ERE2.1 | Energy Meteorology
Energy Meteorology
Co-organized by AS1
Convener: Xiaoli Larsén | Co-conveners: Gregor Giebel, Somnath Baidya Roy, Philippe Blanc, Petrina PapazekECSECS
| Thu, 18 Apr, 08:30–12:30 (CEST)
Room 0.96/97
Posters on site
| Attendance Thu, 18 Apr, 16:15–18:00 (CEST) | Display Thu, 18 Apr, 14:00–18:00
Hall X4
Posters virtual
| Attendance Thu, 18 Apr, 14:00–15:45 (CEST) | Display Thu, 18 Apr, 08:30–18:00
vHall X4
Orals |
Thu, 08:30
Thu, 16:15
Thu, 14:00
Wind and solar power are the predominant new sources of electrical power in recent years. Several countries or regions regularly exceed 100% of variable renewable energy in their grids. By their very nature, wind and solar power, as well as hydro, tidal, wave and other renewable forms of generation are dependent on weather and climate. Modelling and measurement for resource assessment, site selection, long-term and short term variability analysis and operational forecasting for horizons ranging from minutes to decades are of paramount importance.
The success of wind power means that wind turbines are increasingly put in sites with complex terrain or forests, with towers extending beyond the strict logarithmic profile, and in 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, e.g.:
• Wind conditions (both resources, siting conditions and loads) on short and long time scales for wind power development.
• 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 etc.).
• Dedicated solar measurement techniques (pyranometric sensors, sun-photometer, ceilometer, fish-eye cameras, etc.) from ground-based and space-borne remote sensing.
• Tools for urban area renewable energy supply strategic planning and control.
Other related topics will be considered by the conveners.

Orals: Thu, 18 Apr | Room 0.96/97

Chairpersons: Xiaoli Larsén, Somnath Baidya Roy
On-site presentation
Janina Bade, Hans-Jürgen Kirtzel, Leon Heinze, Piet Markmann, Gerhard Peters, Christoph Bollig, Sebastian Ulonska, Florian Jordan, and Guntram Huschenbeth

A novel lidar prototype for horizontal Doppler wind measurements with more than 30 km maximum range is presented. The request for such long-range measurements arose from the development of methods for improved prediction of potential and actual feed-in of wind power from offshore wind farms in the project WindRamp. The target is a short-term prediction horizon of up to 30 minutes.

The coherent lidar module is based on a robust fiber amplifier architecture developed within the project. This enables deployment in harsh environments in the future, e.g. at offshore wind farms. The emitted laser beam is eye save (class 1M).

In order to emulate operating conditions of an offshore platform, the system was deployed at the mouth of the Elbe river at 10 m above sea level with unobstructed view in a broad SW-sector. Scans between 204° and 304° azimuth at 0.35° Elevation were performed. The averaging time was 1 s and the angular speed 0.6° s-1.

The lidar performance is demonstrated by observations of wind fronts propagating through the observed area. The weather in North Germany during winter 2023/24 was characterized by unusual persistent precipitation, low hanging clouds and fog, which are unfavourable conditions for lidar operation. Therefore, the observed availability of valid data versus range represents a conservative estimate of the system’s potential.

How to cite: Bade, J., Kirtzel, H.-J., Heinze, L., Markmann, P., Peters, G., Bollig, C., Ulonska, S., Jordan, F., and Huschenbeth, G.: Long range lidar for short term wind predictions for offshore wind parks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17552,, 2024.

On-site presentation
Ebba Dellwik, Sten-Ove Rodén, Johan Arnqvist, Mikael Sjöholm, Corinna Möhrlen, and Andre Gräsman

As wind turbines have grown in size, it has become ever more costly to make the necessary tower-based wind observations needed both for the pre-operation (siting) phase and for wind turbine operations. In response to this challenge, the wind energy scientific community has - over the last decades - focused on evaluating and improving ground-based remote-sensing technology. The development has often been done in close collaboration with the innovative companies dedicated to providing the new solutions for replacing the expensive meteorological towers to the market.

The project EARS4WindEnergy, which started in March 2023, represents one such effort. The project is focused on a re-exploration of the sodar technology, which preceded the later focus on wind lidars. Here, we present a benchmarking of the AQ510 sodar equipped with new signal processing technology with tall-tower data focusing on the three “must-perform” criteria of accurate wind speed, accurate turbulence intensity and a reliable identification of erroneous data. The complementary aspects of data availability and robustness in relation to current wind lidars is also discussed. Most of the presented data are taken at the Østerild test site in Northern Denmark, where a 244m tall tower allows for accuracy quantification over most of the sodar’s measurement range.

How to cite: Dellwik, E., Rodén, S.-O., Arnqvist, J., Sjöholm, M., Möhrlen, C., and Gräsman, A.: Replacement of meteorological towers with ground-based remote-remote sensing sodars: How close are we? , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7587,, 2024.

On-site presentation
Freddy Houndekindo and Taha Ouarda

Wind resource assessment studies over large regions provide the basis for the preliminary identification of locations with promising wind energy prospects. In past studies, several authors have mapped the mean wind speed across large regions using spatial interpolation methods or machine learning models. In recent studies, more emphasis has been placed on mapping the entire wind speed distribution to evaluate the wind resource variability at unsampled locations. Most of these studies have assumed that the wind speed distribution across the entire region belongs to a single family of probability distribution functions and then processed to map the distribution parameters. A flexible non-parametric approach for wind speed distribution mapping is proposed in this study. The new approach is based on mapping various wind speed quantiles at some fixed percentile points in the region using a machine learning model. Then, at any unsampled location, these quantiles are used as input of an asymmetric kernel estimator of cumulative distribution function to recover the whole wind speed distribution. Asymmetric kernel estimators solve the probability leakage problem that appears when fitting symmetric kernels to bounded variables such as wind speed. The non-parametric approach for wind speed distribution mapping was more effective than a traditional approach based on mapping the parameters of a distribution function. In the best scenario, an improvement was observed between 6% (test samples) and 9% (cross-validation) of the Kolmogorov-Smirnov statistic between the observed and estimated wind speed distribution. The non-parametric approach is recommended for regions with highly variable wind regimes that cannot be captured by a single family of distribution functions.

How to cite: Houndekindo, F. and Ouarda, T.: Mapping wind speed distribution across large regions using machine learning and asymmetric kernel estimators., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1432,, 2024.

On-site presentation
Xin Xia and Yong Luo

With the rapid development of wind energy, the imperative for precise wind power predictions has intensified, with the crux lying in forecasting wind speeds. The accurate short-term (1 to 3 days) forecast of wind speeds at the hub height in boundary layer poses a significant scientific challenge. Generating such forecasts for wind farms 1 to 3 days in lead time necessitates reliance on global weather forecast products and the WRF model. In pursuit of heightened accuracy, artificial intelligence (AI) algorithms are employed to refine WRF-predicted wind speeds based on observational data.

This study draws upon observational data from five operational wind farms over three years, employing diverse deep time-series models, to examine the effectiveness and limitations of these models in post-processing corrections for WRF-predicted wind speeds. Based on our examination, we conclude that: 1) Transformer-based models have significant untapped potential, with the Pyraformer model emerging as a well-suited temporal model for post-processing corrections in wind speed and power predictions. 2) Traditional full-attention mechanisms are less effective, highlighting the importance of sparse attention as a vital approach for capturing temporal correlations in such problems. 3) The optimal model demonstrates a reduction of approximately 20% in RMSE for single-point post-processing corrections. In addition, wind speed prediction accuracy reaches around 86%, and power prediction accuracy is approximately 82%. 4) AI-based post-processing corrections may encounter challenges, including the underestimation for high-value and difficulties in reproducing forecasts below the average value.

How to cite: Xia, X. and Luo, Y.: Application of WRF-Based Single-Point Data Artificial Intelligence Post-Processing Correction Method in Practical Short-Term Wind Speed and Power Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16521,, 2024.

On-site presentation
Jeffrey Thayer, Gerard Kilroy, Norman Wildmann, and Antonia Englberger

Convective cold pools routinely pass over the dense network of wind turbines in northern Germany, causing short-term changes in boundary-layer wind speeds (i.e., wind ramp events) and atmospheric stability. These large, rapid, and more-localized variations in the low-level kinematic and thermodynamic structure are difficult for numerical weather prediction models to forecast with sufficient spatial and temporal accuracy for utilization by wind turbine operators. As boundary-layer stability and winds strongly influence wind turbine structural loads, downstream turbulent wake behavior, and power generation, it is important to better understand how rapid changes in dynamic processes evolve within the vertical layer of wind turbine rotor blades (~50 - 150 meters altitude).

Using in-situ observations and high-resolution modeling focused on the WiValdi research wind park in Krummendeich, Germany, we examine how convective cold pool passages during July 2023 impact the inflow and turbulent wakes for two installed turbines with a hub height of 92 meters. Meteorological mast, Doppler wind lidar, and microwave radiometer observations provide upstream and downstream measurements of stability, vertical shear, and turbulence variations at ~1-minute resolution. While this measurement coverage adequately captures the cold pool evolution relative to each turbine, we remain somewhat limited by the fixed instrument locations for measuring upstream conditions and the three-dimensional turbulent wake structure. Therefore, we also utilize the mesoscale model WRF in large-eddy-simulation mode, with inserted generalized actuator disks acting as proxy wind turbines, to analyze far-upstream inflow conditions and three-dimensional wake characteristics during cold pool passages. The proposed work will provide a foundation for future analysis which will more robustly verify WRF output using additional WiValdi instrumentation.

How to cite: Thayer, J., Kilroy, G., Wildmann, N., and Englberger, A.: How do convective cold pools influence the stability and turbulence conditions in the vicinity of wind turbines in Northern Germany?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17394,, 2024.

On-site presentation
Xuefeng Yang and Shengli Chen

The rainfall directly affects wind turbine operation by eroding the turbine blades and changing their aerodynamic performance, however, little research has been conducted on the effects of rainfall on wake evolution. The present study simulates the impact of rainfall on wind turbine wake using a coupled LES-ADMR model, in which a double Euler method is employed for the rainfall injection. The numerical simulation results indicate that the rainfall reduces the wake wind speed in the sweep area while increasing it in the outer region of the upper blade tip, reaching up to 2.1% for increment. Rainfall also weakens the turbulence in the near wake and the outer region of the top tip (as much as 2.0%), with the influence extending up to 10 diameters downstream the wind turbine. These modifications are positively correlated with the rainfall intensity and inversely correlated with wind speed. By analyzing the rainfall-induced changes in MKE (Mean Kinetic Energy) and TKE (Turbulent Kinetic Energy) budget terms, the study reveals that the alteration of turbulent radial transport of MKE is the main cause of changes in wind speed, while the variation of shear production of  TKE is responsible for the turbulent intensity changes. The rainfall-induced change of  reynold stress u'w' is the root cause of the above phenomenons.

How to cite: Yang, X. and Chen, S.: Effect of Rainfall on Evolution of the wind turbine wake:A LES Study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4945,, 2024.

On-site presentation
Zhouyi Liao, Xin Xia, and Yong Luo

As China strides towards its carbon neutrality target by 2060, the strategic planning of renewable energy distribution and power plant installations becomes imperative to fulfill the renewable energy penetration goals. This study presents a comprehensive assessment of the future projections of solar and wind power resources in China, utilizing the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We examine various CMIP6 scenarios to project the geographical and temporal variations of solar and wind energy potential up to 2100. A verification assessment was carried out using terrestrial solar radiation and wind speed data sourced from 17 stations operated by the China Meteorological Administration (CMA). This evaluation revealed that the Meteorological Research Institute Earth System Model version 2-0 (MRI-ESM2-0) demonstrated overall superior performance in terms of correlation coefficients (R) and Root Mean Square Error (RMSE). Then MRI-ESM2-0 was selected to examine the spatial and temporal shifts in solar and wind potential in China. Notably, in the SSP585 scenario, a marked decrease in both PV power potential and wind power potential was observed. Additionally, the future spatial complementarity between solar and wind power in China was evaluated using the Pearson correlation coefficient and Kendall rank correlation coefficient and this was juxtaposed with the present complementarity. These maps provide a crucial reference for guiding the planning and management of renewable energy resources in China.

How to cite: Liao, Z., Xia, X., and Luo, Y.: Future Projections and Complementarity Assessment of Solar and Wind Power in China Using CMIP6 Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18234,, 2024.

On-site presentation
Ruben Borgers, Joaquim Pinto, Johan Meyers, and Nicole van Lipzig

The growing importance of the offshore wind energy sector emphasizes the need for projections of the long-term energy yield for existing and planned wind farm installations. In the North Sea, where wind farms are already pivotal to the electricity mix of the surrounding countries, the production capacity is set to increase tenfold by 2050. Studies suggest that, by 2050, the wind climate over the North Sea basin may differ significantly from the historical climate (Carvalho et al., 2021; Hahmann et al., 2022). Here, we combine an analysis of CMIP6 projections with an ERA5-driven, mesoscale wind farm simulation to further explore the impact of near-future wind climate changes over the North Sea on the energy production. First, an ensemble of 17 GCMs is reduced to 12 GCMs based on an analysis of the ability to represent the historical wind rose at 100 m MSL (1985-2014). Next, we identify future decades for each season where the wind rose exceeds the range of the historical decadal variability. Based on these extreme wind roses, we then apply a sub-sampling to a 30-year, ERA5-driven COSMO-CLM simulation covering the North Sea and incorporating a projected, 250 GW wind farm layout. Based on the sub-sampled datasets, we then quantify the impact of these extreme 10-year wind roses on the energy production of different wind farm clusters and compare this against an historical baseline.

How to cite: Borgers, R., Pinto, J., Meyers, J., and van Lipzig, N.: Future wind energy production over the North Sea for extreme, 10-year wind roses based on CMIP6-informed subsampling of an ERA5-driven RCM simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20273,, 2024.

On-site presentation
Abhirup Bhattacharya and Somnath Baidya Roy

Power production from a renewable energy (RE) source such as a wind farm or urban roof-top solar panel installation is highly sensitive to the obstacles around it, particularly those which are in the upstream direction. RE installations can avoid or minimize the effects of obstacles using proper planning. However, obstacles that come up after the plant is operational can lead to significant loss in power production and revenue. In this study we quantitatively explore two common examples – shading effect of neighbouring buildings on roof-top solar plants and wake effects of upstream wind turbines on offshore wind farms.

The first example considers a horizontal solar panel atop an urban building in a relatively congested neighbourhood. We built a model to quantify the shading effects of neighbouring tall buildings on the solar panel. The model calculates the position of the Sun on the celestial dome at every minute with astronomical accuracy. Then the solar irradiance is calculated for a clear-sky environment. After that the shadow profile is calculated and visualized for obstacle buildings with any height and at any distance. And finally, the loss in available insolation and the power production is calculated. The results show significant power loss due to the building shading effect. For example, a roof-top solar panel surrounded by a 20m taller building at 20m distance can reduce power generation by more than 50%.

The second example is where a new wind farm is constructed upstream of an existing wind farm. We used two different models to quantify the meteorological effects of the upstream wind turbines on downwind turbines. The first one involves Jensen Wake Model (JWM), a static wake recovery model to simulate the wake effects of upstream obstacle turbine on downwind turbine. The second approach makes use of the Wind Turbine Parameterization (WTP) in WRF. This method implements wake loss using a wind turbine power curve data and wake recovery through atmospheric vertical mixing. A case study has been conducted for a hypothetical offshore wind farm situated in Palk Strait between India and Sri Lanka by placing wind farms of different shapes and dimensions in the upwind direction. The results show a range of losses in annual power production between 3 – 12 MW, which roughly converts into €1.1M – €4.1M.

This study demonstrates that the effects of upstream obstacles on RE sources are non-trivial and can have serious impacts on the performance on RE installations. Currently, local zoning laws in India and many countries do not protect RE installations from future constructions that can act as obstacles. Hence, effective policies are required to safeguard the return on investments in the RE industry.

How to cite: Bhattacharya, A. and Baidya Roy, S.: Effects of Upstream Obstacles on Energy Production of Solar and Wind Farms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4688,, 2024.

Coffee break
Chairpersons: Ebba Dellwik, Petrina Papazek
On-site presentation
Angela Meyer and Kevin Schuurman

Short-term solar irradiance forecasts are becoming increasingly important as power grid operators have to deal with the uncertainty in incoming surface solar irradiance (SSI) and the expected photovoltaic (PV) power production. Geostationary satellites are an excellent source of spectral imagery of SSI-relevant atmospheric components over large geographical regions. The spectral measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary Meteosat Second Generation satellite form the basis of many SSI estimation and forecasting techniques [3], [4], [6]. These forecasting techniques usually rely on level 2 products to estimate SSI from reflectance but this induces a significant delay in the forecasting cycle. We demonstrate that using a deep learning regressor to estimate surface solar irradiance can drastically reduce this delay.

Previous machine learning-based methods for estimating SSI from geostationary reflectance imagers show great promise and can outperform state-of-the-art radiative transfer retrieval methods at the ground stations used as training sites [1], [2], [5]. Previous methods only use ground station SSI to train on, but point-wise estimators trained on a group of ground stations do not generalize well to out-of-sample ground stations, possibly because of changes in surface albedo [5].

To improve the generalization, we introduce a deep learning spatial convolution operator which is trained to emulate radiative-transfer SSI retrievals from spectral satellite imagery. Our SSI estimator model is fine-tuned on an extensive network of ground stations as a second training set. In this contribution, we will demonstrate the performance of the radiative transfer emulator, its applications and latency based on independent measurements from ground stations across Europe.


[1] H. Jiang, N. Lu, J. Qin, W. Tang, and L. Yao, “A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data,” Renewable and Sustainable Energy Reviews, vol. 114, p. 109 327, Oct. 1, 2019, ISSN: 1364-0321. doi: 10.1016/j.rser.2019.109327.
[2] D. Hao, G. R. Asrar, Y. Zeng, et al., “DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution,” Earth System Science Data, vol. 12, no. 3, pp. 2209–2221, Sep. 15, 2020, Publisher: Copernicus GmbH, ISSN: 1866-3508. doi: 10.5194/essd-12-2209-2020.
[3] Y. Lu, L. Wang, C. Zhu, et al., “Predicting surface solar radiation using a hybrid radiative transfer–machine learning model,” Renewable and Sustainable
Energy Reviews, vol. 173, p. 113 105, Mar. 1, 2023, ISSN: 1364-0321. doi: 10.1016/j.rser.2022.113105.
[4] Q. Paletta, G. Terren-Serrano, Y. Nie, et al., “Advances in solar forecasting: Computer vision with deep learning,” Advances in Applied Energy, vol. 11,
p. 100 150, Sep. 1, 2023, ISSN: 2666-7924. doi: 10.1016/j.adapen.2023.100150.
[5] H. Verbois, Y.-M. Saint-Drenan, V. Becquet, B. Gschwind, and P. Blanc, “Retrieval of surface solar irradiance from satellite imagery using machine learning: Pitfalls and perspectives,” Atmospheric Measurement Techniques, vol. 16, no. 18, pp. 4165–4181, Sep. 19, 2023, ISSN: 1867-8548. doi: 10.5194/amt-16-4165-2023.
[6] A. Carpentieri, D. Folini, D. Nerini, S. Pulkkinen, M. Wild, and A. Meyer, “Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection,” Applied Energy, vol. 351, doi: 10.1016/j.apenergy.2023.121775.

How to cite: Meyer, A. and Schuurman, K.: Predicting surface solar irradiance from satellite imagery with deep learning radiative transfer emulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2452,, 2024.

On-site presentation
Fei Yao, Paul Palmer, Jianzheng Liu, Hongwen Chen, and Yuan Wang

Particulate matter (PM) in the atmosphere and deposited on solar photovoltaic (PV) panels reduce PV energy generation. Reducing anthropogenic PM sources will therefore increase carbon-free energy generation. However, we lack a global understanding of the sectors that would be the most effective at achieving the necessary reductions in PM sources. We combine well-evaluated models of solar PV performance and atmospheric composition to show that deep cuts in air pollutant emissions from the residential sector substantially benefit Asian PV power output. Specifically, halving residential emissions of PM would lead to an additional 10.3 TWh yr-1 and 2.5 TWh yr-1 of PV energy generation in China and India in 2020, respectively. Compared to the 2020 electricity generation of 261.6 TWh yr-1 and 54.4 TWh yr-1 from solar PV technology in China and India, respectively, these unrealised sources of energy generation represent an improvement of approximately 4-5%. While anthropogenic PM sources originate mainly from producers, they are responding to changes in domestic and international consumer demand. This raises a critical question about the extent to which consumers, who benefit from the emission process, should be responsible for the resulting unrealised, cleaner PV energy generation. Focusing on Northeast Asia (NEA), we investigate the source-receptor relationship of PV energy losses attributable to PM pollution among China, South Korea, and Japan by incorporating a new input-output model into the combined models of solar PV performance and atmospheric composition. Our findings reveal that the solar energy generation losses attributable to PM pollution in NEA caused by emissions produced in China surpass those linked to China’s consumption that stimulates emissions in China and elsewhere, with the disparity amounting to 9.3 TWh yr-1. Conversely, a reverse pattern is observed for solar energy generation losses linked to emissions produced versus induced by consumption in South Korea and Japan, where the disparities are found to be -0.023 TWh yr-1 and -0.231 TWh yr-1, respectively. In other words, when we consider international trade across NEA, we find there is diminished (augmented) responsibility for China (South Korea and Japan) in explaining PV-related energy losses attributable to PM pollution.

How to cite: Yao, F., Palmer, P., Liu, J., Chen, H., and Wang, Y.: Impacts of Air Pollutant Emissions on Solar Energy Generation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4715,, 2024.

On-site presentation
Sandrine Anquetin, Léo Clauzel, Christophe Lavaysse, Guillaume Tremoy, and Damien Raynaud

With its commitment to reduce greenhouse gas emissions and harnessing the potential of renewable energy, the West African region is at the forefront of global environmental challenges. This work focuses on the specific aspect of solar energy, which holds significant promise in the region. High quality solar energy forecasts are necessary for solar plants and power systems management, while they remain poorly developed in this region, in particular because of the specificities of the West African climate. We evaluate the errors in Global Horizontal Irradiance (GHI) operational forecast models for two Sahelian solar power plants, Zagtouli in Burkina Faso and Sococim in Senegal, and investigate their links with local meteorological conditions, with a specific focus on clouds and dust aerosols.

This work begins by assessing aerosol products and our results support the use of the CAMS reanalysis for the assessment of Aerosol Optical Depth (AOD), particularly with respect to dust aerosols. We then assess the performance of three operational GHI forecast products: the Global Forecast System (GFS, NCEP/NOAA), the Integrated Forecast System (IFS, ECMWF), and SteadyMet (SM), developed by French company Steadysun, which is computed from the previously mentioned Numerical Weather Prediction (NWP) model outputs. The analysis reveals that IFS and SM outperform GFS in terms of forecast accuracy, with SM showing a slight advantage due to its probabilistic nature, which provides valuable information on forecast uncertainty.

Closer examination reveals a significant relationship between GHI forecast errors and local meteorological characteristics. These errors are more pronounced during the wet season, primarily attributed to cloud occurrence. Dust events are found to play a secondary role, particularly during the dry season. Correlation analyses underline the main link between forecast errors and cloudiness, while co-occurrence analyses highlight the fact that dust aerosol loading is a secondary factor in forecast errors for the GHI directly or for cloud representation (aerosol-cloud interaction).

How to cite: Anquetin, S., Clauzel, L., Lavaysse, C., Tremoy, G., and Raynaud, D.: West African operational daily solar forecast errors and their links with meteorological conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5042,, 2024.

On-site presentation
Marleen van Soest, Harm Jonker, and Stephan de Roode

The increase in renewable energy production demands forecasting of wind and solar radiation due to their greater variability compared to non-renewable energy sources. The variability in solar energy is primarily caused by clouds. Large Eddy Simulation (LES) proves effective for high-resolution solar radiation prediction, capturing clouds like stratocumulus where large-scale models cannot. LES uncertainty in clouds primarily stems from initial conditions taken from these large-scale models. In this study, an ensemble Kalman filter assimilates observations into LES initial conditions. A large ensemble is created from a limited amount of LES runs by taking advantage of their internal variability. From this ensemble and measurements from the Cabauw measurement site, improved initial conditions are calculated for a range of stratocumulus cases. These cases are simulated without further interference. The method shows a 60% reduction in Root Mean Square Error (RMSE) for shortwave down solar radiation at the initial condition over the unfiltered initial condition. This improvement persists at 45% after 3 hours of simulation, showing the lasting impact of assimilated observational data on predictive accuracy. The decrease can be accounted to a combination of microphysical processes, energy fluxes from the lower boundary condition and the advective tendencies in the model. In future work, possible improvements to these processes will be identified and the method will be evaluated for other sites and cloud conditions.

How to cite: van Soest, M., Jonker, H., and de Roode, S.: Solar Radiation Forecasts from Large Eddy Simulations and Observations using Ensemble Kalman Filtering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18854,, 2024.

On-site presentation
György Varga, Fruzsina Gresina, József Szeberényi, András Gelencsér, and Ágnes Rostási

The expansion of renewable energy sources is a major issue from the sustainability, climate policy and energy security perspectives. All of this expansion can be optimal if its potential is exploited to the best possible effect, and accurate forecasting of irradiance levels, both for existing and planned capacity, is essential.

Solar forecasting is the process of predicting the expected solar output from a photovoltaic (PV) system over a given period. This process is important for power system operators and utility companies who need to ensure that they can meet the electricity demand of their customers by balancing the supply and demand of energy on the grid.

Our research investigated the impact of mineral dust on photovoltaic power generation and day-ahead forecast. We analysed the year 2022, when the number of Saharan dust storm events identified in Hungary (n=16) set a new record. Our methods included satellite measurements, numerical simulations, air mass movement trajectory calculations and synoptic meteorological analyses, as well as laboratory analyses of the dust material that washed out with precipitation during Saharan dust storm events. During some episodes, a deficit of up to 500 MW between actual and predicted output was periodically detected, which required the use of expensive and polluting back-up capacity.

We have shown that the semi-direct effect of atmospheric dust particles on high-level cloud formation rather than their direct irradiance-reducing effect is responsible for the reduced accuracies of e short-term (24-h) PV energy production forecasts during these events.

The results were published in Varga et al. (2024). Effect of Saharan dust episodes on the accuracy of photovoltaic energy production forecast in Hungary (Central Europe). Renewable and Sustainable Energy Reviews 193,

The research was supported by the NRDI projects FK138692 and RRF-2.3.1-21-2021. The research was funded by the Sustainable Development and Technologies National Programme of the Hungarian Academy of Sciences (FFT NP FTA).


How to cite: Varga, G., Gresina, F., Szeberényi, J., Gelencsér, A., and Rostási, Á.: Effect of Saharan dust storm events on the forecast of photovoltaic power generation in Hungary, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22047,, 2024.

On-site presentation
Hannah Bloomfield, Kieran Hunt, and Isa Dijkstra

Energy systems across the globe are evolving to meet climate mitigation targets set by the Paris Agreement. This process requires a rapid reduction on nations’ reliance on fossil fuels and significant uptake of renewable generation (such as wind power, solar power, and hydropower). In parallel to the decarbonisation of the electricity sector, both the heat and transport sectors are electrifying to reduce their carbon intensity. Renewable energy sources are weather-dependent, causing production to vary on timescales from minutes to decades. A consequence of this variability is that there may be periods of low renewable energy production, here termed ‘renewable energy droughts’. This energy security challenge needs to be addressed to provide a consistent power supply and to ensure grid stability. India is chosen here as a study area as a region that already has a large existing proportion of renewable generation (42 GW of wind power, 61 GW of solar power and 51 GW of hydropower were installed as of October 2022) and a region that experiences good sub-seasonal predictability in large-scale patterns.

In this study, we use broad variety of data sources to quantify potential and realised capacity over India from 1979 to 2022 using the ERA5 reanalysis and a range of open source renewable energy installation data. Using gridded estimates of existing installed renewable capacity combined with our historical capacity factor dataset, we create a simple but effective renewable production model for each Indian state and at national level. We use this model to identify the timing of historical renewable energy droughts and then discuss potential weaknesses in the existing grid – particularly a lack of complementarity between wind and solar production in north India – and vulnerability to high deficit generation in the winter. The data produced here have all been made open access and the methods could easily be reproduced over any region of interest.

We then consider the weather patterns that could cause the largest renewable energy droughts over India and investigate potential sources of predictability. Existing large-scale daily weather types (based on large-scale wind map clustering) as well as novel patterns created by k-means clustering of more relevant variables for wind and solar power are used to investigate the different weather patterns causing renewable energy droughts. Renewable energy droughts largely occur during the winter season (January and February) and are caused by low seasonal wind speeds in combination with weather patterns bringing high cloud cover. These are mainly winter anticyclones and western disturbances.

Sources of potential sub-seasonal predictability are considered for the largest renewable energy droughts, including the Madden Julian Oscillation and Boreal Summer Intra-Seasonal Oscillation. Although both have a stronger relationship with high energy production days, links between phases of these two modes of variability and renewable energy droughts have been identified. These could help to provide early warnings for conditions that challenge supply security in the future.

How to cite: Bloomfield, H., Hunt, K., and Dijkstra, I.: Identifying weather patterns responsible for renewable energy production droughts in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7725,, 2024.

On-site presentation
Julie Thérèse Pasquier, Johannes Rausch, Matthias Piot, Julia Schmoeckel, Marco Thaler, Christian Schluchter, and Martin Fengler

The production of renewable energy from wind and solar sources is intricately linked to meteorological conditions, where wind speed and solar radiation play critical roles. Due to the success of renewable energies, wind turbines are increasingly placed in sites with complex terrain, while solar panels are increasingly situated in alpine areas. However, current weather models often struggle to accurately forecast the weather, especially over complicated topography, due to limitations in spatial resolution. This leads to inaccurate predictions of power production, impacting the efficiency and reliability of renewable energy systems. To address this challenge, Meteomatics developed the EURO1k model, the first pan-European weather model with a 1 km² spatial resolution, providing optimal forecasting for wind and solar power.

The EURO1k model offers a 48-hour forecast horizon, generating a new forecast every hour. In addition to standard data sources such as weather stations, radar, satellite data, and radiosondes, the EURO1k model also incorporates data from a network of Meteodrones - small, unmanned aircraft systems developed by Meteomatics - which collect vertical atmospheric profiles up to 6000m in altitude. The high resolution of the EURO1k model enables accurate representation of small-scale weather patterns, resulting in highly accurate and precise forecasts.

Meteomatics uses a forecast system that combines various global and regional weather models to predict wind and solar power, aiming to reduce average errors. Recently, EURO1k has been integrated into this system, improving intraday and day-ahead power production forecasts. The normalized root mean square error (nRMSE) was reduced by up to 8.1% for intraday and by up to 8.5% for the day-ahead wind power forecast. Furthermore, a comparison of day-ahead forecasts with actual production data, combined with balancing energy costs, demonstrates improved earnings with the addition of the EURO1k model. Indeed, the EURO1k shows especially better performance in weather situations with large uncertainties. This underscores the added value of EURO1k in power forecasting, enhancing the cost efficiency of renewable energies and fostering greater integration into the energy mix, thereby reducing CO2 emissions.

How to cite: Pasquier, J. T., Rausch, J., Piot, M., Schmoeckel, J., Thaler, M., Schluchter, C., and Fengler, M.: Improving Renewable Energy Forecasting with Meteomatics EURO1k Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17204,, 2024.

On-site presentation
James M. Wilczak, Elena Akish, Antonietta Capotondi, and Gilbert Compo

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets.  After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist limiting its direct applicability for a wind and solar energy analysis.  Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low.  Errors for the shortest duration, most extreme solar negative anomaly events are found to be statistically reasonably well represented in the ERA5, when completely overcast conditions occur in both ERA5 and observations.  Longer duration events on weekly to monthly timescales, which include partially cloudy days or a mix of cloud conditions, have ERA5-derived solar power errors as large as 40%.  ERA5-derived solar power errors are found to have consistent characteristics across the CONUS region.  The negative bias errors in the ERA5 windspeeds and wind power are largely consistent across the central and northwestern US, and offshore, while the eastern US has an overall small net bias.  For weekly to monthly timescales, the uncorrected ERA5-derived wind power errors approach 50%.  Corrections to the ERA5 are derived using a quantile-quantile method for solar power, and linear regression of wind speed for wind power.  These corrections greatly reduce the ERA5 errors, including for extreme events associated with wind and solar energy droughts, that will be most challenging for electric grid operation, while also avoiding potential over-inflation of the reanalysis variability resulting from differences between point-measurements and the temporally and spatially smoother reanalysis values.

How to cite: Wilczak, J. M., Akish, E., Capotondi, A., and Compo, G.: Evaluation and Bias Correction of the ERA5 Reanalysis for Wind and Solar Energy Applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12053,, 2024.

On-site presentation
David Pozo-Vázquez, Guadalupe Sánchez-Hernandez, Antonio Jiménez-Garrote, Miguel López-Cuesta, Inés Galván-León, Ricardo Aler-Mur, Joaquín Tovar-Pescador, José Antonio Ruiz-Arias, and Francisco Santos-Alamilllos

Renewable energies (RES) will play a central role in national energy systems worldwide in the near future, boosted by the climate change issue and the ever-growing competitiveness of these energies. An example is the Spanish roadmap to produce 80% of its electricity from renewables by 2030.
However, the transition from the current generation mix to decarbonized energy systems is a formidable challenge, as they must be technically reliable and economically viable. To design such systems, the spatial and temporal variability of RES, combined with proper simulation tools, are determinant. In recent decades, energy system models have emerged as valuable tools for conducting these analyses. These models allow, for a specific region, the analysis of the optimal allocation and sizing of new renewable plants, taking into account the variability of generation and demand, energy costs, integration and the issue of transmission. The key input to these models is a database of RES resources in the study region. However, in many cases, the extent to which these databases represent the actual RES for a given country is far from optimal, reducing confidence in the results. In general, current RES databases face two main problems: 1) low reliability of energy estimates and 2) lack of adequate spatial and/or temporal resolution. In most cases, these problems arise from the lack of actual measurements for model training and validation.
In this work, we present SHIRENDA (Spanish High-resolution Renewable ENergies and Demand database), an enhanced open access database of Spanish renewable energies resources and demand. The database consists of hourly values of wind, solar photovoltaic and hydroelectric capacity factors (CF), together with electricity demand, covering the period 1990-2020, for each of the Spanish NUTS3 regions, which is an unprecedented spatial resolution so far. CFs and demand values were derived using state-of-the-art machine learning models based on: 1) actual values of installed RES capacities (Jiménez-Garrote et al, 2023); 2) real energy and demand data derived from the Spanish TSO and 3) meteorological data derived from the ERA5 reanalysis. The database covers the period 1990-2020, with the period 2014-2020 used for model training and validation purposes.
The SHIRENDA database has been developed within the framework of the MET4LOWCAR project, funded by the Government of Spain, and aims to gather the desirable characteristics to carry out reliable studies on modeling and analysis of energy systems, thus contributing to an adequate energy transition. Notably, the high spatial resolution allows the very high spatial variability of RES resources in the study region to be properly taken into account. At the same time, the high temporal resolution, along with the temporal coverage, allows for properly assessing the impact of climate variability, extreme meteorological conditions and compound events in a future decarbonized energy systems in Spain. 


Reference: Jimenez-Garrote et al, 2023.

How to cite: Pozo-Vázquez, D., Sánchez-Hernandez, G., Jiménez-Garrote, A., López-Cuesta, M., Galván-León, I., Aler-Mur, R., Tovar-Pescador, J., Ruiz-Arias, J. A., and Santos-Alamilllos, F.: SHIRENDA: A long-term high-resolution database of electricity demand and wind, hydro and PV renewable resources for Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1609,, 2024.


Posters on site: Thu, 18 Apr, 16:15–18:00 | Hall X4

Display time: Thu, 18 Apr 14:00–Thu, 18 Apr 18:00
Chairpersons: Petrina Papazek, Xiaoli Larsén
Xiaoli Larsén, Jana Fischereit, Konrad Bärfuss, and Astrid Lampert

Over the North Sea, larger and larger part of the water surface is being covered by wind farms. Studies have shown consistent results regarding farm wake effects at hub height, characteristic of reduced wind speed and enhanced turbulence. Close to water surface, published studies using both measurements and modeling have suggested enhanced wind speeds sometimes, and reduced wind speeds some other times. Hence, this study investigates the research question: Do offshore wind farms weaken or enhance surface wind and wave fields?

We use the mesoscale atmosphere-wave-wake coupled modeling system that consists of the Weather Research and Forecast (WRF) model, Spectral Wave Nearshore (SWAN) model with the wave boundary-layer model (Du et al. 2017, Fischereit et al. 2022). We use the Fitch Wind Farm Parameterization scheme (Fitch et al. 2012), with four coefficients for the advection of the wind farm-generated Turbulence Kinetic Energy (TKE): a = 1, 0.25, 0.1 and 0, corresponding to larger and larger TKE advection. The model is used together with flight measurements of wind fields upwind, above and downwind of offshore wind farms, collected during the project WIPAFF (Bärfuss et al. 2019, Lampert et al. 2020). We use two case studies, one following Bärfuss et al. (2021) (with fetch effect) and one following Larsén and Fischereit (2021) (without fetch effect). 

There is no evidence of generally enhanced surface winds and waves in the presence of wind farms. Enhanced surface winds and waves can however be generated numerically when using e.g. a = 1, as a result of numerical distribution of excessive TKE and momentum generated at hub height down to the surface. The study suggests that the wake effect is rather sensitive to the value of a, regarding both horizontal and vertical distribution from the hub height. Measurements are needed to understand the distribution of turbine-generated TKE and to help defining a- value for specific conditions.


Bärfuss, et al. 2019: In-situ airborne measurements of atmospheric and sea surface parameters related to offshore wind parks in the German Bight,, 2019.

Bärfuss et al. 2021: The Impact of OffshoreWind Farms on Sea State Demonstrated by Airborne LiDAR Measurements. J. Mar. Sci. Eng.  9, 644.

Du J., Bolaños R. and Larsén X. 2017: The use of a wave boundary layer model in SWAN. J. Geophys. Res.:Oceans. DOI: 10.1002/2016JC012104, vol. 122, No 1, p42 - 62.

Fischereit, J., Larsén, X.G. and Hahmann A. 2022: Climate impacts of wind-wave-wake interactions in offshore wind farms. Frontier Energy Res. doi: 10.3389/fenrg.2022.881459. Vol. 10., 881459.

Fitch et al. 2012: Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model, Mon. Weather Rev., 140, 3017–3038,

Lampert et al. 2020: In-situ airborne measurements of atmospheric and sea surface parameters related to offshore wind parks in the German Bight, Earth Syst. Sci. Data, 12, 935–946.

Larsén X. and Fischereit J. 2021: A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets. Geo. Mod. Dev., 14(5), 3141-3158.

How to cite: Larsén, X., Fischereit, J., Bärfuss, K., and Lampert, A.: Do offshore wind farms weaken or enhance surface wind and wave fields?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9862,, 2024.

Shaokun Deng and Shengli Chen

Coupling Weather Research and Forecasting (WRF) model with wind farm parameterization can be effective in examining the performance of large-scale wind farms. However, the current scheme is not suitable for floating wind turbines. In this study, a new scheme is developed for floating wind farm parameterization (FWFP) in the WRF model. The impacts of the side columns of a semi-submersible floating wind turbine on waves are firstly parameterized in the spectral wave model (SWAN) where the key idea is to consider both inertial and drag forces on side columns. A machine learning model is trained using results of idealized high-resolution SWAN simulations and then implemented in the WRF to form the FWFP. The difference between our new scheme and the original scheme in a realistic case is investigated using a coupled atmosphere-wave model. Results indicate that the original scheme underestimates the power output of the entire floating wind farm in the winter scenario. On average, the power output of a single turbine is underestimated by a maximum of 694 kW (12 %). The turbulent kinetic energy decreases within the wind farm, with the greatest drop of 0.4 m2 s-2 at the top of the turbine. This demonstrates that the FWFP is necessary for both predicting the power generated by floating wind farms and evaluating the impact of floating wind farms on the surrounding environment.

How to cite: Deng, S. and Chen, S.: A parameterization scheme for the floating wind farm in a coupled atmosphere-wave model (COAWST v3.7), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4887,, 2024.

Lihong Zhou and Igor Esau

In the quest for accurate wind resource assessment crucial for the expansion of wind farms, this study tackles the scientific question of how varying time series lengths and temporal resolutions impact the estimation of wind resources, and introduce uncertainty into the assessment process. Recognizing the significant importance of considering temporal variability in wind speed distribution, we utilize in-situ observations from weather stations provided by the Norwegian Meteorological Institute, analyzing 1-hourly data spanning one to ten years. The study employs a comparative analysis of various wind speed distributions to determine the best-fit distribution for estimating wind resources. This process involves assessing the goodness-of-fit for each distribution under different time series lengths. Additionally, the study investigates the impact of temporal resolutions by examining data collected at 10-minute, hourly, daily, and monthly intervals from the same period and stations. The overarching goal is to systematically quantify uncertainty in wind resource estimation arising from the selection of wind speed distribution based on varying lengths and resolutions of time series data. The outcomes of this research aim not only to enhance the precision of wind resource assessments in the wind power sector but also to provide valuable insights applicable to fields influenced by wind conditions, including risk management and construction design. This study is financed by the Equinor academia project.

How to cite: Zhou, L. and Esau, I.: The impact of time series length and temporal resolution on wind resource assessment: a comparative analysis of wind speed distributions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10849,, 2024.

Aldo Brandi and Gabriele Manoli

In June 2023 Swiss people voted a new climate law that set a net-zero emission goal to be reached by 2050 via a full energetic transition from fossil fuels to renewables. The country’s Energy Strategy estimates that 7% (4.3 TWh) of future total renewable energy will be supplied by wind turbines, which requires an increase in the number of installed devices from the 37 currently operating to 760. Such an objective presents numerous challenges as available space is limited by technical restrictions, the country’s complex terrain, and competition with other types of land use.

Thanks to qualities like small size and weight, low noise emission levels, and the ability to operate with winds blowing from any direction at relatively low speed (> 2 m/s), vertical axis wind turbines (VAWTs) installed in urban areas are an attractive alternative to overcome the issues associated with large wind farms. Despite this, the potential for wind energy micro-generation in complex urban settings remains largely unexplored.

Private households use one third of all energy consumed in Switzerland, and residential renewable energy generation currently consists almost exclusively of photovoltaic (PV) panels which, in 2021, represented 78% of all solar systems operating in the country. No similar statistics are available for residential wind energy generation. Even in the scientific literature, current understanding of the interaction between wind and urban areas is limited, and the knowledge about urban wind resources is markedly inadequate to address the challenges posed by climate change to both local and global energy sectors.

Here we use use the Weather Research and Forecast (WRF) model to simulate mean near-surface wind speed over the cities of Lausanne and Geneva to assess the potential for wind energy generation. We perform simulations at 300 m grid spacing and across 85 vertical model levels, with hourly output interval throughout one entire year to identify diurnal and seasonal wind speed trends. We then use power curves of select VAWTs to translate mean wind speed data into potential electrical output maps and time series, over all model cells classified as urban.  

Our results show that mean wind speed is generally higher in Lausanne than in Geneva, especially at nighttime. Diurnal cycles evolve markedly differently between the two cities, although differences are at times minimized due to seasonal changes. The average potential for wind energy harvesting using VAWTs in urban environments varies with turbine size and geographical area. The average daily total energy generation potential is one order of magnitude greater in Lausanne compared to Geneva. In Lausanne, top generation is expected during the nighttime across most months, allowing for a good integration of photovoltaic generation. The opposite happens in Geneva where already lower peak wind speed, and associated energy generation, always culminate during the afternoon.

This research highlights the potential for urban wind energy micro-generation, drawing attention to the role of regional differences and the need and the importance of numerical simulations for quantitative assessments at the city and regional scales.

How to cite: Brandi, A. and Manoli, G.: Numerical assessment of urban wind energy micro-generation potential: a comparison between two Swiss cities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1770,, 2024.

Assessment of Wind Energy Potential and Future Deployment in the Arabian Peninsula
Latifa Yousef
Nora Helbig, Florian Hammer, Reinhard Bischoff, Michael Lehning, and Sarah Barber

Complex mountain winds provide a largely unknown wind energy potential. Mountainous terrain influences air flow by e.g., wind flow sheltering, ridge acceleration, channelling, deflections, blocking and recirculation. Its impact on the energy production of wind turbines has not yet been thoroughly quantified, but various studies show that it could be significant. To accurately assess the wind energy potential in mountainous terrain, spatio-temporal wind fields capturing local wind-topography interactions are required. Ground measurements can retrieve spatio-temporal wind fields, but even with a dense weather station network, atmospheric models are still needed to capture the full spatial variability. However, it is challenging to generate the necessary fine-scale wind fields over long timescales and large regions computationally efficiently. Wind farm planning in mountainous regions is therefore much more challenging and uncertain than in flat areas.

Here, we present our concept that addresses this challenge by evaluating and enhancing various state-of-the art computationally efficient downscaling methods (statistical and dynamical). These methods generate highly resolved spatio-temporal wind fields, considering dominant local wind-topography interactions. Using these fields, we can derive time-resolved wind energy yield potential. The evaluation involves assessing the methods across fine spatial scales (e.g., dekameter scale), large spatial extents (up to tens of kilometers), high temporal resolution (e.g., hourly scale), and long timescales (several years) in real Swiss mountain settings using wind field and energy production measurements. Our overall goal is to provide wind modelers and energy planners with recommendations for efficient methods for obtaining highly resolved spatio-temporal wind fields, enabling accurate energy yield estimations in mountainous terrain.

How to cite: Helbig, N., Hammer, F., Bischoff, R., Lehning, M., and Barber, S.: Towards efficient methods for estimating spatio-temporal wind energy yields in mountainous regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15512,, 2024.

David Geiger, Dehong Yuan, Thomas Spangehl, Doron Callies, Jaqueline Drücke, Garrett Good, Frank Kasper, and Lukas Pauscher

Wind speed from atmospheric reanalyses is often used as input for modelling wind energy production in energy systems analysis. While some studies compare energy generation of wind turbines to those modelled from reanalysis data sets for specific sites, such analyses are usually aggregated to regional or national levels. However, nationwide evaluations using high quality wind speed measurements at heights relevant for modern wind turbines are still scarce. 

This paper presents a detailed comparison of high quality wind speed measurements of tall profiles with different reanalysis datasets at more than 75 locations in Germany measured by lidars and masts. Among the evaluated model-based products are the regional reanalysis COSMO-REA6, the global reanalysis ERA5 and the new European reanalysis CERRA. They are evaluated at different measurement heights using statistical analysis. All sites include measurement heights above 100 m and are suited for wind energy applications. This evaluation dataset provides good coverage of the relevant terrain ranging from offshore to the low mountain regions. Measurement locations are distributed all over Germany. Data was collected over multiple years (2012 – 2023) and measurement durations at individual locations range from months to multiple years. Many of the measurements were carried out adhering to the current standards used in wind resource assessment or have comparable quality. Thus, the dataset allows for a unique and comprehensive evaluation of the reanalysis datasets with respect to the representation of geographic and topographic features as well as seasonal patterns in the context of wind energy generation. 

To address current advancements in wind power generation, our analysis focuses on heights above 100 m to reflect the height of modern wind turbines. 

First analysis results using ERA5 and COSMO-REA6 indicate a distinct effect of the terrain on the model skill. Both reanalyses have a small median bias across all measurements with larger variations seen for ERA5. There is a height dependency in the bias of the wind speed, with positive (negative) biases for lower (higher) orographic measurement heights – i.e. the terrain height at which the lidar or mast is installed. The bias varies depending on the elevation of the measurement position in hilly/mountainous terrain. A clear correlation can be observed for the bias and the difference of the terrain height at the measurement location and the orographic height of the assigned model grid box. While for elevated lidar/mast positions (higher than the model grid cell) a clear tendency towards higher measured wind speeds can be observed the effect vanishes for measurement sites close to the orographic model height. 

How to cite: Geiger, D., Yuan, D., Spangehl, T., Callies, D., Drücke, J., Good, G., Kasper, F., and Pauscher, L.: An evaluation of wind speed profiles in model-based reanalyses using ground-based measurements of high quality in the context of wind energy generation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18322,, 2024.

Sima Hamzeloo, Xiaoli Guo Larsén, Alfredo Peña, and Jacob Tornfeldt Soerensen

The study aims to couple the Weather Research and Forecasting (WRF) [1] model of the large eddy simulation (LES) module with the MIKE 21 wave [2] model to study the effect of surface waves on the atmospheric flow over the North Sea. We provide a realistic surface wave field with MIKE 21 by forcing Era5 wind speed. We examine the effect of such wave fields on the atmosphere for a variety of met-ocean conditions, from normal to extreme conditions. The methodology involves applying simulated significant wave heights as the surface boundary for the WRF model, employing the LES module to capture the three-dimensional as well as smaller scales of turbulence that are unresolved by WRF-LES. The simulations will be validated using atmospheric and wave measurements in the North Sea, e.g., from the FINO 1 and 3 metocean research platforms. The preliminary results include the model outputs, including the spatial distribution of wind fields under different wave conditions.

[1] Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., … Huang, X. -yu. (2019). A Description of the Advanced Research WRF Model Version 4.1 (No. NCAR/TN-556+STR).


How to cite: Hamzeloo, S., Guo Larsén, X., Peña, A., and Soerensen, J. T.: Investigation of air-sea interaction with a One-Way Coupling: MIKE 3 wave and WRF-LES, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20569,, 2024.

Junqing Zheng, Yong Luo, Rui Chang, and Xiaoqing Gao

The high demand for low-carbon energy sources to mitigate climate change has prompted a rapid increase in ground-mounted solar parks. The implementation of photovoltaic (PV) significantly impacted the local climate and ecosystem, which are both poorly understood. To investigate the effects of a typical solar park on the Gobi ecological system, local microclimate and soil thermal regimes were measured year-round under and between PV arrays, at an applied solar park sited in Xinjiang, China. Our results demonstrated their seasonal and diurnal changes. Under solar PV arrays, the mean annual net radiation and wind speed decreased by 92.68% and 50.53% respectively. In contrast, PV panels caused an increase of the rear sides air by 10.12% with 0.87°C. South-facing PV panels reduced wind speed with the prevailing northerly wind below. In addition, the relative humidity rapidly decreased when snow covered the ground, but slightly increased from April to September. We found the soil under PV panels was cooler and tended to be a sink of energy during spring and summer whereas was more often a source during autumn and winter compared with the soil between PV panels. Observed data developed the understanding of the energy processes of solar parks in Gobi ecosystems and provided evidence to support the sustainable management of the solar park.


Zheng, J., Luo, Y., Chang, R., and Gao, X., 2023. An observational study on the microclimate and soil thermal regimes under solar photovoltaic arrays. Solar Energy. 266, 112159.

How to cite: Zheng, J., Luo, Y., Chang, R., and Gao, X.: An observational study on the microclimate and soil thermal regimes under solar photovoltaic arrays, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-195,, 2024.

Timothy Myers, Allison Van Ormer, Dave Turner, James Wilczak, Laura Bianco, and Bianca Adler

As offshore wind energy development accelerates in the U.S., it is important to assess the accuracy of hub-height wind forecasts from numerical weather prediction models over the ocean.  Leveraging approximately two years of Doppler lidar observations from buoys in the New York Bight, we provide an evaluation of 80-m wind speed forecasts from two weather models: the High-Resolution Rapid Refresh (HRRR) model and the Global Forecast System (GFS).  These two models have different horizontal (3 km vs 13 km) grid spacing, vertical layering, initialization methods, and parameterizations of boundary layer mixing and surface-atmosphere interactions.  Even with these differences, the models demonstrate similar and highly skillful short-term forecasts at three measurement sites (Day 1: root mean square error, RMSE, ≤ 2.4 m/s and r≥0.83; Day 2: RMSE≤3 m/s and r≥0.77).  Day-ahead forecasts also exhibit skill (Critical Success Index > ~0.5) in predicting quiescent winds and winds associated with maximum turbine power.  By Day 10, GFS forecasts on average have almost no skill.  Short-term forecast skill by the HRRR and GFS does not strongly depend on season or time of day, yet we find some dependence of the models' performance on near-surface stability.  Additionally, 5-14 day forecasts by the GFS exhibit lower RMSE during summer relative to other seasons.  The high skill of the HRRR and GFS short-term forecasts establishes confidence in their utility for offshore wind energy maintenance and operation.

How to cite: Myers, T., Van Ormer, A., Turner, D., Wilczak, J., Bianco, L., and Adler, B.: Evaluation of hub-height wind forecasts over the New York Bight, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6578,, 2024.

Jinah Yun, Jinwon Kim, Minwoo Choi, Hee-Wook Choi, Yeon-Hee Kim, Sang-Sam Lee, Ki-Hoon Kim, and Chulkyu Lee

  As the proportion of renewable energy continues to rise, solar energy reaching the Earth's surface holds a significant share compared to other sources such as wind power. Efficient utilization of solar energy necessitates accurate data on surface insolation. Consequently, both domestically and internationally, there's active research into developing insolation mapping using various numerical models based on solar meteorological resources.
The Korea Meteorological Administration's KMAP (Korea-Meteorological Administration Post-processing), hereafter KM, provides insolation data. However, its limitation lies in the inability to realistically account for complex terrains like mountains due to the 1.5 km resolution of the Meteorological Administration's LDAPS (Local Data Assimilation and Prediction System), an operational local forecast model.
 This study analyzes the impact and characteristics of different resolutions of Digital Elevation Models (DEMs) on the accuracy of surface insolation calculations performed by KMAP-Solar, the solar energy mapping system of the Korea Meteorological Administration (1.5 km and 100 m). Comparison and verification against insolation data from 42 Korea Meteorological Administration Automated Synoptic Observation Systems (ASOS) stations reveal that the introduction of high-resolution DEM reduces land-averaged solar radiation biases by up to 32 Wm
−2 at all observation points, particularly accentuating its effect in regions with complex terrains.
The enhanced accuracy due to high-resolution DEMs is attributed to their ability to alleviate errors caused by differences in Sky View Factors (SVF) between high and low-resolution DEMs. Both DEM resolutions exhibit correlations between insolation and terrain elevation (SVF). However, high-resolution DEMs significantly underestimate these relationships compared to low-resolution DEMs, primarily in areas with high elevations where low-resolution DEMs inadequately represent steep terrains and/or small SVFs.
This study demonstrates that high-resolution DEMs provide a more realistic distribution of insolation by integrating a broader range of crucial terrain parameters, thus proving their significance in accurate insolation calculations compared to low-resolution DEMs. It is anticipated that this research will play a crucial role in supporting future solar energy studies, real-time prediction, and management within solar power plant installations and the power grid.

How to cite: Yun, J., Kim, J., Choi, M., Choi, H.-W., Kim, Y.-H., Lee, S.-S., Kim, K.-H., and Lee, C.: Improvement of Korea Meteorological Administration insolation Information by Applying Detailed Terrain Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8687,, 2024.

Petrina Papazek, Pascal Gfäller, and Irene Schicker

Heterogenous, location dependent solar power/PV installations entail individually different production. This is a challenge for power grid operators as to feed-in PV-production, besides its vast output variability, the grid operators need very high-resolution (temporal and spatial) power forecasts, ideally tailored to each of these sites. Technological advances along with the expansion of solar energy will often modify the initial setup of a production site, thereby significantly altering the production data over their record time. Inevitably inconsistent presentations of historic data or short record periods (e.g.: in case of newly build sites) pose challenges in the renewable sector. This induces a common issue in AI driven post-processing:  machine learning and AI powered forecasts heavily rely on sufficient, consistent historic data, more so if simulating expected production peaks in high temporal resolution is part of the requirements. To address the need of such reduced historic data, we aim at generating semi-synthetic data within the ReduceData project by providing a sufficiently represented and continuous data set across multiple data sources. Building on random forest models, we exploit spatial and temporal strongly associated non-reduced auxiliary data, such as satellite data products (e.g.: CAMS) and reanalysis fields (e.g.: ERA5).  Due to their limited nature, PV production records and high-resolution numerical models (e.g.: AROME) will be targeted by our semi-synthetic data generator. The presented case study focuses on nowcasting- to short-range forecasts in 15-minute update frequency tailored to selected solar power production sites in East-Austria. We study to what extent deep learning methods benefit from a consistent semi-synthetic data set built on different raw data sources, highlighting the added value of combining various sources via deep learning. Inputs for the AI-driven post-processing are, for instance, the climatology of satellite data and reanalysis, pvlib’s estimations, AROME surface parameters, and in-house nowcasting models (e.g.: IrradPhyD-Net). Different settings of the semi-synthetic data generator are evaluated by cross-validation. In most studied cases, we achieve a high skill compared to available classical and standard methods (e.g.: persistence, climatology). 

How to cite: Papazek, P., Gfäller, P., and Schicker, I.: An Austrian case study on empowering ReduceData solar power forecasting using a ML-driven semi-synthetic data generator, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8933,, 2024.

Matic Savli and Peter Mlakar
To improve the process of solar energy production, we can utilize the downward
shortwave flux (DSSF) measurement, which constitutes a part of the satellite
derived total and diffuse downward surface shortwave flux (MDSSFTD) product.
MDSSFTD is issued by the Satellite Application Facility on Land Surface Analysis (LSA SAF).
However, its direct application in this area is inhibited by potential systematic
errors in the DSSF product. Therefore, this has to be addressed before the DSSF can be used downstream.

To this end, we implemented a neural network-based post-processing procedure
that uses previous temporal DSSF observations and additional predictors, such
as cloudiness and time of day, to generate a corrected DSSF value. The ground
truth for this regression task are the in-situ measurements across a variety of
locations in Slovenia. Additionally, the neural network produces DSSF estimates
in terms of quantiles, providing an uncertainty estimate of the corrected prediction itself.

We verified our new method on the aforementioned region over a period of
four years. We found that our neural network approach successfully reduces
the presence of systematic differences present in the DSSF. Additionally, the
neural network method outperforms a baseline look-up-table approach in terms
of multiple criteria, such as mean absolute error, bias, and error variability.

How to cite: Savli, M. and Mlakar, P.: Reduction of systematic differences of LSASAF shortwave solar radiation fluxes using neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9396,, 2024.

Lindsay Sheridan, Danielle Preziuso, Caleb Phillips, Dmitry Duplyakin, and Heidi Tinnesand

Distributed wind projects, particularly those involving small wind turbines, are more subject to financial and temporal limitations than utility-scale wind energy. Onsite measurements are often not feasible or economically viable investments, leading to developers, analysts, and customers in the distributed wind community relying on wind resource models to establish generation estimates. One popular wind product used by the distributed wind community in the United States is the global, high-resolution Global Wind Atlas from the Technical University of Denmark and the World Bank Group.

Wind resource models are valuable tools for siting and establishing generation expectations but are not entirely accurate, which can lead to distributed wind customer dissatisfaction when actual energy generation does not meet pre-construction expectations. To enhance the understanding of the performance and limitations of utilizing Global Wind Atlas for wind resource assessment, this work presents the validation of the model wind speeds using meteorological towers across the diverse geography of the United States with measurement heights relevant to distributed wind hub heights (20 m – 100 m). The analysis expands to quantify the performance of Global Wind Atlas in representation of seasonal, diurnal, and interannual variability in the wind resource along with an assessment of wind shear accuracy at locations with measurements at multiple heights.

How to cite: Sheridan, L., Preziuso, D., Phillips, C., Duplyakin, D., and Tinnesand, H.: Performance of Global Wind Atlas for Distributed Wind Resource Assessment in the United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11653,, 2024.

Olanrewaju Soneye-Arogundade and Bernhard Rappenglueck

Knowledge of solar radiation and its components in a particular area is crucial in studying solar energy and constructing solar energy devices due to the many advantages solar radiation has over fossil fuels. In this two-year study, conducted at a tropical site in Ile-Ife, Nigeria, from January 2016 to December 2017, twenty-one empirical models were proposed to estimate diffuse solar radiation using continuous solar radiation data. The models were divided into five groups and developed using relative sunshine duration and/or clearness index as input variables. The performance of five models from the literature was also examined and compared to measured data. The models' performance was evaluated using the Akaike Information Criteria (AIC), the Global Performance Index (GPI), and various statistical errors. Model 11, a quadratic model with clearness index as an input variable, had the lowest AIC (1.8098), AICC (4.8099), ∆AICC (0.0000), and GPI (-2.1796) values and was the most accurate model for estimating diffuse solar radiation at the study site and other locations with similar climatic conditions. None of the models selected from the literature was suitable for estimating diffuse solar radiation at the study site; hence, the proposed models performed better.

How to cite: Soneye-Arogundade, O. and Rappenglueck, B.: Estimation of Diffuse Solar Radiation Models for a Tropical Site in Nigeria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12318,, 2024.

Boris Morin, Damian Flynn, Conor Sweeney, and Aina Maimo Far

The 2024 Government of Ireland Climate Action Plan aims to increase the share of renewable energy sources (RES) from 38% to 80% by 2030. In 2022, the installed capacity of wind power will surpass 4.5 GW, and the goal is to reach the same level as solar power by 2025. As the proportion of energy generated from these weather-dependent sources increases, there is a need to more accurately quantify periods when the energy generated from such sources is low for an extended period, in order to plan for appropriate reserve capacity.

The terms "Dunkelflaute" and “Renewable Drought” have been used to refer to extended periods of time when the capacity factor of both wind and solar power falls below a given threshold for a set period of time. In this study, we define a Dunkelflaute event as occurring when the combined capacity factor for wind and solar falls below a fixed threshold for at least 24 hours. The effect of choosing different values for this fixed threshold is also investigated in our study.

This study aims to investigate how the expected frequency and duration of Dunkelflaute events identified in different RES datasets may change depending on the assumptions made by the underlying RES datasets.

The first RES dataset investigated is an hourly estimate of electricity generation based on ERA5 climate variables, made by C3S Energy, which was produced using statistical and physical models. The C3S Energy dataset provides a time series of electricity supply from wind and solar photovoltaic and is trained using European Network of Transmission System Operators for Electricity (ENTSO-E) data.

This dataset has certain limitations. First, it assumes a homogeneous spatial distribution of the installed capacity of wind and solar energy production, to maintain a methodological coherence between the two RES sources. Second, the energy conversion models applied, contain simplifying approximations, such as using a single wind turbine model with a fixed hub height for all locations.

The second RES dataset has been created by the authors, which uses more detailed information about the location of the wind and PV farms. Relevant atmospheric variables are interpolated from ERA5 data to the location of each RES farm. In addition, the characteristics of the wind and PV panels at each farm are taken into account.

Both datasets are compared against the actual wind and PV capacity factor data supplied by the national grid operator of Ireland, EirGrid, for the year 2023, to indicate the performance of each model. The two datasets are then analysed across the full range of the time series, from 1979 to 2023, to determine the frequency and duration of all Dunkelflaute events during this period.

Differences in the identified Dunkelflaute events highlight the importance of considering results in the context of the driving data, which would be important for future policy decisions such as planning reserve capacity requirements, or locating future RES farms.

How to cite: Morin, B., Flynn, D., Sweeney, C., and Maimo Far, A.: Comparing PV and Wind Models to Analyse Dunkelflaute Events in Ireland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17213,, 2024.

Florian Ebmeier, Nicole Ludwig, Jannik Thümmel, Georg Martius, and Volker H. Franz

As heating is the largest factor of Greenhouse gases in the household sector, it should
be the focus of our decarbonisation efforts. Solar Thermal Systems (STS), which provide
heat based on solar energy, are a promising technology in this regard. However, STS
are prone to faults due to improper installation, maintenance, or operation, often leading
to a substantial reduction in efficiency, damage to the system, or even an increase in
energy cost. As individual monitoring is economically prohibitive for small-scale systems,
automated monitoring and fault detection should be used to address this issue.
We propose a data-driven neural network approach for fault detection in small-scale
STS, utilising probabilistic reconstructions from a long short-term memory (LSTM) based
Variational Autoencoder (VAE). Key factors in our approach are generalising from faultless
data to previously unseen systems and an anomaly score derived from an ensemble of
reconstructions. We apply this to an operational dataset provided by our industry partner,
which includes systems with different types of faults.
Our results show that our model can detect faults in STS with comparable performance
to the state-of-the-art expert-based system used by our industry partner. Furthermore, our
model can detect previously undetected faults, specifically those resulting from unexpected
behaviour in the control software or behaviours that were entirely unexpected and not
considered in the expert-based system. Thus, a combination of our model and the expert-
based system covers a broader range of faults than either system and is proposed for
further use in the industry partner’s application. Additionally, other providers without a
functioning expert-based system could build upon our work to get a minimal viable product
for fault detection in STS, purely based on data from existing systems and without the
need to install additional sensors or domain-specific knowledge.

How to cite: Ebmeier, F., Ludwig, N., Thümmel, J., Martius, G., and Franz, V. H.: Fault Detection in Solar Thermal Systems using Probabilistic Reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17521,, 2024.

Posters virtual: Thu, 18 Apr, 14:00–15:45 | vHall X4

Display time: Thu, 18 Apr 08:30–Thu, 18 Apr 18:00
Chairpersons: Somnath Baidya Roy, Gregor Giebel
Anthony Kettle

Between late January and early March of 1990 Europe was hit by a sequence of severe winter storms that caused significant infrastructure damage and a large number fatalities. The storm sequence started with Hurricane Daria on 25-26 January 1990, which was one of the most serious events of the storm cluster, especially for the UK.  The low pressure centre moved in the west-northwest direction across Ireland, southern Scotland, and northern Jutland before moving further into the Baltic. The strongest winds south of the trajectory path caused significant damage and disruptions in England, France, Belgium, the Netherlands, and West Germany.   Media reports highlighted building damage, interrupted transportation networks, power outages, and fatalities.  There were also a series of maritime emergencies in the English Channel, North Sea, and Baltic Sea.  This contribution takes a closer look at Storm Daria, presenting an overview of meteorological measurements and the societal impacts, followed by an analysis of the North Sea tide gauge network to understand the storm surge and possible large wave occurrences.  The results for Storm Daria are compared with other serious storms of the past 30 years, highlighting similarities and differences in the patterns of storm impact.  Offshore wind energy was at the planning stage in this early period, but onshore wind energy was established in Europe, and the storm is an important case study of extreme meteorological conditions that that can impact energy infrastructure.  The 1990 winter storm sequence was analyzed in detail by the insurance industry because of the large damage costs, and evidence of an emerging climate change contribution was highlighted.

How to cite: Kettle, A.: Storm Daria: Societal and energy impacts in northwest Europe on 25-26 January 1990, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2922,, 2024.

Rupanjan Banerjee and Somnath Baidya Roy

Long-term wind speed forecasting is still in its early stages, particularly in India. Due to lack of operational forecasts the Indian wind industry is forced to rely on climatological averages, that do not incorporate interannual variability. The overall goal of our study is to evaluate and enhance the capability of the Indian Institute of Tropical Meteorology Coupled Forecast System Version 2.0 (IITM CFSv2) model to forecast the summer monsoon (June-September) 10m wind speeds over India at seasonal scales as a part of the Monsoon Mission III program. The model runs were conducted in hindcast mode for the period 1981-2017. Initially, we conducted a systematic evaluation to assess the quality of the forecasts initialized in February and March for selected stations by comparing them against observations from the Global Summary of the Day (GSOD) dataset. Our findings indicate that the raw forecasts are poor quality with Symmetric Mean Absolute Percentage Error (SMAPE) in the 70% and 90% range.

Next, we developed calibration algorithms using ML techniques to improve the quality of the forecasts. Linear Regression, Random Forest, XGBoost, LSTM, Conv-LSTM, GRU were employed as regression models. The outcomes from the best-performing model demonstrate that calibration significantly enhances the quality of the forecasts. After calibration, the mean absolute error (MAE) values typically fall within the range of 0.5 to 0.9 m/s for most stations, though a few stations exhibit values exceeding 1 m/s, in contrast to the raw forecasts where the error range extends from 1.2 to 2 m/s. The SMAPE is reduced to between 30% and 60% after calibration. When compared with 30-year climatology, the calibrated forecasts in 60% of the stations show a positive Root Mean Square Error Skill Score (RMSESS) ranging from 0.01 to 0.3 whereas the scores for the raw forecasts are showing highly negative skill. This study demonstrates that ML based calibration is a promising technique that can significantly improve the quality of numerical model forecasts and perform significantly better than climatology.

How to cite: Banerjee, R. and Baidya Roy, S.: Long - term wind speed forecasting for the monsoon seasons at station scales over India: Integrating ML and Numerical techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7873,, 2024.

Gregor Giebel, Caroline Draxl, Helmut Frank, John Zack, Corinna Möhrlen, George Kariniotakis, Jethro Browell, Ricardo Bessa, and David Lenaghan
The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast. Therefore, the notionally largest group world-wide discussing renewable forecasts, IEA Wind Task 51 “Forecasting for the Weather Driven Energy System” is reaching out to other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. The three existing Work Packages (WPs) on NWP Improvements (WP1), Power and Uncertainty Forecasting (WP2) and optimal use of Forecasting Solutions (WP3), are complemented by thirteen work streams in a matrix structure.
The three work packages span three distinct areas of challenge in forecasting for the weather driven energy system. The first area is the continuing effort to improve the representation of physical processes in weather forecast models through both new high performance initializations and tailored parameterizations. The second area is the heterogeneity of the forecasters and end users, the full understanding of the uncertainties throughout the modelling chain and the incorporation of novel data into power forecasting algorithms. A third area is representation, communication, and use of these uncertainties to industry in forms that readily support decision-making in plant operations and electricity markets.

Task 51 focuses on facilitating communication and collaborations among international research groups engaged in the improvement of the accuracy and applicability of forecast models and their utility for the stakeholders in the wind industry, in the power sector and in the energy system.

The collaboration is also structured in work streams, more targeted around a particular topic and potentially spanning several work packages [1]. Two of those work streams are aligned around forecasting horizons, the one on Sub-seasonal to Seasonal (S2S) forecasting and the one on minute-scale forecasting. Both work streams had public workshops. The Seasonal Forecasting workshop was in Reading (UK) in May 2023, while the Minute Scale Forecasting workshop  was on 10/11 April 2024 in Risø (DK). While the S2S workshop was done in conjunction with WMO, the Minute Scale workshop had people from several other IEA Wind Tasks (Lidars, Wind Farm Flow Control and Hybrid Power Plants) as well as representatives of IEA PVPS Task 16 for the solar side in the committee. The poster will discuss the results of both workshops.


Reference: [1]   The Task website, last accessed 10 January 2024

How to cite: Giebel, G., Draxl, C., Frank, H., Zack, J., Möhrlen, C., Kariniotakis, G., Browell, J., Bessa, R., and Lenaghan, D.: IEA Wind Task 51 – Minute and Seasonal Scale Forecasting Workshops for the Weather Driven Energy System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17848,, 2024.