Long-term measurement and characterization of boundary layer optical turbulence.

The United States Naval Academy long-term scintillation measurement campaign was a multi-year effort to characterize optical turbulence in the near-maritime atmospheric boundary layer (ABL). At its core, the field experiment consists of in situ measurements of bulk atmospheric and oceanographic parameters, as well as path-averaged measurements of the refractive index structure parameter, C n2, collected using a large-aperture scintillometer. The field experiment ran from January 1st, 2020, through September 26th, 2023, representing the most comprehensive collection of optical turbulence measurements in the near-maritime ABL to date. Long-term measurements enable researchers to evaluate existing theory and develop new models applicable to this environment. The present study characterizes some of the physical relationships that affect optical turbulence. This characterization focuses on the relationship between local optical turbulence and select atmospheric and oceanographic parameters. The impact of temperature gradients on the extent of optical turbulence was analyzed, along with its interactions with relative humidity and wind speed. The diurnal and seasonal variations in measured C n2 were examined, with comparisons drawn against other environments. Further information and the full dataset are publicly available through the optical turbulence benchmark repository [Jellen et al., GitHub, 2023].

Hybrid optical turbulence models using machine-learning and local measurements.

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.

Machine-learning informed macro-meteorological models for the near-maritime environment.

Macro-meteorological models predict optical turbulence as a function of weather data. Existing models often struggle to accurately predict the rapid fluctuations in Cn2 in near-maritime environments. Seven months of Cn2 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 Cn2 and measured environmental parameters. Finally, the Cn2 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 Cn2 prediction accuracy, and were better able to track optical turbulence. Further tuning and machine-learning architectural changes could further improve model performance.

Machine learning informed predictor importance measures of environmental parameters in maritime optical turbulence.

Prediction of the index of refraction structure constant Cn2 in the low-altitude maritime environment is challenging. To improve predictive models, deeper understanding of the relationships between environmental parameters and optical turbulence is required. To that end, a robust data set of Cn2 as well as numerous meteorological parameters were collected over a period of approximately 15 months along the Chesapeake Bay adjacent to the Severn River in Annapolis, Maryland. The goal was to derive new insights into the physical relationships affecting optical turbulence in the near-maritime environment. Using data-driven machine learning feature selection approaches, the relative importance of 12 distinct, measurable environmental parameters was analyzed and evaluated. Random forest nodal purity analysis was the primary machine learning approach to relative importance determination. The relative feature importance results indicated that air temperature and pressure were important parameters in predicting Cn2 in the maritime environment. In addition, the relative importance findings suggest that the air-water temperature difference, temporal hour weight, and time of year, as measured through seasonality, have strong associations with Cn2 and could be included to improve model prediction accuracy.