Numerical simulation of the fog

Abstract

Fog forms, strengthen, and disperses due to complex interactions among diverse local, microphysical, dynamical, radiative, chemical processes, and boundary-layer conditions (Gultepe et al., 2007). Aerosol particles play a vital role in fog formation, development, and dissipation. Furthermore, the fog can also impact aerosol particles' characteristics (e.g., size distribution, chemical composition). The primary sources of particle numbers in cities are traffic, thermal power stations, factories and household emissions, and their concentration is affected by advection and scavenging processes. While scavenging by activation strongly depends on the chemical composition of the particles (e.g., Gilardoni et al., 2014), the collision scavenges of particles is mainly affected by size of the aerosol particles and that of the water drops. The chemical composition and the size distribution of aerosol particles are affected by their sources, gas-phase chemical reactions and microphysical processes, and chemical reactions that occur in the water drops. Dissolution of some ambient gases into droplets and the subsequent aqueous-phase chemical reactions can also modify the particle size (Kerminen and Wexler, 1995; Meng and Seinfeld, 1994). Besides, considering the chemistry in the numerical simulation of fog is a great challenge due to the strong interaction between the different chemical processes and due to the lack of observational data of trace gases and various relevant inorganic and organic compounds for validating the models. In this study a recently developed bin scheme (Schmeller and Geresdi, 2017) was used in a one – dimensional (1D) model to investigate, how the environmental conditions affect the physical and chemical characteristics of the fog. Although the 1D models cannot simulate some crucial characteristics of the fog (e.g., turbulence, radiative cooling, sedimentation), they allow for the very accurate simulation of specified processes such as microphysical and chemical processes. For example, Xue et al. (2019) carried out 1D model experiments using a bulk liquid chemistry scheme incorporating detailed SO2 oxidation chemistry to derive SO4 2- production over the full range of SO2 atmospheric concentrations. Furthermore, rigorous model studies have been made to improve fog forecasting over this region (Pithani et al., 2020, 2019a, 2018). However, there is considerable scope to improve forecasting and detailed understanding of fog microphysics and chemical properties during the fog life cycle.