1. We thank the reviewer for the constructive comments on the clarity and presentation of the abstract. All the points raised have been carefully considered and incorporated into a revised version of the abstract.

•Regarding the description of how the enhanced dust product is refined, the text has been rewritten to more clearly convey the methodology. “The L2A+ product is generated through a series of processing steps that integrate multi-sensor satellite retrievals for cloud screening and aerosol layer characterization, CAMS reanalysis outputs to classify aerosol types, distinguish dust from non-dust fractions, and provide the missing depolarization ratio values required for the Polarization Lidar

Photometer Networking (POLIPHON) technique, along with ground-based lidar measurements used for performance assessment.” This revision directly addresses the reviewer’s concern about clarifying how the dust product is improved.

•The sentence detailing the retrieval algorithms has been rephrased for clarity and conciseness. It now reads: “Both the primary (L2A) and enhanced (L2A+) Aeolus pure-dust backscatter coefficient profiles at 355 nm are retrieved using four different algorithms: the Standard Correct Algorithm (SCA), the Standard Correct Algorithm with middle-bin vertical scaling (SCA-MB), the Maximum Likelihood Estimation (MLE), and AEL-PRO.”

•To avoid any misunderstanding, we have clarified in the beginning of the abstract that the absence of cross-polarization measurements in ALADIN is that it was originally designed as a wind lidar without depolarization measurement capability. Therefore, it detects only the co-polar component of the backscattered signal, limiting the accuracy of optical products in the presence of depolarizing atmospheric layers.

We believe that the above-mentioned revisions address the reviewer’s concerns and improve the clarity, accuracy, and readability of the abstract.

2. We thank the reviewer for this valuable comment and for highlighting the need for greater clarity and dimensional consistency in Equation 1. The section has been revised accordingly. The variable Cₚₘ₁₀ is now explicitly defined as the total aerosol mass concentration (μg/m³), while Xₚ denotes the mass mixing ratio (kg/kg) of aerosol type p (SS₁–₃ for sea salt, DD₁–₃ for dust, OM for organic matter, BC for black carbon, and SU for sulfate). The term inside the parentheses, (pₘ / (Rspec × T)), has been clarified to represent the air density (kg/m³), derived from the ideal gas law. Multiplying the aerosol mass mixing ratio by this term yields the mass concentration in kg/m³, which is then converted to μg/m³. This correction ensures full dimensional consistency and provides a physically sound description of the conversion process. The notation has also been standardized using Xₚ to denote the mixing ratio of each aerosol type, in accordance with the reviewer’s suggestion.

3. We thank the reviewer for this constructive and detailed comment. In the revised version of the manuscript, Section 3.3 (“Quantifying the Aeolus pure-dust component through reconstruction of its missing cross-polar term”) has been substantially rewritten to clarify the aerosol typing methodology and to replace the previous threshold-based approach.

In the updated framework, Aeolus dust identification is no longer based on fixed concentration or fraction thresholds (e.g., 1.3 μg/m³ or 50%), but instead relies on a physically consistent, POLIPHON-based methodology. The one-step POLIPHON technique (Pappalardo et al., 2014) allows the decomposition of the total aerosol load into pure-dust and non-dust components by exploiting particle depolarization ratios. Since Aeolus ALADIN does not provide depolarization measurements, CAMS reanalysis products were integrated into the L2A+ workflow to (i) classify aerosols along the Aeolus overpasses, (ii) distinguish dust from non-dust fractions, and (iii) supply the missing depolarization ratio values needed to apply the POLIPHON method.

A detailed spatiotemporal collocation between Aeolus and CAMS was performed using nearest-neighbor matching, with CAMS outputs selected within ±3 hours of each Aeolus observation. For each Aeolus BRC bin, CAMS aerosol mass concentration profiles were averaged over the corresponding vertical layer. These CAMS-derived parameters, dust mass concentration and dust fraction, were used to qualitatively identify the atmospheric layers dominated by dust. Over these layers, the missing Aeolus cross-polar backscatter component was reconstructed using Eq. (4): 𝛽_totalcross=𝛿𝑐𝑖𝑟𝑐₃₅₅×𝛽_totalco

where 𝛽_totalcross is the missing cross-polar contribution from dust and non-dust aerosols, and 𝛽_totalcois the directly observed Aeolus co-polar backscatter. The particle depolarization ratio 𝛿𝑐𝑖𝑟𝑐355is derived from CAMS-based linear depolarization ratios through the POLIPHON relation (Eqs. 2–3), incorporating theoretical values of δ₃₅₅,d = 0.244 and δ₃₅₅,nd = 0.03 (converted to circular values 0.65 and 0.06, respectively).

This new formulation allows the reconstruction of Aeolus’s missing cross-polar term, enabling the retrieval of complete (L2A+) pure-dust and total backscatter coefficients. It also ensures that the aerosol typing is based on physically meaningful parameters, namely, particle depolarization ratio and backscatter properties, rather than on empirically defined thresholds.

Finally, to clarify the reviewer’s concern, the “median value of the entire dust mass concentration” refered explicitly to the median over all Aeolus BRC bins within the identified dust layers, providing a consistent measure of dust intensity across the profile.

4. In the revised version of the manuscript, Section 3.3 (“Quantifying the Aeolus pure-dust component through reconstruction of its missing cross-polar term”) has been expanded to clarify this point.

To produce the L2A+ dust product, the reconstruction of the missing Aeolus cross-polar component is based primarily on CAMS-derived depolarization ratio values, rather than directly on the dust fraction. Specifically, CAMS data are used to estimate the particle depolarization ratio through the one-step POLIPHON methodology, which enables the separation of dust and non-dust components in a physically consistent way. This approach allows the derived depolarization ratio to implicitly reflect the degree of dust dominance within each layer, without applying an explicit dust-fraction weighting. Therefore, even in cases where the CAMS dust fraction is below 100 %, the retrieved depolarization ratio, and consequently the reconstructed Aeolus cross-polar term, accounts for the partial presence of non-dust aerosols within the mixture.

Regarding the reviewer’s suggestion on using lidar ratios, we fully agree that this parameter provides valuable information for aerosol typing. Although the present study focuses on reconstructing the Aeolus cross-polar signal and evaluating the improvement in backscatter retrievals, the extension of this framework to include lidar-ratio-based analysis and comparison with reference values from EARLINET datasets is foreseen as part of our future work.

Overall, the revised methodology ensures that mixed aerosol conditions are treated consistently through the CAMS-based depolarization ratio retrieval, while establishing a robust foundation for future inclusion of additional constraints such as the lidar ratio.

5. We appreciate the reviewer’s suggestion. The figures have been revised using thinner, less bold lines for the uncertainty ranges, improving clarity and allowing individual curves to be more easily distinguished.

1. We thank the reviewer for this comment. When averaging AEL-PRO measurements per BRC profile, a strict cloud-filtering procedure was applied. Specifically, any BRC containing at least one cloud-contaminated measurement, as identified by both AEL-FM and SEVIRI (MSG), was entirely excluded from the averaging. Furthermore, for the AEL-PRO algorithm, the retrieved cloud-free co-polar backscatter profiles were additionally filtered using the AEL-PRO classification product, retaining only BRC bins classified as tropospheric aerosols (index = 103). As a result, the averaged BRC profiles include exclusively cloud-free aerosol measurements, ensuring that localized clouds or contaminated features do not affect the derived statistics.

2. In our analysis, we applied a combined cloud-screening approach using both the Aeolus Feature Mask (AEL-FM) and the SEVIRI CLAAS-3 cloud-mask product to ensure more robust identification and removal of cloud-contaminated profiles. Although the L2A Baseline 16 processor includes a cloud mask at the BRC level, we chose this combined approach to benefit from the higher horizontal resolution (~3 km) of the AEL-FM product and the complementary cloud information from SEVIRI (~4 km, 15 min temporal resolution). The AEL-FM algorithm, included in the Baseline 16 processor used in this study, provides a feature detection probability index capable of distinguishing between clear-sky, optically thick clouds, and fully attenuated signals, allowing finer discrimination of small-scale cloud structures. The SEVIRI CLAAS-3 binary cloud mask, on the other hand, offers continuous and independent geostationary cloud observations, enabling cross-verification of cloudy scenes detected by Aeolus.

By combining these two datasets, we ensured a more conservative and reliable cloud screening, minimizing the risk of residual cloud contamination in the Aeolus optical profiles used for aerosol analysis.

3. In the earlier version of the manuscript, the identification of dust layers relied on CAMS dust mass concentration (median threshold: 1.3 μg/m³) and dust fraction (50%) to classify Aeolus BRC bins as dust-dominated. However, these thresholds do not necessarily correspond to fully pure-dust layers, as the classified bins may still contain a mixture of dust and non-dust aerosols. This partially explains why the increase in the L2A+ total backscatter in Figure 10 did not appear as strong as the theoretical ~33% underestimation factor expected for purely depolarizing dust.

In the revised version of the study, we implemented an improved, physics-based aerosol typing scheme using the one-step POLIPHON methodology. Through this approach, CAMS reanalysis data were used to estimate the particle depolarization ratio, enabling the physical separation of dust and non-dust components in the aerosol mixture. This method inherently reflects the degree of dust dominance within each layer, without requiring an explicit dust-fraction weighting. As a result, even for partially mixed layers, the retrieved depolarization ratio, and therefore the reconstructed Aeolus cross-polar contribution, appropriately accounts for the presence of non-dust aerosols, leading to a more realistic enhancement of the total backscatter signal.

Additionally, following the reviewer’s suggestion, we have modified the color scale in Figure 11 (previously Figure 10) to a non-linear scheme, which enhances visual contrast and makes the increase in total backscatter after the L2A+ correction more clearly visible.

4. In response to the reviewer’s comment, we would like to clarify that in the initial version of our methodology, the enhanced L2A+ dust product was derived by estimating the missing cross-polarized backscatter component using a fixed depolarization ratio value for dust as a constraint. For this reason, explicit tables or varying values of the circular and linear depolarization ratios at 355 nm were not included in the manuscript.

Specifically, we adopted a fixed particle linear depolarization ratio (δ₃₅₅,d = 0.244) for Saharan dust, which was then converted to its circular equivalent using Eq. (3). This value was taken from the DeLiAn database (Floutsi et al., 2022) and was considered appropriate for our study domain over the Atlantic Ocean, where Saharan dust is the dominant aerosol type.

However, as correctly noted by the reviewer, this fixed-value approach limits applicability to regions dominated by other aerosol types. Therefore, in the revised version of our methodology, we improved the L2A+ dust retrievals by incorporating CAMS reanalysis data. CAMS-derived aerosol information was used to estimate particle depolarization ratios dynamically through the one-step POLIPHON method, enabling a physically consistent separation of dust and non-dust components and the derivation of an improved Aeolus L2A+ dust product, analogous to the approach used in the CALIPSO LIVAS database. The new methodology is now ready to be implemented on a global scale, allowing the production of an enhanced Aeolus dust product with improved accuracy and consistency across different aerosol environments.

Regarding ground-based depolarization measurements, particle linear depolarization ratio data were available only from the eVe lidar system operated during the ASKOS campaign, while the PollyXT system did not provide depolarization measurements for the examined period. However, in our analysis, the depolarization ratio information used to reconstruct the missing Aeolus cross-polar component was derived entirely from CAMS reanalysis outputs through the one-step POLIPHON methodology, rather than from Aeolus observations themselves. For this reason, we did not perform a direct comparison between the CAMS-derived depolarization ratios and the eVe ground-based measurements, since the CAMS-based ratios represent modeled, reanalysis-driven estimates and not Aeolus-measured quantities. A comparison with eVe depolarization data would therefore not serve as a direct validation of the Aeolus-based retrievals but rather of the CAMS product, which lies beyond the scope of this study. This type of comparison could be incorporated in a future study focused on validating CAMS-derived depolarization ratios against both satellite-based and ground-based lidar measurements, further supporting the reliability of CAMS data for aerosol typing applications.

5. We appreciate the reviewer’s observation regarding the Quality Check (QC) flags included in the Aeolus L2A Baseline 16 products. In the current study, the Aeolus QC flags were not applied to the SCA, SCA-MB, or MLE retrievals. This decision was made to avoid a substantial reduction in the number of valid bins per profile per BRC, which would have significantly limited the statistical robustness of the Aeolus–ground-based comparisons. As suggested, we have now clarified this point in the methodology section of the revised manuscript, explicitly noting that the QC flags were not used and explaining the rationale behind this decision.

6. Indeed, the recent implementation of the MLEsub product in the Aeolus L2A Baseline 16 (processor version 3.16) provides improved horizontal resolution by reducing the averaging scale to sub-BRC level, corresponding to approximately 15–18 km per sub-profile. As demonstrated by Trapon et al. (2025), this finer resolution significantly enhances the representation of aerosol layers and mitigates the impact of averaging dilution, particularly in regions with strong spatial variability or partial cloud contamination.

Although our present study relies on SCA, SCA-MB, AEL_PRO and MLE datasets at the standard BRC resolution, we recognize that the use of MLEsub could further improve the cloud-screening accuracy and aerosol retrieval consistency. Following the reviewer’s suggestion, we have added a statement in the revised manuscript version noting this open point and highlighting that future work will include a comparative analysis of Aeolus optical products at BRC scale (e.g., SCA, MLE) versus MLEsub, in order to assess the impact of horizontal averaging on the retrieval of dust and mixed aerosol layers.

7. Specific comments: All these specific points have now been corrected as suggested by the reviewer.