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Deep learning neural network solution for predicting the refractive index structure constant

A lightweight deep learning neural network model for predicting the refractive index structure constant (C2n) was constructed using atmospheric data collected at the TISTEF laser range over a 90 day period. The model used variables such as barometric pressure, wind speed, wind direction, air temperature, corrected average ground temperature, relative humidity, solar kilowatts, solar mega joules, and various derived variables to make its predictions. The model was evaluated using measurements recorded from a Scintec BLS 900. The predictions from the model were highly correlated with the measured values of C2n.