10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
Abstract View
Method to Retrieve Cloud Condensation Nuclei Number Concentrations Using Multiwavelength Raman Lidar
WANGSHU TAN, Chengcai Li, Yingli Yu, Chunsheng Zhao, Peking University
Abstract Number: 1138 Working Group: Clouds and Climate
Abstract The uncertainties associated with the estimated climate forcing attributed to aerosol-cloud interactions still span over a poorly constrained range. Determination of cloud condensation nucleus (CCN) number concentrations (NCCN) in and near clouds is of central importance in aerosol-cloud interaction observations. Ground-based multiwavelength Raman lidars continuously provide optical properties of aerosols up to the cloud base and have potential to provide profiles of CCN-relevant particles. In this work, we propose a novel method to retrieve NCCN with machine learning techniques. Variations of lidar-derived backscatter and extinction coefficients among different wavelengths, usually expressed as Ångström exponent and lidar extinction-to-backscatter ratios, can describe the predominant size of aerosols. Enhancements of backscatter and extinction with relative humidity in vertical direction are utilized to represent the hygroscopicity of aerosols. The feasibility of the method are investigated by theoretical simulations based on Mie theory and κ-Köhler theory using in situ aerosol measurement datasets. Results show that, for lower supersaturation ratios (0.07%, 0.10%, and 0.20%), the mean relative errors of predicted NCCN are within 7% and the determination coefficients R2 are larger than 0.97, but there are larger errors for higher supersaturation ratios (0.40% and 0.80%). Theoretical analyses indicate that optical properties at common lidar wavelengths are insensitive to particle number and hygroscopicity of particles with diameter smaller than 100 nm and lidar backscatter coefficients are complicated among different particle sizes. This new method to retrieve NCCN has great potential in providing long-term CCN data for aerosol-cloud-interaction studies.