A Method for Detecting Atmospheric Lagrangian Coherent Structures Using a Single Fixed-Wing Unmanned Aircraft System

Abstract

The transport of material through the atmosphere is an issue with wide ranging implications for fields as diverse as agriculture, aviation, and human health. Due to the unsteady nature of the atmosphere, predicting how material will be transported via the Earth’s wind field is challenging. Lagrangian diagnostics, such as Lagrangian coherent structures (LCSs), have been used to discover the most significant regions of material collection or dispersion. However, Lagrangian diagnostics can be time-consuming to calculate and often rely on weather forecasts that may not be completely accurate. Recently, Eulerian diagnostics have been developed which can provide indications of LCS and have computational advantages over their Lagrangian counterparts. In this paper, a methodology is developed for estimating local Eulerian diagnostics from wind velocity data measured by a single fixed-wing unmanned aircraft system (UAS) flying in a circular arc. Using a simulation environment, driven by realistic atmospheric velocity data from the North American Mesoscale (NAM) model, it is shown that the Eulerian diagnostic estimates from UAS measurements approximate the true local Eulerian diagnostics and also predict the passage of LCSs. This methodology requires only a single flying UAS, making it easier and more affordable to implement in the field than existing alternatives, such as multiple UASs and Dopler LiDAR measurements. Our method is general enough to be applied to calculate the gradient of any scalar field.

Pollution Transport Patterns Obtained Through Generalized Lagrangian Coherent Structures

“…Future work will extend the analysis to various vertical layers and a fully three-dimensional analysis. We will also extend this analysis to other test cases over various temporal and spatial scales, develop LCS based on field measurements, combine this with data from simulations [49,50], compare LCS forecasts with satellite and remote sensing observations (e.g., as done for ozone in [11]), and study the implication of LCS (weighted by various species) to source locations, atmospheric chemistry, and exposure [5,9].…”

A Near-Field Gaussian Plume Inversion Flux Quantification Method, Applied to Unmanned Aerial Vehicle Sampling

“…However, in practice, limited sampling time constraints (particularly for UAV sampling) may potentially lead to the observation of non-Gaussian q me across the sampling plane due to turbulence (see Figure A3 for the example of the sample data). The NGI method is therefore a trade-off between the need to sample for a long enough time to characterise the time-averaged plume as much as possible and the need to minimise the systematic effect of any potential dynamic changes in atmospheric stability, source strength and prevailing meteorology over the course of measurement (parameters which may be derived from complementary specialised UAV sampling [57]). Such competing needs would need to be considered on a case-by-case basis by considering weather forecasts, the local topography and the nature of the expected sources being studied.…”

“…Schuyler et al [56] used a 5.14 kg UAV, in the form of a balloon-launched glider, to derive measurements of temperature, pressure and humidity from up to 25 km in height, which were used to validate the results of a weather model. Nolan et al [57] developed an Eulerian diagnostics wind velocity model suitable for single UAV flights, which they tested by simulating a UAV flying in circles. Elsewhere, Nolan et al [58] measured wind speed, wind direction and temperature at 0.07 Hz using two UAVs combined with ground-based (tower) measurements to determine wind fields, which were then used to model the atmospheric transport of emissions.…”

Wind Profiling in the Lower Atmosphere from Wind-Induced Perturbations to Multirotor UAS

“…How the ABL evolves with space and time influences phenomena that impact public health and safety [1][2][3][4][5]. For example, the transport of air pollutants, pollen and spores [6][7][8][9], wind power supply to smart grid systems [10][11][12][13][14], forecast of local weather [2][3][4][5], air traffic control at airports [15][16][17][18], the spread and management of wildfires [19][20][21][22], and emissions mitigation of greenhouse gases [23][24][25][26] are all affected by the dynamic state of the ABL. Therefore, mitigation of adverse conditions affected by the dynamic state of the ABL requires accurate measurements of wind velocity over micro-and mesoscale domains [2,27,28].…”