Figure 6Local biases of wind-power production simulations based on AROME for year 2015 for loss factors varying from 0 % to 30 %.

The use of ERA5 as the main data source enables to simulate over several decades (starting from 1950 when ERA5 extension will be available), which is necessary for some applications.

This wind-speed analysis is limited by the small number of sites, which impedes general conclusions. The regional diurnal cycles in Fig. 4c show an interesting phenomenon at night.

To complete this direct validation of wind speeds, an indirect evaluation was conducted using a large number of wind-power observations in France from two public datasets published by the transmission and distribution system operators: 30 min power time series at the regional scale and annual sum of energy at the local scale (cities). The dataset contains the years 2011 to 2017. (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. Another kind of simulations, 50 m microscale simulations were also conducted but are not available for download and thus not investigated. This choice was confirmed by local energy outputs of wind farms in the vicinity of the measurement masts: at places with no wind-speed bias, using λ=15 % enabled to remove the wind-power bias. Simulations are usually based on wind-speed data from climate reanalyses are they are useful data with large spatial and temporal coverage. AROME and COSMO-REA6 have very high correlations (0.986 and 0.976 for France).

See location characteristics in Table 2. Moreover, the ERA5 reanalysis is newest reanalysis produced and few studies have evaluated on ERA5’s performance in the Arctic. (b–h) Average diurnal cycles of the observed (in black) and modelled (in colours) wind speeds at all locations except F3 which is extremely similar to F2 (same time period and very close locations). Considering large-scale global models, the new reanalysis ERA5 shows very good skills. Still the overall availability is always above 85 %. Table 2Information on the wind-speed measurements used in the study: location (approximate for confidential data) and surrounding environment; LIDAR/anemometers heights; measurement time period (1 to 3 years) and availabilities over this period: availability of the observations alone and availability of combined observed and modelled datasets. See region map in Fig. 2.

Energy, 206, 439–450, 2017. a, NEWA consortium: NEWA mesoscale data, available at: https://www.neweuropeanwindatlas.eu/, last access: 20 January 2020. a, b, Olauson, J.: ERA5: The new champion of wind power modelling?, Renew. http://help.emd.dk/knowledgebase/content/TechNotes/TechnicalNote6_EMD-WRF_On-Demand_20180116.pdf. Ramon, J., Lledó, L., Pérez-Zanón, N., Soret, A., and Doblas-Reyes, F. J.: The Tall Tower Dataset: a unique initiative to boost wind energy research, Earth Syst. MERRA-2 reanalysis (Gelaro et al., 2017) from NASA's Global Modeling and Assimilation Office and its predecessor MERRA have been much used in the wind industry (e.g. Olauson, 2018, and references therein). In addition, the thesis will focus on how the assimilated N-ICE2015 observations affect the

Annual energy at the local scale (production from one or a few farms in the same city, summed by year).

Can we trust these modelled wind speeds?

Two public datasets were used: 30 min time series of wind-power production at the regional scale (productions aggregated over the twelve French administrative regions).

Wind-speed measurements at levels close to turbine hub heights are difficult to get access to for confidentiality reasons.

NEWA (in orange) has biases ranging from −0.24 to 0.91 m s−1, with a rather positive tendency. They correspond to ending and starting hours of night-time curtailment: the turbines are slowed down between 22:00 and 07:00 LT (local time) (i.e. between 20:00 and 05:00 UTC in summer and between 21:00 and 06:00 UTC in winter) to reduce noise impacts on neighbour inhabitants. The issue appears at both 10:00 and 22:00 UTC but is more visible in convective atmospheric conditions thus appearing mostly at 10:00 UTC, during the day, over continental France.

The study evaluates two global reanalysis (MERRA-2 from NASA and ERA5 from ECMWF), two high-resolution models (COSMO-REA6 reanalysis from DWD, AROME NWP model from Météo-France) and the New European Wind Atlas mesoscale data.

The turbines power curves were also obtained from The Wind Power database. HErZ – Hans-Ertel Centre for Weather Research (University of Bonn, Germany) and DWD – Deutscher Wetterdienst: COSMO-REA6, available at: Jensen, T. and Pinson, P.: RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system, Sci. Res., 17, 63–77, https://doi.org/10.5194/asr-17-63-2020, 2020. This work investigates five wind-speed datasets, comparing them to actual wind-speed and wind-power observations in France. It is a result of the EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019, Lyngby, Denmark, 9–13 September 2019. AROME is not a reanalysis but an operational model issuing a new forecast every 6 h.

A year is discarded if the installed capacity of the city changed during the year (if a farm was installed or dismantled). Some higher resolution models, such as AROME or COSMO-REA6 show very good skills too, reducing the bias and increasing the correlation in complex topography.

The results in Sect. 4.2 show those that were found as the most interesting ones: correlation coefficients, bias and the average diurnal cycles.

The development shall continue as the country's goals of installed capacity by 2028 are about 40 GW for wind (including 6 GW offshore) and 35 to 44 GW for photovoltaic power (Ministère de la Transition écologique et solidaire, 2020). The author declares that she has no conflict of interest. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a, Silva, V., López-Botet Zulueta, M., Wang, Y., Fourment, P., Hinchliffe, T., Burtin, A., and Gatti-Bono, C.: Anticipating Some of the Challenges and Solutions for 60 % Renewable Energy Sources in the European Electricity System, in: Renewable Energy: Forecasting and Risk Management, Springer Proceedings in Mathematics & Statistics, vol. 254, edited by: Drobinski, P., Mougeot, M., Picard, D., Plougonven, R., and Tankov, P., Springer, Cham, https://doi.org/10.1007/978-3-319-99052-1_9, 2017.

(a) For each region (identified by marker style) and each model (identified by marker colour): bias in percent of the installed capacity (model – observation, on the y-axis) versus correlation coefficient (on the x-axis) of the 30 min wind-power time series. Finally, the study also investigates the AROME-France NWP model (Seity et al., 2011) for its high resolution over France. Attendees of the 2019 EMS conference, especially Frank Kaspar (DWD) and Bjarke Tobias Olsen (DTU), for fruitful discussions. Res.

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. 2019.

Turbines whose power curve was missing in the database (it was the case for 64 farms) were replaced by a turbine with similar characteristics (diameter, rated power, rated speed, cut-in speed, cut-out speed).

commissioning date (and decommissioning date if the farm was dismantled). Res.

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