Weather sensing with structured light

Abstract Environmental conditions, such as temperature and wind speed, heavily influence the complex and rapidly varying optical distortions propagating optical fields experience. The continuous random phase fluctuations commonly make deciphering the exact origins of specific optical aberrations challenging. The generation of eddies is a major contributor to atmospheric turbulence, similar in geometric structure to optical vortices that sit at the center of beams that carry Orbital Angular Momentum (OAM). Decomposing the received optical fields into OAM provides a unique spatial similarity that can be used to analyze turbulent channels. In this work, we present a mode decomposition assisted machine learning approach that reveals trainable features in the distortions of vortex beams that allow for effective environmental monitoring. This technique can be used reliably with Support Vector Machine regression models to measure temperature variations of 0.49 °C and wind speed variations of 0.029 ms −1 over a 36 m experimental turbulent free-space channel with controllable and verifiable temperature and wind speed with a short 3 s measurement. These findings could indicate the presence of an underlying physical relationship between environmental conditions that lead to specific eddy formation and the OAM spiral spectra. Therefore, this relationship could be used to develop next generation optical weather sensors.