随机并行梯度下降算法拟合大气湍流廓线统计模式的研究

掌握湍流基本规律及其廓线结构分布,是大气光学理论与应用研究中的关键问题之一。利用随机并行梯度下降算法对大气湍流廓线统计模式拟合进行了研究。在已获合肥地区整层大气折射率结构常数C2N平均廓线的前提下,以广义Hufnagel-Valley湍流模型为基础,拟合获得了该地区不同时间与不同季节的湍流模式廓线。研究发现,该方法获得的该地区湍流模式廓线在整层大气高度上均能很好地同实测平均廓线相符合,且两者所表征的整层湍流特征参量也能保持非常好的一致性。对寻求基于Hufnagel-Valley模型的通用湍流廓线模式拟合方法进行了有益的探索。

Investigation on Atmospheric Optical Turbulence Profile Statistical Mode by Stochastic Parallel Gradient Descent Algorithm

Understanding vertical profiles of atmospheric turbulence characteristics is one of the most important problems for theoretical and applied research in the fields of atmospheric optics. Stochastic parallel gradient descent (SPGD) algorithm is proposed for turbulence profile mode fitting. Based on the generalized Hufnagel-Valley model, atmospheric turbulence profile models for different seasons and time of day in Hefei have been developed to fit each observed average C2N vertical profile by SPGD algorithm. The results show that, not only the obtained turbulence mode show best accordance with the observed average profiles of C2N over the whole atmosphere, but also the optical turbulence characteristic parameters of the obtained turbulence modes are in good agreement with those for the average profiles of C2N. The investigation is a useful exploration for developing a “universal method” for turbulence profile model fitting based on the generalized Hufnagel-Valley model.