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Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
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Volume 2, issue 2
Geosci. Instrum. Method. Data Syst., 2, 177-187, 2013
https://doi.org/10.5194/gi-2-177-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Instrum. Method. Data Syst., 2, 177-187, 2013
https://doi.org/10.5194/gi-2-177-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 05 Jul 2013

Research article | 05 Jul 2013

Comparison and application of wind retrieval algorithms for small unmanned aerial systems

T. A. Bonin1,2, P. B. Chilson1,2, B. S. Zielke1,2, P. M. Klein1,3, and J. R. Leeman1 T. A. Bonin et al.
  • 1School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA
  • 2Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma, USA
  • 3Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma, USA

Abstract. Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using well-aspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.

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