Parameterization of below-cloud scavenging for polydisperse fine mode aerosols as a function of rain intensity
Yong Pyo Kim , Chang Hoon Jung , Hyung-Min Lee , Dasom Park , Young Jun Yoon , Yongjoo Choi , Junshik Um , Seoung Soo Lee , Ji Yi Lee
DOI:10.1016/j.jes.2022.07.031
Received January 16, 2022,Revised , Accepted July 21, 2022, Available online July 28, 2022
Volume 35,2023,Pages 43-55
The below-cloud aerosol scavenging process by precipitation is one of the most important mechanisms to remove aerosols from the atmosphere. Due to its complexity and dependence on both aerosol and raindrop sizes, wet scavenging process has been poorly treated, especially during the removal of fine particles. This makes the numerical simulation of below-cloud scavenging in large-scale aerosol models unrealistic. To consider the slip effects of submicron particles, a simplified expression for the diffusion scavenging was developed by approximating the Cunningham slip correction factor. The derived analytic solution was parameterized as a simple power function of rain intensity under the assumption of the lognormal size distribution of particles. The resultant approximated expression was compared to the observed data and the results of previous studies including a 3D atmospheric chemical transport model simulation. Compared with the default GEOS-Chem coefficient of 0.00106R0.61 and the observation-based coefficient of 0.0144R0.9268, the coefficient of a and b in Λm = aRb spread in the range of 0.0002- 0.1959 for a and 0.3261- 0.525 for b over a size distribution of GSD of 1.3–2.5 and a geometric mean diameter of 0.01- 2.5 µm. Overall, this study showed that the scavenging coefficient varies widely by orders of magnitude according to the size distribution of particles and rain intensity. This study also demonstrated that the obtained simplified expression could consider the theoretical approach of aerosol polydispersity. Our proposed analytic approach showed that results can be effectively applied for reduced computational burden in atmospheric modeling.
