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The atmospheric boundary layer experiences multiple changes in coastal regions, especially with wind directions from land towards the sea, where the wind speed usually increases due to the smaller roughness of the ocean surface. These effects are of particular relevance for offshore wind energy utilization; they are summarized under the term coastal effects. This paper provides an overview of coastal effects and their potential impact on the operating conditions of offshore wind farms with a focus on the German Bight. Common numerical and experimental tools to study coastal effects and developing internal boundary layers (IBL) are introduced, and a review on the current state of research is given. The German Bight is an interesting example to illustrate impacts of coastal effects on offshore wind energy, because of the large number of wind turbines with a coastal distance of 100 km or less. Phenomena related to the stability of the boundary layer, like low level jets, are discussed. Spatial variations of vertical heat fluxes in the coastal zone related to variable water depths or Wadden Sea areas are analysed. The study illustrates that due to the increasing size of offshore wind farms, horizontal wind speed gradients caused by coastal effects can lead to significant wind variations within a single farm. Research topics which still need further attention are discussed in the framework of the rapidly developing wind energy sector with increasing wind turbine hub heights and rotor diameters as well as growing wind farm sizes. One example is the interaction of coastal effects with offshore wind farm wakes. The necessity to consider a large spectrum of spatial and temporal scales to understand and describe coastal effects is highlighted. We summarize modelling and observation tools, which are suitable for the investigation and prediction of the boundary layer dynamics in coastal areas. Existing applications and results are described based on several examples with collocated observation and model results obtained in the X‑Wakes project. The study puts particular focus on the large potential provided by the combination of different measurements and modelling techniques and gives recommendations for future developments of integrated approaches including the formulation of priorities.

As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For an analysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMs make use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energy project. Typically, numerical data are used for the long-term correlation that is required for estimating the yield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operational phase as an additional reference data set to detect degrading sensors. The value of a VMM directly depends on its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regression is used as state of the art to correct reference data to the site-specific conditions. In this study, a framework of 10 different machine-learning methods is tested to investigated the benefit of more advanced methods on two offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-Nearest Neighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of Gaussian Processes is also very promising. The use of more advanced regression models lead to decreased uncertainties; hence those methods should find its way into industrial applications. The recommended regression models can serve as a starting point for the development of end-user applications and services.