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 $-\mathrm{1.50}\pm \mathrm{1.30}$ 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.

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).

Understanding uncertainties in wind resource assessment associated with the use of the output from numerical weather prediction (NWP) models is important for wind energy applications. A better understanding of the sources of error reduces risk and lowers costs. Here, an intercomparison of the output from 25 NWP models is presented for three sites in northern Europe characterized by simple terrain. The models are evaluated using a number of statistical properties relevant to wind energy and verified with observations. On average the models have small wind speed biases offshore and aloft (< 4 %) and larger biases closer to the surface over land (> 7 %). A similar pattern is detected for the inter-model spread. Strongly stable and strongly unstable atmospheric stability conditions are associated with larger wind speed errors. Strong indications are found that using a grid spacing larger than 3 km decreases the accuracy of the models, but we found no evidence that using a grid spacing smaller than 3 km is necessary for these simple sites. Applying the models to a simple wind energy offshore wind farm highlights the importance of capturing the correct distributions of wind speed and direction.

Low-level jets (LLJs), characterized by wind speed maxima in the lower part of the atmospheric boundary layer, play a crucial role in shaping wind resource availability, particularly as modern wind turbines reach heights exceeding 200 m. Understanding the climatology of LLJs is essential for optimizing wind energy assessments in offshore environments. We leverage wind measurements from lidars and a mast at five sites in the North and Baltic seas to evaluate the ability of the Weather Research and Forecasting (WRF) model and the widely used reanalysis ERA5 to characterize LLJs and use the optimal WRF setup to generate a detailed 5-year climatology. We test the sensitivity of LLJ representation to key WRF model configurations, including grid spacing, vertical resolution, surface layer (SL), and planetary boundary layer (PBL) parameterizations. Results reveal that LLJ representation strongly depends on the PBL scheme, with LLJ frequency varying by more than a factor of 3 across configurations. For example, the Mellor–Yamada–Janjic (MYJ) scheme favored LLJ formation, while Yonsei University (YSU), BouLac (BL), and Mellor–Yamada–Nakanishi–Niino (MYNN) 2.5 (with bl_mynn_mixlength=0) were less prone. The best-performing setup employed scale-aware subgrid mixing (km_opt=5; 3DTKE), accurately capturing LLJ occurrence rates, intensity, and vertical profiles. In contrast, ERA5 significantly underestimated LLJ frequency and failed to resolve key features, highlighting its limitations for detailed LLJ analysis. The 5-year LLJ climatology provides new insights into the spatial and seasonal distribution of LLJs, offering valuable guidance for offshore wind resource assessment and planning in the region. In the North and Baltic seas, LLJs occur along the western sea basins around 10 %–15 % of the time, with average jet heights between 140–220 m, which are well within the height of operation of modern wind turbines. The most LLJ-prone region is east of southern Sweden, especially during spring and summer, where LLJs contribute to up to 30 % of the wind capacity. In spring and summer, strong coastal gradients are observed in jet timing, height, and direction, particularly along eastern shorelines. Strong variations in the mean duration are also seen, with the longest-lasting jets occurring in the Southern Bight.

This is the first of two papers that documents 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. An optimal combination of model setup and parameterisations 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 largest differences in annual mean wind speed at 100 m 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. Many different parameters were varied across the simulations, for example, model version, forcing data, various physical parameterisations and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes on the wind climate in the boundary layer are mostly due to using different physical parameterisations, 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. The degree of similarity between winds simulated by the WRF ensemble members and the observations was assessed 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 distribution, which is important for wind resource assessment. The most realistic ensemble members were identified 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.