Proceedings Article | 27 July 2016
KEYWORDS: Adaptive optics, Atmospheric modeling, Statistical analysis, Statistical modeling, Turbulence, Gemini Observatory, Adaptive optics, Data modeling, Wind measurement, Error analysis, Performance modeling
Wide Field Adaptive Optics (WFAO) systems represent the more sophisticated AO systems available today at large telescopes. One critical aspect for these WFAO systems in order to deliver an optimised performance is the knowledge of the vertical spatiotemporal distribution of the CN2 and the wind speed. Previous studies (Cortes et al., 2012[1]) already proved the ability of GeMS (the Gemini Multi-Conjugated AO system) in retrieving CN2 and wind vertical stratification using the telemetry data. To assess the reliability of the GeMS wind speed estimates a preliminary study (Neichel et al., 2014[2]) compared wind speed retrieved from GeMS with that obtained with the atmospherical model Meso-Nh on a small sample of nights providing promising results. The latter technique is very reliable for the wind speed vertical stratification. The model outputs gave, indeed, an excellent agreement with a large sample of radiosoundings (∼ 50) both in statistical terms and on individual flights (Masciadri et al., 2013[3]). Such a tool can therefore be used as a valuable reference in this exercise of cross calibrating GeMS on-sky wind estimates with model predictions. The main results of Neichel et al. (2014) analysis showed that, on a great number of cases, GeMS could reconstruct very good wind speed estimates. At the same time it has been put in evidence, on a number of cases, not negligible discrepancies from the atmospherical model. However we observed that these discrepancies strongly decreased or even disappear if GeMS data reduction is done with the a priori knowledge of the wind speed stratification provided by the model Meso-Nh. Basically the a priori knowledge helped the data reduction of GeMS acquisitions. In this contribution we achieved a two-fold results: (1) we extended analysis on a much richer statistical sample (∼ 43 nights), we confirmed the preliminary results and we found an even better correlation between GeMS observations and the atmospherical model with basically no cases of not-negligible uncertainties; (2) we evaluate the possibility to use, as an input for GeMS, the Meso-Nh estimates of the wind speed stratification in an operational configuration. Under this configuration these estimates can be provided many hours in advanced with respect to the observations and with a very high temporal frequency (order of 2 minutes or less). Such a system would have a set of advantages: (a) to implement inside GeMS a total temporal and spatial coverage of the wind speed over ∼ 20 km and all along the night not only in real-time but in advance of a few hours, (b) to improve the detection of the CN2 vertical stratification from GeMS because a good wind speed estimation would improve the quality of the cross-correlation peaks detection, (c) the possibility to bypass the complex (and not necessarily reliable) procedures necessary to automatise the wind speed estimate of GeMS due to the relatively low vertical resolution of the system. Such a study can obviously be considered as a demonstrator for multiple operational AO and WFAO systems (AOF, LINC-NIRVANA, RAVEN, ...) of present top-class telescopes and for the forthcoming generation. It might have, therefore, an interest for the AO community well beyond the improvement of GeMS performance.
