This article constitutes the second installment in a series dedicated to exploring the uncertainties associated with pure rotational Raman–Rayleigh temperature measurement LiDARs in the middle-to-upper atmosphere (20–90 km). It presents uncertainty evaluation methods aimed at addressing the challenge of uncertainty assessment. These methods leverage both the Guide to the Expression of Uncertainty in Measurement (GUM) method and the Monte Carlo method (MCM) to ascertain the uncertainty of air density and temperature derived from LiDAR signals. Within the framework of these evaluation methods, various uncertainty sources are considered, encompassing saturation correction, photon noise, background noise, reference temperature, and two special uncertainty sources: atmospheric transmittance correction in Rayleigh LiDAR and the calibration process in Raman LiDAR. An uncertainty evaluation example in accordance with the proposed evaluation method is provided by employing raw signals from full-chain simulation system introduced in another series paper. In this example, we compare the evaluation results of the GUM method with those derived from the MCM. Our findings reveal consistent trends in different uncertainty components between the two methods. In addition, the uncertainty caused by atmospheric transmittance correction is involved with detailed considerations of correlation and iterative methods. The uncertainty resulting from the calibration process in Raman LiDAR, attributed to its nonlinear characteristics, can only be evaluated using the MCM. These findings affirm the applicability of the proposed uncertainty evaluation methods of pure rotational Raman–Rayleigh LiDAR. This research enhances our understanding of the sources of uncertainty in LiDAR systems and provides a convenient approach for evaluating the data quality of temperature LiDAR.

中文翻译：

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中高层大气测温纯旋转拉曼-瑞利激光雷达的不确定度评估方法


本文是致力于探索中高层大气（20-90 公里）中纯旋转拉曼-瑞利温度测量 LiDAR 相关不确定性的系列文章的第二部分。它提出了旨在解决不确定性评估挑战的不确定性评估方法。这些方法利用测量不确定度表达指南 (GUM) 方法和蒙特卡罗方法 (MCM) 来确定从 LiDAR 信号得出的空气密度和温度的不确定度。在这些评估方法的框架内，考虑了各种不确定性源，包括饱和度校正、光子噪声、背景噪声、参考温度以及两个特殊的不确定性源：瑞利激光雷达中的大气透射率校正和拉曼激光雷达中的校准过程。通过使用另一系列论文中介绍的全链仿真系统的原始信号，提供了根据所提出的评估方法的不确定性评估示例。在本例中，我们将 GUM 方法的评估结果与 MCM 方法的评估结果进行比较。我们的研究结果揭示了两种方法之间不同不确定性成分的一致趋势。另外，大气透过率修正带来的不确定性还涉及到相关法和迭代法的详细考虑。由于拉曼激光雷达的非线性特性，其校准过程产生的不确定性只能使用 MCM 进行评估。这些发现证实了所提出的纯旋转拉曼瑞利激光雷达不确定性评估方法的适用性。 这项研究增强了我们对激光雷达系统不确定性来源的理解，并为评估温度激光雷达的数据质量提供了一种便捷的方法。