As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses.@en

In the next few decades climate mitigation efforts will transform the North Sea into one of the most important energy sources. The present wind energy capacity on the North Sea is expected to increase by almost a factor 5 in 2030 and almost a factor 10 in 2050. It is therefore of paramount importance to know how wind farms influence the atmosphere. Wind farms extract kinetic energy from the atmosphere and in doing so decrease the wind speed and increase turbulence levels. More turbulence means more mixing of vertical layers in the atmosphere and a change in humidity and temperature profiles. This may lead to cloud forming or dissipation. Wind farms are also an obstacle to the flow, which is what is called the blockage effect, as opposed to the wake effect behind the wind farm. This report is about the wake effect, mainly on wind, but we also analysed temperature and humidity profiles. In order to assess and quantify the wake effect, we compared two high resolution re-analyses for the year 2019 on a 2000 by 2000 km North Sea domain. The high resolution re-analyses with a 2.5 km horizontal grid spacing is based on global re-analysis ERA5 and downscaled with mesoscale weather model HARMONIE-AROME which is used operationally at KNMI. One of the re-analyses is without the effect of wind farms (referred to as control or HarmCY43-CTL in this report) and one with the Fitch wind farm parametrization that was recently incorporated in HARMONIE-AROME (HarmCY43-WFP). From the differences between the two we can isolate the wind speed deficits, or wakes, from the wind farms. Earlier validation studies have shown that a previous version of the HARMONIE-AROME model (HarmCY40) produces accurate wind climatology for undisturbed wind fields (period 2008-2018) and validates well against disturbed tower, aircraft and lidar measurements from 2016. In these studies the wind climatology is not validated for different stability regimes. In this study we do make that distinction and use measurements from 2019 for validation of HarmCY43-CTL and HarmCY43-WFP. * Generally HarmCY43-WFP outperforms HarmCY43-CTL in wake areas. HarmCY43-WFP even seems to capture the wind in wind farms reasonably well, although the WFP is not designed for that. * The selection criterion that we used to select disturbed (in wakes) and undisturbed wind directions (outside wakes) seems to work well: the WFP reduces the wind speed bias for disturbed winds significantly, but hardly affects undisturbed winds. * Our results confirm earlier studies that wakes are strongest for situations with stable stratification: we observed wake lengths as long as about 50 km. We can conclude that HarmCY43-CTL tends to underestimate the wind speed for stable stratification and overestimate the wind speed for weakly stable and unstable stratification, mainly for the lidar measurements. As expected HarmCY43-WFP reduces the wind speed in the wake. This means that HarmCY43-WFP validates better against measurements for weakly stable and unstable stratification. However, for stable stratification HarmCY43-WFP makes the underestimation of the measurements worse (note that this does not imply the wake deficits are biased). This could even become worse if wind turbines are not performing according to the power curve or are not turning at all because of maintenance or legislation, the WFP will not be aware of that and will extract too much energy, overestimate the wake effect and underestimate the wind speed. * Earlier studies have shown that HarmCY40-CTL captures the diurnal cycle well. HarmCY43-CTL does as well and including the WFP does not seem to affect that. The results of this study give us confidence that the present HARMONIE-AROME model configuration, including the Fitch WFP, can be used to assess the influence of the anticipated wind farm infrastructure in 2050 on the wind climatology. @en

High-resolution, regularly gridded air-temperature maps are frequently used in climatology, hydrology, and ecology. Within the Netherlands, 34 official automatic weather stations (AWSs) are operated by the National Met Service according to World Meteorological Organization (WMO) standards. Although the measurements are of high quality, the spatial density of the AWSs is not sufficient to reconstruct the temperature on a 1-km-resolution grid. Therefore, a new methodology for daily temperature reconstruction from 1990 to 2017 is proposed, using linear regression and multiple adaptive regression splines. The daily 34 AWS measurements are interpolated using eight different predictors: diurnal temperature range, population density, elevation, albedo, solar irradiance, roughness, precipitation, and vegetation index. Results are cross-validated for the AWS locations and compared with independent citizen weather observations. The RMSE of the reference method ordinary kriging amounts to 2.6 °C whereas using the new methods the RMSE drops below 1.0 °C. Especially for cities, a substantial improvement of the predictions is found. Independent predictions are on average 0.3 °C less biased than ordinary kriging at 40 high-quality citizen measurement sites. With this new method, we have improved the representation of local temperature variations within the Netherlands. The temperature maps presented here can have applications in urban heat island studies, local trend analysis, and model evaluation.

@en