Since the start of the European Space Agency's Aeolus mission in 2018, various studies were dedicated to the evaluation of its wind data quality and particularly to the determination of the systematic and random errors in the Rayleigh-clear and Mie-cloudy wind results provided in the Aeolus Level-2B (L2B) product. The quality control (QC) schemes applied in the analyses mostly rely on the estimated error (EE), reported in the L2B data, using different and often subjectively chosen thresholds for rejecting data outliers, thus hampering the comparability of different validation studies. This work gives insight into the calculation of the EE for the two receiver channels and reveals its limitations as a measure of the actual wind error due to its spatial and temporal variability. It is demonstrated that a precise error assessment of the Aeolus winds necessitates a careful statistical analysis, including a rigorous screening for gross errors to be compliant with the error definitions formulated in the Aeolus mission requirements. To this end, the modified Z score and normal quantile plots are shown to be useful statistical tools for effectively eliminating gross errors and for evaluating the normality of the wind error distribution in dependence on the applied QC scheme, respectively. The influence of different QC approaches and thresholds on key statistical parameters is discussed in the context of the Joint Aeolus Tropical Atlantic Campaign (JATAC), which was conducted in Cabo Verde in September 2021. Aeolus winds are compared against model background data from the European Centre for Medium-Range Weather Forecasts (ECMWF) before the assimilation of Aeolus winds and against wind data measured with the 2 µm heterodyne detection Doppler wind lidar (DWL) aboard the Falcon aircraft. The two studies make evident that the error distribution of the Mie-cloudy winds is strongly skewed with a preponderance of positively biased wind results distorting the statistics if not filtered out properly. Effective outlier removal is accomplished by applying a two-step QC based on the EE and the modified Z score, thereby ensuring an error distribution with a high degree of normality while retaining a large portion of wind results from the original dataset. After the utilization of the described QC approach, the systematic errors in the L2B Rayleigh-clear and Mie-cloudy winds are determined to be below 0.3 m s⁻¹ with respect to both the ECMWF model background and the 2 µm DWL. Differences in the random errors relative to the two reference datasets (Mie vs. model is 5.3 m s⁻¹, Mie vs. DWL is 4.1 m s⁻¹, Rayleigh vs. model is 7.8 m s⁻¹, and Rayleigh vs. DWL is 8.2 m s⁻¹) are elaborated in the text.

中文翻译：

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来自联合 Aeolus 热带大西洋运动的 Aeolus L2B 风的质量控制和误差评估

自 2018 年欧洲航天局的 Aeolus 任务开始以来，各种研究致力于评估其风数据质量，特别是确定在提供的瑞利晴和米氏多云风结果中的系统性和随机误差Aeolus Level-2B (L2B) 产品。分析中应用的质量控制 (QC) 方案主要依赖于 L2B 数据中报告的估计误差 (EE)，使用不同且通常主观选择的阈值来拒绝数据异常值，从而阻碍了不同验证研究的可比性。这项工作深入了解了两个接收器通道的 EE 计算，并揭示了其作为测量实际风误差的局限性，因为它的空间和时间可变性。事实证明，对 Aeolus 风的精确错误评估需要仔细的统计分析，包括严格筛选严重错误以符合 Aeolus 任务要求中制定的错误定义。为此，修改了Z 分数和正态分位数图被证明是有用的统计工具，可有效消除粗差，并分别根据所应用的 QC 方案评估风误差分布的正态性。在 2021 年 9 月在佛得角开展的联合 Aeolus 热带大西洋运动 (JATAC) 的背景下讨论了不同 QC 方法和阈值对关键统计参数的影响。将 Aeolus 风与来自欧洲中心的模型背景数据进行了比较用于风神风同化之前的中期天气预报 (ECMWF) 和使用 2  µm测量的风数据猎鹰飞机上的外差探测多普勒测风激光雷达 (DWL)。这两项研究表明，米氏多云风的误差分布严重偏斜，如果没有正确过滤，大部分正偏风结果会扭曲统计数据。通过基于 EE 和修改后的Z分数应用两步 QC 来实现有效的异常值去除 ，从而确保具有高度正态性的误差分布，同时保留原始数据集的大部分风结果。在使用所描述的 QC 方法后，相对于 ECMWF 模型背景和 2  µm，L2B 瑞利晴风和米氏多云风中的系统误差被确定为低于 0.3 m s ^-1DWL。相对于两个参考数据集的随机误差差异（Mie 与模型为 5.3 m s ^-1，Mie 与 DWL 为 4.1 m s ^-1，Rayleigh 与模型为 7.8 m s ^-1，Rayleigh 与 DWL 为 8.2 m s ⁻¹ ) 在正文中进行了详细说明。