Free space optical communication (FSOC) is a promising technology for satellite-to-earth communication systems, where vector beams, especially orbital angular momentum (OAM), can further increase the capacity of the optical link. However, atmospheric turbulence along the path can introduce intensity scintillation, wavefront aberrations and severe distortion of spatial patterns, leading to data degradation. Forecasting atmospheric turbulence allows for advanced scheduling of satellite-to-earth data transmission links, as well as the use of adaptive optics (AO) to compensate for turbulence effects and avoid data transmission link performance degradation. Therefore, atmospheric turbulence forecasting is critical for practical applications. In this work, we proposed a hybrid atmospheric turbulence forecasting model based on a two-stage variational mode decomposition (TsVMD) and autoregression model. The variational mode decomposition (VMD) algorithm is first used, to our best knowledge, to denoise the observed atmospheric turbulence dataset, and then is used again to decompose the datasets into several intrinsic mode functions (IMFs). Finally, the autoregression model is used to predict each IMF independently. And the predictions of each IMF are combined to obtain the final atmospheric turbulence predictions. Experiments employing the observed turbulence datasets and two additional methodologies were carried out to verify the performance of the proposed model. The experimental results show that the performance of the proposed model is much superior to that of the comparative methods.

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