Joint multiscale cloud detection algorithm for ground-based lidar

Abstract

A ground-based lidar is a powerful tool for studying the vertical structure and optical properties of clouds. A layer detection algorithm is important to determine the presence and spatial position of clouds from vast lidar signals. However, current detection algorithms for ground-based lidar still involve substantial missing and false detections for tenuous layers and layer edges. Here, a joint multiscale cloud layer detection algorithm is proposed. The algorithm can effectively capture the tenuous layers and layer edges by using joint multiscale detection methods based on a trend function and the Bernoulli distribution assumption. Results show that the proposed algorithm detects 10.45% more cloud layers than the official cloud product of Micro Pulse Lidar Network (MPLNET) does. Specifically, 7.93% and 12.57% more cloud layers are detected at daytime and nighttime, respectively. The evaluation based on depolarization properties proves that the additional cloud layers detected by the joint multiscale algorithm are reliable. These additional detected clouds have important implications for cloud climatology and climate change research. The new algorithm remarkably enhances the cloud detection capability of ground-based lidar and potentially be widely used by the community.