Surface layer profiles of air temperature and humidity measured 
from unmanned aircraft

Hobbs, Stephen; Dyer, David F.; Courault, Dominique; Olioso, Albert; Lagouarde, J.-P.; Kerr, Yann; McAneney, John; Bonnefond, Jean Marc

doi:10.1051/agro:2002050

Cited by 19 publications

(11 citation statements)

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“…Unmanned aerial vehicles on the other hand provide mobility, yet do not allow a comprehensive sensor package due to payload restrictions (e.g. Egger et al, 2002;Hobbs et al, 2002;Martin et al, 2011;Thomas et al, 2012). Here the weightshift microlight aircraft (WSMA) can provide an alternative at low cost-, transport-and infrastructural demand.…”

Section: Introductionmentioning

confidence: 99%

Eddy-covariance flux measurements with a weight-shift microlight aircraft

et al. 2012

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Abstract. The objective of this study is to assess the feasibility and quality of eddy-covariance flux measurements from a weight-shift microlight aircraft (WSMA). Firstly, we investigate the precision of the wind measurement (σ u,v ≤ 0.09 m s −1 , σ w = 0.04 m s −1 ), the lynchpin of flux calculations from aircraft. From here, the smallest resolvable changes in friction velocity (0.02 m s −1 ), and sensible-(5 W m −2 ) and latent (3 W m −2 ) heat flux are estimated. Secondly, a seven-day flight campaign was performed near Lindenberg (Germany). Here we compare measurements of wind, temperature, humidity and respective fluxes between a tall tower and the WSMA. The maximum likelihood functional relationship (MLFR) between tower and WSMA measurements considers the random error in the data, and shows very good agreement of the scalar averages. The MLFRs for standard deviations (SDs, 2-34 %) and fluxes (17-21 %) indicate higher estimates of the airborne measurements compared to the tower. Considering the 99.5 % confidence intervals, the observed differences are not significant, with exception of the temperature SD. The comparison with a largeaperture scintillometer reveals lower sensible heat flux estimates at both tower (−40 to −25 %) and WSMA (−25-0 %). We relate the observed differences to (i) inconsistencies in the temperature and wind measurement at the tower and (ii) the measurement platforms' differing abilities to capture contributions from non-propagating eddies. These findings encourage the use of WSMA as a low cost and highly versatile flux measurement platform.

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Section: Introductionmentioning

confidence: 99%

Eddy-covariance flux measurements with a weight-shift microlight aircraft

et al. 2012

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“…The convection is not the biggest contribution to the increased scatter anymore; instead, the fact that the flow information cannot compensate for excessively low change rates in the heading along the averaging window leads to the differences. The solution of the overdetermined matrix in Equation (14) cannot compensate for the occurrence of small-scale fluctuations if the ground speed and heading become almost constant inside the averaging window M. The results for the SNT flight over complex terrain are, on the other hand, remarkably good for these harsh conditions. Here, the benefit of the algorithm compared to the NFSA is shown for situations when there is less than a full racetrack inside the averaging window and, therefore, a quite high temporal resolution.…”

Section: Short Averaging Periods For Enhanced Temporal Resolution (M mentioning

confidence: 99%

“…Atmospheric boundary layer (ABL) studies are increasingly complemented by in situ measurements using small unmanned aircraft systems (sUAS) [1][2][3][4][5][6][7][8]. Atmospheric sampling using sUAS dates back to 1961 [9] and has since been applied to atmospheric physics and chemistry [10][11][12][13], boundary-layer meteorology [14][15][16][17][18][19][20][21][22][23][24][25], and, more recently, also to wind-energy meteorology [26][27][28]. The capabilities of sUAS for meteorological sampling range from mean values for wind, thermodynamics, species concentration, etc., to highly resolved turbulence measurements, and from an accurate and diverse but larger sensor payload, down to small aircraft that can be operated from almost anywhere, with minimal logistical overhead.…”

Section: Introductionmentioning

confidence: 99%

Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft

et al. 2018

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One of the biggest challenges in probing the atmospheric boundary layer with small unmanned aerial vehicles is the turbulent 3D wind vector measurement. Several approaches have been developed to estimate the wind vector without using multi-hole flow probes. This study compares commonly used wind speed and direction estimation algorithms with the direct 3D wind vector measurement using multi-hole probes. This was done using the data of a fully equipped system and by applying several algorithms to the same data set. To cover as many aspects as possible, a wide range of meteorological conditions and common flight patterns were considered in this comparison. The results from the five-hole probe measurements were compared to the pitot tube algorithm, which only requires a pitot-static tube and a standard inertial navigation system measuring aircraft attitude (Euler angles), while the position is measured with global navigation satellite systems. Even less complex is the so-called no-flow-sensor algorithm, which only requires a global navigation satellite system to estimate wind speed and wind direction. These algorithms require temporal averaging. Two averaging periods were applied in order to see the influence and show the limitations of each algorithm. For a window of 4 min, both simplifications work well, especially with the pitot-static tube measurement. When reducing the averaging period to 1 min and thereby increasing the temporal resolution, it becomes evident that only circular flight patterns with full racetracks inside the averaging window are applicable for the no-flow-sensor algorithm and that the additional flow information from the pitot-static tube improves precision significantly.

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“…The use of UAVs for atmospheric turbulence research is still in its infancy; although initially focusing on remotely piloted measurements of temperature, wind and humidity profiles [29,30], autopilot-guided measurements are now becoming increasingly common [31][32][33][34][35][36][37][38]. Typically, these measurements employ wind velocity probes with a temporal response that is little better than that of sonic anemometers.…”

Section: Introductionmentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle for the Measurement of Turbulence in the Atmospheric Boundary Layer

2017

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This paper describes the components and usage of an unmanned aerial vehicle developed for measuring turbulence in the atmospheric boundary layer. A method of computing the time-dependent wind speed from a moving velocity sensor data is provided. The physical system built to implement this method using a five-hole probe velocity sensor is described along with the approach used to combine data from the different on-board sensors to allow for extraction of the wind speed as a function of time and position. The approach is demonstrated using data from three flights of two unmanned aerial vehicles (UAVs) measuring the lower atmospheric boundary layer during transition from a stable to convective state. Several quantities are presented and show the potential for extracting a range of atmospheric boundary layer statistics.

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Surface layer profiles of air temperature and humidity measured from unmanned aircraft

Cited by 19 publications

References 0 publications

Eddy-covariance flux measurements with a weight-shift microlight aircraft

Eddy-covariance flux measurements with a weight-shift microlight aircraft

Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft

Development of an Unmanned Aerial Vehicle for the Measurement of Turbulence in the Atmospheric Boundary Layer

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