Turbulence Mapping with Autonomous Vehicle Fleets

The most representative sensors that make up the Autonomous Vehicle sensors’ ecosystem are RADAR, LiDAR (Light Detection and Ranging), ultrasonic, GNSS and cameras. These onboard sensors measure wave sources (mostly based on phase observations), allow for redundancies, and have distinct properties to perform specific tasks, such as positioning or obstacle detection. They either measure one-dimensional ranges, record 3D point clouds of their environment or process signals from medium Earth orbit satellites. Each of those phase observations is affected by their path through the atmosphere and allows, with suitable manipulation and filtering, the derivation of the spectrum of turbulent phase fluctuations and the estimation of its parameters, such as the cutoff frequency using the valuable von Kárman assumption. The strength of the fluctuations can be additionally estimated, a quantity related to the structure constant of the refractive index. In this presentation, I will show how it would be possible to “extract” the turbulence spectrum from AV sensors measurements with the goal to advance atmospheric turbulence research, especially within urban settings, by enhancing real-time monitoring through a dense network of sensors deployed via Autonomous Vehicle fleets. I will present some outcomes, spanning from noise analysis, Large Eddy Simulation validation, and extreme weather nowcasting uncertainty reduction.