Open AccessUsing an artificial neural network approach to estimate surface-layer optical turbulence at Mauna Loa, Hawaii
Author(s) -
Yao Wang,
Sukanta Basu
Publication year - 2016
Publication title -
optics letters/optics index
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.524
H-Index - 272
eISSN - 1071-2763
pISSN - 0146-9592
DOI - 10.1364/ol.41.002334
Subject(s) - artificial neural network , observatory , turbulence , meteorology , atmospheric turbulence , environmental science , atmospheric optics , remote sensing , layer (electronics) , optics , computer science , geology , artificial intelligence , physics , materials science , astronomy , composite material
In this Letter, an artificial neural network (ANN) approach is proposed for the estimation of optical turbulence (Cn2) in the atmospheric surface layer. Five routinely available meteorological variables are used as the inputs. Observed Cn2 data near the Mauna Loa Observatory, Hawaii are utilized for validation. The proposed approach has demonstrated its prowess by capturing the temporal evolution of Cn2 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).