Jellen Christopher, Nelson Charles, Burkhardt John, Brownell Cody
Appl Opt. 2023 Jun 20;62(18):4880-4890. doi: 10.1364/AO.487280.
Accurate prediction of atmospheric optical turbulence in localized environments is essential for estimating the performance of free-space optical systems. Macro-meteorological models developed to predict turbulent effects in one environment may fail when applied in new environments. However, existing macro-meteorological models are expected to offer some predictive power. Building a new model from locally measured macro-meteorology and scintillometer readings can require significant time and resources, as well as a large number of observations. These challenges motivate the development of a machine-learning informed hybrid model framework. By combining a baseline macro-meteorological model with local observations, hybrid models were trained to improve upon the predictive power of each baseline model. Comparisons between the performance of the hybrid models, selected baseline macro-meteorological models, and machine-learning models trained only on local observations, highlight potential use cases for the hybrid model framework when local data are expensive to collect. Both the hybrid and data-only models were trained using the gradient boosted decision tree architecture with a variable number of in situ meteorological observations. The hybrid and data-only models were found to outperform three baseline macro-meteorological models, even for low numbers of observations, in some cases as little as one day. For the first baseline macro-meteorological model investigated, the hybrid model achieves an estimated 29% reduction in the mean absolute error using only one day-equivalent of observation, growing to 41% after only two days, and 68% after 180 days-equivalent training data. The data-only model generally showed similar, but slightly lower performance, as compared to the hybrid model. Notably, the hybrid model's performance advantage over the data-only model dropped below 2% near the 24 days-equivalent observation mark and trended towards 0% thereafter. The number of days-equivalent training data required by both the hybrid model and the data-only model is potentially indicative of the seasonal variation in the local microclimate and its propagation environment.
准确预测局部环境中的大气光学湍流对于评估自由空间光学系统的性能至关重要。为预测一种环境中的湍流效应而开发的宏观气象模型，应用于新环境时可能会失效。然而，现有的宏观气象模型仍有望提供一定的预测能力。基于本地测量的宏观气象数据和闪烁仪读数构建新模型可能需要大量时间和资源，以及大量观测数据。这些挑战促使了一种机器学习辅助的混合模型框架的发展。通过将基线宏观气象模型与本地观测数据相结合，训练混合模型以提高每个基线模型的预测能力。混合模型、选定的基线宏观气象模型以及仅基于本地观测数据训练的机器学习模型之间的性能比较，凸显了在本地数据收集成本高昂时混合模型框架的潜在应用案例。混合模型和仅基于数据的模型均使用梯度提升决策树架构进行训练，训练时使用了数量可变的现场气象观测数据。结果发现，混合模型和仅基于数据的模型在某些情况下，即使观测数据较少（少至一天），也优于三种基线宏观气象模型。对于所研究的第一个基线宏观气象模型，混合模型仅使用一天等效观测数据时，平均绝对误差估计降低了29%，仅两天后降至41%，180天等效训练数据后降至68%。与混合模型相比，仅基于数据的模型通常表现出相似但略低的性能。值得注意的是，混合模型相对于仅基于数据的模型的性能优势在接近24天等效观测标记时降至2%以下，此后趋于0%。混合模型和仅基于数据的模型所需的等效训练天数可能表明了当地微气候及其传播环境的季节变化。
