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Improved method for gas temperature and pressure retrieval in Brillouin lidar remote sensing

机译:布里渊利来遥感中的气体温度和压力检索方法

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摘要

The Rayleigh-Brillouin scattered spectrum is an important tool for analyzing the temperature and pressure of gas in Brillouin lidar remote sensing. The Tenti-S6 model has been widely used to retrieve atmospheric temperatures. However, the retrieval accuracy of this method is unsatisfactory. We analyzed the influence of several factors on the retrieval accuracy of this method and developed an improved method for temperature and pressure retrieval. First, the Rayleigh-Brillouin spectral baseline was corrected using a new fitting procedure, and an experimental spectrum that is of high coincidence with the line shape of the S6 model could subsequently be obtained. Second, the influence of the Airy function on the retrieval accuracy was analyzed, and the retrieval error could be decreased using the Tenti-S6 model without the Airy function. We found that the gas parameters could be precisely detected under low-pressure conditions. Compared with the traditional method, our improved method could effectively reduce the temperature and pressure retrieval errors. The experimental results of nitrogen scattering in the laboratory and air scattering demonstrate the effectiveness, universality, and viability of the proposed improved method. (C) 2021 Optical Society of America
机译:瑞利-布里渊散射光谱是布里渊激光雷达遥感中分析气体温度和压力的重要工具。Tenti-S6模型已广泛用于反演大气温度。然而,这种方法的检索精度并不令人满意。分析了几种因素对该方法反演精度的影响,提出了一种改进的温度和压力反演方法。首先,使用新的拟合程序对瑞利-布里渊光谱基线进行校正,随后可以获得与S6模型线形高度一致的实验光谱。其次,分析了Airy函数对检索精度的影响,并使用Tenti-S6模型,在不使用Airy函数的情况下,可以减小检索误差。我们发现在低压条件下可以精确地检测气体参数。与传统方法相比,改进后的方法可以有效地减小温度和压力反演误差。实验室氮散射和空气散射的实验结果证明了改进方法的有效性、通用性和可行性。(2021)美国光学学会

著录项

  • 来源
    《Applied optics》 |2021年第3期|共10页
  • 作者

    Zhang Peng; Liang Kun;

  • 作者单位

    Huazhong Univ Sci &

    Technol Coll Elect Informat &

    Commun Wuhan 430074 Peoples R China;

    Huazhong Univ Sci &

    Technol Coll Elect Informat &

    Commun Wuhan 430074 Peoples R China;

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  • 正文语种 eng
  • 中图分类 应用;
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