Abstract

In this Letter, an artificial neural network (ANN) approach is proposed for the estimation of optical turbulence ($C_n^2$) in the atmospheric surface layer. Five routinely available meteorological variables are used as the inputs. Observed $C_n^2$ data near the Mauna Loa Observatory, Hawaii are utilized for validation. The proposed approach has demonstrated its prowess by capturing the temporal evolution of $C_n^2$ remarkably well. More interestingly, this ANN approach is found to outperform a widely used similarity theory-based conventional formulation for all the prevalent atmospheric conditions (including strongly stratified conditions).

© 2016 Optical Society of America

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