Development of a new semi-empirical parameterization for below-cloud scavenging of size-resolved aerosol particles by both rain and snow

A parameter called the scavenging coefficient Lambda is widely used in aerosol chemical transport models (CTMs) to describe below-cloud scavenging of aerosol particles by rain and snow. However, uncertainties associated with available size-resolved theoretical formulations for Lambda span one to two orders of magnitude for rain scavenging and nearly three orders of magnitude for snow scavenging. Two recent reviews of below-cloud scavenging of size-resolved particles recommended that the upper range of the available theoretical formulations for Lambda should be used in CTMs based on uncertainty analyses and comparison with limited field experiments. Following this recommended approach, a new semi-empirical parameterization for size-resolved Lambda has been developed for below-cloud scavenging of atmospheric aerosol particles by both rain (Lambda(rain)) and snow (Lambda(snow)). The new parameterization is based on the 90th percentile of Lambda values from an ensemble data set calculated using all possible "realizations" of available theoretical Lambda formulas and covering a large range of aerosol particle sizes and precipitation intensities (R). For any aerosol particle size of diameter d, a strong linear relationship between the 90th-percentile log(10)(Lambda) and log10(R), which is equivalent to a power-law relationship between Lambda and R, is identified. The log-linear relationship, which is characterized by two parameters (slope and y intercept), is then further parameterized by fitting these two parameters as polynomial functions of aerosol size d. A comparison of the new parameterization with limited measurements in the literature in terms of the magnitude of Lambda and the relative magnitudes of Lambda(rain) and Lambda(snow) suggests that it is a reasonable approximation. Advantages of this new semi-empirical parameterization compared to traditional theoretical formulations for Lambda include its applicability to below-cloud scavenging by both rain and snow over a wide range of particle sizes and precipitation intensities, ease of implementation in any CTM with a representation of size-distributed particulate matter, and a known representativeness, based on the consideration in its development, of all available theoretical formulations and field-derived estimates for Lambda(d) and their associated uncertainties.