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 some 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, the 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 is expensive to collect. Both the hybrid and data-only models were
trained using the Gradient Boosted Decision Tree (GBDT) 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 mean absolute error (MAE) using
only one days-equivalent of observation, growing to 41% after only two days,
and 68% after 180 days-equivalent training data. The number of days-equivalent
training data required is potentially indicative of the seasonal variation in
the local microclimate and its propagation environment.

构建了一个以机器学习为基础的混合模型框架，通过结合基准宏观气象模型和局部观测数据，提高了每个基准模型的预测能力。该研究表明，在某些情况下，只需很少的观测数据，混合模型和仅使用局部观测数据的模型就能胜过三个基准宏观气象模型的性能。