Distributed wind measurements with multiple quadrotor unmanned aerial vehicles in the atmospheric boundary layer

Abstract

Abstract. In this study, a fleet of quadrotor unmanned aerial vehicles (UAVs) is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to obtain horizontal wind speed and direction is designed for hovering flight phases and is based on the principle of aerodynamic drag and the related quadrotor dynamics. During the FESST@MOL campaign at the boundary layer field site (Grenzschichtmessfeld, GM) Falkenberg of the Lindenberg Meteorological Observatory – Richard Assmann Observatory (MOL-RAO), 76 calibration and validation flights were performed. The 99 m tower equipped with cup and sonic anemometers at the site is used as the reference for the calibration of the wind measurements. The validation with an independent dataset against the tower anemometers reveals that an average accuracy of σrms<0.3 m s−1 for the wind speed and σrms,ψ<8∘ for the wind direction was achieved. Furthermore, we compare the spatial distribution of wind measurements with the fleet of quadrotors to the tower vertical profiles and Doppler wind lidar scans. We show that the observed shear in the vertical profiles matches well with the tower and the fluctuations on short timescales agree between the systems. Flow structures that appear in the time series of a line-of-sight measurement and a two-dimensional vertical scan of the lidar can be observed with the fleet of quadrotors and are even sampled with a higher resolution than the deployed lidar can provide.

Spatially distributed and simultaneous wind measurements with a fleet of small quadrotor UAS

“…At the boundary layer field site (Grenzschichtmessfeld, GM) Falkenberg of the Meteorological Observatory Lindenberg -Richard Aßmann-Observatory (MOL-RAO), two campaigns were carried out associated to the FESST@MOL 2020 and the FESSTVaL 2021 activities at the MOL-RAO. With data from the FESST@MOL 2020 experiment, we compared the spatial distribution of horizontal wind in main wind direction, as obtained from the UAS fleet to tower vertical profiles and DWL scans before [16]. In order to calculate turbulence parameters which are essential to understand loads and wake dynamics of WT, it is desirable to measure with high temporal resolution in all three dimensions.…”

“…This feature simplifies and increases the accuracy of the wind algorithm, due to more similar relative inflow of the wind towards the quadrotor, resulting in less variability of the aerodynamic drag. The hardware and software have not changed significantly compared to the 2020 campaign so that we refer to Wetz et al (2021) [16] for more details.…”

“…In a first approach the wind forces were calculated according to Wetz et al (2021) [16], assuming zero motion of the UAS during hovering (see Eq. ( 6)).…”

“…The calibration target values for the longitudinal wind component are 2 s average wind speeds of the corresponding sonic anemometer measurements. The short average time compared to Wetz et al (2021) [16] is chosen for better statistics in the calibration of the velocity variance. First, the proportional parameter c x and the exponent b x of Eq.…”

Enhanced resource assessment and atmospheric monitoring of the research wind farm WiValdi

“…To validate the remote sensing data prior to the installation of measurement masts at the WiValdi site, a two-week long campaign was carried out in September 2021. 14 radiosondes were launched in this period and in parallel to each radiosonde launch, a meteorological multicopter UAS (see [9]) was launched to obtain soundings in the lowest 100 meters. The radiosonde data up to 10 km are particularly useful to assess the performance of the MWR retrievals.…”