## 1 Introduction  
Wind power is the second largest renewable energy resource, with a market share of 7.8%, or about 2325 TWh, of the total electricity production in the world in 2023 [1], and a global increase in installed wind power capacity of 11%, from 1021 GW to 1136 GW, in 2024 [2]. As an intermittent resource, wind power production strongly depends on the local wind conditions, and wind resource assessment is an important part of the development of new wind farms. There are several options for wind resource assessment, including observational data from wind measurements, numerical weather modelling (NWM) and/or computational fluid dynamics, followed by statistical post-processing of one or a combination of these methods [3, 4, 5]. A common setup for wind resource assessment for a wind power plant is to use locally measured data, and then microscalenumerical wind simulations to determine wind variations in the area for optimal placement of wind turbines [6; 7; 8; 9]. During the last decade, numerical weather models have proven to be a natural step for mesoscale wind resource assessment when considering large areas and/or long time intervals [10], as in country-wide wind atlases for Spain [11], Finland [12], Greece [13], Canada [14; 15] and Russia [16]. Several reports show that the differences between NWM and site-specific wind measurements are in the order of 5% or less [10], although complex terrain typically increases the errors [8; 11; 17; 18]. Already in this early period of NWM for mapping, ref. [11] performed validation test of a wind map based on the mesoscale Skiron against measurement masts in different terrain types in Spain. This showed that the mean average error was larger, and with more spread, for complex terrain compared to flat terrain. Ref. [8] used two wind power park locations in complex terrain, with measurements from several wind turbines and meteorological masts for comparison, and showed that the Weather Research and Forecasting model (WRF) resulted in improved wind speed estimates compared to the commercial microscale models WindSim and WAsP. One of the great advantages of NWM is that it typically includes global coverage where meteorological measurements are incorporated, and although these measurements might be far from the site under scrutiny, the physical modelling allows for downscaling and creation of virtual mast data that can successfully be used individually for weather research assessment [8] or as input for traditional microscale computational fluid dynamics (CDF) modelling for wind power [12; 18]. With increasing computational power, NWM has moved into the microscale flow regime where parametric planetary boundary layer parametrisation is replaced by large eddy simulations (LESs) to resolve terrain effects [19; 20] and/or includes wind turbines in the wind field [21; 22; 23; 24]. The case study in [20] shows how WRF LESs can be used to study the wind changes over 42 h for 274 individual wind turbine locations at one wind park in a mountainous area. Thus, NWM may be able to provide an initial detailed wind resource assessment without the need for site measurements or additional microscale software tools such as WAsP for the New European Wind Atlas [25]. Although several papers have studied and used NWM for wind power applications, its tendency to underestimate high-wind-speed events has recently been reported in both complex terrain [26; 27] and offshore [28; 29]. In this paper, we investigate the mesoscale Weather Research and Forecasting model (WRF) [30] using the Fakken wind power plant located in complex terrain in northern Norway. This site is surrounded by several fjords and steep mountains, and the effect of increased resolution and LESs on the modelled wind flow is compared with measurements during the year of 2017.