Here, we quantify relationships between wind farm effi ciency and wind speed, direction, turbulence and atmospheric stability using power output from the large offshore wind farm at Nysted in Denmark. Wake losses are, as expected, most strongly related to wind speed variations through the turbine thrust coeffi cient; with direction, atmospheric stability and turbulence as important second order effects. While the wind farm effi ciency is highly dependent on the distribution of wind speeds and wind direction, it is shown that the impact of turbine spacing on wake losses and turbine effi ciency can be quantifi ed, albeit with relatively large uncertainty due to stochastic effects in the data. There is evidence of the 'deep array effect' in that wake losses in the centre of the wind farm are under-estimated by the wind farm model WAsP, although overall effi ciency of the wind farm is well predicted due to compensating edge effects.When wind turbines are arranged in large wind farms, there will inevitably be a loss of power output due to wind turbine wakes. This means that within the wind farm, wind speeds do not recover to their freestream value after encountering the fi rst turbine (or row of turbines), and thus are lower than the freestream at subsequent turbines because kinetic energy has been extracted. Although this is a well-known phenomenon, power losses due to wakes are diffi cult to predict accurately due to the temporal and spatial variability of wind speed, direction, turbulence and atmospheric stability. Accurate quantifi cation of power losses due to wind turbine wakes in differing wind climates and wind farm layouts is essential to optimal wind farm design. In small and medium size offshore wind farms (less than three rows), wake effects have been quantifi ed experimentally, 1,2 Using the observational data, performance of both state-of-the-art wake and wind-farm models has been demonstrated to be at least satisfactory. 3,4 However, preliminary model results from the fi rst large offshore wind farms suggests that current wind farm models using standard parameter values under-estimate power losses in large wind farms due to wind turbine wakes, leading to over-prediction of power output. 5,6 Better prediction can be obtained by adjusting parameters or using 'added roughness' over the area of the wind farm. 7,8 The implied 'deep array effect' has prompted renewed research into wake behaviour. The focus is to understand and predict power losses from wakes in large wind farms with reduced uncertainty.In the simplest form, the velocity defi cit of an individual wind turbine wake is dictated by the thrust coeffi cient (c t ) (and thereby to wind speed; see Figure 1) and the downstream distance, x [9]. The formula used in WAsP which has a fl at velocity profi le in the wake is: Wake losses at Nysted offshore wind farm R. J. Barthelmie and L. E. Jensen 574 V V c D D kx t 0 0 0 2 Wind Energ. 2010; 13:573-586

A grand challenge from the wind energy industry is to provide reliable forecasts on mountain winds several hours in advance at microscale (∼100 m) resolution. This requires better microscale wind-energy physics included in forecasting tools, for which field observations are imperative. While mesoscale (∼1 km) measurements abound, microscale processes are not monitored in practice nor do plentiful measurements exist at this scale. After a decade of preparation, a group of European and U.S. collaborators conducted a field campaign during 1 May–15 June 2017 in Vale Cobrão in central Portugal to delve into microscale processes in complex terrain. This valley is nestled within a parallel double ridge near the town of Perdigão with dominant wind climatology normal to the ridges, offering a nominally simple yet natural setting for fundamental studies. The dense instrument ensemble deployed covered a ∼4 km × 4 km swath horizontally and ∼10 km vertically, with measurement resolutions of tens of meters and seconds. Meteorological data were collected continuously, capturing multiscale flow interactions from synoptic to microscales, diurnal variability, thermal circulation, turbine wake and acoustics, waves, and turbulence. Particularly noteworthy are the extensiveness of the instrument array, space–time scales covered, use of leading-edge multiple-lidar technology alongside conventional tower and remote sensors, fruitful cross-Atlantic partnership, and adaptive management of the campaign. Preliminary data analysis uncovered interesting new phenomena. All data are being archived for public use.

We present empirical downscaling of 5 state-of-the-art AOGCMs (Atmosphere-Ocean General Circulation Models) to investigate potential changes in wind speeds and energy density in northern Europe. The approach is based on downscaling the Weibull parameters of wind speed probability distributions from AOGCM-derived 500 hPa relative vorticity and sea-level pressure gradients, and is demonstrated to generate accurate depictions of the wind climate during the transfer function conditioning period. Bootstrapping is used to develop 100 realizations for each downscaling period and these are used to assess the uncertainty in the results due to stochastic effects in the AOGCM-derived downscaling predictors. Projected changes in the wind speed probability distribution vary with the AOGCMs from which the predictors are derived, but generally it is shown that mean wind speeds, 90th percentile wind speeds and energy density are slightly lower in the 2081-2100 climate projection period than during at the majority of the 46 stations studied. Conversely it is found that there is no significant difference between conditions during 2046-2065 and 1961-1990 based on the ensemble of downscaling results. Equally, the winter time of 2046-2065 is largely indistinguishable from for the majority of stations, while the winters of 2081-2100 appear to be associated with lower mean and 90th percentile wind speeds and energy density. KEY WORDS: Wind speeds · AOGCM · Climate projections · Empirical downscaling Resale or republication not permitted without written consent of the publisherClim Res 29: [183][184][185][186][187][188][189][190][191][192][193][194][195][196][197][198] 2005 formance than regional coupled models for some parameters (Kidson & Thompson 1998, Goddard et al. 2001. Empirical downscaling may prove preferable in the current context due to the need to reproduce local rather than mesoscale wind speeds for many applications (dynamical models run at scales of 10 to 40 km are unable to capture the full variability of 10 m wind speeds; de Rooy & Kok 2004, Räisänen et al. 2004. Pryor et al. (2005c) present a novel approach to empirical downscaling of wind speeds where the predictands are the parameters of the wind speed probability distribution at a specific location (rather than time series of wind speeds) (Sailor et al. 2000, Lionello et al. 2003 and the predictors are parameters of the probability distributions of the sea-level pressure gradient and 500 hPa relative vorticity. We propose that this approach is advantageous because: (1) It avoids a focus on mean conditions and underestimation of variance (due to truncation of the probability distribution), and hence generates a more robust representation of the upper percentiles of the wind speed probability distribution. (2) It generates output that is accessible to, and more strongly coupled to, the needs of user communities such as the wind energy industry who are familiar with Weibull distribution statistics and use them routinely to assess wind energy density. ...

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