Uncertainty Quantification and Global Sensitivity Analysis for Radio Wave Propagation in Evaporation Duct

Accurate prediction of radio wave propagation in evaporation ducts is critical for radar systems but faces significant environmental uncertainties. This study presents an uncertainty quantification and global sensitivity analysis framework comparing three surrogate models: Polynomial Chaos Expansion, Ordinary Kriging, and Polynomial-Chaos Kriging. Using a parabolic equation solver, we quantify how five parameters—mean duct height, duct height slope, potential refractivity gradient, frequency, and root mean square (RMS) wave height—affect propagation loss. We assess predictive accuracy, perform Sobol-based sensitivity analysis, and explore how surrogate performance relates to the normalized frequency V, a parameter characterizing modal complexity. Results show that Kriging consistently outperforms the others: its local interpolation capability proves essential for capturing rapid spatial oscillations caused by multimode interference. We observe a statistically significant negative correlation between Kriging’s prediction error and V, suggesting that its local interpolation becomes increasingly advantageous as the modal complexity of the field (quantified by V) increases. This provides a physically interpretable, though not yet predictive, link between surrogate model choice and the underlying propagation physics. Sensitivity analysis reveals that mean duct height dominates uncertainty at short-to-medium ranges, while the potential refractivity gradient becomes increasingly influential at longer ranges. RMS wave height exhibits localized effects near multipath nulls, particularly at higher frequencies. These findings provide quantitative guidance for prioritizing environmental measurements and offer a physically interpretable basis for surrogate model selection in evaporation duct problems.