科研之友
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摘要
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).
- 出版日期2016-5-15
