A case study of the life cycle of a stratus‐lowering coastal‐fog event in Newfoundland, Canada

Abstract

We present a case study of a coastal‐fog stratus‐cloud‐lowering event on September 13–14, 2018, during the C‐FOG field campaign conducted along the east coast of Newfoundland, Canada. The goal of this work is to understand the mechanisms governing the life cycle of a 4‐hr‐long coastal‐fog event that resulted from the complex interplay of dynamic, thermodynamic, and microphysical processes. In addition to standard meteorological measurements, turbulence, irradiance, droplet‐size spectra, tethered‐balloon wind and thermodynamic profiles, visibility, precipitation, and spatial heterogeneity of microphysics measurements are presented to discriminate and interpret the fog formation, development, and dissipation. After sunset, strong radiative cloud‐top cooling induced top‐down convection length scales that can be characterised with the Thorpe scale. Top‐down mixing and turbulence kinetic energy generated due to buoyant/shear mixing are characterised using the flux and bulk Richardson number near the surface. Use of these parameters is unique in the analysis of fog events and helped describe mixing processes. Downward mixing led to fog droplet formation that precipitated from the cloud base, which in turn cooled the sub‐cloud layers via droplet evaporation and moistened the air beneath the cloud. Once fog formed, it was affected by dry‐air entrainment from its top. As a result, the fog thinned, creating patchy fog that was characterised by remarkable oscillations in visibility near the surface. Dissipation of the fog was driven by strong turbulence above the fog layer and horizontal thermal advection demonstrated using the temperature tendency equation. This work provides novel measurements and analysis techniques that have previously not been used to understand the mechanisms governing stratus‐lowering events. These observations and analyses help highlight processes and explain mechanisms related to the fog life cycle that are inherently challenging to predict in mesoscale models.