## 3 Data and Methods  
### Data Processing  
The data from the land-based AWS-FMI is routinely checked and processed by the Finnish Meteorological Institute and can thus be used as is. All other data were visually inspected for obvious errors. Furthermore, system specific data processing procedures were applied. The slow-response AWS-ice data were checked for their physical range, and obviously erroneous data were removed. The directional offsets of the wind vanes were corrected to face true north, and all three wind vanes were aligned to result in the same wind direction under conditions with neutral stratification. Due to the distance and large difference in measurement height, no such correction was applied in order to align our wind direction observations with the ones taken over land at AWS-FMI. The short-wave radiation (\(I\)) showed small negative values during the night, which were used to apply an offset correction to the entire data set by forcing the minimum value to equal zero. The EC data was processed using the TK3.11 EC software package [92] producing 30-min, 10-min, and 1-min averaged turbulence quantities, like variance, turbulent fluxes of sensible and latent heat, and momentum. The following settings and corrections were applied: de-spiking by applying a 7-SD threshold; 10% maximum allowed number of missing/bad values; double rotation; Moore, Schotanus, and WPL density corrections; cross-correlation to maximize covariance; stationarity tests; and integral tests on developed turbulence. The resulting data was quality flagged using a three-level flagging system, ranging from 0 to 2. In accordance with the Spoleto agreement, a flag of 0 indicated data of high quality, 1 indicated intermediate quality and 2 indicated poor quality [92]. For the following analyses, we included all EC data with a flag of 0 or 1. In addition to the directly measured parameters, like \(T\), \(RH\), and \(p\), obtained by the SUMO, the horizontal wind speed (\(U\)) and direction (\(Dir\)) were estimated by applying the \"no-flow-sensor\" wind estimation algorithm described by [68]. All SUMO data were interpolated to a common frequency of 4 Hz in order to provide a consistent data set. The Bebop2 Met also provides direct profile measurements of \(T\), \(RH\) and \(p\), of which only data during ascent was used due to possible downwash contamination during decent. The pressure data at the time of takeoff and landing were used to remove linear trends in the surface pressure which have commonly been observed to cause altitude errors of a few meters towards the end of a flight. Getting reliable altitude information is crucial, especially for observations of the lowermost layers if, e.g., surface-based inversions are to be resolved correctly. Like for the SUMO system, all data were interpolated to a common frequency of 4 Hz. In addition, attempts were made to retrieve wind speed and direction estimates from the aircraft pitch and roll information, following the method of [93]. Due to the design of the Bebop2 with a long but slim body, this method can only be applied reliably if the cross-wind component affecting the aircraft is much smaller than the front-wind component. Since an autopilot algorithm for turning the aircraft into the wind was not implemented during the campaign, the wind speed and direction data from the Bebop2 Met have to be considered experimental with corresponding larger uncertainties. The AMOR pressure data, used to compute the height above ground level of the UAV, was smoothed by applying a moving average. The data of the humidity sensors were recomputed, taking into account the response time and thereby, mapping the correct values to the corresponding heights. All SODAR data with a signal-to-noise ratio (SNR) below 2 dB were removed from the further analyses. From the filtered SODAR data, we computed 10-min averaged profiles of the vertical velocity and its variance. The attenuated backscatter signal, measured directly by the SODAR, was used to estimate the ABL height.