Estimation of ground-based atmospheric turbulence strength (<i>C</i> <i>n</i>2) by neural network architecture

Estimating the atmospheric turbulence strength ( C n 2 ) becomes significant in the research field of electromagnetic radiation transmission through the atmosphere, particularly optical waves. As turbulence strength increases, the quality and strength of these optical waves may decrease and cause much trouble as they pass through the atmosphere. Throughout the years, C n 2 has been formulated by different research groups for various geographical locations and seasons using macro-meteorological variables empirically and theoretically. However, since these models are based on the data collected from numerous places and conditions, such as deserts or coastal areas, they do not provide accurate C n 2 predictions for our experimental site, as demonstrated for three well-known models in the paper. In this study, a novel, to our knowledge, artificial neural network (ANN) model named as quadratic Fourier neural network (QFNN) is trained to estimate C n 2 from experimentally measured ground-based atmospheric turbulence strength and macro-meteorological variables during the winter season in a rural area. The trained model gives reliable estimations, achieving a value of R 2 =0.92 for experimental C n 2 values.