Reference Optical Turbulence and Wind Profiles for Single Conjugate and Extreme Adaptive Optics

TL;DR

This paper introduces a method to extract representative optical turbulence and wind profiles from large datasets for adaptive optics simulations, capturing atmospheric variability relevant to system performance.

Contribution

The paper develops a new approach to select and compress atmospheric profiles, including wind data, for more accurate adaptive optics modeling and simulation.

Findings

Profiles cover a broad range of atmospheric variability

Correlates atmospheric parameters with AO performance

Profiles enable realistic AO simulation scenarios

Abstract

We present a simple method of extracting a small number of reference optical turbulence and wind profiles from a large dataset for single conjugate and extreme adaptive optics simulations. These reference profiles can be used in slow end-to-end adaptive optics simulations to represent the variability of the atmosphere. The method is based on the assumption that performance for these systems is correlated with integrated atmospheric parameters $r_0$, $\theta_0$ and $\tau_0$. Profiles are selected from a large dataset that conform concurrently to the distributions of these parameters, and hence represent the variability of the atmosphere as seen by the AO system. We also extend the equivalent layers method of profile compression to include wind profiles. The method is applied to stereo-SCIDAR data from ESO Paranal to extract five turbulence and wind profiles that cover a broad range in atmospheric variability, and we show using analytical AO simulation that this correlates to the equivalent range of AO-corrected Strehl ratios.