the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Dec 2025
Evaluation of the vertical microphysical properties of fog as simulated by Meso-NH during the SOFOG3D experiment
Abstract. This study evaluates the representation of fog microphysics in high-resolution simulations from the Meso-NH model using the two-moment LIMA microphysical scheme, based on data from the SOFOG3D field campaign. This campaign combines remote sensing and vertical microphysical observations from a tethered balloon. Two fog events were simulated in order to assess the model's ability to reproduce their life cycle and identify any missing physical processes. The analysis focuses on the vertical microphysical structure, observed consistently by the various instruments, and on the simulated processes during the different phases of fog development (i.e. before, during, and after the transition from thin to thick fog). The model realistically reproduces the thermodynamic and dynamic evolution of fog, resulting in a satisfactory simulation of its development stages. A comprehensive analysis of microphysical processes is conducted throughout the entire height of the fog, based on a comparison with observations and a budget of modelled processes. While microphysics is generally well represented, certain systematic errors emerge: excessive liquid water content values vertically during the thin-to-thick transition and adiabatic phases, due to excessive condensation; an inaccurate representation of the droplet side distribution, with the absence of the largest droplets; and an inability to capture the droplet concentration vertical gradient, with values that are too high near the ground. While some of these shortcomings can be explained by dynamic biases, and more cases are needed to confirm our results, various recommendations are proposed. These include assessing the impact of drizzle and representations that could benefit all warm clouds.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5528', Anonymous Referee #1, 18 Dec 2025
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RC2: 'Comment on egusphere-2025-5528', Anonymous Referee #2, 20 Feb 2026
This study presents a high-potential evaluation of fog microphysics using a sophisticated dataset (SOFOG3D) that combines tethered balloon observations with remote sensing. The use of the Meso-NH model with the two-moment LIMA scheme is state-of-the-art and addresses a significant gap in our understanding of the vertical structure of fog.
However, the manuscript currently lacks a clear link between the described dynamic causes (e.g., advection, initialization) and the microphysical evidence (e.g., reflectivity vs. liquid water content). Specifically, the "reflectivity paradox" identified in IOP 6—where the model overestimates LWC but underestimates reflectivity—points to a fundamental constraint in the droplet size distribution (DSD) parameterization that requires deeper discussion. Additionally, inconsistencies in model initialization between the two cases and the treatment of measurement biases need clarification before publication.
I recommend Major Revisions to address these physical inconsistencies and sharpen the narrative.
- Unclear attribution of model biases to microphysics vs. dynamics — needs stronger evidence and clearer separation.
Explanation: the manuscript frequently attributes the LWC and Nc biases both to thermodynamic/dynamical biases (e.g., wet/cold/TKE biases) and to structural microphysics limits (single-mode gamma DSD). These arguments appear repeatedly (e.g. discussion of LWP/LWC overestimate and TKE link). The causal chain is plausible but not demonstrated strongly enough: is excess condensation producing high Nc, or is excessive activation due to turbulence producing excess LWC? See discussion and budget summaries. add (or more clearly present existing) quantitative diagnostics that separate dynamical from microphysical causes. Show time/height cross-correlations (or regression) between modeled TKE perturbations and activation rate / Nc production to support the "TKE → activation → Nc → LWC" chain. (Add a short figure or table with correlation coefficients or a simple sensitivity test.) If available, present a sensitivity experiment (or at least a quantitative estimate) where only microphysics is changed (e.g., change activation parameter) and separately where only dynamics (e.g., TKE or surface fluxes) is nudged — to demonstrate which change reduces the LWC/Nc bias most. The manuscript mentions sensitivity tests (not shown) — if you ran them, include a concise table/figure; if not, state explicitly that these are planned. - Budget analysis is promising but hard to follow — improve clarity and quantitative presentation.
Figures 11–12 and the text describe rc/Nc budgets, but the reader struggles to see magnitudes, relative contributions, and integrated tendencies. The text presents qualitative descriptions but few numbers. Add a compact table that reports integrated (over height or over the fog layer) mean tendencies for each budget term during each phase (formation, transition, adiabatic, dissipation). This will let the reader see which process dominates in each phase. - Representation of the DSD: single-mode gamma assumption limits conclusions — quantify the impact and discuss alternatives.
The text repeatedly notes that LIMA’s single-mode/gamma DSD prevents realistic bimodality and under/overestimates large droplet population (e.g. missing 10 µm mode or >30 µm tail). This is a central limitation that affects reflectivity and sedimentation conclusions. Add an explicit sensitivity test or offline exercise: take observed LWC and Nc and compute the gamma-law DSD (you have the "pink" distributions already). Then compute radar reflectivity and compare to observed reflectivity to quantify how much of the reflectivity bias is due to shape vs. other sources. Discuss possible microphysics remedies (multi-mode DSD, explicit coalescence parameterization, drizzle scheme) and estimate (qualitatively or quantitatively) which would be most effective. - Instrumentation and representativeness caveats need clearer placement and stronger emphasis.
The CDP measures 2–50 µm; the manuscript notes that droplets >50 µm could be important and that BASTA/CDP reflectivity mismatches suggest missing large drops. But this important measurement limitation is discussed intermittently rather than in one explicit limitations paragraph; it is central to confidence in comparisons. Create a short dedicated subsection “Observational limits and impact on comparisons” that lists instrument ranges/uncertainties and then explicitly states how these affect each diagnostic (LWC, Nc, reflectivity). Provide a short table summarizing instrument vertical coverage, diameter ranges and uncertainties (you already have Table 1 but expand to explicitly link limitations to specific model comparisons). - Phase definition thresholds and sensitivity — be explicit about robustness.
Thin-to-thick transition is defined by several thresholds (TKE, dT/dz, LWnet/LWP, CTH) and the manuscript notes sensitivity to averaging windows and layer definitions. This affects phase timing and subsequent aggregations. Provide a concise sensitivity test (e.g., show how transition start/end shift when using alternate thresholds or averaging windows) or at least a quantification of uncertainty (± hours). A short table or supplementary figure would suffice. This will strengthen confidence in phase-aggregated comparisons. - Conclusions: strengthen practical takeaways for model developers/operational users.
The Discussion lists useful suggestions (e.g., drizzle probes, radar Doppler) but the Conclusions are relatively general. The paper will be more impactful if it gives concrete, prioritized recommendations for model improvement and for observational follow-ups. Add a short bullet list in Conclusions: (1) short-term model changes to try (e.g., adjusting deposition velocity, implementing two-mode DSD or drizzling scheme), (2) diagnostics to routinely output in future Meso-NH runs, and (3) highest-priority observations to collect in future campaigns.
Minor comments are given in the attached doc.
- Unclear attribution of model biases to microphysics vs. dynamics — needs stronger evidence and clearer separation.
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- 1
Review of ‘Evaluation of the vertical microphysical properties of fog as simulated by Meso-NH during the SOFOG3D experiment’
December 2025
Overview
This paper uses observations of microphysics in fog from the SOFOG3D campaign to evaluate the performance of the two-moment LIMA microphysical scheme within the Meso-NH high resolution model (using the Arome model for initialisation/boundary conditions). The observations include vertical profiles of microphysical parameters within two fog cases. Whilst similar observations have been collected before, for example during the LANFEX field campaign, such data are still very rare, but of high value to fog research to better understand the characteristics and evolution of fog. The SOFOG3D data set therefore has significant potential to aid the development of fog modelling, such as in this study.
The paper concludes that the model does a reasonable job simulating two case studies, but certain issues are identified such as an overestimation in liquid water content, and also that the drop size distribution was not correctly modelled, due to limitations of a one-moment scheme. The discussion provides a useful analysis of the strengths and weaknesses of the model.
The paper is generally well laid out and written, though I feel there are some areas where clarification is required, together with some persistent grammatical issues, as pointed out below. Also I feel there could be more discussion on certain topics.
Points to address
1) Model set up. Suggest slight clarification. What is meant by (line 170) ‘..with the configuration previously identified as the most satisfactory.’: you mean as laid out in Cuxart, (2015)? Also, regarding the sizes of the nested grids, how sensitive were the results at the super site to these?
2) Line 200. Suggest explaining why the fog deposition scheme does not also use Stoke’s law. I would have liked to have seen more study on the fog deposition at the surface, given its potential to significantly affect the fog morphology. For example, the reported observed reduction in droplet concentration near the surface maybe principally caused by sedimentation. Given the large number of trees in the SOFOG3D region, one imagines that aerodynamic capture of drops (as suggested by Price 2025) could be large. A section dedicated to deposition would have been good, but it is presented more as an after-thought, in the discussion. At least the authors correctly identify it as a property that requires further study.
3) Line 310. Discussion of TKE. I understand that the BASTA radar was Dopplerised during SOFOG3D? If so, then would using this data have not added more direct insight into the dynamics within the fog? Vertical velocity variance measured there can be very useful. Also, I suggest that it is important to acknowledge that TKE measured during the stable phase of a fog is significantly influenced by the horizontal sloshing of fog from side-to-side, and does not represent turbulence as it is commonly understood, unlike during the adiabatic phase of a fog.
4) Please check that all acronyms are defined.
5) Line 355. Explanation of radiation. Temperature errors would be the most likely explanation?
6) Line 366. ‘vicious circle’ – suggest ‘positive feedback’.
7) Line 409. No need for a new paragraph.
8) Line 412. Meaning of paragraph not clear.
9) Line 441: ‘primarily’.
10) Line 468 and elsewhere. Use of ‘aggregate’. Do you mean ‘average’? Also may be useful to clarify that ‘phase’ refers to the temporal phase of the fog and not of the DSDs! What is meant by ‘sub-phase’ (line 482 and elsewhere)?
11) lines 495/6. ‘dispersion’. Suggest, ‘Variation’?
12) Line 527. ‘small-to medium-sized droplets’. Please define the actual size range for this phrase.
13) Line 553. ‘faults’. Suggest, ‘discrepancies’?
14) Line 569. What is meant by, ‘dryness inducing evaporation’? Where is the dryness from?
15) Lines 578-9. (and L606-7) High humidity for one case and cold temperatures for the other, cannot alone, account for the excessive liquid water production.
16) Lines 584. ‘The sedimentation process mainly contributes to the shift towards larger droplets in the DSD…’. Where is the proof for this statement? If this is an assumption then the statement needs re-wording as a proposition.
17) Line 613. The study by Price (2025) considered deposition of fog droplets at the surface.
18) Line 674. Paragraph. Droplet sedimentation/capture at the surface would seem like a good candidate for causing the gradient in Nc near the surface. Note also that adiabatic fog with low saturation excess could exhibit sub-saturated conditions near the surface if e.g. it advects to a lower altitude, or during the morning period when it starts dissipating (but still remains as fog). Both of these could affect Nc in the lowest part of the fog.
19) line 739. ‘dew deposition’, suggest ‘dew and droplet deposition’.
20) Throughout the paper there are examples of verbs and adjectives that are not qualified, and force the reader into making assumptions about what the authors are talking about. For example, line 460, ‘concentrations’, and line 539, ‘bottom’. Concentrations and bottom of what? Please add appropriate verbs and adjectives to reduce ambiguity throughout.
21) The paper states that 140 profiles were taken collecting microphysical information during SOFOG3D (Costabloz et al. 2024). Looking at the figures I estimate around 40 profiles or partial profiles are presented here. What are the reasons that the majority of the data has not been used in this study? Can we look forward to more model comparisons in future from this data set?
22) Figure 2. Reference to the various panels seems incorrect.
23) Figure 4. Panel h) shows a bottom-driven boundary layer, but we would expect a top-driven one in this situation?
24) Figure 5, panel e). Wind vectors too small to read on A4 printed material.