Understanding the dissipation of continental fog by analysing the LWP budget using idealized LES and in situ observations

Abstract

Funding informationDirection Générale de l'Armement (France), Meteomodem and the Netherlands Organization for Scientific Research (contract 863.10.010 and project number SH-312-15), Physical processes relevant for the dissipation of thick, continental fog after sunrise are studied through observations from the SIRTA observatory and idealized sensitivity studies with the large-eddy simulation model DALES. Observations of 250 fog events over 7 years show that more than half of the fog dissipations after sunrise are transitions to stratus lasting 2 hr or more. From the simulations, we quantify the contribution of each process to the liquid water path (LWP) budget of the fog. Radiative cooling is the main source of LWP, while surface turbulent heat fluxes are the most important process contributing to loss of LWP, followed by the absorption of solar radiation, the mixing with unsaturated air at the fog top and the deposition of cloud droplets. The loss of LWP by surface heat fluxes is very sensitive to the Bowen ratio, which is importantly affected by the availability of liquid water on the surface; in a run without liquid on the surface, fog dissipation occurred 85 min earlier than in the Baseline simulation. The variability of stratification and humidity above fog top is documented by 47 radiosondes and cloud radar. Using DALES, we find that the variability in stratification has an important impact on the entrainment velocity; a three times more rapid fog-top entrainment enables the cloud base to lift from the ground 90 min earlier in weak stratification than in strong stratification in the model. With relatively dry overlying air, the fog evaporates faster than if the air is near saturation, leading to 70 min earlier dissipation in our simulations. Continuous observations of the temperature and humidity profiles of the layer overlying the fog could therefore be useful for understanding and anticipating fog dissipation.

Comparison of a Spectral Microphysics and a Two-Moment Cloud Scheme: Numerical Simulations of the Cloud-Topped Marine Boundary Layer

“…It is noteworthy that by reason of mass conservation, in a one-dimensional model large-scale subsidence should not occur, that is w = 0 in (1). However, without this forcing term a reasonable simulation of fog or stratiform clouds with a one-dimensional model would be impossible (see, e.g., Driedonks and Duynkerke 1989;Bott et al 1996;Waersted et al 2019).…”

Direct Numerical Simulation of Fog: The Sensitivity of a Dissipation Phase to Environmental Conditions

“…Large eddy simulation (LES) has provided an outstanding tool to study fog evolution [10,38,40,[45][46][47][48][49] though it still exhibits some limitations in representing the realistic synoptic processes of fog [50]. One of the limitations can be attributed to the inherent uncertainty of the parameterized turbulence models used in LES schemes [51,52] and partially to the embedded inaccuracy of the large-scale forcing [35].…”

Comment on acp-2020-1314

“…Fog occurs due to multiple processes that lead to saturation of the air near the surface, through cooling of air temperature, such as radiative cooling, turbulent heat exchange, diffusion, adiabatic cooling through lifting, advection, and through moistening of the air, such as evaporation from the surface, evaporation of drizzle, advection of moist air, and vertical mixing (Brown and Roach, 1976;Gultepe et al, 2007;Dupont et al, 2012). Similarly fog dissipates as a result of warming and drying of the air near the surface, and also through the removal of droplets by precipitation (Brown and Roach, 1976;Haeffelin et al, 2010;Waersted et al, 2017Waersted et al, , 2019.…”

“…An adiabatic fog behaves similarly to stratocumulus clouds on top of convective boundary layers (Cermak and Bendix, 2011). The processes of stratocumulus clouds have been studied extensively in the past with large-eddy simulation (LES) and numerical weather prediction (NWP) models (Nakanishi, 2000;Porson et al, 2011;Bergot, 2013Bergot, , 2016Waersted et al, 2019).…”