The design of wind turbines and wind farms can be improved by increasing the accuracy of the inflow models representing the atmospheric boundary layer. In this work we employ one-dimensional Reynolds-averaged Navier–Stokes (RANS) simulations of the idealized atmospheric boundary layer (ABL), using turbulence closures with a length-scale limiter. These models can represent the mean effects of surface roughness, Coriolis force, limited ABL depth, and neutral and stable atmospheric conditions using four input parameters: the roughness length, the Coriolis parameter, a maximum turbulence length, and the geostrophic wind speed. We find a new model-based Rossby similarity, which reduces the four input parameters to two Rossby numbers with different length scales. In addition, we extend the limited-length-scale turbulence models to treat the mean effect of unstable stratification in steady-state simulations. The original and extended turbulence models are compared with historical measurements of meteorological quantities and profiles of the atmospheric boundary layer for different atmospheric stabilities.

The influence of baroclinicity on wind within the planetary boundary layer was investigated using two years of wind lidar measurements collected at a suburban site in northern Germany (Hamburg) and a rural‐coastal site in western Denmark (Høvsøre). Measurements were made up to a height of 950 m. The surface geostrophic wind, the surface gradient wind and the gradient wind were estimated using the pressure and geopotential fields from a mesoscale model. At both sites the atmospheric flow was typically baroclinic. The distribution of the geostrophic wind shear was approximately Gaussian with a mean close to zero and a standard deviation of approximately 3 m s⁻¹km⁻¹. The geostrophic wind shear had a strong seasonal dependence because of temperature differences between land and sea. The mean wind profile in Hamburg, observed during an intensive campaign using radio sounding and during the whole year using the wind lidar, was influenced by baroclinicity. For easterly winds at Høvsøre, the estimated gradient wind decreased rapidly with height, resulting in a mean low‐level jet. The turning of the wind in the boundary layer, the boundary‐layer height and the empirical constants in the geostrophic drag law were found to be dependent on baroclinicity for neutral conditions.

We present a comprehensive database of near-shore wind observations that were carried out during the experimental campaign of the RUNE project. RUNE aims at reducing the uncertainty of the near-shore wind resource estimates from model outputs by using lidar, ocean, and satellite observations. Here, we concentrate on describing the lidar measurements. The campaign was conducted from November 2015 to February 2016 on the west coast of Denmark and comprises measurements from eight lidars, an ocean buoy and three types of satellites. The wind speed was estimated based on measurements from a scanning lidar performing PPIs, two scanning lidars performing dual synchronized scans, and five vertical profiling lidars, of which one was operating offshore on a floating platform. The availability of measurements is highest for the profiling lidars, followed by the lidar performing PPIs, those performing the dual setup, and the lidar buoy. Analysis of the lidar measurements reveals good agreement between the estimated 10-min wind speeds, although the instruments used different scanning strategies and measured different volumes in the atmosphere. The campaign is characterized by strong westerlies with occasional storms.

Applying erroneous roughness lengths can have a large impact on the estimated performance of wind turbines, particularly in forested areas. In this study, a new method called the objective roughness approach (ORA), which converts tree height maps created using airborne lidar scans to roughness maps suitable for wind modelling, is evaluated via cross predictions among different anemometers at a complex forested site with seven tall meteorological masts using the Wind Atlas Analysis and Application Program (WAsP). The cross predictions were made using ORA maps created at four spatial resolutions and from four freely available roughness maps based on land use classifications. The validation showed that the use of ORA maps resulted in a closer agreement with observational data for all investigated resolutions compared to the land use maps. Further, when using the ORA maps, the risk of making large errors (> 25 %) in predicted power density was reduced by 40–50 % compared to satellite-based products with the same resolution. The results could be further improved for high-resolution ORA maps by adding the displacement height. The improvements when using the ORA maps were both due to a higher roughness length and due to the higher resolution.