Retrieval of Cloud Condensation Nuclei Number Concentration Profiles from Lidar Extinction and Backscatter Data

• Main Authors: Lv, Min, Luo, Tao, Ferrare, Richard, Li, Zhangqing, Wang, Zhien
• Format: Other/Unknown Material
• Language: unknown
• Published: 2018
• Subjects: Earth Resources and Remote Sensing; Aerosol Robotic Network
• Online Access: http://hdl.handle.net/2060/20190029243

The vertical distribution of aerosols and their capability of serving as cloud condensation nuclei (CCN) are important for improving our understanding of aerosol indirect effects. Although ground-based and airborne CCN measurements have been made, they are generally scarce, especially at cloud base where it is needed most. We have developed an algorithm for profiling CCN number concentrations using backscatter coefficients at 355, 532, and 1064 nm and extinction coefficients at 355 and 532 nm from multi-wavelength lidar systems. The algorithm considers three distinct types of aerosols (urban industrial, biomass burning, and dust) with bimodal size distributions. The algorithm uses look-up tables, which were developed based on the ranges of aerosol size distributions obtained from the Aerosol Robotic Network, to efficiently find optimal solutions. CCN number concentrations at five supersaturations (0.070.80%) are determined from the retrieved particle size distributions. Retrieval simulations were performed with different combinations of systematic and random errors in lidar-derived extinction and backscatter coefficients: systematic errors range from -20% to 20% and random errors are up to 15%, which fall within the typical error ranges for most current lidar systems. The potential of this algorithm to retrieve CCN concentrations is further evaluated through comparisons with surface- based CCN measurements with near surface lidar retrievals. This retrieval algorithm would be valuable for aerosol-cloud interaction studies for which virtually none has employed CCN at cloud base because of the lack of such measurements.