The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is unique in its ability to perform profiling measurements of aerosol and cloud layers globally. Detecting the layer boundaries of aerosols and clouds is a crucial step in CALIPSO data retrieval. The CALIPSO team uses the selective iterated boundary location (SIBYL) algorithm based on threshold arrays to find aerosol and cloud layers at different horizontal resolutions. However, threshold arrays could obstruct the detection of optically tenuous layers at a high resolution and may cause overestimation when averaging signals of layer and clear air at a low resolution. Here, a multiscale algorithm using a series of sliding window sizes without threshold setting is proposed based on a pre-defined probability. The results over land and marine areas show that the multiscale algorithm detected 37.41% and 16.36% more layer area than the SIBYL at 1–80 km resolutions at daytime and 1–5 km resolutions at night time, respectively. This indicates that the multiscale algorithm does not need a threshold array, allowing more tenuous layers to be detected, especially at low signal to noise ratios (SNRs). In contrast, the SIBYL detects 4.40% more layer area than the multiscale algorithm at 1–80 km resolutions at nighttime, mainly caused by the large proportion of layer area detected by SIBYL at 20 and 80 km resolutions. This implies possible noteworthy overestimation by the SIBYL at low resolutions. Additionally, the evaluation using the depolarization ratio of ice clouds shows that the extra detected layers by the multiscale algorithm are reliable. Besides, simulation tests show that the multiscale and SIBYL algorithms achieve a 100% true detection rate when SNR is approximately 2 and 4, respectively. The new multiscale algorithm could upgrade the resolution and accuracy of the layer detection of space lidars and reduce the underestimation of layer optical depth due to missing layers.

云气溶胶激光雷达和红外探路者卫星观测 (CALIPSO) 的独特之处在于其能够对全球气溶胶和云层进行剖面测量。检测气溶胶和云的层边界是 CALIPSO 数据检索的关键步骤。CALIPSO 团队使用基于阈值阵列的选择性迭代边界定位 (SIBYL) 算法来寻找不同水平分辨率的气溶胶和云层。然而，阈值阵列可能会阻碍以高分辨率检测光学脆弱层，并且在以低分辨率对层和晴空的信号进行平均时可能会导致高估。在这里，基于预定义的概率，提出了一种使用一系列滑动窗口大小而无需设置阈值的多尺度算法。陆地和海洋区域的结果表明，多尺度算法在白天 1-80 km 分辨率和夜间 1-5 km 分辨率下检测到的层面积分别比 SIBYL 多 37.41% 和 16.36%。这表明多尺度算法不需要阈值阵列，可以检测到更脆弱的层，尤其是在低信噪比 (SNR) 时。相比之下，SIBYL 在夜间在 1-80 km 分辨率下检测到的层面积比多尺度算法多 4.40%，主要是由于 SIBYL 在 20 和 80 km 分辨率下检测到的层面积比例较大。这意味着 SIBYL 在低分辨率下可能出现值得注意的高估。此外，利用冰云去极化率的评估表明，多尺度算法检测到的额外层是可靠的。除了，仿真测试表明，当 SNR 分别约为 2 和 4 时，多尺度和 SIBYL 算法实现了 100% 的真实检测率。新的多尺度算法可以提升空间激光雷达层检测的分辨率和精度，减少由于层数丢失而导致层光深度的低估。