerosol to solar radiation from the heliostat to the heat collector in the process of tower photothermal power generation is of critical economic importance for the site selection of power station and the evaluation of power generation efficiency.
How to cite: Jia, B., Shen, Y., and Wang, C.: Vertical distribution of near-surface aerosol extinction over North China and its impacts on tower photothermal power generation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14324, https://doi.org/10.5194/egusphere-egu25-14324, 2025.
The potential for photovoltaic energy in West Africa is high, and the use of this resource is expected to grow in the future. Due to the variability of solar energy, accurate weather forecasts are essential to ensure the smooth operation of the electricity network. In this region, the primary weather prediction challenges include the West African monsoon and the advection of dust from the nearby Sahara Desert.
Currently, SteadySun – a company specializing in power and weather forecasts for renewable energies – relies on low to medium-resolution global models to predict GHI (Global Horizontal Irradiance) for West Africa. It has been previously shown that weather models with higher horizontal resolution provide a more realistic representation of small-scale weather phenomena such as convective clouds. Most global models only take into account aerosols concentration through a monthly climatology which does not give information on AOD (Aerosol Optical Depth) variations on small temporal scales. Given the specific characteristics of the West African climate, employing high-resolution models that account for hourly dust concentration is likely to enhance the forecasting system.
This study aims to assess the benefits, limitations, and differences in GHI predictions from five global models and one high-resolution regional model over Burkina Faso. The five global weather models include IFS (ECMWF), GFS (NOAA), and ICON (DWD), which provide simulations with hourly outputs, as well as ARPEGE (Météo-France) and GDPS (CMC), which provide simulations with 3-hourly outputs. The regional weather model used is an augmentation of the weather model WRF for solar energy forecasting: WRF-Solar (NCAR). It features a spatial resolution of 3 km, outputs data every 15 minutes and integrates hourly aerosol optical depth forecasting data from the global atmospheric composition forecast production system CAMS (ECMWF).
The WRF-Solar forecast is expected to deliver improved accuracy during dust advection events and more realistic variability during cloud passages, potentially benefiting the planned forecasting system. However, the absence of data assimilation in WRF-Solar could result in misplaced convection cells, among other inaccuracies. On the other hand, the global models, with their varied physics and resolutions, may offer some advantages under specific weather conditions. This initial evaluation could also identify models that are less suitable for integration into the planned forecasting system. To perform this assessment, GHI data with a spatial resolution of 3 km and temporal resolution of 15 minutes, derived from MSG (EUMETSAT) satellite imagery, will be used. Two assessment periods have been defined: the first during the monsoon season (July to September 2023) and the second during the dry season (January to March 2024), when dust advections from the Sahara Desert are common.
How to cite: Solbès, A., Cosme, E., Raynaud, D., and Anquetin, S.: Evaluating Global and Regional Weather Models for Solar Energy Forecasting in West Africa: A Case Study in Burkina Faso, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16750, https://doi.org/10.5194/egusphere-egu25-16750, 2025.
Renewable energy development in China relies heavily on accurate forecasts of surface solar irradiance and 10-meter wind speed. This study evaluates the medium-range and sub-seasonal forecast performance of the European Centre for Medium-Range Weather Forecasts operational ensemble (ECMWF-ENS) over China. Forecast data, provided at a 6-hour time step, are assessed against gridded observational datasets from the China Meteorological Administration (CMA). Using metrics such as mean absolute error, root mean square error, and mean bias error, the study examines the forecast accuracy across different seasons and regions in China. Results reveal that the ensemble forecasts effectively capture diurnal cycles and regional variability in solar irradiation and wind speed. However, forecast errors vary significantly based on the climate variable and time of year, with solar irradiation forecasts generally demonstrating higher accuracy during summer months. The study highlights the role of the atmosphere in modulating solar and wind energy potential, emphasizing the critical need for accurate, high-resolution forecasts to support renewable energy applications. The findings demonstrate that spatially continuous hourly predictions can reconstruct regional-scale variations, providing valuable insights for optimizing site selection for solar and wind power plants. This research underscores the importance of reliable medium-range and sub-seasonal forecasting systems for advancing renewable energy planning and addressing China's growing energy demands while supporting climate adaptation strategies.
How to cite: Qian, C., Song, T., Chinta, V., and Wu, K.: Assessment of Medium-Range and Sub-Seasonal Ensemble Forecasts of Solar Irradiance and Wind Speed Over China: Applications for Renewable Energy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17120, https://doi.org/10.5194/egusphere-egu25-17120, 2025.
Aerosols influence surface solar irradiance directly through scattering and absorption and indirectly by acting as cloud condensation nuclei. Dust aerosols, a significant tropospheric component, play a critical role in climate processes by altering atmospheric energy fluxes at the surface and Top of the Atmosphere (TOA) and hence atmospheric temperature. This study examines the optical properties and direct shortwave radiative effects of dust aerosols at Agia Marina Xyliatou, Cyprus (35.04°N, 33.06°E, 535 m), a region impacted by Sahara and Arabian dust intrusions. Ground-based measurements, including AERONET sun photometer data, pyranometer and pyrheliometer records, combined with radiative transfer (RT) modeling (LibRadtran RT package), provide detailed insights into dust dynamics typing and radiative effects.
Analysis of the 2015–2022 dataset reveals a seasonal peak in dust events during spring and autumn, with the Sahara contributing 80% of occurrences. Polly-XT lidar profiles from Limassol station expose the vertical aerosol structure and variability in their extinction, while size distributions show a dominance of coarse-mode particles during intense dust periods. The mean direct Aerosol Radiative Effect (ARE) was −53.01±27.02 W/m² at the surface, indicating substantial cooling, and −16.29 W/m² at the TOA, ranging from −26.33 W/m² in February to −13.96 W/m² in April. March exhibited the strongest radiative effect, associated with the peak in Aerosol Optical Depth (AOD) and the lowest single scattering albedo (SSA) values indicative of more absorbing aerosols. Saharan dust exhibited stronger cooling compared to Middle Eastern dust due to its lower SSA (higher absorption).
This research highlights the significant role of dust aerosols in reducing surface solar radiation, emphasizing the need for detailed aerosol characterization to understand their climatic impacts and optimize solar energy resources in dust-prone regions.
Acknowledgments:
This research is performed under the auspices of the Memorandum of Understanding between the Eratosthenes CoE and The Cyprus Institute. The authors acknowledge the ‘EXCELSIOR’: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 857510, the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development, and the Cyprus University of Technology. This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 856612 and the Cyprus Government (EMME-CARE).
Authors would like to acknowledge the Action Harmonia CA21119 supported by COST (European Cooperation in Science and Technology).
How to cite: Charalampous, G., Fragkos, K., Fountoulakis, I., Marenco, F., Derimian, Y., Karpasitis, A., Nisantzi, A., Mamouri, R.-E., Papachristopoulou, K., Hadjimitsis, D., and Kazadzis, S.: A seven year study on the assessment of shortwave surface solar radiation in Cyprus, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20085, https://doi.org/10.5194/egusphere-egu25-20085, 2025.
Solar irradiance forecasting plays a pivotal role in maximizing the use of solar energy resources and promoting the transition towards a cleaner and more sustainable energy future. This study evaluates the performance of the Weather Research and Forecasting (WRF-Solar) model using two shortwave radiation schemes in estimating Global Horizontal Irradiance (GHI) at two solar power plants in Senegal, i.e. Diass and Ten Merina. The different simulation experiments of WRF-Solar are specifically assessed under different sky conditions using hourly GHI measurements for 2020 from the solar plants operated by energy companies in Senegal. A total of six simulations are performed using different shortwave radiation schemes (Dudhia and RRTMG). There are two simulations run for the RRTMG scheme: one without aerosol optical depth (AOD) and one with AOD (RRTMG_AOD). In addition, the impact of shallow convection on the model performance is investigated. Results indicate that the RRTMG_AERO scheme outperforms other schemes with the highest correlation of 0.85 and the lowest values of RMSE (160 W/m2) and MAE (110 W/m2). It shows superior performance across clear, cloudy, and all-sky conditions. While the inclusion of shallow convection has minimal impact on GHI estimation accuracy under clear skies, some differences are noted under cloudy conditions at Ten Merina. Notably, the model shows biases, particularly under cloudy skies. These findings offer valuable insights that can enhance solar energy forecasting accuracy, support reliable solar power generation and renewable energy optimization, benefiting energy providers, policymakers and communities in Senegal.
How to cite: Ndiaye, A., Sawadogo, W., Bliefernicht, J., Dione, C., Moussa, M., Dungall, L., Gaye, A., and Kunstmann, H.: Sensitivity Analysis of Radiation Schemes in WRF-Solar for SolarEnergy Applications in Senegal, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21843, https://doi.org/10.5194/egusphere-egu25-21843, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 4
EGU25-2114 | ECS | Posters virtual | VPS16
Impact of Climate Change on Offshore Wind Energy Potential over the Arabian Sea using CMIP6 Future Projection.Mon, 28 Apr, 14:00–15:45 (CEST) | vP4.8
Over the past fifty years, the Indian Ocean has experienced a pervasive warming trend, prompting investigations into the causative factors and consequential impacts at a basin-wide scale. Research analyzing sea surface temperature (SST) suggests that the western Indian Ocean has been undergoing warming for more than a century. The increase in SST has triggered a range of effects, including alterations in surface pressure distribution, resulting in variable wind patterns, sea-level rise, and other associated outcomes. Understanding the variability in wind speed holds practical significance, including estimating wind power potential for specific geographic regions and developing future projections for wind wave climates to aid in the planning of coastal activities and coastal zone management. The World Climate Research Programme (WCRP) within the Intergovernmental Panel on Climate Change (IPCC) plays a pivotal role in disseminating comprehensive insights into the past, present, and future trajectories of climate change for the scientific community. The CMIP6 project, introduces a spectrum of shared socio-economic pathways (SSP) projecting radiative forcing values ranging from 1.9 to 8.5 W/m² by the end of the century. For a comprehensive understanding of future climate projections, a thorough evaluation and skill assessment of General Circulation Models (GCMs) within the CMIP6 project, specifically regarding their ability to simulate wind speed, is imperative. In this study, BCC-CSM2-MR model has been leveraged to project future changes in the offshore wind energy potential over the Arabian sea. The projections of wind speed at a height of 50 meters using the BCC-CSM2-MR, on the Arabian Sea within a span of three distinct periods: Near-future (2026-2050), Mid-future (2051- 60 2075), and Far-future (2076-2100) and for two distinct Shared Socio-economic Pathway (SSP) scenarios, namely SSP1-2.6 and SSP3-7.0, have been estimated in this study. The overall trend indicates that wind speed over the Arabian Sea remains relatively constant, showing no significant changes. However, a subtle increase is discernible on the western side of the Arabian Sea, particularly near the Oman coast, evident in the SSP3-7.0 scenario. The Projected change in the wind power density (WPD) for the three distinct period change are evaluated keeping the historical wind data from 1990-2014 as a reference. The WPD is increasing by 10% over the Arabian sea for SSP1-2.6 for near-future (2026- 2050), 8% for mid-future (2051-2075) and 6% for far-future (2076-2100) with respect to historical wind speed (1990-2014). But for SSP3-7.0 the wind speed is showing a decline of 2%to 4 % from near-future to far-future. Correspondingly, wind power density exhibited spatial changes over the Arabian Sea, with the western side showing an increase under SSP1-2.6 and a decrease under SSP3-7.0
Keywords: Wind Speed, Wind Power Density, CMIP6, Arabian Sea, Offshore Wind Energy, Climate Change.
How to cite: Ansari, S. and Behera, M. R.: Impact of Climate Change on Offshore Wind Energy Potential over the Arabian Sea using CMIP6 Future Projection., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2114, https://doi.org/10.5194/egusphere-egu25-2114, 2025.
