Establishing baseline model performances for optical turbulence forecasting

Accurate optical turbulence (OT) forecasts help telescopes observe with maximum efficiency, as the performance of adaptive optics (AO) systems strongly depends on turbulence conditions. Due to the chaotic and non-linear nature of turbulence, OT forecasting remains a challenging problem. A wide range of methods has been explored to address this problem, including numerical weather prediction (NWP) models, machine learning algorithms, and hybrid approaches, each varying in complexity, computational cost, and accuracy. Assessing the performance of forecasting methods is essential to benchmark them against simple, well-defined reference models, known as baselines, which establish reference thresholds. This paper aims to calculate these thresholds for the astroclimatic and atmospheric parameters most relevant for ground-based astronomy related to two of these baseline methods that are suitable for use in two different types of forecast, which are among the most relevant for application to the ground-based astronomy: the forecast of the average of a parameter on a defined timescale and the forecast of the temporal evolution of a parameter on a defined timescale. We apply these baseline methods to a dataset related to a rich statistical sample of many years encompassing key astroclimatic and atmospheric parameters above the sites of the Very Large Telescope and the Large Binocular Telescope, and we discuss the implications of these results and the possibility of extrapolating such values for general rules. Additionally, we analyse the impact of data pre-processing steps, including smoothing, outlier rejection, and sampling frequency, on forecasting performance. We demonstrate that the predictive performance of each reference method depends on the observing sites, the parameter that is forecasted, the forecast timescale, and the type of forecast used. That means that it is meaningless to quantify the performance of OT forecasts in absolute terms, since they depend on the context.