Understanding aerosol-fog interactions during nocturnal radiation fog

Forecasting and modelling fog formation, development, and dissipation is a significant challenge. Fog dynamics involve subtle interactions between small‐scale turbulence, radiative transfer and microphysics. Recent studies have highlighted the role of aerosol and related cloud microphysical properties in the evolution of fog. In this thesis, the impact of aerosol on nocturnal radiation fog is investigated. This has been done using the Met Office NERC Cloud (MONC) model, which can perform very high‐resolution large eddy simulations. MONC has been coupled with a newly developed multi‐moment cloud microphysics scheme (CASIM) designed to model aerosol-cloud interactions. The initial results demonstrate the sensitivity of the fog structure to the properties of the aerosol population (e.g. number concentration). An increase in aerosol concentration results in the fog layer becoming well-mixed too quickly. This highlights the importance of aerosol during the fog’s transitional period and the requirement for an accurate scheme accounting for aerosol activation. A new aerosol activation scheme was developed to better represent the cooling mechanisms in fog. It was shown that this scheme results in a lower droplet number for a given aerosol population and hence transitions to a well-mixed fog more in line with observations. The impact of a nucleation scavenging parameterisation in fog was investigated. It was shown that including nucleation scavenging in simulations of fog resulted in it dissipating too rapidly compared to observations. Turning on nucleation scavenging results in aerosol being depleted through sedimentation. These results also showed that accounting for additional sources of aerosol could not fully negate the impact of the nucleation scavenging parameterisation on fog. Overall, this thesis has demonstrated the importance of aerosol treatment during fog formation and development and has outlined recommendations to help improve the accuracy of short-term fog forecasting.