Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s^-1 to 0.1 m s^-1) and SDCD (5.4–5.6 m s^-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s^-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s^-1 in the Arctic, 6.7 m s^-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s^-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

中文翻译：

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利用 Aeolus Level-2B 风更好地表征大气运动矢量偏差和不确定性

摘要。对高精度大气风观测的需求是科学界的重中之重，尤其是数值天气预报 (NWP)。为了满足这一要求，本研究利用欧洲航天局 (ESA) 提供的 Aeolus 风激光雷达 Level-2B 数据来更好地表征大气运动矢量 (AMV) 偏差和不确定性，最终目标是潜在地改进 AMV 算法。将来自地球静止 (GEO) 和低地球极轨 (LEO) 卫星的 AMV 产品与再处理的 Aeolus 水平视线 (HLOS) 全球风进行比较，这些风是在 8 月和 2019 年 9 月观测到的。来自四种 Aeolus 观测模式中的两种的风是用于与 AMV 进行比较：Rayleigh-clear（来自分子散射信号）和 Mie-cloudy（来自粒子散射）。为了最直接的比较，质量控制 (QC'd) Aeolus 风在空间和时间上与质量控制的 AMV 并置，并且 AMV 被投影到 Aeolus HLOS 方向。平均搭配差异 (MCD) 和这些差异 (SDCD) 的标准偏差 (SD) 是通过基于多种条件的比较确定的，并讨论了它们与已知 AMV 偏差和不确定性估计的关系。更详细地描述了基于 Aeolus 风的 GOES-16 和 LEO AMV 特征。总体而言，QC'd AMV 与 QC'd Aeolus HLOS 风速 (HLOSV) 在瑞利晴天和 Mie-cloudy 观测模式下很好地对应，尽管再处理后 Aeolus 风仍存在偏差。与 Aeolus HLOSV 的比较与已知的 AMV 偏差和热带地区、NH 温带地区和北极地区的不确定性一致，并且在晴天和多云场景中的中上层。SH 比较通常表现出比预期的 SDCD 大，这可能归因于强风区域的高度分配错误和增强的垂直风切变。GOES-16 水汽晴空 AMV 相对于瑞利晴空表现最佳，MCD 较小（-0.6 ms^-1到 0.1 ms ^-1 ) 和 SDCD (5.4-5.6 ms ^-1 ) 在 NH 和热带地区，这些误差值在相对于无线电探空仪风的 AMV 误差值的可接受范围内。与 Mie-cloudy 风相比，AMV在整个对流层表现出相似的 MCD 和更小的 SDCD（~4.4-4.8 ms ^-1）。在极地地区，Mie-cloudy 比较具有较小的 SDCD（北极为5.2 ms ^-1，南极为6.7 ms ^-1）相对于 Rayleigh-clear 比较大 1–2 ms ^-1. AMV 和 Aeolus 风之间的一致性水平因条件组合而异，包括 Aeolus 观测模式与 AMV 推导方法、地理区域和并置风的高度。建议在未来的比较研究和涉及 3D 风的影响评估中考虑这些分层。预计对 Aeolus 数据集的额外偏差校正将进一步完善结果。