Optical turbulence forecast over short timescales using machine learning techniques

A. Turchi; E. Masciadri; L. Fini

doi:10.1117/12.2629501

29 August 2022 Optical turbulence forecast over short timescales using machine learning techniques

A. Turchi, E. Masciadri, L. Fini

Proceedings Volume 12185, Adaptive Optics Systems VIII; 121855W (2022) https://doi.org/10.1117/12.2629501
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada

Conference Poster

Abstract

Forecast of optical turbulence and atmospheric parameters relevant for ground-based astronomy is becoming an important goal for telescope planning and AO instruments optimization in several major telescope. Such detailed and accurate forecast is typically performed with numerical atmospheric models. Recently short-term forecasts (a few hours in advance) are also being provided (ALTA project) using a technique based on an autoregression approach, as part of a strategy that aims to increase the forecast accuracy. It has been proved that such a technique is able to achieve unprecedented performances so far. Such short-term predictions make use of the numerical model forecast and real-time observations. In recent years machine learning (ML) techniques also started to be used to provide an atmospheric and turbulence forecast. Preliminary results indicate however an accuracy not really competitive with respect to the autoregressive method or even prediction by persistence. This technique might be applicable joint to atmospheric model. It is therefore interesting to investigate the main features of their performances and characteristics (also because there is a great number of algorithms potentially accessible) to understand if results achieved so far can be further improved using ML. In this study we focus on a purely machine learning application to short term forecast (1-2 hours) of astroclimatic and other atmospheric parameters above VLT.

Citation Download Citation

A. Turchi, E. Masciadri, and L. Fini "Optical turbulence forecast over short timescales using machine learning techniques", Proc. SPIE 12185, Adaptive Optics Systems VIII, 121855W (29 August 2022); https://doi.org/10.1117/12.2629501

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available