AI 中文总结
本文针对光学湍流预测难题，通过将两种基线方法应用于相关数据集，计算天文气候和大气参数阈值，探讨其性能受观测站点、参数、时间尺度和预测类型影响，指出绝对量化OT预测性能无意义。
AI 中文摘要
准确的光学湍流（OT）预测有助于望远镜高效观测，因为自适应光学（AO）系统性能强烈依赖于湍流条件。OT预测仍是难题，已探索多种方法。本文旨在为与地面天文学相关的天文气候和大气参数计算两种基线方法的阈值，这两种方法适用于不同类型预测。将其应用于包含甚大望远镜和大型双筒望远镜站点关键参数的多年丰富统计样本数据集，讨论结果影响及外推可能性。结果表明各参考方法的预测性能取决于观测站点、预测参数、预测时间尺度和预测类型，绝对量化OT预测性能无意义。
英文摘要
Accurate optical turbulence (OT) forecasts help telescopes observe with maximum efficiency, as the performance of adaptive optics (AO) systems strongly depends on turbulence conditions. 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. 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.
Comments24 figures, 2 tables, A&A, 2026, accepted
