Feasibility of Retrieving CCN Concentrations from Multi-Wavelength Raman Lidar Remote Sensing: Simulation

Atmospheric aerosol particles could affect radiative forcing indirectly via changing the cloud properties by acting as cloud condensation nuclei (CCN), which continues to be the largest source of uncertainty for the prediction of the global climate change. The difficulty of reducing the uncertainty of this effect is a lack of suitable measurements, especially the coincidental CCN concentration and cloud properties measurements, to further improve the processes study. We are exploring the feasibility of retrieving CCN concentrations near the cloud base using a multi-wavelength Raman lidar remote sensing method (MRLS). The retrieval is based on backscatter coefficients at 355, 532, 1064 nm and extinction coefficients at 355 nm and 532 nm. In the calculation, aerosol size distribution is assumed to compose two lognormal modes, denoted as the fine mode and the coarse mode. Besides, a compute-intensive look-up table (LUT) based on aerosol size distributions from Aeronet observations and other field campaigns is generated to construct a stable and accurate inversion of particle size distribution. CCN concentrations at five supersaturations (0.07% ~ 0.80%) are determined from the retrieved particle size distribution, using an assumption that the various constituents are internally mixed. Retrieval sensitivity of the assumptions used in the retrieval is analyzed respectively. Measurement errors up to 15% are considered here, which is within the expected accuracy that cloud be provided for a MRLS system. Results suggest that this approach can retrieve CCN concentration with an uncertainty better than 50%, especially at lower supersaturation rates. Further investigations with observation data to verify this technique will be performed.