Role of thermodynamic and turbulence processes on the fog life cycle during SOFOF3D experiment.

In this study, we use a synergy of in-situ and remote sensing measurements collected during the Southwest FOGs 3D experiment for processes study (SOFOG3D) field campaign in autumn 2019 and winter 2020, to analyze the thermodynamic and turbulence processes related to fog formation, evolution, and dissipation across southwestern France. Based on a unique dataset with a very high resolution and a fog conceptual model, an analysis of the four heaviest fog episodes (two radiation fogs and two advection-radiation fogs) is conducted. The results show that radiation and advection-radiation fogs form under deep and thin temperature inversion, respectively. For both fog categories, the transition period from stable to adiabatic fog and the fog adiabatic phase are driven by vertical mixing associated with an increase in turbulence in the fog layer due to mechanical production (turbulence kinetic energy (TKE) up to 0.4 m² s^-2 and vertical velocity variance (σ_w²) up to 0.04 m² s^-2) generated by brisk wind at the supersite (advection). The dissipation time is observed at night for the advection-radiation fog case studies and during the day for the radiation fog case studies. Night-time dissipation is driven by horizontal advection generating mechanical turbulence (TKE at least 0.3 m² s^-2 and σ_w² larger than 0.04 m² s^-2). Daytime dissipation is linked to the combination of thermal and mechanical turbulence related respectively to solar heating (near surface sensible heat flux larger than 10 W m^-2) and advection. Through a deficit of the fog reservoir of liquid water path, the fog conceptual model estimates the dissipation time at least one hour before the observed dissipation for radiation fog cases. It gives a better estimate of the fog dissipation time for advection-radiation cases. This study also demonstrates the importance of using instrumental synergy (with microwave radiometer, wind lidar, weather station, and cloud radar) and a fog conceptual model to better predict fog characteristics and dissipation time at nowcasting ranges. [ABSTRACT FROM AUTHOR]