大气边界层高度是大气环境、天气和气候研究中最重要的参数之一。随着地基遥感探测技术的发展，对大气边界层高度进行连续监测成为可能。然而，基于地基遥感得出的边界层高度取决于所使用的反演方法，且受复杂大气条件的影响。本研究采用中国科学院平凉陆面过程与灾害天气观测研究站2020年8月27日至2023年8月1日云高仪、累计降水量和天气状况观测记录，以及2023年夏季加强观测期探空等观测资料，结合位温廓线识别的边界层高度，对比分析了几种基于云高仪后向散射梯度反演边界层高度算法的有效性，提出了白天［08:00（北京时，下同）-19:00）］和夜间（20:00至次日07:00）分别采用不同后向散射梯度算法并限制检索高度的边界层高度反演混合算法。结果表明：不同后向散射梯度算法（最大负梯度法、三大负梯度评估法、百分比波动法、拐点法和Flamant法）反演结果存在差异，Fla‐mant法、三大负梯度评估法和最大负梯度法反演的边界层高度与位温廓线反演结果相关性较高，平均绝对偏差较小，拐点法和百分比波动法反演的边界层高度绝对偏差较大。对后向散射廓线进行适当平滑并采用混合算法能显著提升边界层高度识别的准确度，其中SG 25/25平滑方案结合Flamant和最大负梯度混合算法反演效果最好，得到的边界层高度与位温廓线反演结果的相关性为0. 56，平均绝对偏差约为406 m，与云高仪内部算法结果的相关性为0. 64。混合算法可很好地识别和反映边界层高度的日变化特征，当边界层较高时，其反演结果波动较大，通过时间平均能有效提升其连续性。利用本文给出的混合算法可以得到连续性及分辨率高的边界层高度信息，可作为边界层高度等基础数据的补充方案。

The atmospheric boundary layer height（ABLH）is one of the most important parameters in the study of atmospheric environment，weather and climate. With the development of ground-based remote sensing technology，continuous monitoring of ABLH has become possible. However，the ABLH derived based on groundbased remote sensing depends on the inversion method used and is affected by complex atmospheric conditions. In this study，we use the data from ceilometer and rain gauge，weather records from 27th August 2020 to 1st Au‐ gust 2023 and radiosonding records obtained during the 2023 summer extensive observation period at the Pingliang Land Surface Process and Severe Weather Research Station，Chinese Academy of Sciences. The effective‐ ness of several commonly used algorithms for inverting ABLH based on backscatter profiles from ceilometer are evaluated by comparing with the ABLH identified by potential temperature profiles. A hybrid algorithm that employs different backscatter gradient inversion methods for daytime［08:00（Beijing time，same as after）-19:00］ and nighttime（from 20:00 to 07:00 the next day）is proposed with constrained retrieval heights tailored for the study area. The results reveal notable differences in the inversion results among various algorithms，including the maximum negative gradient method，the three-major negative gradient evaluation method，the percentage fluctuation method，the inflection point method，and the Flamant method. Specifically，the ABLHs derived by the Flamant method，the three-major negative gradient evaluation method，and the maximum negative gradient method correlated well with that determined by the potential temperature profile and give lower mean absolute deviations. In contrast，the ABLHs derived by the inflection point method and the percentage fluctuation method give large absolute deviations. Appropriate smoothing of the backscatter profiles，combined with the hybrid algorithm，significantly improved the accuracy of the derived ABLH. Among the investigated methods，the SG 25/ 25 smoothing scheme combined with the Flamant and maximum negative gradient hybrid algorithm yielded the best results，achieving a correlation of 0. 56 with the ABLH determined by the potential temperature profile and an average absolute deviation of approximately 406 m. The correlation between the ABLHs derived from the hybrid algorithm and that from the ceilometer’s internal algorithm is 0. 64. The hybrid algorithm can effectively capture the diurnal variation of ABLH. The proposed hybrid algorithm can be used to obtain continuous，highresolution ABLH information，serving as a valuable supplementary method for obtaining fundamental data on ABLH and related parameters.