Jellen Christopher, Oakley Miles, Nelson Charles, Burkhardt John, Brownell Cody
Appl Opt. 2021 Apr 10;60(11):2938-2951. doi: 10.1364/AO.416680.
Macro-meteorological models predict optical turbulence as a function of weather data. Existing models often struggle to accurately predict the rapid fluctuations in 2 in near-maritime environments. Seven months of 2 field measurements were collected along an 890 m scintillometer link over the Severn River in Annapolis, Maryland. This time series was augmented with local meteorological measurements to capture bulk-atmospheric weather measurements. The prediction accuracy of existing macro-meteorological models was analyzed in a range of conditions. Next, machine-learning techniques were applied to train new macro-meteorological models using the measured 2 and measured environmental parameters. Finally, the 2 predictions generated by the existing macro-meteorological models and new machine-learning informed models were compared for four representative days from the data set. These new models, under most conditions, demonstrated a higher overall 2 prediction accuracy, and were better able to track optical turbulence. Further tuning and machine-learning architectural changes could further improve model performance.
宏观气象模型根据气象数据预测光学湍流。现有模型在准确预测近海洋环境中光学湍流的快速波动方面常常存在困难。沿着马里兰州安纳波利斯市塞文河上一条890米的闪烁仪链路收集了七个月的光学湍流现场测量数据。该时间序列通过当地气象测量数据进行补充，以获取大气整体气象测量数据。在一系列条件下分析了现有宏观气象模型的预测准确性。接下来，应用机器学习技术，利用测量得到的光学湍流数据和环境参数来训练新的宏观气象模型。最后，针对数据集中四个具有代表性的日子，比较了现有宏观气象模型和新的机器学习模型所生成的光学湍流预测结果。在大多数情况下，这些新模型展现出更高的光学湍流总体预测准确性，并且能够更好地跟踪光学湍流。进一步的调整和机器学习架构的改变可能会进一步提升模型性能。
