Spatiotemporal Nowcasting of Atmospheric Optical Turbulence for LEO Satellite-to-Ground Laser Communication Networking

Satellite-to-ground laser communication (SGLC) is a viable technology for future high-speed feeder links in mega Low Earth Orbit (LEO) constellation networks. However, it is constrained by the dynamic and non-stationary nature of atmospheric optical turbulence during rapid communication windows. To address the operational requirements for network scheduling, this study captures transient atmospheric states by deriving high-resolution Fried parameter fields. This study proposes the Diffusion Schrödinger Bridge Graph Transformer (DSBGT) for minute-scale spatiotemporal nowcasting. The DSBGT architecture employs a Graph Wavelet Network-based Transformer (GWNformer) backbone to predict spatiotemporal patterns via dynamic graphs and parallel attention mechanisms. To address the non-stationarity of atmospheric optical turbulence, a Diffusion Schrödinger Bridge is incorporated to establish probabilistic optimal transport mappings from current Fried parameter distributions to its future states . Experimental results indicate that the DSBGT model achieves a mean absolute error of less than 0.24 cm for 30-min forecasts across all four seasons, outperforming baseline models. These results suggest the potential for facilitating proactive operational link scheduling and dynamic resource optimization in future LEO-SGLC networks.