Optical turbulence forecast over short timescales using machine learning techniques

TL;DR

This paper investigates the potential of machine learning techniques to improve short-term forecasts of optical turbulence and atmospheric parameters for ground-based astronomy, aiming to enhance telescope planning and adaptive optics performance.

Contribution

It provides an analysis of machine learning methods for short-term atmospheric forecasting, comparing their performance with existing autoregressive approaches.

Findings

ML techniques show potential but currently lag behind autoregressive methods

Combining ML with numerical models could improve forecast accuracy

Study focuses on 1-2 hour ahead predictions for VLT atmospheric parameters

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.