Abstract
Abstract. A multi-hole probe mounted on an aircraft provides the air velocity vector relative to the aircraft, requiring knowledge of the aircraft spatial orientation (e.g., Euler angles), translational velocity and angular velocity to translate this information to an Earth-based reference frame and determine the wind vector. As the relative velocity of the aircraft is typically an order of magnitude higher than the wind velocity, the extracted wind velocity is very sensitive to multiple sources of error including misalignment of the probe and aircraft coordinate system axes, sensor error and misalignment in time of the probe and aircraft orientation measurements in addition to aerodynamic distortion of the velocity field by the aircraft. Here, we present an approach which can be applied after a flight to identify and correct biases which may be introduced into the final wind measurement. The approach was validated using a ground reference, different aircraft and the same aircraft at different times. The results indicate a significant reduction in wind velocity variance at frequencies which correspond to aircraft motion.
Highlights
IntroductionThe past few decades have witnessed a significant increase in the utilization of small unmanned aerial systems (sUASs) in a wide range of atmospheric research areas, such as the evolution and structure of the atmospheric boundary layer (see, for example, van den Kroonenberg et al, 2007, 2008; Cassano et al, 2010; Bonin et al, 2013; Lothon et al, 2014; Wildmann et al, 2015; Bärfuss et al, 2018; de Boer et al, 2018; Kral et al, 2018; Bailey et al, 2019), turbulence (Balsley et al, 2013; Witte et al, 2017; Bailey et al, 2019; Mansour et al, 2011; Calmer et al, 2018; Båserud et al., 2016), analysis of aerosols and gas concentrations in the atmosphere (Bärfuss et al, 2018; Platis et al, 2016; Corrigan et al, 2008; Schuyler and Guzman, 2017; Illingworth et al, 2014; Zhou et al, 2018), cloud microphysics (Ramanathan et al, 2007; Roberts et al, 2008), and observation and analysis of extreme weather events such as hurricanes (Cione et al, 2016)
The biases described by can be removed following Eqs. (7) to (11) prior to a final determination of [U (t)]I. To validate this correction procedure, we applied it to measurement data acquired during the LAPSE-RATE campaign described in de Boer et al (2018)
We first demonstrate the correction procedure in flights compared to a ground reference, followed by a demonstration of the improvements made to vertical profiles of the wind velocity and direction
Summary
The past few decades have witnessed a significant increase in the utilization of small unmanned aerial systems (sUASs) in a wide range of atmospheric research areas, such as the evolution and structure of the atmospheric boundary layer (see, for example, van den Kroonenberg et al, 2007, 2008; Cassano et al, 2010; Bonin et al, 2013; Lothon et al, 2014; Wildmann et al, 2015; Bärfuss et al, 2018; de Boer et al, 2018; Kral et al, 2018; Bailey et al, 2019), turbulence (Balsley et al, 2013; Witte et al, 2017; Bailey et al, 2019; Mansour et al, 2011; Calmer et al, 2018; Båserud et al., 2016), analysis of aerosols and gas concentrations in the atmosphere (Bärfuss et al, 2018; Platis et al, 2016; Corrigan et al, 2008; Schuyler and Guzman, 2017; Illingworth et al, 2014; Zhou et al, 2018), cloud microphysics (Ramanathan et al, 2007; Roberts et al, 2008), and observation and analysis of extreme weather events such as hurricanes (Cione et al, 2016). These scalar quantities are relatively straightforward to acquire, obtaining all three components of the wind velocity vector is complicated by the presence of the continual translation and rotation of the measurement platform, resulting in different approaches developed to determine the wind vector
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