The authors present cloud micro-physical measurements taken by the UCASS optical particle spectrometer aboard a small uncrewed aircraft (SUA) during the Pallas Cloud Experiment 2022. The manuscript provides a good overview on the used instrument and data products, and illustrates an example of measurements. I am lacking some information on the retrievals used and a discussion of their uncertainties and limitations. I recommend publication of the manuscript after the following comments have been addressed by the authors.

General comments

1. Sec 3.2 “Data Analysis and QA”: The data analysis part of this Section is very short. I would suggest to add a separate Section 4 titled e.g. “Overview of Cloud Properties” summarizing the measured properties that the title of the manuscript advertises. Within this Section, more general information on the cloudy conditions should be presented, e.g. by summarizing where (geographically; also with respect to cloud base and top) clouds were profiled; if the whole cloud layer or only parts of it were measured, and where profiling relative to cloud base and top was performed; how does the data set fits in with other cloud data sets obtained during the campaign? How did profiles of the same cloud vary within one flight? What are the errors of the retrieved variables?
2. I am missing an overview and discussion of the retrievals applied to the measurements. How are the variables
   
   mass and number concentration and effective radius (Tab 1) calculated and what errors and limitations arise from the retrievals and assumptions therein? How is phase information taken into account when retrieving in mixed-phase or ice cloud conditions? I would suggest to add a sub-section to either Sec 2 or 3 illustrating this currently missing information on the dataset.

Specific comments

Abstract

• Line 2: add […] super site during the Pallas Cloud Experiment (PACE) in Autumn 2022.
• L 4: How did measurements differ compared to previous data sets taken? (also see next comment)

Sec 1: Introduction

• An additional paragraph is necessary to introduce the PACE experiment in more detail to the reader. Information should be added on other cloud measurements, previous in-situ observations within the PACE experiments, and how this dataset differs to previous ones.
• L10: rather: Earth system models
• L 11, ff: references given should be more specific and target gaps in the knowledge of clouds specifically observed at the PACE site.
• L 13: also see comment above; which cloud processes are targeted with the measurements taken?
• L 14: „One of the reasons for this“: unclear to what this is referring to
• L 22: A short paragraph on the UCASS system should be added here to introduce it to the reader, including framing the system within other similar SUA systems. Some of the information given L 30 ff could be moved to an earlier stage of the manuscript.

Sec 2: Methods

• Section title might be misleading. I would rather propose “Instrument and Flight strategy” as a title. 
• L 41, 56, 116: abbreviation (noa, b) needs to be explained
• See GC2: a sub-section explaining the retrievals in more detail should be added here or in Sec 3.
• Fig 3: it would be nice for the reader to get an understanding on where all flights were generally taking place. Is the selected plotted flight a random or representative example of the flight trajectory? It would be nice to see all flights on one map, e.g. color-coded by flight number; alternatively, if the plot gets too busy, a selection of representative or highlight flights could be added which could then be illustrated further in new Section 4 (see GC 1), including an illustration of how variable conditions were from profile to profile within the same flight. Additionally, an illustration of the vertical flight trajectories e.g. in a sub-figure (b) would add valuable information to the flight strategy.

Sec 3: Data

• L 80: the global time step was set to 0.5s. What vertical and horizontal resolution, respectively, do the output variables have due to this averaging applied, and how does it change throughout the vertical profiles illustrated in Figs 5+6? This information would also be important to discuss in the Results Section (see GC 1)
• L 87: “using one of a number of algorithms” should be specified in more detail; which ones were used? References should be added.
• L 93: only discuss QA here (see general comment 1)
• L 95: unspecific; constraints and limitations should be further discussed e.g. in previous Section
• L 100: how were the threshold values chosen, and what uncertainty do they introduce to the masking?
• L 101: unclear, sentence should be double-checked
• L 103, 107: wind direction and speed were taken from Sammaltunturi station. From Fig 3, it seems like the flights were operated >3km away from the station. Were wind measurements only taken at ground? The authors should discuss in more detail how the wind information error due to the distance and orography affects processing, retrievals and data uncertainty.
• Fig 5, 6: what is the minimal height that the instruments operate at? The plot seems to start around 400m; is this a system limitation or due to the observed cloud conditions or orography? Information should be added on where the particles were collected with respect to the cloud boundaries (see GC 2).
  
  Minor xtick labels should be added to all respective flights sub-panels at the bottom. Are all panels showing the same xrange? It would be nice to add ‘10’ and ‘20’ to the top x-ticks.
  
  It seems like lines are connecting points throughout clear air patches (eg Fig 6 profiles 25-28 between 750-1200m). Replacing lines with plotted dots would further illustrate the vertical resolution of the measurements.
• Tab 2: Information on how long each flight lasted should be added. More information on the meaning of airspeed type ‘normal’ vs ‘corrected’ is needed in the text.
• L 123 (also see GC 1): how variable are profiles change within each flight and how far spaced geographically?

Summary

• A more concise outlook should be added to how this data set will be used in the future with respect to the questions and research areas raised in the introduction (L 12-14), and within other PACE measurements and modeling activities.

Technical Corrections

• L 74: reference incomplete
• L 94: spell out QA in Section title
• L 109: Section title on new page
• L 115, 116: what do ‘SHT’, ‘BME’, ‘AG’ stand for?
• Fig 5, 6: top x-ticks don’t need 0 to match numbers given in Tab 2

The manuscript presents a dataset of in situ data of polar stratus clouds collected from a fixed-wing small uncrewed aircraft (SUA) during the Pallas Cloud Experiment (PaCE) 2022 at the Pallas atmosphere-ecosystem super site. The dataset over 39 flights with 84 quasi-vertical profiles includes data on cloud droplet size distributions including derived products (number concentration, liquid water content, effective radius), meteorological variables, and SUA flight data. A quality check was performed on the data and different processing depths (“a1” and “c1”) were applied.

General comments:  

1. I’m not sure if the dataset now only includes measurements of “arctic warm stratus” (line: 32), or also arctic cold stratus / mixed-phase clouds (line 104: “ice while flying”, line: 136 mixed-phase clouds).
2. If you were also measuring mixed-phase clouds and aerosols (line: 53) how did you handle the discrimination between cloud droplets, ice crystals, and aerosols? Also, due to their very different refractive indices that could affect the measurement. This could strongly influence the measure cloud droplet size distribution and derive variables.
   
   If you measure cold cloud or mixed-phase conditions a flag in the data set would be helpful, and some short sentences on how you define these temperature regimes, i.e., using the BME temperature or other instruments.
3. You could also include a “QA masking impact”, i.e., how much of the respective flight/profile (e.g., the fraction) was masked.
4. What is largely missing in the manuscript is a compact uncertainty budget for (a) sizing (e.g., due to refractive-index / model dependence), (b) counting / concentration (e.g., sample volume), (c) LWC (e.g., density assumption, mixed-phase conditions), and (d) airspeed substitutions when the pitot tube froze (e.g., ground-station wind - based correction). Particularly the latter considering the wind speeds were presumably measured on ground level.
5. Please extend the analysis part of the data on how the variables are derived from the particle size distribution measurements. Which processes “proc” were actually used.
6. Summary/Conclusion: This part is very short and missing an outlook on what the dataset could be used for or how it could support other datasets if used in synergy. Mainly what is the fundamental value for future studies.

Specific comments: 

1. Line 117-119: This should go into the data analysis part, where you introduce the masks.
2. Figure 5: Number concentration of cloud droplets, aerosols or both?
   
   Do you always show the same x-range?
   
   Are the concentration values saturated (e.g., profile 6) or just not displayed due to the x-axis range?
3. Figure 6: The x-axis label is confusing looks like “510”, please at least include the ticks for every profile. Also here particularly evident is that you need to give some error estimates. Comparing profile 002 with 011 already indicates that due to counting statistics the uncertainty for 002 is higher compared to profile 011.
4. Line 120: Level “c1” means that the flight controller data was available and “a1” where it was not available? Please clarify.
5. Line 117: What do you mean by “The data level is also included here.” This needs more explanation, what is a data level?

Minor comments:

• Line 44: what is (noa,a) after pitot tube?
• Line 56, 116: what is (noa,b)?
• Line 68: include space between 15° and angle
• Lines 69,70, 99, 100: include small space between unit m\,s
• Line 74: check reference year number
• Line 75: Period missing
• Line 101: remove first “time”
• Table 1: kg\,m^-3
• 115: I thin the company is called Sensirion not Senserion
• 116: Change “Bosh” to “Bosch”
• 119: altitude not attitude :)
• Table 2: define n/a in description (not available vs not applicable)
• Throughout: change in-situ to in situ

The manuscript describes a dataset collected in 2022 to understand cloud microphysics using a small UAS. The article contains useful information regarding the UCASS sensor and the available data. The platform would be beneficial for addressing the data gap in in-situ cloud observations. However, the manuscript lacks some key information about data processing.

General Comments:

I think it would benefit the reader if there was more description about how this article differs and expands upon the work from Girdwood et al. 2022b. As the introduction stands now, I do not see how this dataset is unique or what was learned from the previous campaign.

There should be more description on the data processing mentioned in Software. The authors mention that there is space for customization but do not describe the standard practice used on the dataset herein. Overall, the description of the QA process needs to be expanded. I do not understand the processes which went into creating the c1 level files. 

The meteorological observations (T and RH) are mentioned but never described in their data quality or availability. I would imagine condensation on the sensors could pose an issue, but this is not mentioned, nor do we see any examples of meteorological data plotted in the manuscript. Were there any calibration offsets or post-processing to ensure accurate measurements?

The doi links in the Abstract and Data Availability statement do not work.

Specific Comments:

L4: It would be nice to add the year of the Girdwood 2022b campaign to distinguish the two datasets better.

L20: Have there been other UAS to gather cloud physics observations? If so, they should be mentioned in the introduction.

L41: What is (noa, a)?

L44-L47: All of the pointing to references to describe the data processing makes it hard to follow.

L54-56: Are there any differences between these two sensors? Or in their respected data quality?

Fig 3: You might consider adding a satellite image to understand the cloud types and coverage in the flight path.

L102: Are there differences in data quality between ascending profiles and descending? How is the concern for rotor-wash influence on the environment mediated?

L104: This seems like a fundamental issue given the goals of the platform. How often was the pitot tube unusable? Does that reduce the frequency of observations? 

L128: Fix the citation