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Nolan PJ, Pinto J, González-Rocha J, et al. Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs). Sensors (Basel). 2018;18(12)doi: 10.3390/s18124448.
Nolan, P. J., Pinto, J., González-Rocha, J., Jensen, A., Vezzi, C. N., Bailey, S. C. C., de Boer, G., Diehl, C., Laurence, R., Powers, C. W., Foroutan, H., Ross, S. D., & Schmale, D. G. (2018). Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs). Sensors (Basel, Switzerland), 18(12), . https://doi.org/10.3390/s18124448
Nolan, Peter J, et al. "Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)." Sensors (Basel, Switzerland) vol. 18,12 (2018). doi: https://doi.org/10.3390/s18124448
Nolan PJ, Pinto J, González-Rocha J, Jensen A, Vezzi CN, Bailey SCC, de Boer G, Diehl C, Laurence R, Powers CW, Foroutan H, Ross SD, Schmale DG. Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs). Sensors (Basel). 2018 Dec 15;18(12). doi: 10.3390/s18124448. PMID: 30558335; PMCID: PMC6308849.
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Sensors (Basel). 2018 Dec 15;18(12). doi: 10.3390/s18124448.

Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs).

Sensors (Basel, Switzerland)

Peter J Nolan, James Pinto, Javier González-Rocha, Anders Jensen, Christina N Vezzi, Sean C C Bailey, Gijs de Boer, Constantin Diehl, Roger Laurence, Craig W Powers, Hosein Foroutan, Shane D Ross, David G Schmale

Affiliations

  1. Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA. [email protected].
  2. National Center for Atmospheric Research, Boulder, CO 80305, USA. [email protected].
  3. Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA 24061, USA. [email protected].
  4. National Center for Atmospheric Research, Boulder, CO 80305, USA. [email protected].
  5. Department of Mechanical Engineering, University of Kentucky, Lexington, KY 40506, USA. [email protected].
  6. Department of Mechanical Engineering, University of Kentucky, Lexington, KY 40506, USA. [email protected].
  7. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80132, USA. [email protected].
  8. UAS Colorado, PO Box 1824, Monument, CO 80132, USA. [email protected].
  9. Integrated Remote and In Situ Sensing, University of Colorado, Boulder, CO 80132, USA. [email protected].
  10. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA. [email protected].
  11. Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA. [email protected].
  12. Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA. [email protected].
  13. School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA. [email protected].

PMID: 30558335 PMCID: PMC6308849 DOI: 10.3390/s18124448

Abstract

Concentrations of airborne chemical and biological agents from a hazardous release are not spread uniformly. Instead, there are regions of higher concentration, in part due to local atmospheric flow conditions which can attract agents. We equipped a ground station and two rotary-wing unmanned aircraft systems (UASs) with ultrasonic anemometers. Flights reported here were conducted 10 to 15 m above ground level (AGL) at the Leach Airfield in the San Luis Valley, Colorado as part of the Lower Atmospheric Process Studies at Elevation-a Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) campaign in 2018. The ultrasonic anemometers were used to collect simultaneous measurements of wind speed, wind direction, and temperature in a fixed triangle pattern; each sensor was located at one apex of a triangle with ∼100 to 200 m on each side, depending on the experiment. A WRF-LES model was used to determine the wind field across the sampling domain. Data from the ground-based sensors and the two UASs were used to detect attracting regions (also known as Lagrangian Coherent Structures, or LCSs), which have the potential to transport high concentrations of agents. This unique framework for detection of high concentration regions is based on estimates of the horizontal wind gradient tensor. To our knowledge, our work represents the first direct measurement of an LCS indicator in the atmosphere using a team of sensors. Our ultimate goal is to use environmental data from swarms of sensors to drive transport models of hazardous agents that can lead to real-time proper decisions regarding rapid emergency responses. The integration of real-time data from unmanned assets, advanced mathematical techniques for transport analysis, and predictive models can help assist in emergency response decisions in the future.

Keywords: Lagrangian Coherent Structure (LCS); Unmanned Aircraft System (UAS); Weather Research and Forecasting (WRF)

References

  1. Chaos. 2011 Sep;21(3):033122 - PubMed
  2. Annu Rev Phytopathol. 2015;53:591-611 - PubMed
  3. Chaos. 2016 May;26(5):053110 - PubMed
  4. Sensors (Basel). 2016 Jul 12;16(7): - PubMed

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