Eddy covariance measurements for prediction of optical turbulence

Differential heating and turbulence in the atmosphere results in regions of spatially varying temperature, density, and humidity. Laser light traveling along a path with varying optical properties will experience excessive beam spread, loss of coherence, and diminished irradiance on target. This phenomenon is called optical turbulence, and is typically quantified by the index of refraction structure parameter C_n². We aim to develop both traditional and machine learning models for determining C_n² from sonic anemometer data. High frequency wind velocity, temperature, and gas flux through the atmosphere has been measured at three different heights within the surface layer. This data will be applied to similarity theory and direct methods to calculate C_n². Using statistical regression models and supervised machine learning techniques, a new model will be developed and trained to identify C_n² for a local environment and thus predict laser performance.