CALIPSO (cloud-aerosol lidar and infrared pathfinder satellite observation) provides unique opportunities for profiling global cloud and aerosol. It is crucial to accurately detect the boundaries of cloud and aerosol layers from CALIPSO observation because the detecting error will be passed to further retrieval. Considered superior to other layer detection methods, the threshold method is the core of the selective iterated boundary location (SIBYL) algorithm developed for producing the CALIPSO official products. However, the threshold method can miss many tenuous layers, and the use of the slope method to refine the layer base in SIBYL leads to considerable uncertainty due to its high sensitivity to noise. This study proposed a new layer detection algorithm based on an automatic segmentation method with a minimum cost function. Results show that the new algorithm determines 21% and 13% more layers than SIBYL at 1 km and 1-5 km resolution, respectively, which indicates that the new algorithm has higher detection efficiency. Moreover, the layers detected by the new algorithm are 170 m thicker than that detected by SIBYL on average, which indicates that the SIBYL misses layer edges where the signal to noise ratio is low. The new algorithm can improve the accuracy and resolution of the layer products of CALIPSO as well as other space-based lidars.

CALIPSO（云气溶胶激光雷达和红外探路卫星观测）为分析全球云和气溶胶提供了独特的机会。从CALIPSO观测中准确检测云层和气溶胶层的边界至关重要，因为检测误差将传递给进一步的检索。阈值方法被认为优于其他层检测方法，是为生产CALIPSO官方产品而开发的选择性迭代边界定位（SIBYL）算法的核心。但是，阈值方法可能会遗漏许多脆弱的层，并且由于其对噪声的高度敏感性，使用倾斜方法来细化SIBYL中的层基础会导致相当大的不确定性。该研究提出了一种基于最小成本函数自动分割方法的层检测算法。结果表明，新算法在1 km和1-5 km分辨率下比SIBYL确定的层分别多21％和13％，这表明新算法具有更高的检测效率。此外，新算法检测到的层平均比SIBYL检测到的层厚170 m，这表明SIBYL错过了信噪比低的层边缘。新算法可以提高CALIPSO以及其他天基激光雷达层产品的精度和分辨率。这表明SIBYL错过了信噪比低的层边缘。新算法可以提高CALIPSO以及其他天基激光雷达层产品的精度和分辨率。这表明SIBYL错过了信噪比低的层边缘。新算法可以提高CALIPSO以及其他天基激光雷达层产品的精度和分辨率。