摘 要: 星载激光雷达能够观测云-气溶胶特性的垂直剖面分布，是云-气溶胶研究和监测的独特手段。正交偏振云-气溶胶激光雷达 (CALIOP) 已在轨运行多年，提供了大量云-气溶胶剖面观测的数据资料。从激光雷达数据中检测云和气溶胶层次的空间位置，是精确反演和提取层次信息的前提。CALIOP官方算法运用经验阈值检测层次，存在较多的层次漏检。当前以一维简单多尺度算法 (1D-SMA) 为代表的假设检验算法，通过检验给定信号是否符合背景大气分布假设，避免了设置传统的经验阈值阵列，提高了检测准确性。然而，上述方法均未考虑层次信号在二维垂直剖面场景中的空间连续性，漏检现象仍有发生。因此，本文进一步提出了基于伯努利概率分布的二维简单多尺度算法 (2D-SMA)，运用统计概率模型替换经验阈值阵列，并结合覆盖多根廓线的二维层次检测窗口，实现对相邻廓线信号空间关联的利用。新算法在全水平分辨率 (5-80 km) 综合检测的层次总面积，比CALIOP官方算法多50.45%，比一维方法1D-SMA多32.45%。新算法仅在5-20 km水平分辨率就能实现与官方算法全分辨率面积相当的层次检出量。最后，本文通过冰云退偏比评估，验证了新算法检测的可靠性。 关键词:星载激光雷达，CALIOP，云和气溶胶，层次检测，多尺度

Abstract: Objective Spaceborne lidar is a unique approach for the research and monitoring of cloud and aerosol due to its ability to observe their vertical properties in the atmospheric profile. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite has been operational in orbit for many years and has provided a large amount of profiling measurements of cloud and aerosol. Detecting the spatial locations of cloud and aerosol layers from lidar data is a prerequisite for retrieving and extracting layer information accurately. While the official CALIOP algorithm detects layers using empirical thresholds, many layers are missed in its output, which may explain the systematic underestimation of aerosol optical depth (AOD) offered by CALIPSO compared to that by the Moderate Resolution Imaging Spectroradiometer (MODIS). Current hypothesis testing methods represented by the 1D-Simple Multiscale Algorithm (1D-SMA) determine whether a given signal belongs to a layer by verifying whether it conforms to the distribution hypothesis of background air. These methods eliminate the traditional empirical threshold array and improve the accuracy of layer detection. However, none of the methods above have taken into account the spatial continuity of the layer signals in the 2D vertical profiling scene, and missed layers still occur in their cases. Method This study further proposes a 2D-Simple Multiscale Algorithm (2D-SMA) based on Bernoulli probability distribution, which replaces the empirical threshold with a statistical probability model. For a background atmosphere bin, the probabilities that its signal intensity is greater or less than the ideal value are both 1/2. Assuming that the signals at each bin are independent, the distribution of the number of signal bins within the detection window that are greater or less than ideal value follows a multiple Bernoulli experiment. We design a probability of belonging to a layer based on the signal intensity of all the bins in the detection window, reflecting their overall deviation from the ideal background atmosphere bins. The new algorithm marks that the center bin of the detection window as layer bin when the probability is small to a certain extent, which means it deviates far from background atmosphere. To utilize the spatial correlation of signals on adjacent profiles, we use 2D layer detection windows covering multiple profiles at different horizontal resolutions. Result Statistical comparing experiment based on the continuous global observations of CALIOP in December, 2017 shows that at full horizontal resolution (5-80 km), the new algorithm detects 50.45 % and 32.45 % more layer area than that of the official CALIOP algorithm and 1D-SMA, respectively. Applied at a horizontal resolution of 5-20 km, the new algorithm achieves a comparable or greater area of detected layers than that of the official algorithm at 5-80 km horizontal resolutions. Moreover, this paper demonstrates the reliability of the layers identified by the new algorithm by evaluating the depolarization ratio of ice clouds. Conclusion In general, the new algorithm effectively reduces the missing of weak layers compared with the official CALIOP algorithm, simple and fast to implement, with certain research potential and application prospects. Key words: spaceborne lidar, CALIOP, cloud and aerosol, layer detection, multiscale Supported by National Natural Science Foundation of China (No. 42322109); National Key Research and Development Program of China (No. 2024YFF0809400)