Knowledge of the Earth's atmospheric optical turbulence is critical for astronomical instrumentation. Not only does it enable performance verification and optimisation of existing systems but it is required for the design of future instruments. As a minimum this includes integrated astro-atmospheric parameters such as seeing, coherence time and isoplanatic angle, but for more sophisticated systems such as wide field adaptive optics enabled instrumentation the vertical structure of the turbulence is also required.Stereo-SCIDAR is a technique specifically designed to characterise the Earth's atmospheric turbulence with high altitude resolution and high sensitivity. Together with ESO, Durham University has commissioned a Stereo-SCIDAR instrument at Cerro Paranal, Chile, the site of the Very Large Telescope (VLT), and only 20 km from the site of the future Extremely Large Telescope (ELT).Here we provide results from the first 18 months of operation at ESO Paranal including instrument comparisons and atmospheric statistics. Based on a sample of 83 nights spread over 22 months covering all seasons, we find the median seeing to be 0.64 with 50% of the turbulence confined to an altitude below 2 km and 40% below 600 m. The median coherence time and isoplanatic angle are found as 4.18 ms and 1.75 respectively.A substantial campaign of inter-instrument comparison was also undertaken to assure the validity of the data. The Stereo-SCIDAR profiles (optical turbulence strength and velocity as a function of altitude) have been compared with the Surface-Layer SLODAR, MASS-DIMM and the ECMWF weather forecast model. The correlation coefficients are between 0.61 (isoplanatic angle) and 0.84 (seeing).

Context. The wind-driven halo is a feature that is observed in images that were delivered by the latest generation of ground-based instruments that are equipped with an extreme adaptive optics system and a coronagraphic device, such as SPHERE at the Very Large Telescope (VLT). This signature appears when the atmospheric turbulence conditions vary faster than the adaptive optics loop can correct for. The wind-driven halo is observed as a radial extension of the point spread function along a distinct direction (this is sometimes referred to as the butterfly pattern). When this is present, it significantly limits the contrast capabilities of the instrument and prevents the extraction of signals at close separation or extended signals such as circumstellar disks. This limitation is consequential because it contaminates the data for a substantial fraction of the time: about 30% of the data produced by the VLT/SPHERE instrument are affected by the wind-driven halo. Aims. This paper reviews the causes of the wind-driven halo and presents a method for analyzing its contribution directly from the scientific images. Its effect on the raw contrast and on the final contrast after post-processing is demonstrated. Methods. We used simulations and on-sky SPHERE data to verify that the parameters extracted with our method can describe the wind-driven halo in the images. We studied the temporal, spatial, and spectral variation of these parameters to point out its deleterious effect on the final contrast. Results. The data-driven analysis we propose provides information to accurately describe the wind-driven halo contribution in the images. This analysis confirms that this is a fundamental limitation of the finally reached contrast performance. Conclusions. With the established procedure, we will analyze a large sample of data delivered by SPHERE in order to propose post-processing techniques that are tailored to removing the wind-driven halo.

AOtools is a Python package which is open-source and aimed at providing tools for adaptive optics users and researchers. We present version 1.0 which contains tools for adaptive optics processing, including analysing data in the pupil plane, images and point spread functions in the focal plane, wavefront sensors, modelling of atmospheric turbulence, physical optical propagation of wavefronts, and conversion between frequently used adaptive optics and astronomical units. The main drivers behind AOtools is that it should be easy to install and use. To achieve this the project features extensive documentation, automated unit testing and is registered on the Python Package Index. AOtools is under continuous active development to expand the features available and we encourage everyone involved in adaptive optics to become involved and contribute to the project.

We present the results from a Monte Carlo computer simulation of adaptive optics (AO) pre-compensated laser uplink propagation through the Earth’s atmospheric turbulence from the ground to orbiting satellites. The simulation includes the so-called point-ahead angle and tests several potential AO mitigation modes such as tip/tilt or full AO from the downlink beam, and a laser guide star at the point ahead angle. The performance of these modes, as measured by metrics relevant for free-space optical communication, are compared with no correction and perfect correction. The aim of the study is to investigate fundamental limitations of free-space optical communications with AO pre-compensation and a point-ahead angle, therefore the results represent an upper bound of AO corrected performance, demonstrating the potential of pre-compensation technology. Performance is assessed with varying launch aperture size, wavelength, launch geometry, ground layer turbulence strength (i.e. day/night), elevation angle and satellite orbit (Low-Earth and Geostationary). By exploring this large parameter space we are able examine trends on performance with the aim of informing the design of future optical ground stations and demonstrating and quantifying the potential upper bounds of adaptive optics performance in free-space optical communications.

Knowledge of the optical turbulence profile is important in adaptive optics (AO) systems, particularly tomographic AO systems such as those to be employed by the next generation of 40 m class extremely large telescopes (ELTs). Site characterisation and monitoring campaigns have produced large quantities of turbulence profiling data for sites around the world. However AO system design and performance characterisation is dependent on Monte-Carlo simulations that cannot make use of these large datasets due to long computation times. Here we address the question of how to reduce these large datasets into small sets of profiles that can feasibly be used in such Monte-Carlo simulations, whilst minimising the loss of information inherent in this effective compression of the data. We propose hierarchical clustering to partition the dataset according to the structure of the turbulence profiles and extract a single profile from each cluster. This method is applied to the Stereo-SCIDAR dataset from ESO Paranal containing over 10000 measurements of the turbulence profile from 83 nights. We present two methods of extracting turbulence profiles from the clusters, resulting in two sets of 18 profiles providing subtly different descriptions of the variability across the entire dataset. For generality we choose integrated parameters of the turbulence to measure the representativeness of our profiles and compare to others. Using these criterion we also show that such variability is difficult to capture with small sets of profiles associated with integrated turbulence parameters such as seeing.