The transport of material in the atmosphere is a problem with important implications for agriculture, aviation, and human health. Given the turbulent nature of the atmosphere it can be difficult to predict where a particle, such as a plant pathogen, will wind up. Tools from dynamical systems theory, such as Lagrangian coherent structures (LCSs), can help us to understand how particles in a flow will evolve. The study of atmospheric transport from a dynamical systems perspective has long focused on the study of large scale phenomena. This has been largely due to the larger scale grid spacing of readily available atmospheric model data and the lack of high resolution atmospheric measurements on a scale large enough to calculate Lagrangian data. Furthermore, few works have attempted to find ways to detect LCSs in the field. In the authors used wind velocity measurements from a dopler LiDAR to detect LCS which had passed through Hong Kong International Airport. Rather than measure the wind velocity to try and detect LCSs, the authors in looked at sudden changes in pathogen concentrations in the atmosphere. They were then able to link those changes to the passage of LCSs using atmospheric velocity data from the North American Mesoscale (NAM) model. Yet to date, we are unaware of any attempts to develop a means of directly sense LCSs which could be readily implemented by operators in the field. Recent advances in dynamical systems theory, such as new Eulerian diagnostics, and new atmospheric sensing technology, such as unmanned aircraft systems (UAS), have brought the local detection of LCSs within reach.
Comments: 25 Pages. In preparation for journal submission
[v1] 2018-11-23 23:21:08
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