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Knowledge of the optical turbulence profile is important in adaptive optics(AO) systems, particularly tomographic AO systems such as those to be employedby the next generation of 40 m class extremely large telescopes (ELTs). Sitecharacterisation and monitoring campaigns have produced large quantities ofturbulence profiling data for sites around the world. However AO system designand performance characterisation is dependent on Monte-Carlo simulations thatcannot make use of these large datasets due to long computation times. Here weaddress the question of how to reduce these large datasets into small sets ofprofiles that can feasibly be used in such Monte-Carlo simulations, whilstminimising the loss of information inherent in this effective compression ofthe data. We propose hierarchical clustering to partition the dataset accordingto the structure of the turbulence profiles and extract a single profile fromeach cluster. This method is applied to the Stereo-SCIDAR dataset from ESOParanal containing over 10000 measurements of the turbulence profile from 83nights. We present two methods of extracting turbulence profiles from theclusters, resulting in two sets of 18 profiles providing subtly differentdescriptions of the variability across the entire dataset. For generality wechoose integrated parameters of the turbulence to measure therepresentativeness of our profiles and compare to others. Using these criterionwe also show that such variability is difficult to capture with small sets ofprofiles associated with integrated turbulence parameters such as seeing.

Representative optical turbulence profiles for ESO Paranal by hierarchical clustering