Accurate modeling of wind conditions is vital for the efficient operation and management of wind farms. This study investigates the enhancement of weather simulations by assimilating local offshore light detection and ranging (lidar) and/or supervisory control and data acquisition (SCADA) data into a numerical weather prediction model while considering the presence of neighboring wind farms through wind farm parameterization. We focus on improving model output during storms impacting the Belgian–Dutch wind farm cluster located in the Southern Bight of the North Sea via the four-dimensional data assimilation (nudging) technique in the Weather Research and Forecasting (WRF) model. Our findings indicate that assimilating wind observations significantly reduces the relative root-mean-square error for wind speed at a wind farm located 47 km downwind from the offshore lidar platform. This leads to more accurate power production outputs. Specifically, at wind turbines experiencing wake effects, the wind speed error decreased from 10.5 % to 5.2 %, and the wind direction error was reduced by a factor of 2.4. A proposed artificial configuration, leveraging the upwind lidar measurements, showcases the potential for improving hour-ahead wind and power predictions. Moreover, we perform a thorough study to investigate the sensitivity to nudging parameters during versatile atmospheric conditions, which helps to identify the best assimilation practices for this offshore setting. These insights are expected to refine wind resource mapping and reanalysis of weather events, as well as motivate more measurement campaigns offshore.

Characterizing wind and precipitation conditions is essential for the durability and maintenance of wind turbine components. Precipitation-driven leading edge erosion of turbine blades has emerged as a significant concern, as it compromises aerodynamic performance and shortens blade lifespan. This study investigates wind and precipitation patterns across a large region of Europe, with a particular focus on the Southern Bight of the North Sea. Using ten years of ERA5 atmospheric reanalysis data, we analyze wind and precipitation conditions, and derive an erosion risk map based on the combined effects of precipitation and blade tip speed. To capture local-scale variability, we employ high-resolution WRF simulations over a three-year period to downscale ERA5 data for the Southern Bight region. These simulations are used to generate detailed seasonal maps of wind speed, precipitation, and erosion risk on a 3 km grid. Additionally, we compare precipitation estimates from ERA5, as well as from NASA's IMERG satellite product, NORA3 hindcast archive, and from the WRF model output against three Belgian weather stations. We emphasize the added value of high-resolution modeling in capturing precipitation heterogeneity that influences blade erosion rates. Integrating both large-scale and regional weather data supports site screening in early-stage wind farm planning, material selection in blade coatings, and maintenance prioritization, especially offshore, thus contributing to the cost-effectiveness of wind energy projects.

With the rapid increase in wind farm developments, it is essential to evaluate the impacts of newly constructed wind farms on adjacent existing and planned wind farms. Various numerical models have been used to study the interactions between adjacent farms, spanning from fast-running engineering models to numerical weather prediction models. These models are essential to anticipate and mitigate wake effects in future wind energy deployments. Since the atmospheric conditions are variable over the year, it is important to characterize the variation of the wake interactions over the year, important for wind farm operation and planning. Due to the higher computational cost of numerical weather prediction models compared to fast-running engineering models, they are limited in their capacity to evaluate a wide range of design scenarios or very long simulation periods. In this study, we investigate the annual variation of wake effects coming from a new wind farm cluster on adjacent, existing wind farms in the North Sea using a simulation of a representative year. We compare results from a numerical weather prediction model (the Weather and Research Forcasting model) and for multiple fast-running engineering wake models (Gauss-BPA, Jensen (kw = 0.02), Jensen (kw = 0.04), Cumulative curl, TurbOPark (A = 0.04), TurbOPark (A = 0.06)), providing insights into variations in wake loss predictions among the models. Indeed, both the numerical weather prediction model and the engineering models make different assumptions to predict wake interactions between wind farms. Throughout this study a distinction is made between external wake losses, caused by the newly built wind farm cluster only, and internal wake losses, which are generated by the individual wind farms of the existing cluster. Temporal variations in stability are the main driver of hourly and seasonal variations in external wake losses, while internal losses are also determined by seasonal variations in wind speed. While yearly averaged external wind farm losses from the numerical weather prediction model are limited to 4 %, the internal wake losses reach as high as 3 % for the closest adjacent, existing wind farm to the new wind farm cluster. Additionally, all engineering models considered predict lower wake losses compared to the numerical weather prediction model, but predictions exhibit a very large range of magnitudes, ranging from 98 % to 33 % difference for external wakes and 59 % to 14 % for internal wakes compared to the numerical weather prediction model. Not only do the results differ quantitatively but also qualitatively between model strategies, i.e. yearly spatial distributions, especially for the external wake predictions of the former fast-running engineering models (Gauss-BPA, Jensen (kw = 0.02), Jensen (kw = 0.04)) and the numerical weather prediction model. These engineering models do not capture the same qualitative trend as the numerical weather prediction model while the newly designed engineering models (Cumulative curl, TurbOPark (A = 0.04), TurbOPark (A = 0.06)) do. For the internal wake losses, qualitatively, both engineering models and the numerical weather prediction model show higher internal wake losses for turbines located in the center of the wind farm, with highest losses for densely spaced turbines, and lower losses at the edge of the wind farm, however all models show different magnitudes of losses.

The importance of wind energy as an alternative energy source has increased over the latest years with more focus on offshore winds. A good estimation of the offshore winds is thus of major importance for this industry. Up to now the effect of the wind–wave (mis)alignment has not yet been taken into account in coupled atmosphere–wave models to study the vertical wind profile and power production estimations of offshore wind farms. In this study the roughness length parametrization of Drennan et al. in 2003, and its extension addressing the wind–wave (mis)alignment proposed by Porchetta et al. in 2019, are investigated in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This study shows that the yearly mean wind estimation at hub height (100 m) is improved by the roughness length parametrization of Porchetta et al. compared to Drennan. This is mainly due to the increased roughness of the former parametrization compare to the latter, even in aligned wind–wave conditions. This difference in roughness is caused by the dataset used to obtain the constants, deep-water conditions versus mixed offshore conditions. Moreover, the roughness length parametrization of Porchetta et al. performs better in two of three alignment categories. Furthermore, similar model performances are obtained if we exclude the wind directions from the wind shadow zone of the measurement mast or the wind directions from the recently built Alpha Ventus wind farm, which is in close vicinity of the measurement mast. Investigating different wind conditions shows that the new roughness length parametrization of Porchetta et al. performs best for both offshore and onshore winds. Additionally, we show that the coupled model estimations of the vertical wind are only slightly affected by significant wave height estimations. Similar model performances for different accuracies of significant wave height estimations are presented. One exception is the perpendicular alignment category where the new roughness length of Porchetta et al. outperforms the roughness length of Drennan when investigating the wind estimations related to significant wave heights with a higher accuracy. The roughness length parametrization of Porchetta et al. reduced the power production overestimation of the coupled model from 5.7 to 2.8%. We also show that the standalone atmospheric model including the roughness length of Charnock in 1955 has a degraded performance compared to the coupled model including the roughness length parametrization of Porchetta et al. for yearly average wind profiles.

Two-way feedback occurs between offshore wind and waves. However, the influence of the waves on the wind profile remains understudied, in particular the momentum transfer between the sea surface and the atmosphere. Previous studies showed that for swell waves it is possible to have increasing wind speeds in case of aligned wind–wave directions. However, the opposite is valid for opposed wind–wave directions, where a decrease in wind velocity is observed. Up to now, this behavior has not been included in most numerical models due to the lack of an appropriate parameterization of the resulting effective roughness length. Using an extensive data set of offshore measurements in the North Sea and the Atlantic Ocean, we show that the wave roughness length affecting the wind is indeed dependent on the alignment between the wind and wave directions. Moreover, we propose a new roughness length parameterization, taking into account the dependence on alignment, consisting of an enhanced roughness length for increasing misalignment. Using this new roughness length parameterization in numerical models might facilitate a better representation of offshore wind, which is relevant to many applications including offshore wind energy and climate modeling.