Fog can significantly impact communication systems, free-space optics, and remote sensing applications, which are critical for defence and security in multidomain operations. Forecasting fog remains a complex challenge, particularly in assessing threshold operational conditions crucial for decision-making processes. A long-term characterization of the fog events at the airport of Lisbon was performed using 20-year METAR data. This study explores the classification of fog types using decision-tree-based ensemble methods applied to a comprehensive set of preconditioning features. By extending the Tardif and Rasmussen classification framework and incorporating additional key variables, this research aims to improve the understanding and mitigation of fog events. The classified fog types are correlated with typical runway visual range (RVR) values, a crucial parameter for low visibility operations at airports. As some fog types exhibit RVR values below the 550-m threshold for low visibility operations, this study proposes an RVR-based operational impact diagram to support decision-making in very low visibility conditions, thereby enhancing situational awareness and operational safety in aviation environments.