A Review of Coastal Fog Microphysics During C-FOG

Our goal is to provide an overview of the microphysical measurements made during the C-FOG (Toward Improving Coastal Fog Prediction) field project. In addition, we evaluate microphysical parametrizations using the C-FOG dataset. The C-FOG project is designed to advance understanding of liquid fog formation, particularly its development and dissipation in coastal environments, so as to improve fog predictability and monitoring. The project took place along eastern Canada's (Nova Scotia and Newfoundland) coastlines and open water environments from August-October 2018, where environmental conditions play an important role for late-season fog formation. Visibility, wind speed, and atmospheric turbulence along coastlines are the most critical weather-related factors affecting marine transportation and aviation. In the analysis, microphysical observations are summarized first and then, together with three-dimensional wind components, used for fog intensity (visibility) evaluation. Results suggest that detailed microphysical observations collected at the supersites and aboard the Research Vessel Hugh R. Sharp are useful for developing microphysical parametrizations. The fog life cycle and turbulence-kinetic-energy dissipation rate are strongly related to each other. The magnitudes of three-dimensional wind fluctuations are higher during the formation and dissipation stages. An array of cutting-edge instruments used for data collection provides new insight into the variability and intensity of fog (visibility) and microphysics. It is concluded that further modifications in microphysical observations and parametrizations are needed to improve fog predictability of numerical-weather-prediction models.

Automated summary

The C-FOG project aims to improve coastal fog prediction through microphysical measurements and parametrization evaluation in Eastern Canada. The study finds a correlation between microphysical observations and three-dimensional wind factors in fog intensity evaluation, highlighting the need for adjustments in microphysical observations and parametrizations to enhance the predictability of numerical weather prediction models.