We use cookies on our website to ensure you get the best experience. 2015 is the only year above the long-term average and 2016–2017 are very low years. commissioning date (and decommissioning date if the farm was dismantled). The lack of accessible wind-speed measurements at turbine hub heights is usually the major blocking point for evaluating modelled wind speeds in the scope of wind-power applications. depending on the scope of the study. The period of the study is very short considering the large variability of wind speed on inter-annual to decadal time scales. The interest is not about those isolated singular points but about the general pattern over France and in particular inside each administrative region. Meteorol. Whatever the choice in steps, the method is not seamless, so jumps are observed every 6 h, at the hours when changing from one run to the other. Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over... EDF Lab Paris-Saclay R & D OSIRIS, 7 Bd Gaspard Monge, 91120 Palaiseau, France, (for more information on this spin-up issue, seeÂ. Q & A: Energ., 143, 91–100. MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. This is caused by imbalances in the 3D-Var analysis (for more information on this spin-up issue, see Brousseau et al., 2016). The vertical resolution is 20 m and the 100 m level was mostly used. 1 numerical weather prediction (NWP) model: AROME from Météo-France (about 3 km). The theoretical power curve is smoothed with a Gaussian filter in a similar way as Staffell and Pfenninger (2016). The diurnal cycles are not well depicted, especially at the sites with complex terrain. It was indeed one of the first reanalyses to produce data at an hourly resolution and output winds not only at 10 m above ground but also at 50 m, closer to turbine hub heights. Figure 1Schematic of the reconstruction of a continuous hourly signal by selecting 6 steps (+6 to +11 h here) in each forecast run from an operational NWP model issuing a new forecast every 6 h. Météo-France: Données de modèle atmosphérique à aire limitée à haute résolution, available at: Ministère de la Transition écologique et solidaire: Synthèse de la programmation pluriannuelle de l'énergie, available at: Monforti, F. and Gonzalez-Aparicio, I.: Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union, Appl. (a) For each location (identified by marker style) and each model (identified by marker colour): bias (model – observation, on the y-axis) versus correlation coefficient (on the x-axis) of the 30 min time series. At the local scale, the median bias is −2.0 % in the northern regions and −10.9 % in the southern mountainous regions (resp. −8 % and −48 % of the observed production). See further details. Spatial aggregation over cities and time aggregation over the years. Wind farms are mostly located in HDF and GE. Tetzner, Dieter; Thomas, Elizabeth; Allen, Claire. These sites are likely to provide accurate proxy calibrations for future palaeoclimatic reconstructions. Sci. The climate reanalysis used to perform a proxy calibration should accurately reproduce the local climate variability. Removing losses from gross production to get “real-life” net production is a very important part of the wind resource assessment methodology and under-estimating the losses have led to over-estimating the resource in many past projects (Brower, 2012, see Sect. 16.6). The diurnal cycles show that the overestimation comes mostly from the night time (Fig. 4b). This under-estimation is also seen in the diurnal cycles of the corresponding regions: OCC & PACA in Fig. 4e and ARA (not shown).

There were almost 300 masts over Europe (including 30 in In early planning stages, numerical datasets are used in the form of wind atlases, which commonly provide summarized statistics about local wind speeds. A resolution of a few dozens of kilometres is not able to capture small-scale features which may be important for accurately simulating production at the local scale.

Another field of applications is power forecasting from hourly (Giebel and Kariniotakis, 2017, for a review) to seasonal timescales (e.g. Lledó et al., 2019) based on operational numerical weather prediction (NWP) models. However this value is surely not exact for all wind farms. As variable renewable energies are developing, their impacts on the electric system are growing.