We thank Reviewer #1 for his/her analysis and comments on the paper. The responses to major and minor comments are given below. We marked the reviewer’s and the author’s comments by “RC:” and “AC:”, respectively.

General comments

First of all, we want to admit that a simplistic conversion of scattering ratios provided in the first version of the manuscript appeared to be a source of confusion for the reviewers and we apologize for this. Moreover, the reviews helped us to recall that there are two definitions of scattering ratio itself and even though they both are aimed at estimating the contributions of particulate and molecular components to the backscattered radiation, they are not the same. In the present version, we added a section with all necessary definitions and conversion formulae. This section also appears to be helpful in the discussion of the potential discrepancy sources. The collocated dataset has been reprocessed and the new scattering ratios at 532nm have been calculated and analyzed. Despite changes in wavelength conversion methodology, the results and conclusions did not change much. But, we noted a certain improvement of the overall agreement between the ALADIN and CALIPSO datasets (e.g. see the numbers representing the normalized cloud detection agreement at different heights).

Major comments

RC: The title does not reflect the content of the paper. In fact, the authors focus only on the cloud detection capability based on scattering ratios.

AC: The present version of the article puts more stress on the scattering ratios profiles. In addition, we updated the title to “Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles”

RC: Furthermore, the whole instruction deals only with clouds and not a single word about scattering ratios is written

AC: We now have a whole new section dedicated to definitions, including those of scattering ratios

RC: The scattering ratio which is the essential part of this manuscript has never been properly defined. According to the reference which is given, I assume that, “the ratio between the total backscatter by particles and molecules and the molecular backscatter” (according to Flamant, 2008) is meant, i.e. the ratio between the total backscatter (represented by particles and molecules) to the molecular backscatter.

AC: We agree that the scattering ratio was not properly defined in the previous version. Please, see the general comments above. Indeed, the quoted definition is what is used in ALADIN product, but a different definition is used in the literature for CALIPSO scattering ratio (as CALIPSO is not a HSRL lidar contrarily to ALADIN). A more sophisticated processing is needed than what was provided in the initial version of the manuscript, to convert the scattering ratio from ALADIN to a scattering ratio similar to CALIOP. We believe that this time both the definitions and the conversion are OK.

RC: The conversion the authors use to account for the different wavelengths of CALIOP and AEOLUS is poor. For example, I have made a sketch using an arbitrary atmospheric molecular backscatter coefficient profile and a height-constant particle backscatter coefficient (equal at both wavelengths) of 7e-6m^-1 sr^-1 in order to obtain a scattering ratio at 532 nm shortly above 5 as given by the authors as detection threshold for clouds

AC: First of all, we’d like to thank the Reviewer #1 for his/her efforts to estimate the SRs and the applicability of thresholds. Second, we were not using the same definition of SR as the reviewer in the previous version of the manuscript. Please, read the Section 3 of the present version of the manuscript, which should clarify SR definition, the wavelength conversion and the cloud detection threshold.

RC: Despite all my own doubts concerning this conversion, the authors themselves state: “We would like to stress here that no linear scaling applied uniformly to SRs at all heights could change the ratio of high cloud detection frequency to low cloud detection frequency of ALADIN.” Therefore, I wonder: Why they are doing so?

AC: In the present version of the manuscript, we apply a proper conversion to SR’_532 and we discuss the potential sources of bias associated with the parameters of this conversion. We show that by adjusting the parameters of the conversion one can change the ratio between high- and low-level clouds, but there are physically defined limits for this “tweaking”.

RC: The choice of this threshold SR>5 is not clear to me and seems very arbitrary and without justification.

AC: First of all, we draw the Reviewers’ attention to the fact that the threshold is applied to “CALIOP-like” SR and not to “ALADIN-like” one (please, see Section 3 for the definitions). Second, the threshold SR>5 is used in CALIPSO-GOCCP product (Chepfer et al., 2008, 2013). It is derived from in depth analyses of the CALIPSO SNR in day time at vertical resolution 480m and horizontal resolution 330m, that has been defined within CFMIP for numerous scientific reasons. SR>5 is the threshold value that avoids false cloud detection in day-time due to low SNR induced by solar photons. Even though we used the nighttime cases for CALIOP, ALADIN’s observations are in the twilight zone, so we decided to keep this threshold and to apply it uniformly to both instruments at all latitudes and heights.

RC: What happens if this threshold changes?

AC: The impact of this threshold change is discussed in (Chepfer et al. 2013) for CALIPSO. As for the present manuscript, we discussed the redistribution of the YES_YES, YES_NO and NO_YES cases with respect to threshold value in lines 269-274 of the previous version and we updated this discussion in Section 5.3 of the present version. Briefly, a uniform increase or decrease of the threshold for both SR products will not change the ratio between the ALADIN and CALIOP clouds because both will decrease or increase simultaneously. At the same time, a technical adjustment of the threshold for ALADIN’s SR_532 could improve the agreement between the datasets, but there’s a tradeoff between the YES_YES and NO_YES cases: by increasing the threshold we reduce the number of unexplained (see the text) NO_YES cases, but we reduce the number of good YES_YES cases. By lowering the threshold, we reduce the number of YES_NO cases, but we increase the number of NO_YES cases, a part of which is already difficult to explain. Nevertheless, the new plot with zonal cloud fractions (Fig. 7) looks promising.

RC: The different vertical resolution for Aeolus and Calipso is not sufficiently discussed

AC: In Section 3.1 and 3.2 of the present version that correspond to Sections 2.1 and 2.2 of the original one, we provide the information about the sampling of the instruments and about the resolution of the products used in collocation. Moreover, we apply the same cloud detection thresholds, on both SR(z)_CALIOP and SR(z)_ALADIN at the same vertical and horizontal resolutions.

RC: Language and phrasing need to be improved. It is hardly understandable and not well explained. Please use simple sentences.

AC: The text has been simplified and proof-read by a professional. We hope that this has improved the readability of the article.

RC: Furthermore, “insider information of Aeolus” need to be explained otherwise it is not understandable for non-Aeolus experts.

AC: We have removed internal variable names from the text and rewritten some explanations related to Aeolus in Section 4.5.

Specific comments in addition to pdf

RC: Some statements are either simply wrong or wrongly phrased, e.g.: “…is characterized by lower sensitivity to high clouds above ~7 km than CALIOP, that we explain by lower SNR for ALADIN at these heights that is due both to physical reasons (smaller backscatter at 355 nm)”. Why should there be a smaller backscatter at 355 nm? This is in absolute contradiction to all my knowledge! The particle backscatter coefficient could be equal in clouds (Angström of 0), but the molecular backscatter coefficient is for sure higher (see plots) and thus the total backscatter is for sure also higher! Could you please comment?

AC: This statement is true and, indeed, the phrasing was misleading, we apologize for that. We meant the contribution of the particles to the total (particulate + molecular) signal. Even though the total backscatter is larger at 355nm, the particulate part can be buried in molecular return because the molecular backscatter is larger at 355nm while the backscatter from cloud particles is about the same. If the signal-to-noise ratio is small, then the cross-talk correction will be noisy and the particulate signal will be retrieved with large uncertainty. To avoid the confusion, in the present version of the manuscript we refer to the formalism defined in the second section and explain what we mean.

RC: Abstract: Just one of many examples: “(b) the cloud detection agreement is better for the lower layers. Above ~7 km, the ALADIN product demonstrates lower sensitivity because of lower backscatter at 355 nm” I do not understand this statement. First of all: What do you mean? The volume backscatter coefficient, the particle backscatter coefficient, the molecular backscatter coefficient? It is not clear! And I also do not know why any of these should be lower at 355 nm compared to 532 nm (and 1064 nm)

AC: We have rewritten the abstract for clarification.

RC: Abstract last sentence: Is not understandable. What values are this? What is a cloud detection agreement value? Abstracts should be self-explaining and understandable.

AC: Thank you for pointing this out. We have added the definition to the abstract. Please, see new Section 3.5 for the details.

RC: Not all references are in alphabetical order

AC: Fixed, thanks.

RC: Some mistakes in the names of the references, please check

AC: Fixed, thanks.

For the rest of the reviewer’s comments in PDF, please, see the attached file.

We thank Reviewer #2 for his/her analysis and comments on the paper. The responses to major and minor comments are given below. We marked the reviewer’s and the author’s comments by “RC:” and “AC:”, respectively.

Major  comments

RC: These findings are quite valuable to understand how to interpret both data sets and also valuable to construct longer time records than those obtained by lidar on a single satellite. There is a lack of clarifications in the current form of manuscript.

AC: We thank the Reviewer for pointing out the importance of the work for merging the different spaceborne datasets into one long-term record. As for the clarifications, we have added the definition of the Scattering Ratio, the necessary formalism to convert the scattering ratio from 532 to 355nm and the definition of the different variables (Sect. 3). We have also updated the figures and the corresponding text, and we have addressed all the comments of all the reviewers.

RC: Theoretical justification of using the simple SR conversion factor method between 355nm and 532nm in Equation (1) is not sufficient.

AC: We agree with this statement. We have added a section with all necessary definitions and conversion formulae. This section also appears to be helpful in the discussion of the potential sources of discrepancy between CALIPSO and ALADIN. The collocated dataset has been reprocessed and the conversion has been re-calculated and analyzed

RC: When model outputs are used, there is no need to rely on the conversion factor and the SR for 355nm and 532nm/1064nm can be estimated independently.

AC: This is true, but we do not used this conversion factor for the model+simulator part. We have re-written the simulation section and we added a flowchart to clarify the steps of this simulation experiment. 

RC: The choice in Equation (1) seems to be essential to the theoretical derived value (0.81) for cloud detection agreement between CALIOP and ALADIN. That is, the treatment of model output as well as cloud detection algorithm affect the estimation of the value of 0.81.

AC: Please, see the answer to the previous question. The theoretically estimate of the best achievable normalized cloud detection agreement (= value of 0.81, refined in this version) does not use Eq. 1. As we show in Fig. 4 of the new version of the manuscript, the value is mostly determined by difference in observation geometry and orbital parameters leading to non-ideal collocation.

RC: There are no descriptions about the output parameters for EAMv1 model used in this article.

AC: The outputs of the EAMv1 model are the usual standard inputs for COSP/lidar (e. g. Chepfer et al. 2008; Tang et al. 2019). But, we added several modifications to a standard model+COSP/lidar simulation for this study. Those are presented in the flowchart (Fig. 3) and described in Section 4: (a) subscale horizontal cloud variability; (b) instrumental noises for ALADIN and CALIOP; (c) diurnal variation of cloud fraction.

RC: The actual signals in the CALIOP and ALADIN contain the aerosols as well as clouds and molecules. Aerosol signals at 355nm might be larger than those at 532nm and it is naturally expected that the discrimination between clouds and aerosols is more challenging at ALADIN compared with CALIOP.

AC: First, we did not try to build the cloud detection scheme based on ALADIN-defined SR (see Eq. 2 in new version). As for the CALIOP-like defined SRs (new Fig. 5), the SRs from CALIOP are equal or larger than those estimated from ALADIN, so the cloud-aerosol discrimination problem mentioned in the question is not revealed.

RC: It is not clear how to incorporate the wavelength dependence of aerosols into the equation (1). It is not clear whether aerosols are contained in the EAMv1 model or not. There is no description about how multiple scattering effects for CALIOP and ALADIN are treated in the simulations in section 2.

AC: Again, the simulation experiment does not use Eq. 1. We apologize for a lack of clarity in the previous version of the manuscript regarding the simulations and we hope the new Sect. 4 is helpful. However, the question about multiple scattering is relevant and it is included into the present version of the manuscript in its new theoretical part (Sect. 2) as well as in the discussion of possible reasons for the discrepancy of low-level clouds.

RC: It seems to be possible to apply practically the same cloud detection algorithms used in the ALADIN L2A as well as CALIOP GOCCP products in the theoretical analyses in section 2. If one will do so, it would give a different cloud detection agreement of 0.77. The above-mentioned information is important to interpret the results in section 3 and conclusions.

AC: Since we did not convert the SRs for the simulation study (but only for the actual observations), we actually apply the same detection algorithms to the ALADIN an CALIPSO theoretical analyses. We agree that it was not well described in the previous of the manuscript, we hope the new Sect. 4 and the flowchart help.

RC: There are also lack of clarifications in the treatment of CALIOP clouds for the comparisons. It seems there is no sub-grid scale treatment for 87km-ALADIN L2A products so that 0 or 1 cloud fraction for each 87km-grid.

AC: First, the sub-grid treatment of ALADIN is a part of a Prototype v_3.10 algorithm from ESA, which is not available for the end user. The current end-user ALADIN dataset contains the backscatter and extinction profiles at 355nm that are standard for an HSRLidar (but not for non-HSRL like CALIOP). There’s no 0 or 1 in this ALADIN dataset nor does it define the cloud fraction itself. Therefore, we performed a conversion from ALADIN’s backscatter and extinction at 355 to SR’_532 and apply the uniformly defined cloud detection threshold on this SR’_532 profile (see Section 2 in the updated version of the manuscript). Second, we used high-resolution CALIOP data on 333m grid, averaged its AMB(z) and ATB(z) profiles at the same vertical and horizontal resolution as ALADIN and calculated SR_532(z). These procedures ensure that the two averaged profiles (SR’_532 derived from ALADIN and SR_532 derived from CALIOP) are comparable.

RC: On the other hands, CALIOP product has finer resolution (333m or 1km). It is not clear how to treat cloud fraction for CALIOP after 67km averaging for the comparisons compared with ALADIN in sections 2 and 3.

AC: We do not use the existing cloud fraction from CALIOP. As mentioned above, we averaged ATB and AMB(=ATBmol) over similar resolution as ALADIN and only then do compute SR and apply the cloud detection threshold. We are well aware of the fact that this might lead to an overestimation of cloud fraction in the boundary layer, but we perform this procedure to ensure the comparability of two datasets.

RC: Brief description of Aeolus L2A cloud product is also instructive.

AC: Such a product doesn’t exist (yet), we defined the cloudy or non-cloudy bins by applying the cloud detection threshold to SR_532(z) values.

RC: The SR for CALIOP was originally estimated to create CALIPOSO GOCCP products where Equation (1) is not needed. It is not convincing why equation (1) is used to simulate SR at 532nm.

AC: In the present version of the manuscript, we do not use Eq. 1 anymore. Instead, we use a more precise recalculation approach presented in Section 3. But, the idea of converting ALADIN’s 355 data to 532nm was to compare apples to apples and apply the same cloud detection threshold to the ‘same’ SR profile at the same spatial resolution.

RC: After reading the manuscript several times, any reasonable explanation was not found why the upper clouds are smaller for ALADIN compared with CALIOP, though CALIOP did not detect most of PSCs where ALADIN detected (in shown in the Figure 4a and f).

AC: Actually, we discussed PSC detection in lines 230-231, 301-303, and 374-376 of the previous version of the manuscript, but in the rest of the manuscript there was a confusing explanation regarding the particulate backscatter and we apologize for this. As we wrote in response to the Reviewer #1’s question, we meant the detection of the particles. Even though the total backscatter is larger at 355nm, the particulate part can be buried in molecular return. If the signal-to-noise ratio is small, then the cross-talk correction (used in High Spectral Resolution lidar) will be noisy and the particulate signal will be retrieved with large uncertainty. We do not know the details of the L2 algorithm computing SR, extinction and backscatter used in ALADIN products, but a common sense tells us that if the signal is noisy then there’s a high chance that the algorithm will reject it. Summarizing, our explanation of smaller ALADIN’s sensitivity to high clouds is linked with a combination of weaker-than-planned SNR and smaller particulate backscatter compared to molecular one.

RC: The authors attributed the lower sensitivity of high clouds for ALADIN to smaller backscatter at 355nm without conducting further analysis.

AC: Please, see the previous answer for the corrected explanation. The text of the manuscript has been also updated to avoid misunderstanding.

RC: More discussion of the discrepancies in the cloud detections are requested. It is also noted that it is well established that CALIOP has a good capability to detect PSCs so that Figure 4a is strange.

AC: Please, check the new version of Fig. 4 (now Fig. 5) where we show the SRs starting from SR=3. In Fig. 5, one can also see the PSCs detected by CALIOP with SR>5. Note that this threshold is not optimized for PSC that can be optically thin. And, last, but not least, Fig. 8a does contain the PSCs, but their frequency of occurrence is low.

RC: There are several CALIOP based global cloud products, including NASA Langley’s VFM products, GOCCP, DARDAR and KU cloud products and large differences were reported in (Cesana et al., 2016) JGR among GOCCP, NASA standard and KU products, indicating the different cloud detection methods caused the differences. There are several ways to bridge gaps between CALIOP and AEOLUS. Some comments are needed in this regard.

AC: The works mentioned by the reviewer are all using the same source that is L1 collected by CALIPSO. For comparing ALADIN and CALIPSO, the main challenges are because of the difference of nature of their L1 data: (1) ALADIN measures APB and AMB (and not ATB) because it is an HSRL, while CALIPSO measures ATB (and not APB and AMB) because it is a non-HSRL (See Eqs. in Sect. 3), (2) the wavelengths are different (355 nm vs 532nm), (3) the orbits and overpass times are different (see Sect. 2). We tried to state these points more clearly in the new version of the manuscript. 

Specific comments

RC: p.6 line 182-184, need clarification for the methods and typical values of noises for Aeolus and CALIOP in the target data sets.

AC: We have updated the methodological part (see new Section 3). As for the noise values, we estimated them from the upper part of the vertical profiles, which are cloud-free and contain only molecular return, which is supposed to be smooth. We added this information to the manuscript (Section 4.1)

RC: p.25 Figure 6, zonal mean cloud frequency for CALIOP and ALADIN would be preferable prior to Figures 6a-d.

AC: Thank you for this suggestion. We added the requested figure and the corresponding text. It is interesting to note that visually the cloud distributions for the compared instruments are much more alike than the SR distributions. But, cloud detection threshold for higher clouds is crossed reached less frequently for ALADIN than for CALIOP.

We thank Reviewer #3 for his/her analysis and comments on the paper. The responses to major and minor comments are given below. We marked the reviewer’s and the author’s comments by “RC:” and “AC:”, respectively.

Major  comments

RC: The authors should state clearly in the title that this study is dedicated to cloud products only.

AC: The present version of the article puts more stress on the absolute values of scattering ratios themselves. In addition, we updated the title to “Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles”

RC: The study should include a quantification to some extent, and discussion, on the percentage of the clouds not detected from the 2 lidars with the methodology used. Additionally, a discussion is needed on the effect of these cloud-miss-detections on the results of the intercomparison per altitude (low, mid, high-level clouds).

AC: If we understand this question correctly, it is related to the evaluation of clouds in the GCMs, and this question has been already addressed in (Chepher et al., 2008). For the current work, we are looking for similarities/differences in scattering ratio and cloud fraction profiles between the two lidar missions, if some clouds are filtered out in our approach, they are filtered out in the same way for both lidars.

RC: Although the title clearly states that this is a comparison of the scattering ratio products retrieved from the 2 systems, in the discussion throughout the paper the authors comments are attributed to the 2 systems only. It should be more clear that different approaches for cloud detection products from the 2 missions could lead to different results. See also specific comment below.

AC: We agree with the statement that different approaches for cloud detection products from the 2 missions could lead to different results. But, the idea of the paper was not to reconcile cloud product by “tweaking” the cloud detection algorithm, but rather to compare the fundamental differences. Therefore, here we used the same cloud detection for the two system. We agree that after having fully understood and quantify the differences due to the 2 systems (like we try to do here), the future work will include the algorithm adaptation to retrieve the same clouds and to build a long-term cloud record. We added the corresponding text in the conclusion as an interesting and exciting outlook.

Specific comments

RC: Page 1, line 22: “the ALADIN product demonstrates lower sensitivity because of lower backscatter at 355 nm”: This statement is not clear. The backscatter at 355 nm is not expected to be lower than at 532nm. Please explain and revise accordingly.

AC: This is an important comment made by all three reviewers. Indeed, there was a confusing explanation regarding the particulate backscatter and we apologize for this. As we wrote in response to the Reviewer #1’s question, we meant the contribution of the particles to the total (particulate + molecular) signal. Even though the total backscatter is larger at 355nm, the particulate part can be buried in molecular return because the molecular backscatter is larger at 355nm while the backscatter from cloud particles is about the same. If the signal-to-noise ratio is small, then the cross-talk correction will be noisy and the particulate signal will be retrieved with large uncertainty.

RC: Page 2, line 43: “Despite an excellent daily coverage and daytime/nighttime observation capability (Menzel et al., 2016; Stubenrauch et al., 2017), the height uncertainty of the cloud products retrieved from the observations performed by these spaceborne instruments is limited by the width of their channels’ contribution functions, which is on the order of hundreds of meters, and the vertical profile of the cloud cannot be retrieved with accuracy needed for climate feedback analysis.” The sentence is confusing. Consider revising to make it easier to follow. Possible suggestion: “…is limited by the width of their channels’ contribution functions (which is on the order of hundreds of meters), and their uncapability to retrieve the vertical profile of the cloud with accuracy needed for climate feedback analysis.

AC: Thank you for this suggestion, we have simplified the text of this paragraph.

RC: Page 2, line 47: “This drawback is eliminated by active sounders, the very nature of which is based on altitude-resolved detection of backscattered radiation, and the vertical profiles of the cloud parameters are available from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar (Winker et al., 2003)and CloudSat radar (Stephens et al., 2002) since 2006, CATS (Cloud-Aerosol Transport System) lidar on-board ISS provided measurements for over 33 months starting from the beginning of 2015(McGill et al., 2015).”: Too big sentence, difficult to read. Consider revising.

AC: We have simplified it, thanks.

RC: Page 4, line 106: “In Fig.1(a-c), we show the observation geometry and sampling of ALADIN’s L2A product as well as three variables retrieved from its observations..”: consider revising as: “…as three simulated variables that can be retrieved from its observations..”.

AC: Since other Reviewers found this plot difficult to understand, we have replaced it with a 3D view of the orbits and observation geometries. Correspondingly, the description of Fig. 1 has changed.

RC: Page 4, line 106: “In Fig.1(a-c), we show the observation geometry and sampling of ALADIN’s L2A product as well as three variables retrieved from its observations..”: consider revising as: “…as three simulated variables that can be retrieved from its observations..”.

AC: Thank you for the suggestion, but in the new version of the manuscript we have a different Fig. 1 with a somewhat different discussion.

RC:  Page 4, line 120: “The cloud variability along the satellite’s track has been estimated from the gridded EAMv1 data using the parameterization of (Boutle et al., 2014). Figure1 also serves as an illustration to theoretically achievable cloud detection agreement discussed below.”: Although the cloud variability is estimated, in the plot the scene is cloud free. As the paper mainly investigates clouds, it would be interesting to have a cloudy demonstration also in addition to Figure 1.

AC: Fig. 1 does not exist in its previous form anymore, but in any case, the scene was not cloud free. The horizontal structures with large ATB values corresponded to the clouds.

RC: Page 4, line 123: “…scattering ratio (SR)..”: Please write how the scattering ratio is calculated.

AC: This is a good point. In the new version of the manuscript, we have a whole new section (Sect. 3) dedicated to the definitions and formalism.

RC: Page 4, line 124: “An important companion of such a column is a corresponding quality flag column,…… which can be then compared with that of CALIOP.”: The description is vague, please write more clearly what filtering you used in the data.

AC: We have updated the text to “The important companions of these profiles are quality flag columns. For our analysis, we kept only the layers, which are marked either by a high Mie SNR flag or by high Rayleigh SNR flag, and by a flag indicating an absence of signal attenuation.”

RC: Page 5, line 141: “Since the CALIOP is not a HSRL, the detailed information on AMB and APB is not available, and one has to compare the SR products.”: One could also use the temperature and pressure profiles from NWP (provided with Aeolus & CALIPSO) to produce the particulate backscatter coefficient, and convert/compare these parameters. So this part should be revised to highlight the choice of this study and not state it as the only option.

AC: Thank you for this suggestion, that’s exactly how it’s done in the new version of the manuscript. There’s a small correction, though – the molecular backscatter coefficient is recalculated using P/T profiles, and not the particulate one.

RC: Page 5, line 145-150: “The choice of the fitting parameter is not crucial for the purposes of the present work … collocated data.”: I strongly advise the authors to follow the comment of the first reviewer regarding the wavelength conversions. Alternatively, if they decide to keep the analysis as is, then please provide a detailed discussion on the uncertainties induced from this simplified conversion.

AC: For the new version we have updated the wavelength conversions and we discuss the uncertainties associated with it.

RC: Page 6, line 167: ”To avoid the risks associated with the solar contamination, we picked up only the night-time cases”: As Aeolus is in dusk-dawn, still variability is expected in the PBL with the CALIPSO nighttime observations above land. Can you comment on that in the manuscript?

AC: This is a valid point and, indeed, the diurnal cycle can spoil the comparison. Our answer is in our Fig. 3 (now Fig. 4), which estimates the diurnal effects along with the geometric and sampling differences. In addition, we rebuilt our new Fig. 5 (SR-height histograms) and Fig. 7 (cloud fraction profile per latitude) for the daily data without temporal difference filtering (these versions are not shown in the manuscript). In this approach, the diurnal effects are compensated because both local times are used for both instruments. Still, the SR-height histograms (Fig. 5) and cloud fraction profiles (Fig. 7) plots look about the same for this enhanced dataset as they do for a subset used in the manuscript, so one can conclude that the diurnal effects cannot explain the observed behavior.

RC: Page 6, line 172: “…we have performed a numerical experiment using the same calculated data as we used in Fig.1”: Shouldn’t they be stated as “simulations”?

AC: This is correct, but now we have a different Fig. 1 and a new section dedicated to the simulations, so this phrase does not exist anymore.

RC: Page 6, line 173 – 180: “This time… the passive observations”: It is very hard to follow the approach. A scheme/flowchart would be useful

AC:  We added a flowchart and we simplified the text, thanks for the suggestion.

RC: Page 6, line 182: “Overall, we considered about 1E5 pairs of pseudo-collocated data and we present the results of cloud detection in Fig.3”: Please include also the region and season(s) used to produce these pseudo-collocated data, which represent the outputs of Figure 3.

AC: We have updated the text of the paragraph and added a flowchart (Fig. 3). Briefly, we used 15 simulated orbits of one day in autumn equinox that cover both hemispheres and give, therefore, a representative snapshot of various atmospheric scenarios.

RC: Page 6, line 184: “or each altitude bin, the cloud detection agreement is a ratio of a number of cases when both instruments have detected a cloud (SR>5) ….”: Please elaborate this choice of cloud cut off (e.g. literature) and comment on the uncertainties on the cloud detection induced from this choice for different altitudes. Could you include in results (Figure 3) and discuss, the percentage of the clouds missed to be detected, from the 2 sensors in your simulation, with the presented methodology?

AC: As for the choice of cutoff, we’d like first to refer to our answers to Reviewer #1’s questions and to the two definitions of SR existing in the community. Indeed, a threshold applied to the SR defined as in Eq. 2 of present version of the manuscript should be altitude-dependent. But, as it is shown in (Chepfer et al., 2008, 2013) a fixed threshold can be applied to a SR defined as in Eq. 3 of the manuscript to estimate the difference between the two lidars. Future work will include a more advanced cloud detection algorithm to build a long-term cloud record. But this will be a whole new study.

RC: Page 7, section 3.1. It should be stated clearly in the section that the discussion refers to the SR retrieved products used in this study from the 2 sensors. As for example, a study with the cloud statistics from the Atlid L2A and CALIPSO L2 backscatter coefficient product products may provide different results.

AC: This is true, we hope that the new title clarifies that point.

RC: Page 8, line 224: “In Appendix A, we demonstrate the correlation between individual pairs of CALIOP and ALADIN SR profiles; the conclusion of this exercise is that it justifies using Eq.1, but the uncertainties of the analysis do not allow to refine the conversion coefficients”. This statement is very strong. One could refine the conversion coefficients, independently of the uncertainties of the analysis. I support that the authors should formulate this statement to correctly reflect the choices and limitations.

AC: In the new version of the manuscript, we do not use Eq. 1 and we do not want to retrieve or validate its parameters anymore, so we do not seek to rebuild this plot.

RC: Page 8, line 229: “This observation gives a hint that the instrumental part provides the backscatter information sufficient for some cloud detection up to 20km, but the detection algorithm suppresses noisy solutions.” This sentence is not clear. Please improve the phrasing.

AC: We added some explanations after this sentence.

RC: Page 8, line 246: “Below, we will also discuss the YES_YES statistics normalized to cloud amount, but at this point we also want to study the other cases, which cannot be normalized this way” Consider to improve the phrasing.

AC: We have rewritten this section.

RC: Page 9, line 283: “This exercise is not aimed at revealing any altitude offset in backscatter signal registration, because this part of experimental setup is robust in both instruments”. Consider improving the phrasing.

AC: We have changed it to “We note that we are not looking for an altitude offset here. The altitude detection of both instruments is beyond question. Instead, we would like to check …”

RC: Page 9, line 10: “For each local peak found, we have searched for a peak or for a maximal value of CALIOP’s SR profile in the vicinity of ±3km from the peak height determined from ALADIN”. Consider including the information that only the 82% of the clouds are used for this comparison (according to the statistics presented in line 296-297.

AC: We added the proposed information in the following form: “By imposing the ±3 km search criteria, we filter out about 12% of the cases linked to natural variability, but at the same time we lower the rate of picking up the peak from a different cloud layer.”

RC: Page 9, line 304: “As for the clouds between ~3km and ~10km height, the height sensitivity effects skew the effective cloud height detected by ALADIN downwards by 0.5−1.0km”, It is not clear which are the high sensitivity effects between 3 to 10 km. Maybe the authors could summarize them in a sentence again here. Also, please comment to what extent could the actual 100-km-cloud-variability at these altitudes be responsible for the deviation in the altitudes seen by Aladin and Caliop in these altitudes. It is not clear if the authors point out on the Aeolus capability to detect the top of the cloud, on the SR methodology capability for the same, or on the effect of the natural variability between the 2 instruments on their products.

AC: We have updated the figure due to an improved recalculation of SR. The text has been updated, correspondingly. As for the possibility of 100km variability to be responsible for the observed shift, it is unlikely. The very nature of this variability is random and we do not expect it to have a bias. Moreover, the figure does not change that much if we loosen the collocation criteria thus adding even more random variability.

RC: Figure 1: “…ALADIN’s observation paths for centers of averaged profiles …”: How they are averaged? In Aladin L2A resolution?

AC: We have a new version of Fig. 1 and the caption is now different, too.

RC: Figure 1: “ This inclination is schematically shown as an inclined line lying in lidar curtain plane whereas the real projection to the same plane should be a vertical line”: This part is hard to understand. Same comment for the part inside the manuscript.

AC: This figure has been replaced with a 3D view and the text has been modified correspondingly.

RC: Figure 2: Can the authors comment on the absence of collocated points between 0-60° lon at Δtime < 6hrs?

AC: This is a good point. The problem is purely technical: in this part, the data at 6 h difference come from another day and our collocation used the same day files. The collocation procedure is already heavy enough on resources, so we opted out of reading the other day’s files. Technically, this is possible, but practically we would get only ~10% more of the collocated cases in the geographic area, which is not crucial for the comparison.

RC: Figure 7: No data is difficult to be distinguished from the -2km color, both have dark purple. Consider changing the no data color.

AC: We have changed the no data color.

RC: Figure 9: Consider adding the colorbar here also in the upper panel. Additionally, consider stating what the error bars account for.

AC: We have merged old Fig. 8 and Fig. 9 to a new Fig. 10. Correspondingly, all color panels share now the same color bar. As for the error bars, they correspond to r.m.s. of 1-week chunks of analyzed altitude subsets.

RC: Figure A1: The red points are not scaled in the same frequency ranges as the occurrence

frequencies. Wouldn’t that be better?

AC: This figure was removed from the new version of the manuscript.

Technical corrections

RC: Page 4, line 101: “According to Flamant et al. (2017).”

AC: Fixed.

RC: Page 6, line 182: “Ansmann et al. (2007)”

AC: We do not quote this work in this context anymore. Please, see the next-to-last answer to the Reviewer #1 comments.

RC: Page 7, line 195: “…between the two products..”

AC: This sentence has been rewritten

RC: Page 7, line 200: “..for the thw instruments”

AC: Fixed

RC: Page 7, line 203: “Analyzing the Fig. 4”

AC: Fixed

RC: Page 8, line 242: consider rephrasing to “from the sensitivity study..”

AC: This part has been rewritten

RC: Page 8, line 237: consider rephrasing to “..behavior of the SR cloud detection product agreement”

AC: We have updated the phrasing here.

RC: Figure 3: “...to the total number of simulations ..”

AC: The whole caption of Fig. 3 (now Fig. 4) is different in the new version

RC: Figure 7: “...+-3km vertical vicinity…

AC: Fixed, thanks.