Predicting refractive index structure parameter in the marine wave boundary layer using machine learning models: a thesis in Data Science

The refractive index structure parameter, ??ₙ², quantifies optical turbulence and strongly influences the performance of electromagnetic and free-space optical systems operating within the marine wave boundary layer. Predicting ??ₙ² remains challenging due to nonlinear interactions among atmospheric variables and shows strong changes between daytime and nighttime, especially in coastal environments. This thesis examines these challenges using high-frequency meteorological data from ATI sonic anemometers and co-located scintillometer observations collected at the SMAST Pier in Buzzards Bay, Massachusetts, during the summer and fall of 2025. A random forest (RF) modeling framework is developed to evaluate not only predictive accuracy, but also the conditions under which machine-learning-based estimates of ??ₙ² agree or disagree with observations. Atmospheric data are strongly time-dependent, meaning that common random train-test splits can introduce information leakage and overestimate model skill. To avoid this problem, the analysis uses a blocked, leakage-free train-gap-test strategy based on decorrelation time scales. Low-frequency and seasonal background trends are removed using variance-preserving spectral filtering, and time-lagged features are included based on correlation analyses to better represent the physical memory of the system. Separate RF models developed for summer and fall are evaluated using within-season validation. Results show moderate overall predictive skill, with clear differences between daytime and nighttime performance. Nighttime predictions consistently explain more of the observed variability in log₁₀(??ₙ²) than daytime predictions, particularly during summer. This improved nighttime performance may be linked to more stable and less varying turbulence conditions after sunset. In contrast, daytime predictions show larger errors, likely due to stronger convection and rapidly changing surface-layer processes. Overall, this study shows that the reliability of machine-learning-based ??ₙ² predictions depend strongly on temporal structure, diurnal regime, and how autocorrelation is handled. The iv findings highlight the importance of using regime-aware validation and variance-based diagnostics rather than relying only on single accuracy metrics. This work provides practical insight into when ML-based turbulence predictions can be trusted in coastal marine environments and offers guidance for developing more robust and physically consistent forecasting approaches.