Methods for Evaluating the Temperature Structure-Function Parameter Using Unmanned Aerial Systems and Large-Eddy Simulation

Wainwright, Charlotte E.; Bonin, Timothy A.; Chilson, Phillip B.; Gibbs, Jeremy A.; Fedorovich, Evgeni; Palmer, Robert D.

doi:10.1007/s10546-014-0001-9

Cited by 32 publications

(24 citation statements)

References 37 publications

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“…A second method to estimate C 2 n (DH ISR) that follows the modifications discussed earlier of the method of Wainwright et al (2015) and Bonin et al (2015) was implemented only for the ascent part of the trajectory. Reflectivity N was divided into small portions of 18.75 m that contained at least one half rotation on the helix trajectory to reduce the contribution of vertical variability of N.…”

Section: Resultsmentioning

confidence: 99%

“…From now on, this method will be called "DH direct". However, as stated by Wainwright et al (2015) and Bonin et al (2015), it is important to account for the advection of turbulence past the DH platform.…”

Section: In Situ Dh Measurement Of C 2 Nmentioning

confidence: 99%

“…Another approach for calculating C 2 n is adapted from the work of Wainwright et al (2015) and Bonin et al (2015), where estimates of the structure function parameters are calculated from circular trajectories at constant heights to account for horizontal variations of n. Here, estimates of ( n) 2 are calculated for a set of possible separations δ E in each turn of the helical trajectory (shown by different colored lines in Fig 8); then these estimates are separated into δ E length bins to estimate D n = ( n) 2 . Finally, C 2 n and the ISR are estimated, where C 2 n is constant and independent of δ E , at the average altitude of that turn.…”

Section: In Situ Dh Measurement Of C 2 Nmentioning

confidence: 99%

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Simultaneous observations of structure function parameter of refractive index using a high-resolution radar and the DataHawk small airborne measurement system

et al. 2016

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Abstract. The SOUSY (SOUnding SYstem) radar was relocated to the Jicamarca Radio Observatory (JRO) near Lima, Peru, in 2000, where the radar controller and acquisition system were upgraded with state-of-the-art parts to take full advantage of its potential for high-resolution atmospheric sounding. Due to its broad bandwidth (4 MHz), it is able to characterize clear-air backscattering with high range resolution (37.5 m).A campaign conducted at JRO in July 2014 aimed to characterize the lower troposphere with a high temporal resolution (8.1 Hz) using the DataHawk (DH) small unmanned aircraft system, which provides in situ atmospheric measurements at scales as small as 1 m in the lower troposphere and can be GPS-guided to obtain measurements within the beam of the radar. This was a unique opportunity to make coincident observations by both systems and to directly compare their in situ and remotely sensed parameters.Because SOUSY only points vertically, it is only possible to retrieve vertical radar profiles caused by changes in the refractive index within the resolution volume. Turbulent variations due to scattering are described by the structure function parameter of refractive index C 2 n . Profiles of C 2 n from the DH are obtained by combining pressure, temperature, and relative humidity measurements along the helical trajectory and integrated at the same scale as the radar range resolution. Excellent agreement is observed between the C 2 n estimates obtained from the DH and SOUSY in the overlapping measurement regime from 1200 m up to 4200 m above sea level, and this correspondence provides the first accurate calibration of the SOUSY radar for measuring C 2 n .

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Section: Resultsmentioning

confidence: 99%

Section: In Situ Dh Measurement Of C 2 Nmentioning

confidence: 99%

Section: In Situ Dh Measurement Of C 2 Nmentioning

confidence: 99%

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Simultaneous observations of structure function parameter of refractive index using a high-resolution radar and the DataHawk small airborne measurement system

et al. 2016

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“…Whereafter, Knuth et al [22,23] measured the temperature, humidity, and pressure regarding the atmospheric structure of Terra Nova Bay during the UAV flights at high vertical and temporal resolutions to confirm the results derived from AWS (automatic weather station) and satellite data. Furthermore, Wainwright et al [24] adopted "SMARTSonde" UAV equipped with temperature, pressure, and relative humidity sensors to describe the smallscale temperature perturbations elaborately. UAV platforms have also been proven to be an effective means of observing the temperature and humidity profiles.…”

Section: Introductionmentioning

confidence: 99%

A Correction Method for UAV Helicopter Airborne Temperature and Humidity Sensor

Fan

et al. 2017

Mathematical Problems in Engineering

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This paper presents a correction method for UAV helicopter airborne temperature and humidity including an error correction scheme and a bias-calibration scheme. As rotor downwash flow brings measurement error on helicopter airborne sensors inevitably, the error correction scheme constructs a model between the rotor induced velocity and temperature and humidity by building the heat balance equation for platinum resistor temperature sensor and the pressure correction term for humidity sensor. The induced velocity of a spatial point below the rotor disc plane can be calculated by the sum of the induced velocities excited by center line vortex, rotor disk vortex, and skew cylinder vortex based on the generalized vortex theory. In order to minimize the systematic biases, the bias-calibration scheme adopts a multiple linear regression to achieve a systematically consistent result with the tethered balloon profiles. Two temperature and humidity sensors were mounted on "Z-5" UAV helicopter in the field experiment. Overall, the result of applying the calibration method shows that the temperature and relative humidity obtained by UAV helicopter closely align with tethered balloon profiles in providing measurements of the temperature profiles and humidity profiles within marine atmospheric boundary layers.

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“…Advances in sensor miniaturization have improved the viability of small unmanned air vehicles (UAVs) for a wide range of applications, including precision agriculture, search and rescue, and aerial surveillance. In addition to these vision-sensing applications, small UAVs can also be used to take meteorological measurements [1,2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Filtered dynamic inversion for altitude control of fixed-wing unmanned air vehicles

Mullen

Bailey

Hoagg

2016

Aerospace Science and Technology

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Instrumented unmanned air vehicles (UAVs) represent a new way of conducting atmospheric science, particularly within the atmospheric boundary layer where the air is turbulent. However, using autonomous UAVs for airborne measurement requires active control methods capable of following altitude commands despite unknown and turbulent disturbances to the air. Filtered dynamic inversion (FDI) is a control method with desirable command-following and disturbance-rejection properties for this application. FDI requires limited model information and is thus robust to parametric uncertainty, which arises in modeling UAV dynamics. In this paper, FDI is implemented in an altitude-flight-control system for an autonomous fixed-wing UAV. The control system is validated in simulation with a nonlinear dynamic model of a small fixed-wing UAV. The control system is also implemented and validated in flight experiments with turbulent wind conditions. Experimental results show that FDI yields improved altitude and pitch command following as compared to a classical (e.g., proportional-integral) flight-control system. In particular, experimental data demonstrate that the average power of the altitude and pitch command-following errors with FDI is smaller than those with proportionalintegral control.

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Methods for Evaluating the Temperature Structure-Function Parameter Using Unmanned Aerial Systems and Large-Eddy Simulation

Cited by 32 publications

References 37 publications

Simultaneous observations of structure function parameter of refractive index using a high-resolution radar and the DataHawk small airborne measurement system

Simultaneous observations of structure function parameter of refractive index using a high-resolution radar and the DataHawk small airborne measurement system

A Correction Method for UAV Helicopter Airborne Temperature and Humidity Sensor

Filtered dynamic inversion for altitude control of fixed-wing unmanned air vehicles

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