scholarly journals Measuring Wind Speed Using the Internal Stabilization System of a Quadrotor Drone

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 23 ◽  
Author(s):  
Magdalena Simma ◽  
Håvard Mjøen ◽  
Tobias Boström
Keyword(s):  
Wind Speed ◽  
Test Site ◽  
Horizontal Wind ◽  
Mean Square ◽  
Flight Tests ◽  
Wind Speeds ◽  
Internal Stabilization ◽  
Ultrasonic Anemometer ◽  
Wind Measurements ◽  
The Cost

This article proposes a method of measuring wind speed using the data logged by the autopilot of a quadrotor drone. Theoretical equations from works on quadrotor control are utilized and supplemented to form the theoretical framework. Static thrust tests provide the necessary parameters for calculating wind estimates. Flight tests were conducted at a test site with laminar wind conditions with the quadrotor hovering next to a static 2D ultrasonic anemometer with wind speeds between 0–5 m/s. Horizontal wind estimates achieve exceptionally good results with root mean square error (RMSE) values between 0.26–0.29 m/s for wind speed, as well as between 4.1–4.9 for wind direction. The flexibility of this new method simplifies the process, decreases the cost, and adds new application areas for wind measurements.

scholarly journals Coastal Wind Measurements Using a Single Scanning LiDAR

2020 ◽  
Vol 12 (8) ◽  
pp. 1347 ◽  
Author(s):  
Susumu Shimada ◽  
Jay Prakash Goit ◽  
Teruo Ohsawa ◽  
Tetsuya Kogaki ◽  
Satoshi Nakamura
Keyword(s):  
Wind Speed ◽  
Goodness Of Fit ◽  
Sonic Anemometer ◽  
Data Availability ◽  
Horizontal Wind ◽  
Research Station ◽  
Lidar Measurement ◽  
Wind Speeds ◽  
Wind Measurements ◽  
Scanning Lidar

A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method.

scholarly journals Wind sensing with drone-mounted wind lidars: proof of concept

2020 ◽  
Vol 13 (2) ◽  
pp. 521-536
Author(s):  
Nikola Vasiljević ◽  
Michael Harris ◽  
Anders Tegtmeier Pedersen ◽  
Gunhild Rolighed Thorsen ◽  
Mark Pitter ◽  
...  
Keyword(s):  
Wind Speed ◽  
Continuous Wave ◽  
Position Control ◽  
Horizontal Wind ◽  
Lidar Measurement ◽  
Proof Of Concept ◽  
Horizontal Wind Speed ◽  
Wind Speeds ◽  
Potential Applications ◽  
Wind Measurements

Abstract. The fusion of drone and wind lidar technology introduces the exciting possibility of performing high-quality wind measurements virtually anywhere. We present a proof-of-concept (POC) drone–lidar system and report results from several test campaigns that demonstrate its ability to measure accurate wind speeds. The POC system is based on a dual-telescope continuous-wave (CW) lidar, with drone-borne telescopes and ground-based optoelectronics. Commercially available drone and gimbal units are employed. The demonstration campaigns started with a series of comparisons of the wind speed measurements acquired by the POC system to simultaneous measurements performed by nearby mast-based sensors. On average, an agreement down to about 0.1 m s−1 between mast- and drone-based measurements of the horizontal wind speed is found. Subsequently, the extent of the flow disturbance caused by the drone downwash was investigated. These tests vindicated the somewhat conservative choice of lidar measurement ranges made for the initial wind speed comparisons. Overall, the excellent results obtained without any drone motion correction and with fairly primitive drone position control indicate the potential of drone–lidar systems in terms of accuracy and applications. The next steps in the development are outlined and several potential applications are discussed.

scholarly journals Doppler wind lidar activities at Cabauw

2021 ◽  
Author(s):  
Steven Knoop ◽  
Fred Bosveld ◽  
Marijn de Haij ◽  
Arnoud Apituley
Keyword(s):  
Wind Speed ◽  
Wind Profile ◽  
Continuous Wave ◽  
Wind Farms ◽  
Data Availability ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Low Clouds ◽  
Wind Measurements ◽  
Wind Lidar

<p>Atmospheric motion and turbulence are essential parameters for weather and topics related to air quality. Therefore, wind profile measurements play an important role in atmospheric research and meteorology. One source of wind profile data are Doppler wind lidars, which are laser-based remote sensing instruments that measure wind speed and wind direction up to a few hundred meters or even a few kilometers. Commercial wind lidars use the laser wavelength of 1.5 µm and therefore backscatter is mainly from aerosols while clear air backscatter is minimal, limiting the range to the boundary layer typically.</p><p>We have carried out a two-year intercomparison of the ZephIR 300M (ZX Lidars) short-range wind lidar and tall mast wind measurements at Cabauw [1]. We have focused on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213m tall meteorological mast. We have found an overall availability of quality-controlled wind lidar data of 97% to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. This instrument is being deployed within North Sea wind farms.</p><p>Recently, a scanning long-range wind lidar Windcube 200S (Leosphere/Vaisala) has been installed at Cabauw, as part of the Ruisdael Observatory program [2]. The scanning Doppler wind lidars will provide detailed measurements of the wind field, aerosols and clouds around the Cabauw site, in coordination with other instruments, such as the cloud radar.</p><p>[1] Knoop, S., Bosveld, F. C., de Haij, M. J., and Apituley, A.: A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw, Atmos. Meas. Tech., 14, 2219–2235, 2021</p><p>[2] https://ruisdael-observatory.nl/</p>

scholarly journals Dutch Offshore Wind Atlas Validation against Cabauw Meteomast Wind Measurements

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6558
Author(s):  
Steven Knoop ◽  
Pooja Ramakrishnan ◽  
Ine Wijnant
Keyword(s):  
Wind Speed ◽  
Offshore Wind ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
Weather Model ◽  
Significant Difference ◽  
Wind Measurements ◽  
Sea Wind ◽  
Global Datasets ◽  
Meteorological Institute

The Dutch Offshore Wind Atlas (DOWA) is validated against wind speed and direction measurements from the Cabauw meteorological mast for a 10-year period and at heights between 10 m and 200 m. The validation results are compared to the Royal Netherlands Meteorological Institute (KNMI) North Sea Wind (KNW) atlas. It is found that the average difference (bias) between DOWA wind speeds and those measured at Cabauw varies for the different heights between −0.1 m/s to 0.3 m/s. Significant differences between DOWA and KNW are only found at altitudes of 10 m and 20 m, where KNW performs better. For heights above 20 m, there is no significant difference between DOWA and KNW with respect to the 10-year averaged wind speed bias. The diurnal cycle is better captured by DOWA compared to KNW, and the hourly correlation is slightly improved. In addition, a comparison with the global European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and ERA5 reanalyses (used for KNW and DOWA, respectively) is made, highlighting the added skill provided by downscaling those global datasets with the weather model HARMONIE.

scholarly journals A statistical study of underestimates of wind speeds by VHF radar

1997 ◽  
Vol 15 (6) ◽  
pp. 805-812 ◽  
Author(s):  
L. Thomas ◽  
I. Astin ◽  
R. M. Worthington
Keyword(s):  
Statistical Treatment ◽  
Vertical Direction ◽  
Spatial Separation ◽  
Horizontal Wind ◽  
Vhf Radar ◽  
Height Range ◽  
Wind Speeds ◽  
Radar Echoes ◽  
Wind Measurements ◽  
Radar Measurements

Abstract. Comparisons are made between horizontal wind measurements carried out using a VHF-radar system at Aberystwyth (52.4°N, 4.1°W) and radiosondes launched from Aberporth, some 50 km to the south-west. The radar wind results are derived from Doppler wind measurements at zenith angles of 6° in two orthogonal planes and in the vertical direction. Measurements on a total of 398 days over a 2-year period are considered, but the major part of the study involves a statistical analysis of data collected during 75 radiosonde flights selected to minimise the spatial separation of the two sets of measurements. Whereas good agreement is found between the two sets of wind direction, radar-derived wind speeds show underestimates of 4–6% compared with radiosonde values over the height range 4–14 km. Studies of the characteristics of this discrepancy in wind speeds have concentrated on its directional dependence, the effects of the spatial separation of the two sets of measurements, and the influence of any uncertainty in the radar measurements of vertical velocities. The aspect sensitivity of radar echoes has previously been suggested as a cause of underestimates of wind speeds by VHF radar. The present statistical treatment and case-studies show that an appropriate correction can be applied using estimates of the effective radar beam angle derived from a comparison of echo powers at zenith angles of 4.2° and 8.5°.

Estimation of Wind Vector Profile Using a Hexarotor Unmanned Aerial Vehicle and Its Application to Meteorological Observation up to 1000 m above Surface

2018 ◽  
Vol 35 (8) ◽  
pp. 1621-1631 ◽  
Author(s):  
Tomoya Shimura ◽  
Minoru Inoue ◽  
Hirofumi Tsujimoto ◽  
Kansuke Sasaki ◽  
Masato Iguchi
Keyword(s):  
Wind Speed ◽  
Profile Measurement ◽  
Wind Vector ◽  
Doppler Lidar ◽  
Wind Speeds ◽  
Wide Range ◽  
Lidar Measurements ◽  
Ultrasonic Anemometer ◽  
Aerial Vehicle ◽  
Meteorological Observations

AbstractSmall unmanned aerial vehicles (UAVs), also known as drones, have recently become promising tools in various fields. We investigated the feasibility of wind vector profile measurement using an ultrasonic anemometer installed on a 1-m-wide hexarotor UAV. Wind vectors measured by the UAV were compared to observations by a 55-m-high meteorological tower, over a wide range of wind speed conditions up to 11 m s−1, which is a higher wind speed range than those used in previous studies. The wind speeds and directions measured by the UAV and the tower were in good agreement, with a root-mean-square error of 0.6 m s−1 and 12° for wind speed and direction, respectively. The developed method was applied to field meteorological observations near a volcano, and the wind vector profiles, along with temperature and humidity, were measured by the UAV for up to an altitude of 1000 m, which is a higher altitude range than those used in previous studies. The wind vector profile measured by the UAV was compared with Doppler lidar measurements (collected several kilometers away from the UAV measurements) and was found to be qualitatively similar to that captured by the Doppler lidar, and it adequately represented the features of the atmospheric boundary layer. The feasibility of wind profile measurement up to 1000 m by a small rotor-based UAV was clarified over a wide range of wind speed conditions.

scholarly journals Evaluation of wind profiles from the NERC MST Radar, Aberystwyth, UK

2014 ◽  
Vol 7 (5) ◽  
pp. 4589-4621
Author(s):  
C. F. Lee ◽  
G. Vaughan ◽  
D. A. Hooper
Keyword(s):  
Wind Speed ◽  
Standard Error ◽  
Random Error ◽  
Sampling Error ◽  
Weather Prediction ◽  
Horizontal Wind ◽  
Wind Speeds ◽  
Mst Radar ◽  
Wind Profiles ◽  
Aspect Sensitivity

Abstract. This study quantifies the uncertainties in winds measured by the Aberystwyth Mesosphere-Stratosphere-Troposphere (MST) radar (52.4° N, 4.0° W), before and after its renovation in March 2011. 127 radiosondes provide an independent measure of winds. Differences between radiosonde and radar-measured horizontal winds are correlated with long-term averages of vertical velocities, suggesting an influence from local mountain waves. These local influences are an important consideration when using radar winds as a measure of regional conditions, particularly for numerical weather prediction. In those applications, local effects represent a source of sampling error additional to the inherent uncertainties in the measurements themselves. The radar renovation improved the SNR of measurements, with correspondingly improved altitude coverage. It also corrected an under-estimate of horizontal wind speeds attributed to beam formation problems, due to component failure pre-renovation. The standard error in radar-measured winds averaged over half-an-hour increases with wind speed and altitude, and is 0.6–2.5 m s−1 (5–20% of wind speed) for post-renovation horizontal winds. Pre-renovation values are typically 0.4 m s−1 (0.03 m s−1) larger. The standard error in radial velocities is < 0.04 m s−1. Eight weeks of special radar operation are used to investigate the effects of echo power aspect sensitivity. Corrections for echo power aspect sensitivity remove an underestimate of horizontal wind speeds, however aspect sensitivity is azimuthally anisotropic at the scale of routine observations (≈ 1 h). This anisotropy introduces additional random error into wind profiles. For winds averaged over half-an-hour, the random error is around 3.5% above 8 km, but as large as 4.5% in the mid-troposphere.

scholarly journals Air/sea DMS gas transfer in the North Atlantic: evidence for limited interfacial gas exchange at high wind speed

2013 ◽  
Vol 13 (5) ◽  
pp. 13285-13322 ◽  
Author(s):  
T. G. Bell ◽  
W. De Bruyn ◽  
S. D. Miller ◽  
B. Ward ◽  
K. Christensen ◽  
...  
Keyword(s):  
Wind Speed ◽  
North Atlantic ◽  
Interfacial Stress ◽  
Horizontal Wind ◽  
Gas Transfer ◽  
High Wind ◽  
High Wind Speed ◽  
Wind Speeds ◽  
The North ◽  
The North Atlantic

Abstract. Shipboard measurements of eddy covariance DMS air/sea fluxes and seawater concentration were carried out in the North Atlantic bloom region in June/July 2011. Gas transfer coefficients (k660) show a linear dependence on mean horizontal wind speed at wind speeds up to 11 m s−1. At higher wind speeds the relationship between k660 and wind speed weakens. At high winds, measured DMS fluxes were lower than predicted based on the linear relationship between wind speed and interfacial stress extrapolated from low to intermediate wind speeds. In contrast, the transfer coefficient for sensible heat did not exhibit this effect. The apparent suppression of air/sea gas flux at higher wind speeds appears to be related to sea state, as determined from shipboard wave measurements. These observations are consistent with the idea that long waves suppress near surface water side turbulence, and decrease interfacial gas transfer. This effect may be more easily observed for DMS than for less soluble gases, such as CO2, because the air/sea exchange of DMS is controlled by interfacial rather than bubble-mediated gas transfer under high wind speed conditions.

scholarly journals Development of a small unmanned aircraft system to derive CO<sub>2</sub> emissions of anthropogenic point sources

2020 ◽  
Author(s):  
Maximilian Reuter ◽  
Heinrich Bovensmann ◽  
Michael Buchwitz ◽  
Jakob Borchardt ◽  
Sven Krautwurst ◽  
...  
Keyword(s):  
Wind Speed ◽  
Point Sources ◽  
Unmanned Aircraft ◽  
Horizontal Wind ◽  
Observation System ◽  
Calibration Model ◽  
Unmanned Aircraft System ◽  
Airborne Measurements ◽  
Wind Measurements ◽  
Aircraft System

Abstract. A reduction of the anthropogenic emissions of CO2 (carbon dioxide) is necessary to stop or slow down man-made climate change. To verify mitigation strategies, a global monitoring system such as the envisaged European Copernicus anthropogenic CO2 monitoring mission (CO2M) is required. Those satellite data are going to be complemented and validated with airborne measurements. UAV (unmanned aerial vehicle) based measurements can provide a cost-effective way to contribute to this activities. Here we present the development of a sUAS (small unmanned aircraft system) to quantify the CO2 emissions of a nearby point source from its downwind mass flux without the need for any ancillary data. Specifically, CO2 is measured by a NDIR (non-dispersive infrared) detector and the wind speed and direction is measured with a 2D ultrasonic acoustic resonance anemometer. By means of laboratory measurements and an in-flight validation at the ICOS (Integrated Carbon Observation System) atmospheric station Steinkimmen (STE) near Bremen, Germany, we estimate that the individual CO2 measurements have a precision of 3 ppm and that CO2 enhancements can be determined with an accuracy of 1.3 % or 0.9 ppm, whichever is larger. We introduce an anemometer calibration method to minimize the effect of rotor downwash on the wind measurements. This method derives the fit parameters of a linear calibration model accounting for scaling, rotation, and a potential constant bias. For this purpose it analyzes wind measurements taken while following a suitable flight pattern and assuming stationary wind conditions. From the calibration and validation experiments, we estimate the single measurement precision of the horizontal wind speed to be 0.40 m s−1 and the accuracy to be 0.33 m s−1. By means of two flights downwind of the ExxonMobil natural gas processing facility in Großenkneten about 40 km east of Bremen, Germany, we demonstrate how the measurements of elevated CO2 concentrations can be used to infer mass fluxes of atmospheric CO2 related to the emissions of the facility.

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

2021 ◽  
Vol 14 (5) ◽  
pp. 3795-3814
Author(s):  
Tamino Wetz ◽  
Norman Wildmann ◽  
Frank Beyrich
Keyword(s):  
Boundary Layer ◽  
Spatial Distribution ◽  
Wind Speed ◽  
Atmospheric Boundary Layer ◽  
Unmanned Aerial Vehicles ◽  
Horizontal Wind ◽  
Vertical Profiles ◽  
Aerial Vehicles ◽  
Wind Measurements ◽  
Quadrotor Unmanned Aerial Vehicles

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.

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