Role of thermodynamic and turbulence processes on the fog life cycle during SOFOG3D experiment

Abstract

Abstract. 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 and winter 2019–2020 to analyse the thermodynamic and turbulent processes related to fog formation, evolution, and dissipation across southwestern France. Based on a unique measurement dataset (synergy of cloud radar, microwave radiometer, wind lidar, and weather station data) combined with a fog conceptual model, an analysis of the four deepest 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 inversions, 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 (σw2) up to 0.04 m2 s−2) generated by increasing wind and wind shear. Our study reveals that fog liquid water path, fog top height, temperature, radar reflectivity profiles, and fog adiabaticity derived from the conceptual model evolve in a consistent manner to clearly characterise this transition. The dissipation time is observed at night for the advection–radiation fog case studies and after sunrise 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 σw2 larger than 0.04 m2 s−2). Daytime dissipation is linked to the combination of thermal and mechanical turbulence related to solar heating (near-surface sensible heat flux larger than 10 W m−2) and wind shear, respectively. This study demonstrates the added value of monitoring fog liquid water content and depth (combined with wind, turbulence, and temperature profiles) and diagnostics such as fog liquid water reservoir and adiabaticity to better explain the drivers of the fog life cycle.

Vertical Profiles of Liquid Water Content in fog layers during the SOFOG3D experiment

“…But Dione et al (2023) found much larger values (0.01 < σ 2 w < 0.04 m 2 .s −2 ) during the stable/adiabatic transition of the 4 deepest fog events of SOFOG3D and an increase in TKE of up to 0.4 m 2 .s −2 . These discrepancies may be explained by the contrasting environment between the two measurement areas (Thornton et al, 2023).…”

“…They found that α closure eq depends mainly on the CTH and pointed out that thinner fog with LWP values lower than 20 g.m −2 has α eq values below 0.6 and can even reach negative values. Dione et al (2023) used this model to analyze the four deepest fogs of SOFOG3D and revealed that α closure eq increases from 0 to 0.5 during the thin-to-thick transition and reaches 0.5 when LWP > 20 g.m −2 . We now use the CDP measurements to analyze fog adiabaticity derived from in situ measurements.…”

“…They demonstrated that LWC retrieval is highly sensitive to the prescribed droplet concentration, and that agreement with in situ data is highly dependent on cloud-fog heterogeneity. Dione et al (2023) combined remote sensing measurements with the conceptual adiabatic fog model to analyze the thermodynamic and turbulent processes involved in fog formation, development and dissipation, focusing of the four deepest case studies : two radiation fogs and two advection-radiation fogs. They defined the different phases characterizing the fog's life cycle and provided quantitative analyses of key paramemeters and conditions that drive their temporal evolutions.…”

“…Given such vertical developpment they have clearly undergone a transition from thin to thick. The life cycle of these cases has been examined by Dione et al (2023) to analyze the thermodynamics and turbulent processes involved in fog formation, evolution and dissipation. Most of the fog layers sampled at the Charbonnière site, however, reached much lower thickness than these 3 cases.…”

An analysis of fog and low stratus life‐cycle regimes over central Europe

“…Similar environmental features to those found to influence the mean daily FLS occurrence in Pauli et al (2020) are used, specifically air temperature (t2m) and specific humidity (q) at 2 m, wind speed (ws) at 10 m, and mean surface pressure (msp). While the FLS life cycle is also influenced by large-scale dynamic and thermodynamic conditions (Andersen et al, 2020;Dione et al, 2023), in this study we focus on the relationships of the FLS life cycle to environmental conditions near the ground and at the local scale. The ERA5 data set is rescaled to the spatial resolution of the FLS data set using nearest-neighbour interpolation.…”