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We propose a modification to the Fitch wind farm parameterization implemented in the Weather Research and Forecasting (WRF) model. This modification, derived from 1D momentum theory, employs a wind-speed-dependent induction factor to correct the local grid wind speed back to freestream before computing the turbine's power and thrust. While the original implementation underestimates power, the modified version shows good agreement with the power curve. We strongly recommend employing the modification for all studies that model at maximum one turbine per WRF grid cell. For simulations with more turbines per grid cell, additional inner-cell wake losses have to be considered. [ABSTRACT FROM AUTHOR]

This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the downscaling using WAsP. We show the main results from the final wind atlas and present a comprehensive evaluation of each component of the NEWA model chain using observations from a large set of tall masts located all over Europe. The added value of the WRF and WAsP downscaling of wind climatologies is evaluated relative to the performance of the driving ERA5 reanalysis and shows that the WRF downscaling reduces the mean wind speed bias and spread relative to that of ERA5 from -1.50±1.30 to 0.02±0.78 m s -1. The WAsP downscaling has an added positive impact relative to that of the WRF model in simple terrain. In complex terrain, where the assumptions of the linearized flow model break down, both the mean bias and spread in wind speed are worse than those from the raw mesoscale results. [ABSTRACT FROM AUTHOR]

This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study. [ABSTRACT FROM AUTHOR]

Our aim with this paper was the analysis of the influence of offshore cluster wakes on the power of a far-distant wind farm. We measured cluster wakes with long-range Doppler light detection and ranging (lidar) and satellite synthetic aperture radar (SAR) in different atmospheric stabilities and analysed their impact on the 400MW offshore wind farm Global Tech I in the German North Sea using supervisory control and data acquisition (SCADA) power data. Our results showed clear wind speed deficits that can be related to the wakes of wind farm clusters up to 55 km upstream in stable and weakly unstable stratified boundary layers resulting in a clear reduction in power production. We discussed the influence of cluster wakes on the power production of a far-distant wind farm, cluster wake characteristics and methods for cluster wake monitoring. In conclusion, we proved the existence of wake shadowing effects with resulting power losses up to 55 km downstream and encouraged further investigations on far-reaching wake shadowing effects for optimized areal planning and reduced uncertainties in offshore wind power resource assessment. [ABSTRACT FROM AUTHOR]

Airborne wind energy systems (AWESs) aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind light detection and ranging (lidar) measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This study investigates whether assimilating measurements into the mesoscale Weather Research and Forecasting (WRF) model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the lidar measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long-term statistics. Finally, wind speed data are used to estimate the optimal traction power and operating altitudes of AWES. Observation nudging improves the WRF accuracy at the measurement location. Close to the surface the impact of nudging is limited as effects of the air-surface interaction dominate but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed frequency distribution shows a multi-modality caused by changing atmospheric stability conditions. Therefore, wind speed profiles are categorized into various stability conditions. Based on a simplified AWES model, the most probable optimal altitude is between 200 and 600 m. This wide range of heights emphasizes the benefit of such systems to dynamically adjust their operating altitude. [ABSTRACT FROM AUTHOR]

The goal of the NEWA project was to create the New European Wind Atlas together with relevant uncertainty information. As the wind energy is proportional to the third order of wind speed, it is necessary to consider the whole distribution of wind speed in addition to mean values.During the project a multi-physics ensemble of WRF (Weather Research and Forecast model) results with different parametrization schemes was created. It included members with different combinations of Planetary Boundary Schemes, Surface Schemes (where possible) and Land Surface Models, with additional members representing different SST dataset or changes in nudging methodology (grid nudging vs spectral nudging). ERA-5 reanalysis is used for lateral boundary conditions and initialization. In total around 20 members providing significant spread were identified.The analysis is carried out for two different regions of Europe – one covers central Europe, including Denmark, northern Germany and North Sea (302x296 grid points), second covers Greece representing mountainous terrain (302 x 284 grid points), with 3 km target spatial resolution. The dataset represents one complete year of calculations, with each run being one-week long, using nudging towards reanalysis data.There exists several metrics that can compare two distributions in a single grid-point, such as Chi-square or EMD (Wasserstein metric). In this work the two main topics of interest are (1) how to compare the spread in neighboring grid-points and if it is possible to formulate any regional level conclusions about the spatial characteristics of the spread and (2) what are the physical processes the representation of which results in the most uncertainty.In the context of NEWA project such approach was used to guide the development of smaller ensembles that could replicate the same properties of uncertainty as the larger ensemble, however such assessment serves as important insight in the sources and causes of model uncertainties, therefore paving way in reducing them.Results show that changes in SST input data can provide spread over the North Sea, but the spread over the land is influenced by land use category and surface properties.Corresponding author is grateful to the project "Mathematical modelling of weather processes - development of methodology and applications for Latvia (1.1.1.2/VIAA/2/18/261)" for financial support. [ABSTRACT FROM AUTHOR]

The New European Wind Atlas (NEWA) project aims to develop a new reference method for wind resource assessment and wind turbine site suitability based on a mesoscale to microscale model-chain. This new approach will produce a more reliable wind characterisation than current models, leading to a significant reduction of uncertainties on wind energy production and wind conditions that affect the design of wind turbines.The selection of the Weather Research and Forecasting (WRF) model configuration for the mesoscale production run was based on assessing a large ensemble of WRF model simulations with different model setups. Simulations covered a full year (2015) for a domain covering Germany, Denmark, the Netherlands and the North Sea with 3-km grid spacing. The parameters tested in the nearly 50 ensemble members include, among others: model version, atmospheric and sea surface temperature input, land surface model as well as surface and PBL parameterisations. The wind speed climatology of each member is compared to the reference configuration and to wind observations at tall masts using annual averages, RMSE, and wind speed distributions. This comparison revealed that no single setup was 'best' for all the available measurement masts, but one performed slightly better at 2-3 sites. This configuration was chosen as the base setup for 30 years of simulations for 10 overlapping domains over Europe at a spatial grid spacing of 3 km x 3 km. These data will be released to the public in June 2019. Results of the methods used to select ensemble members, the spread found in different regions and their possible relationship to the uncertainty of the mesoscale wind atlas will also be presented. A further ensemble of simulations was done for other domains and time periods. Interestingly, and perhaps not unexpected, a different set of ensemble members is needed in different regions in Europe. The results showed that changes in SST input data can provide spread over the North Sea, but the spread over the land is influenced by land use category and surface properties. The spread among all ensemble members and combinations of ensemble members was estimated. Furthermore, data clustering techniques were applied, together with the integral of differences in parameter probability distributions, to find a reduced (5-10) number of ensemble members that provided the largest spread. This final ensemble will be the backbone of the NEWA probabilistic wind atlas which serves to provide information about model uncertainties. [ABSTRACT FROM AUTHOR]