Optical turbulence forecasts at short time-scales using an autoregressive method at the Very Large Telescope
Date Issued
2023
Author(s)
Description
The authors thank the Meso-NH user support team, who work constantly to maintain the model by developing new packages in progressing model versions. Initialization data come from the GCM HRES of the ECMWF. This study has been co-funded by the FRCF foundation through the ‘Ricerca Scientifica e Tecnologica’ action
–N.45103 and by the EU Horizon 2020 research and innovation programme under grant agreement No. 824135 (SOLARNET). The digital ele v ation model at high resolution has been obtained thanks to the Shuttle Radar Topography Mission (SRTM). The authors thank Angel Otarola and ESO Santiago and Garching staff supporting this study. This study made use of the scikit-learn PYTHON packages. We thank Leslie Hunts for her precious hints on the English form of the text.
Abstract
In this study we demonstrate that we can provide forecasts of all the main astroclimatic parameters (seeing, wavefront coherence time, isoplanatic angle, and ground-layer fraction) on time-scales of 1 and 2 h (the most critical ones for the service mode) with a root-mean-square error (RMSE) that is smaller than or, at worst, comparable to the instrumental uncertainty (i.e. the standard deviation between instrument estimates). The seeing RMSE is 0.08 arcsec. Results are achieved thank to the use of the autoregressive method (AR) in our automatic forecast system and the study is applied to the Very Large Telescope (VLT). The AR method is a hybrid method taking into account forecasts of a non-hydrostatical mesoscale model jointly with real-time observations made in situ. We demonstrate that the AR method allows an improvement in forecast performance of roughly a factor of three or more with respect to the standard forecasts at a long time-scale (beginning of the afternoon for the coming night), depending on the parameter and the time-scale (1 and 2 h). The AR method also allows roughly a factor of two gain with respect to prediction by persistence. We also show that the AR method provides significantly better performance than a random-forest machine-learning algorithm. An extended analysis of the AR performance is provided following different strategies. Results achieved in this study are therefore very promising and tell us that we can provide real assistance to the service mode of the VLT instrumentation supported by adaptive optics systems.
Volume
523
Issue
3
Start page
3487
Issn Identifier
0035-8711
Ads BibCode
2023MNRAS.523.3487M
Rights
open.access
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