We are currently funded on a three year project through the ONR Young Investigator Program.

Fog obscures visibility, presents travel safety hazards, and can be a primary source of liquid water in many otherwise arid regions of the world. It is difficult for models to represent correctly due to the complex interactions between cloud microphysics, radiation, surface fluxes, and dynamics that control its evolution. The first component of our project aims to identify the sources of uncertainty within the microphysics scheme for predictions of fog and to understand how the scheme complexity impacts fog forecasts. We are running a large suite of simulations to understand the influence of individual microphysical processes on the evolution of fog. Targeted case study simulations will allow us to assess which configurations of the microphysics scheme provide the highest quality forecasts. Another major issue with fog is its detection from satellite data as there is no simple way to distinguish fog from low clouds. We are planning to leverage machine learning techniques to improve our ability to identify marine fog from remote sensing observations.