## 2 Data and Methods
### Synthetic Aperture Radar (SAR) Derived Wind Field  
The analysis of the horizontal wind speed profiles is based on C-band SAR data from the satellites Sentinel-1A and Sentinel-1B, which are operated in the framework of the Copernicus program [22; 23]. The Sentinel-1A/B overlights over the German Bight (Figure 1) are around 5 UTC for descending or 17 UTC for ascending orbits (Figure 2a). With both satellites each location is sampled with identical imaging geometry every 6 days. The investigation of horizontal wind speed maps from SAR is performed from January 2017 to December 2020 and focused on the imaging geometries that cover almost the entire German Bight and totally encompass the transects considered for the study (see Section 2.3 and Figure 2b). This leads to 244 and 243 SAR scenes for morning and evening overflights, respectively. Figure 1: Water depths in the German Bight [24]. The black dots correspond to offshore wind turbines installed by June 2020. The blue arrow indicates the location of the LIDAR (see Section 2.5.1). The conversion of the SAR radar cross section into 10 m wind speed is traditionally performed through geophysical model functions (GMFs). There are two main approaches for the SAR wind speed retrieval. In the first approach, the GMF relates the calibrated normalized radar cross section (NRCS) to the wind speed, the wind direction and a radar incidence angle typically between 20\({}^{\circ}\) and 45\({}^{\circ}\). The wind direction is yet unknown, but can be obtained either from streak-like features on the SAR image [25; 26], or provided by an external data source (e.g., meteorological datasets). A limitation of this approach is uncertainties in the wind direction. The determination of the wind direction through an NRCS image can be challenging, as there is no guarantee of having visible streak-like features. Furthermore, this procedure often contains some 180\({}^{\circ}\) ambiguity. However, an external source of wind direction has often a coarser resolution, which means that wind direction variability at smaller scales is potentially neglected. Nonetheless, the use of external wind direction usually works well where wind direction gradients are smooth. In the second approach, wind speed can be retrieved directly from SAR image without any wind direction information [27; 28; 29]. For instance, in [PERSON] et al. [28], the SAR wind algorithm is based on the smearing of the SAR image in flight direction (azimuth) caused by the orbital motion of the sea surface. However, in coastal and nearshore areas, this technique has limitations as the shoaling and refraction of ocean waves can modulate the radar backscatter caused by local winds [30]. In addition, this approach underestimates wind speeds in situations of offshore winds, as the fetch and depth-limited waters affect the growth of the wave spectrum [31]. Since this study focuses on the variability of offshore winds, the second approach for wind retrieval was not used for the analysis presented here. Hourly wind data from the model run by the German Weather Service (DWD) are used to provide directional information for the first approach (see Section 2.5.2) to avoid any ambiguities and for consistency. The raw satellite signal is calibrated using the SNAP software tool [32]. To eliminate the effect of speckle noise from the radar signal, the retrieved wind speed is smoothed down to a resolution of 400 m. This procedure is a reasonable compromise between noise reduction and the preservation of a high spatial resolution of the retrieved wind field. The GMF used for the derivation of wind speed from SAR in this study is CMOD5.N [33], which is tuned for winds speeds at neutral conditions. Figure 2: Sentinel-1A/B coverage of the entire German Bight (**a**). Red swath corresponds to ascending orbits (acquired around 17:16 UTC) and the blue frame refers to descending orbits (acquired around 05:48 UTC). (**b**) Map showing bathymetry contours with transects over which the wind speed gradients are estimated.