A22D-05:
A New Cloud and Aerosol Layer Detection Method Based on Micropulse Lidar Measurements
Tuesday, 16 December 2014: 11:20 AM
Qianqian Wang, Beijing Normal University, Beijing, China, Chuanfeng Zhao, Beijing Normal University, College of Global Change and Earth System Science, Beijing, China, Yuzhao Wang, Beijing Institute of Space Mechanics and Electricity, Beijing, China, Zhanqing Li, Univ of Maryland College Park, College Park, MD, United States, Zhien Wang, University of Wyoming, Laramie, WY, United States and Dong Liu, Anhui Institute Of Optics and Fine Mechanics,Chinese Academy of Science, Hefei, China
Abstract:
A new algorithm is developed to detect aerosols and clouds based on micropulse lidar (MPL) measurements. In this method, a semi-discretization processing (SDP) technique is first used to inhibit the impact of increasing noise with distance, then a value distribution equalization (VDE) method is introduced to reduce the magnitude of signal variations with distance. Combined with empirical threshold values, clouds and aerosols are detected and separated. This method can detect clouds and aerosols with high accuracy, although classification of aerosols and clouds is sensitive to the thresholds selected. Compared with the existing Atmospheric Radiation Measurement (ARM) program lidar-based cloud product, the new method detects more high clouds. The algorithm was applied to a year of observations at both the U.S. Southern Great Plains (SGP) and China Taihu site. At SGP, the cloud frequency shows a clear seasonal variation with maximum values in winter and spring, and shows bi-modal vertical distributions with maximum frequency at around 3-6 km and 8-12 km. The annual averaged cloud frequency is about 50%. By contrast, the cloud frequency at Taihu shows no clear seasonal variation and the maximum frequency is at around 1 km. The annual averaged cloud frequency is about 15% higher than that at SGP.
