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作 者:朱黎明 孙刚 张汉九 许满满 陈多龙 邵士勇 武鹏飞 李学彬 Zhu Liming;Sun Gang;Zhang Hanjiu;Xu Manman;Chen Duolong;Shao Shiyong;Wu Pengfei;Li Xuebin(Key Laboratory of Atmospheric Optics,Anhui Institute of Optics and Fine Mechanics,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,Anhui,China;Institutes of Physical Science and Information Technology,Anhui University,Hefei 230601,Anhui,China;School of Environmental Science and Optoelectronic Technology,University of Science and Technology of China,Hefei 230031,Anhui,China)
机构地区:[1]中国科学院合肥物质科学研究院安徽光学精密机械研究所大气光学重点实验室,安徽合肥230031 [2]安徽大学物质科学与信息技术研究院,安徽合肥230601 [3]中国科学技术大学环境科学与光电技术学院,安徽合肥230031
出 处:《光学学报》2022年第12期82-89,共8页Acta Optica Sinica
基 金:中国科学院科技创新重点实验室基金(CXJJ-19S028)。
摘 要:在海洋地区利用相干多普勒激光风廓线雷达反演可以得到边界层内全天候大气光学湍流廓线。采用机器学习方法-后向传播神经网络进行训练得到边界层光学湍流估算模型,以探空实测常规气象参数作为模型输入参数估算不同日期不同时刻的边界层内大气光学湍流廓线,并与实测值进行比较。通过误差分析可知,白天和夜晚的估算光学湍流廓线均方根误差分别为0.4332和0.5626,相关系数分别为0.8899和0.7673。该研究表明,相干多普勒激光风廓线雷达反演的光学湍流廓线可以通过神经网络模型实现全天候对海洋大气边界层光学湍流廓线估算的功能,且效果较好,在光电工程及天文选址方面具有重要工程参考意义。In the marine area,the all-weather atmospheric optical turbulence profile within the boundary layer is obtained by inversion by coherent Doppler laser wind profile radar.The machine learning approach and back propagation neural network is used to train the optical turbulence estimation model of the boundary layer.The conventional meteorological parameters measured by sounding are used as model input parameters to estimate the atmospheric optical turbulence profile in the boundary layer on different days and different times,and compared with the measured values.The error analysis shows that the root mean square errors of the estimated optical turbulence profiles at day and night are 0.4332 and 0.5626,respectively,and the correlation coefficients are 0.8899 and 0.7673,respectively.The research proves that the optical turbulence profile inversion of coherent Doppler laser wind profile radar can realize the function of all-weather estimation of optical turbulence profile of the oceanic atmospheric boundary layer through the neural network model,and the effect is good.It has great engineering reference significance in photoelectric engineering and astronomical site selection.
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