Evaluation of near-surface and boundary-layer meteorological conditions that support cold-fog formation using Cold Fog Amongst Complex Terrain field campaign observations

Cold fog refers to a type of fog that forms when the temperature is below 0°C. It can be composed of liquid, ice, and mixed-phase fog particles. Cold fog happens frequently over mountainous terrain in the cold season, but it is difficult to predict. Using observations from the Cold Fog Amongst Complex Terrain (CFACT) field campaign conducted in Heber Valley, Utah, in the western United States during January and February of 2022, this study investigates the meteorological conditions in the surface and boundary layers that support the formation of wintertime ephemeral cold fog in a local area of small-scale mountain valleys. It is found that fog formation is susceptible to subtleties in forcing conditions and is supported by several factors: (1) established high pressure over the Great Basin with associated local clear skies, calm winds, and a stable boundary layer; (2) near-surface inversion with saturation near the surface and strong moisture gradient in the boundary layer; (3) warm (above-freezing) daytime air temperature with a large diurnal range, accompanied with warm soil temperatures during the daytime; (4) a period of increased turbulence kinetic energy (above 0.5 m²·s⁻²), followed by calm conditions throughout the fog's duration; and (5) supersaturation with respect to ice. Then, the field observations and identified supporting factors for fog formation were utilized to evaluate high-resolution (˜400 m horizontal grid spacing) Weather Research and Forecasting (WRF) model simulations. Results show that the WRF model accurately simulates the mesoscale conditions facilitating cold-fog formation but misses some critical surface and atmospheric boundary conditions. The overall results from this paper indicate that these identified factors that support fog formation are vital to accurately forecasting cold-fog events. At the same time, they are also critical fields for the NWP model validation.