the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Aug 2026
Correction for above-clouds aerosols in geostationary satellite cloud property retrievals
Abstract. The high-temporal-resolution observations from Spinning Enhanced Visible and InfraRed Imagers (SEVIRI) and its successor Flexible Combined Imager (FCI) of cloud optical thickness (COT) and cloud particle effective radius (CER), suffer from biases due to absorption by aerosols, when smoke is overlying the cloud scene. This is often the case over the south-east Atlantic Ocean (SEAO), which is an important part of the field of view of these instruments, which are onboard the geostationary Meteosat satellites series, stationed around 0° latitude and longitude. The SEAO is an important area to study the impact of smoke on clouds, because the semi-permanent stratocumulus cloud decks over the dark ocean effectively reflect solar radiation and have a strong cooling effect on the Earth climate system. During the annual dry seasons in Africa, smoke from vegetation fires is advected over the clouds over the SEAO and mixes with them, making this region a natural laboratory to study aerosol-cloud-radiation interactions. In this paper, the biases in the COT and CER are quantified for typical smoke over cloud scenes, using an adaptation of the traditional cloud retrieval algorithms, including an extra channel in the usual bispectral minimisation procedure. This allows the simultaneous retrieval of COT, CER and above-cloud aerosol optical thickness (ACA AOT), which has been shown in several papers. The impact of smoke on the SEVIRI retrievals was investigated during July – October 2017 for a small region of the SEAO in the satellite field of view. The SEVIRI COT and CER were increased by a factor of 1.9 and 1.4, respectively, as a result of accounting for overlying smoke during two days with an average ACA AOT (550 nm) of 0.82. A similar event in 2025 during one day saw an increase in FCI COT and CER of 2.2 and 1.3, respectively, for an average ACA AOT of 0.95. Furthermore, the distribution of COT changed significantly when accounting for the overlying aerosols, showing a much wider distribution of COT values.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-4481', Anonymous Referee #1, 07 Sep 2026
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RC2: 'Comment on egusphere-2026-4481', Anonymous Referee #2, 06 Oct 2026
Manuscript title: Correction for above-clouds aerosols in geostationary satellite cloud property retrievals
Authors: Martin de Graaf et al.
Dear authors,
Thank you for the opportunity to review your manuscript. The manuscript addresses an important and relevant topic concerning the impact of above-cloud aerosols on cloud property retrievals. However, I believe that substantial revisions are needed before the manuscript can be considered for publication. I therefore recommend major revision. My comments below provide further details and suggestions that I hope will help strengthen the manuscript.
General Comments
The manuscript addresses an important issue, since cloud property retrievals can indeed be significantly affected by the presence of aerosols above clouds. However, I have several concerns regarding the scope, motivation, generality, and maturity of the study that should be addressed.
First, it is not clear to me why the problem is presented specifically in the context of geostationary (GEO) satellite observations. Do the authors consider that similar aerosol-related biases may also affect cloud property retrievals from polar-orbiting satellites? If so, it would be useful to clarify what is specific to GEO observations and why the proposed correction is particularly relevant for GEO missions. If the focus on GEO satellites is primarily related to the characteristics of the SEVIRI and FCI instruments, this should be explicitly explained and motivated.
Abstract
The abstract needs substantial revision. In particular, the terminology used to describe the aerosol type appears too restrictive. The authors refer mainly to smoke, although other aerosol types, such as mineral dust, can also occur above clouds and potentially affect the retrieved cloud properties. The authors themselves acknowledge this issue at several points in the manuscript (e.g. lines 189–191). The scope of the proposed method should therefore be stated more clearly: is it specifically designed for absorbing biomass-burning smoke, or is it intended to be applicable to above-cloud aerosols more generally?
More generally, some statements in the abstract are overly technical and do not clearly communicate the scientific motivation, the main methodological contribution, and the significance of the results. The abstract should be revised to emphasize the main scientific findings rather than the technical details of the implementation.
Title
I also find the current title somewhat too broad and not fully representative of the specific contribution of the manuscript. Since the main focus appears to be the correction or estimation of biases in retrieved cloud properties in the presence of absorbing smoke aerosols, I would suggest a title that reflects this more explicitly. For example:
“Bias correction of cloud properties in the presence of absorbing smoke aerosols: application to SEVIRI and FCI over the Southeast Atlantic Ocean”
This would make the specific scientific contribution and the application domain clearer to the reader.
Scope and presentation of the manuscript
In several places, the manuscript reads more like a technical documentation or an Algorithm Theoretical Basis Document (ATBD) than a scientific research paper. This is particularly noticeable in the detailed descriptions of the SEVIRI/FCI implementation and retrieval procedure. I would encourage the authors to reduce some of the implementation-specific technical details and place more emphasis on the scientific aspects and the general conclusions that can be drawn from the study.
At present, the manuscript sometimes gives the impression of being written as technical documentation for an operational retrieval algorithm, potentially for EUMETSAT applications, rather than as a scientific study. If this is indeed the intended purpose, this should be made clearer; otherwise, the manuscript would benefit from a stronger effort to generalize the findings beyond the specific SEVIRI and FCI implementations.
Number of case studies and robustness of the conclusions
A major concern is the relatively limited number of cases used to demonstrate and quantify the impact of above-cloud smoke aerosols. The analysis appears to rely on only two SEVIRI cases (28 August and 6 September 2017) and one FCI case (14 August). It is therefore difficult to assess whether the reported biases in cloud optical thickness (COT) and cloud effective radius (CER) are representative of the broader range of conditions encountered in the Southeast Atlantic.
The authors should provide a stronger justification for the selection of these cases and discuss the extent to which the conclusions can be generalized. In particular, how can the reader be confident that the reported COT and CER biases are representative rather than being strongly dependent on the specific aerosol and cloud conditions of these individual cases? If additional cases are not available, the limitations associated with the small sample size should be discussed much more explicitly.
Choice of study region
The choice of the Southeast Atlantic Ocean (SEAO) as the study region should also be better motivated. The SEAO is indeed an important region for studying aerosol–cloud interactions and above-cloud smoke, and it has been extensively investigated in the context of aerosol–cloud interaction research. However, it is not clear whether the choice of the region is primarily motivated by its scientific relevance, by the availability of suitable smoke cases, or by the characteristics and coverage of the SEVIRI and FCI instruments.
The authors should clarify why the proposed correction is evaluated only over the SEAO and discuss whether the methodology is expected to be applicable to other regions and aerosol regimes. This is particularly important given that the manuscript currently makes statements that could be interpreted as having broader applicability than is demonstrated by the presented cases.
General methodological concern: A recurring issue throughout the manuscript is that the correction appears to depend strongly on the assumed aerosol type and aerosol optical/microphysical properties, while the observations themselves do not always appear to provide an independent verification of these assumptions. This raises an important question regarding the robustness and transferability of the proposed correction. I encourage the authors to quantify the sensitivity of the retrieved COT and CER corrections to the assumed aerosol properties and to discuss more explicitly the conditions under which the proposed approach can be considered reliable.
Overall, I think that the manuscript addresses a relevant problem, but its scope and scientific contribution need to be presented more clearly. In particular, the abstract, motivation, generality of the method, and strength of the conclusions should be substantially improved. At present, the manuscript remains quite technically focused and the limited number of demonstration cases makes it difficult to assess the robustness and broader applicability of the proposed correction.
Specific Comments
- Line 7: “During the annual dry seasons in Africa” — Could the authors also specify the relevant months? This would make the statement more informative and precise.
- Lines 8: “mixes with them” — I am not sure that “mixes with them” is the most appropriate terminology here. Several physical processes can affect the interaction between aerosols and clouds. I suggest that the authors briefly discuss these processes and provide appropriate references. Explicitly identifying the relevant processes would also help to better establish the scientific motivation and broader applicability of this study.
- Lines 10–11: “...an extra channel in the usual bi-spectral minimization procedure” — I recommend moving the description of the retrieval procedure to the main body of the manuscript rather than introducing it in the abstract. I would also avoid the word “usual”, since it is not necessarily clear that all readers will be familiar with this particular bi-spectral minimization procedure.
- Line 12: “several papers” — Please provide the relevant references. As currently written, this general statement does not add much information.
- Line 15: “A similar event in 2025 during one day saw an increase in FCI COT and CER of 2.2 and 1.3 respectively for an average ACA AOT of 0.95.” — This sentence is difficult to understand. Please rephrase it and clarify whether 2.2 and 1.3 refer to absolute or relative changes in COT and CER.
- Line 16–17: “Furthermore, the distribution of COT changed significantly when accounting for the overlying aerosols, showing a much wider distribution of COT values.” — I do not find this statement sufficiently informative in its current form. Could the authors provide a quantitative assessment of the change in the COT distribution rather than making a general statement?
- Lines 21–22: “The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) …” — This paragraph currently reads somewhat like an institutional or product description, particularly in relation to EUMETSAT CM SAF. I suggest rephrasing it to focus on the scientific relevance rather than on the EUMETSAT product itself. Otherwise, the manuscript risks reading more like technical documentation for EUMETSAT operations.
- Line 26: Abbreviations — According to the Copernicus manuscript guidelines, abbreviations should be defined in the abstract and then defined again at their first occurrence in the main text. Please therefore check that SEVIRI, FCI, and SEAO are defined again when they first appear in the main text.
- Line 28: “A prominent region in the geostationary satellites... SEAO” — This appears to repeat information already presented in the abstract. I suggest moving some of the relevant scientific motivation from the abstract into the Introduction rather than repeating it here.
- Line 35: “thickening during the night” — Does “thickening” refer to an increase in optical thickness or to an increase in geometrical cloud thickness? Please clarify.
- Line 36: “equator-ward” — Please use the standard spelling “equatorward” and check for consistency throughout the manuscript.
- Line 45: “Cloud Condensation Nuclei” — Please define the abbreviation CCN at its first occurrence and use the abbreviation thereafter.
- Line 57: “a bi-spectral” — Please consider replacing “the common bi-spectral...”.
- Line 58: “well-known” — I suggest removing this expression or replacing it with a more specific statement supported by an appropriate reference.
- Line 66: “which is well documented” — I suggest rephrasing this and providing specific references rather than describing the phenomenon as “well documented”.
- Line 81: I suggest removing “in the ultra-violet (UV) and VIS” from this sentence and introducing this information in a separate sentence for better readability.
- Line 87: Why is “probabilistic” given in brackets? Do the authors mean that the cloud mask is binary? Please clarify.
- Line 90: I suggest rephrasing this sentence as: “In the CLAAS-3 retrievals, the COT is retrieved at 0.64 μm, while the CER is retrieved at 1.6 or 3.9 μm.”
- Line 91: “Note that the effect of aerosol absorption may be smaller for the 3.9 μm channel.” — Please either provide some quantitative assessment of the aerosol absorption effect at 3.9 μm or remove this statement.
- Line 92 / Figure 1: It is not clear what specific information the reader is expected to obtain from Figure 1 in relation to the preceding text. The figure appears to show maps of COT and CER over a relatively large spatial domain over the SEAO. Please clarify the purpose of the figure and its connection to the discussion.
- Line 95: Please remove the sentence “In the next edition ... will be added.” This is more appropriate for product documentation than for a scientific manuscript.
- Lines 101–104: If AOT = 0, the simulation represents an aerosol-free atmosphere, while COT = 0 corresponds to a cloud-free atmosphere. Since the objective is to investigate aerosol layers above cloud layers, why are these scenarios included in the simulations? Please explain their purpose.
- Line 101: The wavelength is given as 1.63 μm here, whereas 1.6 μm was used previously. Is there a specific reason for using different levels of precision? A similar inconsistency occurs in Figure 2, where the wavelength is sometimes given as 0.82 μm and sometimes as 0.824 μm. Please use a consistent precision throughout the manuscript.
- Line 103: Can the lower bound of COT really be 0? If COT is subsequently transformed logarithmically, how is the zero value treated?
- Line 104: Why was 256 selected as the upper bound for COT? Please provide a justification for this choice.
- Figure 2: Why is this particular viewing/illumination geometry selected for the simulation? Is there a specific reason why this geometry is representative or particularly relevant?
- Figure 2: There is also a typo in “cloud droplet properties”.
- Lines 102–104: “One kilometer thick clouds were placed between 0–1 km altitude, while one kilometer thick aerosol layer was present between 2–3 km altitude.” — I find this configuration difficult to understand physically. Does this mean that a cloud layer is always present from the surface to 1 km, followed by an aerosol-free layer between 1 and 2 km, and an aerosol layer between 2 and 3 km? Please explain the rationale for this particular vertical configuration and how it was selected.
- Lines 121–122: “The spectrally broad SEVIRI channels include ... is possible” — This sentence is difficult to follow and should be rephrased for clarity.
- Table 1: How were the aerosol microphysical properties listed in Table 1 selected? Are these values derived from airborne in-situ campaigns? The authors mention that the aerosol layer above clouds may also consist of dust or pollution rather than smoke. Would it therefore be more appropriate to investigate a range of aerosol microphysical and optical properties representing different aerosol types?
- Table 1: What is the physical meaning of a cloud with COT = 0 in this context? Furthermore, if the logarithm of COT is used in the retrieval/simulation, how is COT = 0 treated? Please explain the motivation for including this value.
- Lines 135–136: “The area is known to be severely affected by smoke from annual burning in Africa during the monsoonal dry season.” — This information is repeated several times throughout the manuscript. I suggest reducing the repetition and providing the relevant motivation once in the Introduction.
- Lines 142–144: “Near the coast and over the continent...” — What is the evidence that the observed aerosol is smoke rather than dust or another aerosol type? Can the authors provide supporting evidence from an independent aerosol product or sensor in addition to Terra/MODIS?
- Lines 147–149: “This is due to the correction for the light-absorption by the aerosols over the clouds...” — This explanation is currently not sufficiently convincing and the sentence is difficult to follow. Please explain more clearly the physical mechanism by which aerosol absorption modifies the measured radiance and how this translates into biases in the retrieved COT and CER.
- Line 156: “but this was not attempted here” — Please explain why this approach was not attempted in the present study.
- Line 161: “to Figure 13 in Peers et al. (2019)” — Do the authors mean Figure 13 of Peers et al. (2019)? Please clarify.
- Line 166: Please define the terms “glory” and “cloud bow” when they are first introduced, and briefly explain their physical origin and relevance to the analysis. In particular, please clarify how these features affect the observed radiances and/or the retrieved cloud properties in the context of this study.
- Line 171: “Both the COT and CER are higher when corrected for the overlying aerosols, as before.” — The increase in COT and CER following aerosol correction has already been emphasized several times. I suggest avoiding repetition here and instead focusing on the new information provided by this particular case.
- Lines 174–175: “Between 10:00 and 15:00 the COT is decreasing during the day, which is expected in this area, where clouds build up during the night and the overhead sun burns away the clouds during the day.” — This sentence is difficult to interpret physically and should be rephrased.
- Figure 5: Please ensure that the figure complies with the journal's requirements for a colour-blind-accessible colour palette.
- Lines 183–184: “The distribution for CER did not change much, but instead shifted to somewhat higher values.” — Where is the evidence for this statement? Please provide quantitative information about the shift observed.
- Lines 189–193: “The results from this case study...” — If the aerosol is not necessarily smoke but may also be dust or pollution, the assumed aerosol optical and microphysical properties in Table 1 may no longer be appropriate. This limitation should be explicitly discussed. In particular, different aerosol species have different absorption properties and would therefore produce different retrieval biases.
- Lines 192–193: Please remove “The results are illustrated in Figures 7–9.” This is unnecessary as the figures are already cited in the surrounding text.
- Line 199: Please clarify what is meant by “point cloud”.
- Lines 203–204: “...which is in the middle of the smoke plumes” — How is the presence and location of the smoke plume established? Please provide supporting evidence or an independent aerosol product.
- Figure 9: Would it be possible to combine panels (a) and (b) into a single figure? This could make the comparison easier for the reader.
- Lines 218–219: “The differences between the left and the right panels of Figure 9 point to retrieval uncertainties due to the assumed aerosol model and limitation in the RTM.” — This statement further supports the idea that the two panels should perhaps be combined into one figure, with the differences between the retrieval configurations discussed directly in the text.
- Glory/cloud bow: The terms “glory” and “cloud bow” appear to be used without sufficient explanation. Please define these features and explain their relevance to the retrieval analysis.
- Verification of smoke: More generally, how is the presence of smoke verified for the individual case studies? Since the aerosol type is a central assumption of the proposed correction, an independent verification of the aerosol type would significantly strengthen the analysis.
- Figure 10: This is a very useful comparison and, in my opinion, deserves a more detailed discussion in the text. I would also suggest showing the GOME-2B AAI in the lower panel and restricting the upper panel to the AOT information from ACA and CAMS. From the current figure, it appears that the GOME-2B AAI correlates reasonably well with the OMI-MODIS DRE. This relationship could be worth discussing further.
- Figure 10: Why are there differences between the yellow lines in the upper and lower panels? For example, there appears to be a peak around 20 July and another around mid-October in the upper panel that are not apparent in the lower panel. Please explain these differences.
- Figure 11: This figure is useful, but I suggest making it larger to improve readability.
- Figure 12: The maps are currently too small and difficult to interpret. Please consider increasing their size and enlarging the axis labels.
- Line 299: “In the FCI retrievals, the cloud height was set to a fixed height of 2 km.” — Please explain the basis for this assumption. Why is 2 km considered representative for the clouds investigated here, and how sensitive are the results to this assumption?
- Line 310: “Plane parallel LUT calculations” — Please discuss whether other radiative-transfer configurations could provide a better representation of the relevant cloud/aerosol geometry. In particular, is the plane-parallel assumption expected to introduce significant uncertainties for the cases investigated?
- Section 6: I suggest shortening the Discussion and Conclusions section. Some of the points currently presented there repeat results or interpretations already discussed in the preceding sections. The section would be stronger if it focused more directly on the main findings, limitations, and implications of the study.
- Line 351: “based on one aerosol type” — I have concerns about whether a single aerosol type is sufficient to support the conclusions of the study. Given that the manuscript itself acknowledges the possible presence of dust and pollution aerosols, the limitations associated with using a single aerosol model should be discussed more explicitly. Ideally, the sensitivity of the correction to different aerosol types should also be investigated.
References: Please carefully check and update the reference list. In particular:
- Benas et al. (2019): The cited paper is currently listed as an Atmospheric Measurement Techniques Discussions paper (“Atmos. Meas. Tech. Discuss., 2019”). Please check whether the paper has since been published in the final Atmospheric Measurement Techniques volume and update the reference accordingly, including the final bibliographic information and DOI.
- Bretherton and Wyant (1997): Please verify and correct the DOI formatting/link. The DOI currently appears to contain an incorrect or malformed link.
- Lenoble et al. (1982): Please verify and correct the DOI. The DOI is currently broken/incomplete in the reference list (“https://doi.org/10.1175/1520 0469…”).
- Nakajima and King (1990): Please verify and correct the DOI formatting/link. The current DOI appears to be malformed.
- Paluch and Lenschow (1991): Please verify and correct the DOI formatting/link.
- Randall et al. (1984): Please verify and correct the DOI formatting/link.
- Wyant et al. (1997): Please verify and correct the DOI formatting/link.
Citation: https://doi.org/10.5194/egusphere-2026-4481-RC2
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- 1
egusphere-2026-4481
Manuscript title: Correction for above-clouds aerosols in geostationary satellite cloud property retrievals
Authors: Martin de Graaf et al.
General Comments:
Authors, in the submitted paper, quantifies the biases in the retrieved cloud optical thickness (COT) and cloud effective radius (CER) produced by the carbonaceous smoke aerosols overlaying the low-level stratocumulus cloud deck in the southeastern Atlantic Ocean—a natural laboratory for studying effects of absorbing aerosols on clouds. Based on Peers et al (2019) above-cloud aerosol (ACA) algorithm and aerosol model, ACA aerosol optical thickness (AOT), aerosol-corrected COT and CRE were derived using the time-resolved, geostationary METEOSAT second generation (MSG) SEVIRI observations over the region during July-October 2017. When compared against the standard CLASS-3 COT retrieval dataset not corrected for aerosol effects, the ACA COT and CER retrievals demonstrated by the authors were found to be higher by factors of 1.9 and 1.4, respectively. An extension of the same technique to recent third generation sensor FCI for an event in 2025 for one day resulted an increase in FCI COT and CER of 2.2 and 1.3, respectively.
While the authors demonstrated the effects of aerosol attenuation, primarily which are primarily driven by the absorption effects, on the cloud retrievals, the submitted work miss citing and discussing similar analyses of aerosol effects on cloud retrievals in the UV (Jethva et al., 2018, 2024) and visible (Meyer et al. 2015) domains.
Authors are suggested to also present theoretical calculations, based upon the RT simulations/LUT, of biases in COT retrievals as a bifunction of ACA AOT and COT.
The OMI-OMAERUV Collection 4 and NASA TROPOMAER aerosol datasets also contain ACA retrievals globally. Bringing one of these datasets for the SEAO region in the present work for relative comparison would further elaborate the findings discussed in the paper.
The Absorbing Aerosol Index (AAI) datasets synchronized from TOMS, GOME-1, SCIAMACHY, OMI, GOME-2A, GOME-2B, and GOME-2C sensors has been shown in the paper (Figure 11). However, the details, at least at the preliminary level, of such synchronization that also accounts for wavelength and footprint differences, is missing.
Detailed comments on specific sections and text are included below with this review report.
Overall, the paper is well-written and organized, although the language can be improved. The theme of the presented work very well fit into the scope of the journal and also important for cloud remote sensing in the presence of absorbing aerosols. Given some additional analysis suggested here, the paper is recommended as “major revision”.
Thanks for this review opportunity.
Specific Comments:
Abstract:
Page 1
line 3: Not just smoke but dust over clouds too.
Line 4-5: “which are onboard the geostationary Meteosat satellites series, stationed around 0-degree latitude and longitude”. This sentence looks out of place.
Introduction:
Page 2, line 40: “During northern hemisphere monsoonal and dry season in southern Africa…”
Page 3, line 59: There are several papers quantified the effects of absorbing smoke layer over clouds in SEAO, such as, Jethva et al. (2013, 2018), Meyer et al. (2015), and few others. Citing a couple of these papers here would further strengthen this statement, since this is the center topic of the presented work.
Section 2.2
Page 4, line 94-95: this statement needs a reference.
Section 2.3
Page 4, lines 101-103: CALIOP-CALIPSO vertical curtain plots, over the southeastern Atlantic Ocean, often show aerosol layers residing between 2-4 km (in some instances, the aerosol layer also touch the cloud-top) and cloud layer between ~800-1500 meters.
Page 5, lines 129-131: This is the best way to quantify the errors in the cloud retrievals eliminating remaining biases between the CLASS-3 and CPP.
Table 1: Aerosol particle size distribution (PSD) parameters look reasonable, based on the in-land AERONET stations (Mongu, Mongu_Inn). However, assuming wavelength-independent imaginary part (0.029) of the refractive index, representative of black carbon dominated particles, seems to be inappropriate for smoke aerosols. AERONET ground inversions show spectral dependence of the imaginary part from 440 nm to 675 nm, with minimal spectral effects at longer wavelengths. What value of Extinction Angstrom Exponent, describing spectral dependence of AOT, was assumed in the simulations?
So, from Figure 2, it is interpreted that first ACA AOT is derived using 0.824 and 0.64 microns (left panel). In the next step, the derived ACA AOT was used to estimate the errors in the retrieved COT and CER caused by smoke aerosols. Please confirm.
Section 3.1
Figure 3. It is worthwhile to combine Figure 1 and Figure 3 to facilitate a direct comparison between the original CLASS-3 COT-CER retrievals with that retrieved after accounting for aerosols over clouds.
Section 3.2
Figure 4. Can author plot percent different plot of COT, i.e., (ACA-CPP)/CPP.
Figure 5. What could be the reason for a sharp increase in ACA AOT at higher solar zenith angle past 15:00 UTC? Is it due to the limitations in the RT simulations/look-up tables at extreme geometries? Does RT simulation correctly account for the sphericity of the atmosphere. This should be discussed here.
Figure 6: It is understood that ACA COT is retrieved at 0.84 microns and assumed to be spectrally neutral, which is a fair assumption in the VIS-NIR spectral domain. This assumption allows us to directly compare 0.84 microns ACA COT retrievals with CLASS-3 standard (no aerosol correction) COT dataset. The “apparent” COT retrieved without aerosols being accounted for, is a function of wavelength, since the aerosol attenuation led by absorption over clouds exhibits spectral dependence, i.e., higher absorption (hence lower COT) at shorter wavelength and lower absorption (hence relatively higher COT) at longer wavelength. I wonder if author can directly compare their “apparent” COT using retrieval domain presented in Figure 2 assuming AOT=0.0 and compare it with the true COT retrieved after aerosol correction. It is expected that the difference between ACA COT and non-corrected COT retrieved at 0.84 microns would be lower than that calculated at 0.64 microns. It is worth to add the suggested analysis in Figure 6.
Section 3.2.1
Page 10, lines 188-189: While the assumed imaginary part of the refractive index a representative value, it exhibits variability with the same month as well as seasonally (Eck et al., 2013).
Figure 7: the y-axis metric (CACA-CCPP)/(CACA+CCPP) is a bit hard to interpret. Instead, a simple percent change (CACA-CCPP)/CCPP) * 100 would be a straightforward way to understand the effects of AOD on COT derivation. Furthermore, the dependence of the COT correction as a function of aerosol absorption optical thickness (AAOT) can also be examined. AAOT can be calculated as the retrieved AOT times (1-assumed SSA).
Figure 7 demonstrates that the effects of absorbing smoke aerosols on COT retrievals do not strictly follow a linear relation. Instead, both are related in, what appear to be, quadratic way. Authors are encouraged to refer to similar analysis published in Figure 11 of Jethva et al. (2018) and Figure 11 Jethva et al. (2024), where the percent difference in COT, caused by aerosol absorption, was parameterized as a bifunction of abs. AOT and COT. For instance, for a fixed value of AAOT over clouds, the difference between corrected and non-corrected COT depends on the true COT underneath the aerosol layer. Authors can carry out similar analysis here to show the dual dependence of COT differences on ACA AOT and ACA COT.
Section 4
Line 234-235: AAI is also a strongly dependent on the spectral dependence of absorption.
Figure 10: Alternatively, these data can also be represented in running-mean fashion. Just a suggestion. A good agreement between ACA AOT and CAMS AOT dataset indicates that most part of the transported aerosol load in vertical column resided over the clouds. The continental biomass burning source region is elevated at about 1 km terrain height. Under the prevailing circulation, smoke particles are further carried over in westward direction and over semi-permanent cloud deck over the southeastern Atlantic Ocean.
OMI’s latest Collection 4 OMAERUV aerosol product now includes the pixel-level direct radiative effects for above-cloud aerosol scenes. The DRE is calculated by contrasting the TOA fluxes estimated for non-corrected and aerosol-corrected COT. Authors are encouraged to bring this new dataset in Figure 10 to see how it compares with the estimates from the OMI-MODIS technique that the first author has pioneered. The OMAERUV product can be freely accessible from NASA’s EarthData at https://www.earthdata.nasa.gov/data/catalog/ges-disc-omaeruv-004.
Section 4.2
The AAI has been calculated using a pair of wavelengths in the near-UV region. Different sensors mentioned here carry different pair of wavelengths, rendering the same AAI quantity but tailored to distinct wavelength pair. The caption of Figure 11 states that AAI from this series of UV-capable sensors was synchronized to account for wavelength and footprint differences. Does it mean that AAI dataset from these sensors were converted to a common, reference wavelength pair in the UV? A brief description on the AAI synchronization method is needed here.
Line 269-270: I would suggest rewording this statement, such as “In very bright scenes on the other hand, like over clouds or snow- or ice-covered surfaces, the scattering by aerosols is negligible compared to the reflected light from the underlying background surface. Under such scenario, the absorption of the background-reflected radiation by aerosols becomes very pronounced”.
Section 5
Figure 13: The results derived from FCI are mostly consistent to those obtained from SEVIRI. Stark differences in ACA COT between the near-noon and early morning-late afternoon retrievals are concerning. A 4- to 8-fold increase in COT seems unrealistic. The diurnal variation of cloud cover is well-captured from time-resolved SEVIRI and DSCOVR-EPIC (and now with drifting Terra-Aqua MODIS too) over the southeastern Atlantic Ocean. These observations show increased regional cloud fraction during the morning and evening hours relative to the near-noon timeframes. However, this may not translate into multifold increase in COT. Such unexpected COT behavior is likely caused by the limitation of plane-parallel RT simulations, which is noted earlier in the paper.
Meyer et al. (2015) noted small differences in the MODIS-based CER retrievals for above-cloud aerosols scenes in the same region. However, the MODIS CER retrievals used 2.1 microns instead of 1.6 microns of SEVIRI/FCI employed in this study. Author should include a discussion on these differences and the results of Meyer et al. (2015).
Section 6
Line 354: While it is true that 0.44 microns has stronger absorption effects than at 0.64 microns, the uncertainty in ACA retrievals arising from the aerosol model (SSA, AE, AAE, PSD) still remain.
Last paragraph: Similar to the OMI-OMAERUV Collection 4 retrieval dataset, NASA TROPOMAER UV aerosol algorithm also produces above-cloud aerosol retrievals, including ACA AOT, ACA COT, and apparent (non-aerosol corrected) COT at 388 nm. Authors are encouraged to look at and use this dataset for their future intended study.
One last comment, which should have been added earlier in this review report: In addition to comparing the ACA COT retrievals against CLASS-3 non-corrected COT dataset, authors should also conduct theoretical estimates of the difference in COT retrievals (i.e., aerosol corrected minus non-corrected) using RT calculations or LUT shown in Figure 2. The suggested analysis can be carried out in two ways by treating 1) 0.84 microns on x-axis to retrieve COT, and 2) 0.64 microns observations on x-axis to retrieve COT. Both exercises should result in different errors in COT, with 0.64 microns COT bias is expected to be higher than that at 0.84 microns due to stronger absorption at former wavelength than at longer one. By performing such analysis, authors can establish the theoretical framework for estimating biases in COT as a function of AOT and underlying true COT, thereby adding further value to the paper.
Although the SEAO is a regional hotspot for observing and studying absorbing smoke aerosols over clouds, other regions, such as tropical Atlantic Ocean (Saharan dust outflow over clouds), Arabian Sea, Southeast Asia (agricultural burning smoke over thick clouds in southern China and adjacent ocean), and North American wildfire smoke transport in North Atlantic, also frequently encounter aerosol-cloud overlap situation on seasonal scale. The cloud remote sensing, and resultant aerosol-cloud interactions, are also important in these regions. Including a brief discussion of these regions will further increase the scientific value of the presented work.