Physics-Informed Residual Learning for Vertical Profile Reconstruction of Atmospheric Optical Turbulence from Tethered UAV Observations over the Ngari Plateau

Abstract

High-resolution measurements of near-surface optical turbulence over the Tibetan Plateau are essential for optical propagation studies, astronomical site characterization, and adaptive optics applications. In this study, a multi-level tethered UAV system was deployed in Ngari Prefecture, China, to obtain synchronous in situ observations of atmospheric optical turbulence from 10 to 190 m above ground. Using measurements at 10–90 m as inputs, we developed a physics-informed reconstruction framework to estimate Cn2 profiles at 110–190 m, thereby approximately doubling the vertical range covered relative to the directly used observations. The framework decomposes the turbulence structure into an equilibrium component describing the large-scale vertical profile and a residual component representing departures from equilibrium. The equilibrium structure is modeled using a dynamically fitted power-law relationship, while a residual-learning module captures short-term variability associated with evolving thermal and dynamical processes. On the independent test period from the same field campaign, the reconstructed profiles achieve an average RMSE below 0.6 in lg(Cn2) and an average R2 above 0.75, outperforming empirical extrapolation and representative data-driven baselines. The proposed framework provides reliable estimates of upper-layer optical turbulence under daytime and transition-period conditions over the Ngari Plateau.