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This study investigates the performance of pumping-mode ground-generation airborne wind energy systems (AWESs) by determining cyclical, feasible, power-optimal flight trajectories based on realistic vertical wind velocity profiles. These 10 min profiles, derived from mesoscale weather simulations at an offshore and an onshore site in Europe, are incorporated into an optimal control model that maximizes average cycle power by optimizing the trajectory. To reduce the computational cost, representative wind conditions are determined based on k-means clustering. The results describe the influence of wind speed magnitude and profile shape on the power, tether tension, tether reeling speed, and kite trajectory during a pumping cycle. The effect of mesoscale-simulated wind profiles on power curves is illustrated by comparing them to logarithmic wind profiles. Offshore, the results are in good agreement, while onshore power curves differ due to more frequent non-monotonic wind conditions. Results are references against a simplified quasi-steady-state model and wind turbine model. This study investigates how power curves based on mesoscale-simulated wind profiles are affected by the choice of reference height. Our data show that optimal operating heights are generally below 400 m with most AWESs operating at around 200 m.

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 (Dörenkämper et al., 2020).

The New European Wind Atlas (NEWA), the largest European project on wind resource assessment technology, has developed mesoscale-to-microscale wind atlas and site assessment methodologies alongside a validation strategy that leverages data from large field experiments as well as wind resource campaigns from industry through a formal verification and validation process. A probabilistic wind atlas approach, based on a multi-physics ensemble, provides means to quantify the uncertainty associated to the mesoscale configuration. Offline meso-micro coupling has been adopted to provide a modular approach for microscale models of different fidelities to share common mesoscale input data. An open-source model chain based on WRF and OpenFOAM codes has been released as reference for future model development and validation activities in connection to wind assessment best practices and standards.

This report summarizes RANS and LES simulation results from model runs of the Rödeser Berg site near Kassel and is separated into two parts. The first part focuses on LES simulation results from a series of sensitivity studies carried out at the Rödeser Berg site to investigate the model’s sensitivity towards forest representations and grid resolution. The results indicate that a representation of the forest in terms of roughness only is not capable in correctly resolving the wind conditions on top of a forested hill, while a forest parametrization that accounts for the drag in the lowest grid cells improves the results significantly. The second part of the report summarizes the (anonymous) results of the first round of benchmarks carried out at the Rödeser Berg site where several RANS and LES modelers handed in simulated wind profiles at several locations during a southwesterly flow. These model results are compared to wind profile measurements at different positions across the Rödeser Berg. The results indicate that neutrally stratified situations can be modeled well by most models but, especially in forested areas, the model results strongly depend on the forest model chosen. In stably stratified situations the model results strongly differ, especially in areas downstream of topography features, i.e. the wakes of hills. Further research is needed in these areas. The Rödeser Berg experimental data offer a great opportunity for future further validation of models and verification of new models and parameterizations against measurement data.

It is common practice to evaluate mesoscale meteorological models against in-situ mast measurements of one or several met masts. With the NEWA Ferry Lidar Experiment and Benchmark conducted as part of the New European Wind Atlas (NEWA) project we further develop the concept of spatially distributed wind profile measurements. A vertically scanning Doppler lidar was placed on a ferryboat travelling on a regular route through the Southern Baltic Sea between Kiel (Germany) and Klaipeda (Lithuania). During four months between February and June 2017, the lidar continuously measured wind profiles between 65 m and 275 m above sea level along the ship track. The obtained wind profile data were corrected for ship motions (i.e. ship velocity and heading). To our knowledge this is the first dataset of wind profile measurements with lidar along such a long track. As the lidar moves with the ship and covers a distance that is comparable to a typical mesoscale model domain, this dataset is very suitable to be compared against meteorological mesoscale model data. In the associated modelling benchmark, participants from the international mesoscale modelling community were invited to simulate the period of the experiment with different mesoscale models and model setups. The participants were encouraged to either use their best practice setup or to test several setups. The objectives of the benchmark are: To assess how well today’s mesoscale models can reproduce the wind conditions offshore and, in particular, in the Southern Baltic Sea where quite often coastal effects are present. To compare different models as well as different model setups of the same model against the measurements. To gain experience with this unique kind of data (moving wind profiles) and explore its strengths and weaknesses. Several participants used the WRF model with different setups and forcing data but also ICON-EU operational forecasts and MM5 are participating in the benchmark. The simulated time series are compared with the measured time series in different heights, and vertical profiles are evaluated at specific locations. Furthermore, error statistics are presented.

Within the framework of the New European Wind Atlas (NEWA) project, a consortium of several partners from 7 research institutions is currently preparing a mesoscale wind atlas for Europe. The atlas covers all EU countries including Norway, Switzerland, the Balkans and Turkey as well as the entire North and Baltic Seas and other offshore areas up to 100 km from the coast. For this purpose, the mesoscale model WRF was used and numerous setups where tested over a period of about two years to find an optimal set of model configuration in terms of wind resources. For the final calculation of the wind atlas, the simulated area was split into ten smaller domains to facilitate the handling of the computations. These were made possible by a PRACE tier-0 allocation at the MareNostrum high-performance computer in Barcelona, Spain. The simulation for the final wind atlas product took about six months, where the tasks were distributed among a team of 13 members from the project consortium that was responsible for submitting, surveying, processing and transferring hundreds of individual runs 24/7. To avoid human errors, the setup of the individual jobs was automatized as much as possible. The about 6000 Terabytes of raw mesoscale model data where on the fly post-processed and interpolated to the final grid, leading to a much smaller but still impressive amount of 200 Terabytes of raw data that build the final wind atlas. This mesoscale data was then used for downscaling the winds to 50 m resolution using the WaSP approach. The final product will be available to the public by late June 2019. Besides some first results from the atlas, the presentation will focus on how the setup for the production runs for the wind atlas was selected, how the simulations were coordinated and what computational and organizational effort was made to produce the atlas product.

This report describes the sensitivity studies performed with the mesoscale model WRF in preparation of the mesoscale wind atlas production runs. The objective of this work was to find a model setup that is not just a best practice setup but well-founded and based on scientific evaluation. We started with performing some initial sensitivity experiments changing the PBL scheme and the initialisation of the model. The work was distributed among several partners, each conducting the same set of experiments but on a different domain. The objective of this first phase was to ensure that everybody speaks the same language in terms of applying WRF in the context of NEWA. The results were analysed and compared in terms of the mean wind climate. To draw conclusions regarding the quality of the experiments, the results of one domain were compared to tall mast observations. Overall the model showed a good performance with slightly better results for one of the two tested PBL schemes (MYNN) and weekly initialisation of simulations (compared to daily). In the next phase, further sensitivity tests were conducted for one of the previously defined domains, varying a multitude of parameters as e.g. model version, vertical resolution, forcing data and land surface parameterisation. These studies showed that virtually each parameter change is affecting the results in some way, while significant effects on the wind climate are mostly obtained by changes in physical parameterisation e.g. PBL scheme, representation of the land surface and surface roughness. However, also non-physical parameters as the simulation length and the domain size affects the results considerably. The results suggest to use rather small domains and not too long simulations (in the order of 1–2 weeks). One of the objectives of NEWA is to create a probabilistic wind atlas, i.e. to provide uncertainty information to the mesoscale wind atlas (see Deliverables D3.1 and D4.4). This will be achieved by generating an ensemble of WRF simulations with different model configurations. While the final ensemble to be run over the complete NEWA domain will only include a few members, a much larger ensemble was run for a smaller sub-domain to find the ensemble members that generate the largest spread and will be used in the final NEWA ensemble. A second objective of this initial large ensemble was to find an optimal setup for the mesoscale production run. Based on the experience gained in the previous sensitivity experiments, a 47-member ensemble was assembled and run. The individual members were compared against each other, as well as against tall mast observations. Different metrics were explored to assess the performance of the members, i.e. not only the usual statistical measures as RMSE, BIAS and correlation but also metrics that compare the wind speed distributions. In the final part of this report we present the ultimate WRF setup for the NEWA production run that was run between August 2018 and March 2019 on the MareNostrum supercomputer in Barcelona.

A central part of the New European Wind Atlas (NEWA) is the generation of a mesoscale wind atlas which is based on 30 years of simulations with the WRF (Weather Research and Forecast) model. The atlas will cover all EU countries (plus Norway, Switzerland, the Balkans and Turkey) and associated offshore areas up to 100 km off the coast including the complete North and Baltic Seas. The first part of the presentation gives an overview of the work that was done to prepare the mesoscale wind atlas production runs. To arrive at a final model configuration for the production run, a large ensemble of nearly 50 WRF simulations with different model setups was performed. The parameters tested in the ensemble members include, among others: model version, atmospheric and sea surface temperature input, land surface model as well as surface and PBL parameterisations. Information from the ensemble runs will also be used to provide uncertainty information to the wind atlas. After presenting the final setup of the production runs, some information is given about the management of the production runs, which were performed on the Tier-0 supercomputer MareNostrum. Finally, results from the production runs are shown.