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

Area of Science:

• Atmospheric Science, Fluid Dynamics, Environmental Monitoring

Background:

• Atmospheric material transport impacts agriculture, aviation, and health.
• Predicting atmospheric transport is challenging due to unsteady wind fields.
• Lagrangian diagnostics (e.g., LCSs) are effective but computationally intensive and rely on accurate forecasts.

Purpose of the Study:

• To develop a novel methodology for estimating local Eulerian diagnostics using a single unmanned aircraft system (UAS).
• To demonstrate the feasibility of using UAS-based Eulerian diagnostics for predicting material transport and Lagrangian Coherent Structures (LCSs).

Main Methods:

• Developed a method to estimate local Eulerian diagnostics from wind velocity data collected by a fixed-wing UAS flying in a circular arc.
• Utilized a simulation environment driven by North American Mesoscale (NAM) model atmospheric velocity data.
• Validated the method by comparing UAS-derived Eulerian diagnostic estimates with true local values and LCS passage predictions.

Main Results:

• Eulerian diagnostic estimates derived from single UAS measurements closely approximate true local Eulerian diagnostics.
• The UAS-based methodology successfully predicts the passage of Lagrangian Coherent Structures (LCSs).
• The approach offers computational advantages and requires only a single UAS, reducing complexity and cost.

Conclusions:

• The proposed methodology provides an efficient and accessible way to estimate Eulerian diagnostics for atmospheric transport studies.
• This single-UAS approach is a viable and cost-effective alternative to existing methods like multiple UASs or Doppler LiDAR.
• The generalizable method can be applied to calculate the gradient of any scalar field, broadening its applicability.