Validation of Ice Cloud Microphysical Properties Retrieval Using a Markov Chain Monte Carlo Algorithm

The Markov chain Monte Carlo (MCMC) algorithm has been used for retrieving ice cloud microphysical properties in this paper. The retrieval data include effective radius (r(e)), ice water content (IWC), number density (N-0), and distribution width parameter (w). First we examine the algorithm feasibility using simulated W-band radar reflectivity. The retrievals locate in a solution space around the exact results. Two methods are applied to produce a unique solution with maximum likelihood function, that is, the mean value of all acceptable sets of parameters in the chain and the first convergence sample. The results of the second method have a better performance compared with that of the first method. The algorithm has better performance when one particle size distribution (PSD) parameter is known than that when three PSD parameters are unknown. Then we apply the algorithm for CloudSat Cloud Profiling Radar (CPR) observations. Four habits, including column, plate, bullet rosette and sphere, are considered to analyze the influence on the retrieval accuracy. The averaged deviation of r(e)s and IWCs obtained from MCMC algorithm among different habit assumptions has a maximum of about 10% and 5%, respectively. It appears clearly that the MCMC algorithm retrieval for short hexagonal column (HEXS) are the closest to that of the official products (2B-CWC-RO), while thick hexagonal plate (HEXF) has the largest bias. Retrieved r(e), IWC, N-0, and w from radar reflectivity are in reasonable agreement with 2B-CWC-RO product, which indicate that the MCMC algorithm can produce reliable cloud properties from radar observations. Plain Language Summary Ice clouds are globally widespread and their microphysical properties play an important role in the radiative balance of Earth, which are still not well understood and poorly represented. Thus, the algorithm that drive the microphysical properties of ice clouds precisely from radar observations is necessary. Here, we address this issue using Markov chain Monte Carlo algorithm for deriving ice cloud microphysical properties. The feasibility of Markov chain Monte Carlo (MCMC) algorithm has been tested using simulated W-band radar reflectivity and CloudSat Cloud Profiling Radar observations. The retrievals are in reasonable agreement with CloudSat 2B- CWC-RO product. The study will be very useful for analyzing the role that ice clouds play in climate.

Automated summary

The study utilizes the Markov chain Monte Carlo (MCMC) algorithm for retrieving ice cloud microphysical properties, demonstrating that the algorithm performs well in both simulated and observational tests.