| 28 Apr 2026
Calculation of the impacts of aerosols on cloud microphysical properties by combining ground- and space-based measurements
Abstract. The effect of aerosols on cloud properties remains poorly constrained, in part because ground-based measurements are highly local while space-based retrievals are spatially imprecise. Here, we show how the two viewpoints can be combined using colocated surface aerosol measurements and thermodynamic profiling from the peri-urban ATOLL (ATmospheric Observatory in LiLLe) site and geostationary-satellite cloud microphysical retrievals from the SEVIRI instrument. Focusing on low-level stratiform clouds that formed under thermodynamically stable conditions, we relate cloud properties to aerosol light scattering as a proxy for cloud condensation nucleus concentrations for two cloud liquid water path bins of 20–100 and 100–200 g m–2. Relative changes of cloud droplet number concentrations and effective radii are compared to relative changes in aerosol light scattering coefficient to find respective susceptibilities. The susceptibility of the cloud droplet number concentration to the aerosol scattering coefficient (Sn), also called nucleation efficiency, is retrieved around 0.29 for LWP (Liquid Water Path, from 20 to 100 g m-2) and around 0.30 for higher LWP (between 100 to 200 g m-2). For the same LWP ranges, the susceptibility of the effective radii to the aerosol burden (Sre) is around 0.08 and 0.07, respectively. These values are consistent with, although on the lower side of, prior studies of continental stratus. Uncertainties in this study are dominated by a small sample size and satellite retrieval biases. However, the approach is readily extensible to other ground-based sites measuring boundary layer aerosol concentrations pointing towards a method for better constraining susceptibility calculations globally.
Crumeyrolle et al. are interested in quantifying the role of aerosol perturbations for cloud microphysics. They aim to do so by a statistical analysis of observational data, combining ground-based observations with passive microwave ground-based remote sensing and cloud retrievals from a geostationary satellite. The method is meant as a proof-of-concept study.
With this aim, the study may be ok for Atmos Chem Phys. The study itself, however, does not provide a substantial new scientific insights. There is a lot of data pre-processing, where some of the applied filters are poorly motivated and seem partly contradictory. Ultimately, a mere 188 data points remain for analysis. This severe lack of data precludes meaningful statistical analysis. The uncertainty quantification consequently shows ranges of almost ±100%.
If the editor decides the topic still fits Atmos Chem Phys, I would have a number of concerns, partly major, partly minor, to be addressed before further consideration.
L14 What exactly is meant by “spatially imprecise”?
L48 This could also refer to a new paper by Jia et al Sci Adv 2026
L92 What are “total..coefficients”?
L129 It would be useful to briefly report the method to retrieve LWC here, and discuss its uncertainties.
L140 This line reads like VIS channels would have 3 km resolution and NIR, 1 km, but that is not true. Only the HRV channel has 1km.
L155 say the “idealised” involves the assumption of adiabaticity
L159 Sentence unclear. The comparison with CALIPSO reveals biases of 30%. But isn’t that a function of the instrument sensitivity? See the GEWEX assessment, Stubenrauch et al. Surveys in Geophysics 2024. The sentence continues with “is less than 10% above the ATOLL station”. What is meant? The bias vs. CALIPSO? Or the liquid fraction itself?
L162 AMSR2 and CLAAS LWP are not directly comparable. Microwave LWP is more like an all-sky retrieval, NIR one, in-cloud. See Seethala and Horváth JGR 2010.
L163 Why jump between r and r²?
L161-164 Please report confidence intervals for the CLAAS retrievals, from the comparison to the reference data, rather than correlation coefficients.
L165 Typically, the statistical error is considered as uncertainty, while if a bias is known, it should be corrected for.
L173 (Table 1) Why not LWP from CLAAS?
L200 This is an awkward definition of stratiform clouds. It should rather be defined by some homogeneity criterion, such as temporally constant LWP and cloud base altitude over some time from the ground-based measurement (or distance from the satellite).
L201 How does a cloud base height threshold filter out rain? There should be a better way to define precipitation from the ground-based measurements.
L214 This is now a second, redundant, definition of stratiform clouds. Why would that criterion be “effective”?
L215 Why do the authors want to minimise convective mixing? Just a few lines earlier (L201) the aim was, on the contrary, to ascertain the aerosol in the boundary layer mixes into the clouds.
L216 Why this lower bound on the BLH? Just a little earlier (L208) the authors explained they want to limit themselves to stable conditions. Here it now seems that on the contrary, convectice situations are sought.
L216 Didn’t the earlier filtering criteria already reduce the data to 18% of its original size (L203)?
L222 I find this criterion puzzling. How is the 60% motivated? This doesn’t seem to me similar at all, if stratiform, and therefore spatially homogeneous clouds, are considered. If the point is to avoid overlying clouds, aren’t there better criteria to filter from the satellite retrievals, e.g. selecting scenes with liquid clouds only?
L223 Now another number for how much data is left is provided, 45%. Why this? What with the 18% (L203) and the 40% (L216)?
L228 This is true for well-mixed boundary layers, but weren’t criteria applied that tried to avoid these?
L232 How are these constrained if no information about CCN is available?
L239 (Table 2) Why this very low number of observations in the initial database? The upper bound would be 4 years * 365 days/year * 12 hours daylight / day * 4 15-min steps / hour = 70.000 time steps. Why only an order of magnitude fewer data points?
L245 My own experience with analysing clouds and sensitivities of microphysical quantities is that one needs a few thousands to obtain robust results. The remaining 188 data points are too few to yield meaningful insights.
L252 The authors should provide some evidence for this statement. For example, they could assess what happens to the conclusions if they further reduce the number of data points. If the results remain unchanged for a reduction by a further quarter or third, that would be convincing.
L263 I agree that Panel (f) is useful, to demonstrate there is very little data from Aug – Oct but otherwise an approximately equal seasonal distribution. What about the distribution between the years? For (a) – (e) it would be more useful to show the overall histrograms, it seems not to make sense to show the seasonal cycle. There is simply not enough data for meaningful conclusions about this.
L271 It is not understandable why the anyway very small dataset is further split into two. What is the motivation?
L276 “higher scattering coefficients are often associated with increased aerosol concentrations” why is that a meaningful statement? Are there situations where the opposite could be true?
L278 “enhanced scattering is indicative of larger particle sizes or higher particle number concentrations” same question
L298 It may be useful to clarify that a hard upper bound for S_n is 1 (every aerosol activates into a droplet), unless mistakes in the definition are made (e.g. 1.59 is physically nonsense).
L300 Schmidt et al. ACP 2015 (doi:10.5194/acp-15-10687-2015) compiled a list of S_n, maybe it is useful to refer to this here.
L302 / Section on r_e: Is this not entirely redundant with the analysis of n_d? i.e., isn’t n_d computed from the retrieved r_e?
L331 A “case study” would be for one case only (but in-depth), here there are 188 cases statistically analysed.