## 1 Introduction  
Wind turbine planning requires reliable estimation of the wind power potential at a prospective site which necessitates knowledge of the wind conditions. The assessment of the wind conditions is recommended to be based on representative wind measurements at projected hub height for at least 12 months with a data availability of at least 80% [1, 2]. Representativeness must include the consideration of seasonal differences in wind speed. In Germany, the average hub height of newly installed turbines in 2018 was 133 m above ground [3]. In general, the optimal hub height depends on the type of the wind turbine, the wind regime at the location, and the economic costs and benefits with regard to different planning scenarios [4, 5]. Currently, the trend for the next generation of turbines goes up to 160 m hub heights and will most likely increase up to 180 m in the future [6]. It is of the utmost importance for planning and financing of wind parks at a certain position that the wind situation at hub height, and thus the expected wind energy revenue, can be properly estimated in advance of a construction. Most inland wind parks are planned in complex topography and higher elevations due to higher wind speeds at these elevations [6; 7; 8]. Unfortunately, there are hardly any wind measurements available in these mostly remote and rural planning areas, particularly not at hub height. Reanalysis data or even data from wind atlases might help, but the results are not always reliable, particularly in complex terrain [9; 10; 11]. Traditionally, measurements utilized meteorological towers, which are very expensive due to the need of tower construction up to hub height, requiring permits, acquisition of suitable building sites, and the deconstruction of the tower after the temporally limited measurement campaign [12]. A more cost-effective alternative is the use of coherent wind Doppler lidar (CWDL) technology, which has supplemented or even substituted towers in recent years [8; 12; 13; 14; 15]. While lidar measurements are cost-effective and flexible compared to meteorological masts, it is known that fog or low stratus events (FLS) can lead to loss of measurement data [16; 17]. During FLS conditions it can be observed that the high number of fog droplets leads to higher reflectivity of the signal, thus resulting in an increase of the Carrier-to-Noise Ratio (CNR) at lower measurement ranges and a greater decrease at longer ranges [17]. Consequently, the availability, which is defined as the percentage of CNR values above a predefined threshold during a certain time period [18], also decreases at the higher measurement ranges during such events. This has a negative impact on the overall availability of a lidar measurement campaign. In order to minimize low availability periods, it is advantageous to be able to estimate the lidar data availability at potential deployment sites when planning the lidar measurement strategy. While FLS is one of the most important drivers of low availability, its spatial and temporal variabilities are mostly unknown for remote planning sites without any visibility observations. Weather satellites have long been used successfully to detect the presence of fog and low stratus [19; 20; 21; 22], and therefore can give useful information about FLS on high spatiotemporal scales. A newly developed scheme based on machine learning technology called HYFOG allows the spatially explicit calculation of ground fog, based on the retrieval of FLS cloud base altitudes (CBA) for the entire area of Europe from data obtained from the Meteosat Second Generation (MSG) SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument [23; 24]. The aim of the current paper is to show that the CBA product from the HYFOG algorithm can support the prediction of low availability of lidar wind measurements in time and space.