The presented paper constitutes a significant contribution to the field of atmospheric science and aerosol-cloud interactions. The authors introduce a Cloud-by-Cloud (C×C) approach, merging geostationary satellite observations with polar-orbiting lidar data, to systematically study aerosol-cloud interactions. By matching cloud development with height-resolved Cloud Condensation Nuclei (CCN) and Ice Nucleating Particle (INP) concentrations, they investigate the aerosol cloud interactions. The application of this novel methodology to data from MSG-SEVIRI and CALIPSO over Europe and Northern Africa in 2014 yields compelling insights into the sensitivity of liquid and ice-containing clouds to changes in aerosol concentrations. This methodological innovation provides a platform for advancing our understanding of atmospheric dynamics and climate processes.

The overall study is well-written, well-structured, characterized by scientific sufficiency and therefore falls into the scope of EGU community, thus could be published as paper. I only have some minor revisions/ comments below.

In Figure 1, the authors present a flow chart outlining the various steps in the data analysis. This visual aid serves as a guide for readers, providing references to the relevant literature and corresponding sections in the paper, enhancing the accessibility of the complex data analysis process. This is a helpful asset in understanding the study's methodology in the first look.

In Subsection 2.1 maybe consider adding a brief explanation or reference for Particle Image Velocimetry (Adrian and Westerweel, 2010) for readers who may not be familiar with the technique.

The criteria for filtering the cloud trajectories based on cloud top height, clear air development, and daytime occurrence are well-defined and reasonable. It might be helpful to mention the concept behind choosing a factor of 4 for the difference in area threshold.

In subsection 2.2, the flow chart of the OMCAM algorithm is a useful addition, aiding in the comprehension of the complex aerosol concentration retrieval process. The expansion of OMCAM to derive INP concentrations is an important extension. Clearly articulating this expansion and its relevance enhances the understanding of the methodology.

Reference to Figure 2: Consider briefly summarizing the key steps in the OMCAM algorithm or referring to specific elements in Figure 2 to direct the reader's attention to the relevant parts.

In subsection 2.3 the introduction of TrackMatcher and its purpose is clear. Consider briefly mentioning the significance of intercept points between cloud trajectories and the CALIPSO ground track in the context of the overall research objective. The step-by-step breakdown of the algorithm is well-detailed, helping the readers who are not familiar with the TrackMatcher tool. A brief context for the operator-set auxiliary information extracted at or around the intercept points would also be helpful.

The explanation regarding the time difference between cloud tracks and CALIPSO satellite is clear. However, it might be helpful to explain why the chosen range of ±90 minutes was suitable for this study.

Providing a brief clarification on what criteria determine the dismissal of CALIPSO aerosol data would enhance understanding.

The addition of Figure 3 is particularly beneficial for visualizing the described process.

Subsection 2.4 describes the quality-assurance process. The four criteria for assessing realistic cloud development are well-detailed. Lines 214-216: Consider rephrasing to avoid redundancy, e.g., "if more than one of the criteria is met" could be streamlined to "if multiple criteria are met." Also, the authors could articulate the context behind the high level of scrutiny and data reduction and explain how this rigorous approach ensures the physical meaningfulness of findings from the bottom-up database.

In subsection 3.2 the rationale for excluding the upper and lower 5% of cases is clear. Consider briefly mentioning the impact of this exclusion on data analysis.

The comparison with studies based on passive CCN proxies adds context (Subsection 3.3). However, consider expanding on the potential reasons for the differences observed, providing a more nuanced interpretation. The mention of future research and the need for a longer time series is appropriate.

The discussion of the temperature-dependent relationship between T_top and n_INP is insightful (Subsection 3.4). Consider emphasizing the implications of this finding for a more nuanced understanding of aerosol-cloud interactions. The comparison to studies using dust concentrations as an INP proxy is also valuable. Briefly discussing potential differences and similarities in findings could be helpful. The emphasis on the need for a larger data set for more robust conclusions is well-stated.

The Summary and outlook section is well-organized and effectively communicates the achievements of the study, as well as the planned future directions. Enhancing the interpretative aspects and providing a bit more context for readers less familiar with the field could further strengthen the summary. Here are some questions that could possibly be answered in this section:

What could the observed sensitivities mean for our understanding of aerosol-cloud interactions on a broader scale?

While there is a mention of the findings being at the lower end of commonly inferred values, consider further discussing the significance of this difference. How might this influence the broader understanding of aerosol-cloud interactions, and what factors could contribute to these variations?

In the outlook section, provide a bit more detail on the rationale behind each future step. For instance, why is shifting or widening the study region important, and how might it enhance the robustness of the study?

In the outlook section, provide a bit more detail on the rationale behind each future step. For instance, why is shifting or widening the study region important, and how might it enhance the robustness of the study?

Briefly discuss the potential impacts or applications of the research, especially if successful. How might the findings contribute to our understanding of climate processes or inform future satellite missions?

The paper by Alexandri et al. introduces a Cloud-by-Cloud (C×C) approach, merging geostationary satellite observations with polar-orbiting lidar data, to assess aerosol-cloud interactions and demonstrates the application of the C×C approach on some studies. In general, the authors provide the necessary information and guidance to understand the C×C approach and the manuscript is well written and structured. I recommend the manuscript for publication after some comments detailed below in my review.

Minor comments:

Line 125. “The best match within set thresholds is kept if the difference in area does not exceed a factor of four.” The authors must explain the reason for choosing a factor of 4 in the cloud-tracking analysis.  Is it an empirical estimate?

Line 178-179. “In this study, we didn’t limit the time difference for finding matches between a cloud track and the CALIPSO satellite but most cases fell within a range between 0 and ±90 minutes.” The time difference can be a critical factor in the retrievals. Why didn’t authors try to limit the criteria? How different could be the results?

Consider moving Figure 4 to appendix.

Figure 6 should be made larger, as it is not readable in the current size.

Figure 8b. Should explain more the existence of the second peak of the ice-containing clouds group and  also, provide a reference for their explanation.

Line 374-376.  It would be better to stress earlier the limitations of the C×C approach.