Abstract. Wind data collection in the atmospheric boundary layer benefits from short-term wind speed measurements using unmanned aerial vehicles. Fixed-wing and rotary-wing devices with diverse anemometer technology have been used in the past to provide such data, but the accuracy still has the potential to be increased. A lightweight drone for carrying an industry-standard precision sonic anemometer was developed. Accuracy tests have been performed with the isolated anemometer at high tilt angles in a calibration wind tunnel, with the drone flying in a large wind tunnel and with the full system flying at different heights next to a bistatic lidar reference. The propeller-induced flow deflects the air to some extent, but this effect is compensated effectively. The data fusion shows a substantial reduction of crosstalk (factor of 13) between ground speed and wind speed. When compared with the bistatic lidar in very turbulent conditions, with a 10 s averaging interval and with the unmanned aerial vehicle (UAV) constantly circling around the measurement volume of the lidar reference, wind speed measurements have a bias between −2.0 % and 4.2 % (root-mean-square error (RMSE) of 4.3 % to 15.5 %), vertical wind speed bias is between −0.05 and 0.07 m s−1 (RMSE of 0.15 to 0.4 m s−1), elevation bias is between −1 and 0.7∘ (RMSE of 1.2 to 6.3∘), and azimuth bias is between −2.6 and 7.2∘ (RMSE of 2.6 to 8.0∘). Key requirements for good accuracy under challenging and dynamic conditions are the use of a full-size sonic anemometer, a large distance between anemometer and propellers, and a suitable algorithm for reducing the effect of propeller-induced flow. The system was finally flown in the wake of a wind turbine, successfully measuring the spatial velocity deficit and downwash distribution during forward flight, yielding results that are in very close agreement to lidar measurements and the theoretical distribution. We believe that the results presented in this paper can provide important information for designing flying systems for precise air speed measurements either for short duration at multiple locations (battery powered) or for long duration at a single location (power supplied via cable). UAVs that are able to accurately measure three-dimensional wind might be used as a cost-effective and flexible addition to measurement masts and lidar scans.

In this work, the Stark effect is shown to be mainly responsible for wrong elemental allocation by automated laser-induced breakdown spectroscopy (LIBS) software solutions. Due to broadening and shift of an elemental emission line affected by the Stark effect, its measured spectral position might interfere with the line position of several other elements. The micro-plasma is generated by focusing a frequency-doubled 200 mJ pulsed Nd/YAG laser on an aluminum target and furthermore on a brass sample in air at atmospheric pressure. After laser pulse excitation, we have measured the temporal evolution of the Al(II) ion line at 281.6 nm (4s(1)S-3p(1)P) during the decay of the laser-induced plasma. Depending on laser pulse power, the center of the measured line is red-shifted by 130 pm (490 GHz) with respect to the exact line position. In this case, the well-known spectral line positions of two moderate and strong lines of other elements coincide with the actual shifted position of the Al(II) line. Consequently, a time-resolving software analysis can lead to an elemental misinterpretation. To avoid a wrong interpretation of LIBS spectra in automated analysis software for a given LIBS system, we recommend using larger gate delays incorporating Stark broadening parameters and using a range of tolerance, which is non-symmetric around the measured line center. These suggestions may help to improve time-resolving LIBS software promising a smaller probability of wrong elemental identification and making LIBS more attractive for industrial applications.

Abstract. Wind data collection in the atmospheric boundary layer benefits from short term wind speed measurements using unmanned aerial vehicles. Fixed and rotary wing devices with diverse anemometer technology have been used in the past to provide such data, but the accuracy still has the potential to be increased. We developed a light weight drone (weight including sensor  45 min) for carrying an industry standard precision sonic anemometer. Accuracy tests have been performed with the isolated anemometer at high tilt angles in a calibration wind tunnel, with the drone flying in a large wind tunnel, and with the full system flying at different heights next to a bistatic lidar reference. The propeller-induced flow deflects the air to some extent, but this effect is compensated effectively. Our data fusion shows no signs of crosstalk between ground speed and wind speed. When compared with the bistatic lidar in very turbulent conditions, with 10 seconds averaging interval and with the UAV constantly circling around the measurement volume of the lidar reference, wind speed measurements have an average absolute bias of 1.9 % (0.073 m s−1), wind elevation average absolute bias is 0.5°, and wind azimuth average absolute bias is 1.5°, indicating excellent accuracy under challenging and dynamic conditions. The system was finally flown in the wake of a wind turbine, successfully measuring the spatial velocity deficit distribution during forward flight, yielding results that are in very close agreement to lidar measurements and the theoretical distribution. We believe that the results presented in this paper can provide important information for designing flying systems for precise air speed measurements either for short duration at multiple locations (battery powered) or for long duration at a single location (power supplied via cable). UAVs that are able to accurately measure three-dimensional wind might be used as cost effective and flexible addition to measurement masts and lidar scans.

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