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29 August 2022 Towards operational optical turbulence forecast systems at different time scales
Elena Masciadri, Alessio Turchi, Luca Fini
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Proceedings Volume 12185, Adaptive Optics Systems VIII; 121851Q (2022) https://doi.org/10.1117/12.2629287
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada
Abstract
The forecast on a time scale of 1 or 2 hours is crucial for all kind of new generation facilities (ELTs) instrumentation supported by the adaptive optics that will be mainly operated in Service Mode. In a recent study (Masciadri et al. 2020) we have showed that we can forecast the seeing and atmospheric parameters at such short time scales using an autoregressive method achieving unprecedented model accuracies with a substantial gain with respect to forecasts performed the day before (i.e. on longer time scales) obtained with an atmospherical mesoscale model. Equally we showed a gain with respect to the method by persistence using simply real-time measurements in situ on the same short time scale (1-2 hours). The auto-regressive method makes use of the forecasts done with mesoscale atmospherical models and real-time measurements and since 2019 has been implemented in the operational forecast system ALTA Center supporting LBT observations. In this contribution we apply the same approach to the VLT site extending the method to all the main astroclimatic parameters i.e. the seeing, the wavefront coherence time, the isoplanatic angle and the ground layer fraction. We prove that such a method offers unprecedented forecast accuracies for all the astroclimatic parameters with clear gains with respect to the prediction by persistence. Preliminary calculations indicate also better accuracies than those obtained with the machine learning based approach using in situ measurements. We will apply soon such a method in an operational forecast system that we conceived for the VLT.
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Elena Masciadri, Alessio Turchi, and Luca Fini "Towards operational optical turbulence forecast systems at different time scales", Proc. SPIE 12185, Adaptive Optics Systems VIII, 121851Q (29 August 2022); https://doi.org/10.1117/12.2629287
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KEYWORDS
Atmospheric modeling
Autoregressive models
Optical turbulence
Machine learning
Turbulence
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