| 13 Jul 2026
Droplet sedimentation regulates liquid water in marine advection fog: Large-eddy simulations with interactive sectional microphysics
Abstract. Marine advection fog strongly affects visibility and maritime operations, yet numerical models frequently overestimate its liquid water content. Although fog-top radiative cooling is known to promote condensation, how size-dependent gravitational droplet sedimentation redistributes and removes the resulting liquid water remains poorly quantified. Here, we use the University of California, Los Angeles Large-Eddy Simulation model coupled with the sectional aerosol–cloud microphysics module from a Lagrangian perspective to quantify this process during a typical advection-fog event over the northwestern Pacific. Longwave radiative cooling near the fog top promotes condensational growth and the formation of large droplets, generating intermittent sedimentation signals that propagate downward through the fog layer. Lead–lag correlations show that sedimentation initiated near the fog top reaches the fog base after approximately 1 h. The liquid water path budget identifies fog-top radiative cooling as the dominant source (about 30 g m-2 h-1) and gravitational sedimentation as the largest microphysical sink (approximately 20 g m-2 h-1), offsetting nearly two-thirds of the radiatively driven production. Droplets with radii larger than 10 μm dominate the sedimentation flux. Sensitivity experiments confirm the robustness of this mechanism: removing sedimentation causes excessive liquid-water accumulation and substantially deepens the fog layer, while suppressed collision–coalescence or enhanced aerosol loading weakens sedimentation by limiting large-droplet formation. These results identify droplet sedimentation as a vertically coupled control on liquid-water redistribution and fog-layer evolution, implying that marine-fog parameterizations need to represent sedimentation and large-droplet microphysics explicitly.
Review of the manuscript egusphere-2026-2980
Droplet sedimentation regulates liquid water in marine advection fog: Large-eddy simulations with interactive sectional microphysics
Summary
This study uses the UCLA large eddy simulation model to learn more about the liquid water budget of a maritime fog. The model is coupled to a specific high resolution, multi-bin microphysics model. The study focusses in particular on the role of droplet sedimentation in governing the LWP, the radiative cooling at the fog top and the fog depth. They conclude the representation of droplet sedimentation is key for the fog evolution and visibility. Fog is challenging to forecast in NWP models, so more fundamental understanding from the LES point of view. The paper is also well written, concise and with high quality figures. Here and there some additional details in the modelling setup are needed, and in my view the conclusion section need to be rewritten.
Recommendation: Major revisions
Major comments:
-1. In my view the Conclusion section needs to be rewritten. The section is very long and does at first instance only repeat the results section. The Conclusion section should answer a well-posed research question that is postulated in the Introduction section. Moreover I prefer to have the Discussion section in front of the conclusion section. The Discussion section should focus on the framing what has been added by this study compared to earlier work, and what is still to be done.
-2. I would love to see an assessment of the UCLA model distribution of subgrid vs resolved scales in the modelled fog over time and in the vertical. At the fog top usually the stratification is strong and small scales are not resolved. This may inhibit the use of LES as technique. Please add some assessment.
-3. Related to the previous point: I will mention it below as well, but some kind of sensitivity study to the model vertical resolution will be appreciated, and will mature the manuscript. A DZ of 5 m as used here is relatively coarse these days, especially for a weather phenomenon dominated by small scales. The mentioned Waerstad study already used 2 m grid spacing 8 years ago (an earlier generation supercomputer), so I would love to see the robustness of the model results in this study against the vertical resolution.
- 4. Analysis. The paper focusses very much on the LWC profiles in time, and tries to relate that to the sedimentation flux, which is logical given the goal of the paper. However I think it would be good show some time series of u* and/or w* and/or TKE at a relevant level to show how the turbulence develops throughout the simulation. Turbulence is a key transport mechanism for liquid water as well, so it would be good how it interacts with radiative cooling at the top, and with the sedimentation flux.
Minor comments:
Ln 48: models also are characterized by excessive mixing, so sometimes fog and low clouds disappear too quickly or do not form. Please rewrite
Introduction: role of initialization and lead time on the NWP/modelling skill is not discussed in the Introduction. If a model is initialized over the ocean with sparse observations, one cannot blame the model physics, neither the poor resolution. In addition, although over land I have seen several studies for the Cabauw tower sites where the 24-48 h forecast was better than the 0-24 h forecast. So spin up and lead time play a role. Please discuss.
Ln 162: 2-m visibility?
Ln 175 (and later on in more detail). A quality assessment of ERA5 for the selected case is not presented? ERA5 usually does not do a good job on fog, for the modelling reasons provided in the introduction. Same for MERRA-2. Please provide an assessment what is the quality of your initial conditions.
Moreover, I do not understand why different reanalysis products have been used for the physics and for the air quality. Using CAMS data from ECMWF for the air quality would imply same physics and dynamics would have been used, which is more consistent.
Ln 186: “UCLALES is a three-dimensional boundary-layer model with turbulence closure”. I do not get this, if it is and LES model, then I expect there is NO turbulence closure, since the turbulence should be largely resolved. So the paper need to make more clear whether it works with and LES model or with a RANS model.
Ln 205: … evaluated in simulations of liquid-phase clouds. Please add some words what was the outcome of the evaluation.
Ln 208-213: please add some words of justification for the chosen settings. Is 5 m sufficient vertical resolution not too coarse? Especially at the fog top where we expect a strong inversion, and LES models are known for having problems at strong stratification, especially in coarse resolution (eddies are too small at the top). For example, Wærsted et al (2019) used 2 m vertical resolution for modelling a dense radiation fog with substantial gravitational settling of droplets.
Ln 236: was the geowind constant with height? Please add.
Ln 242: was the divergence constant with height? At the surface there cannot be any divergence … (w=0 at surface). So how was the vertical w and divergence profile?
Ln 266: the 10-m wind speed was here set the same as the geostrophic wind speed. This violates the idea that the 10-m wind speed is reduced compared to the geowind by boundary layer friction. Please comment.
Ln 281: the reference to Waersted et al. (2018) is missing in the reference list. I think it should be Waersted et al. (2019).
Ln 336: Please add some justification for the use of the Kunkel formula. After 1984 multiple formula’s have been proposed as alternative as well. The Kunkel formula was also not derived for the sea fog, so some more justification of its use is needed. At least some kind of sensitivity assessment is needed to show to what extent your conclusions depend on the specific use of Eq 4.
Ln 337: it is important to note in which unit the LWC needs to be entered in the formula and what is the unit of VIS in that case.
Figure 4: please add an uncertainty measure (measurement uncertainty or confidence interval) to the dots in panel b.
Ln 384: “intermittent downward-extending streak-like features”. Please provide a explanation for this behaviour. Personally I think this is a numerical artefact. In case the fog grows by 1 grid cell, the fog layer as a whole gets extended by 5 m, which means all thermodynamic variables need to be redistributed again. At least this is how I have seen it in fog simulations in1D models