Ferrone and Berne (submitted) summarize data from a unique field campaign seeking to measure orographic precipitation in Antarctica using a distributed network of radars, met stations, radiometers, and other instruments mounted at and around the Princess Elisabeth Antarctica research base out of which the field campaign was conducted. The authors detail the instrument temporal coverage, location, and measurement cycles, followed by a discussion of instrument retrieval processing and an exposition of some of their data. The authors cover a range of topics with their complex multi-radar dataset. Yet the manuscript lacks a section dedicated simply to presentation of the retrieved data variables, does not describe in the manuscript an instrument which is present in the online data archive, and suffers from some editing errors in the text and figures. As such, I recommend major revisions prior to publication.

Major Comments:

• section 2.2: The online listing of the data states `This archive contains the radar variables collected by the W-band Doppler profiling cloud radar (WProf) deployed at PEA. The liquid water path and integrated water vapor (retrieved thanks to the 89 GHz radiometer included in the instrument) has also been included in the files,' but there is no mention of the LWP or IWV retrievals in the paper.
• section 2.2: Two different types of scan cycles are defined for MXPol, though there's no statement of how long each scan cycle takes and when they were performed over the measurement campaign. How long does each cycle take, and how were the two cycles used across the entirety of the measurement campaign? If they were switched at the investigators' discretion, a plot of which scan cycle the MXPol was in for the duration of the field campaign would be useful, or at least a quantification of how frequently each of the scan cycles was used.
• section 2.3: The pointing of MXPol is discussed in detail, but the pointing of the MRR-PROs is not mentioned. What is the elevation and azimuth angle of these instruments? Is their orientation fixed for the duration of their deployment? How was their orientation chosen?
• section 3, 3.1, 3.2: The suite of instruments used is complex, and discussion of the output variables is intermingled with discussion of processing steps. The paper could benefit from the addition of a `dataset' section succinctly summarizing the output file variables and their organization. For the hydrometeor classification, the types are output variables are not specified. The Zenodo listing states that information about the proportion of different hydrometeor types is also calculated, but this is not mentioned in the paper.
• figure 3: Panel labels (a,b) are missing, as are axis labels, axis tick labels, colorbar tick labels, and colorbar labels. This probably occurred from a formatting issue during typesetting.
• fig. 4: This figure has the same issues as figure 3. When I open the PDF file, I see missing labels, missing text, and incorrectly formatted tick labels.
• dataset: I spot-checked the MRR-Pro and MXPol_PPI archives in Python with Xarray for data quality. Not all variables contain a `long_name' or a `standard_name' attribute. For example, the MRR-PRO data variables called `Zea', `width', etc. only contain the `units' attribute. Some MXPol variables do not contain any attributes.
• dataset: When I open the MRR-PRO files with Xarray in Python, I see that valid radar profiles use NaNs to indicate range bins with no meteorological signal, but there is no missing value attribute or data quality variable. Are all processed profiles valid, and NaNs are simply used to indicate no meteorological signal detected? Or do NaNs indicate both `no signal' and `suspect data'? If the latter is the case, then additional information should be provided.

Minor Comments:

• line 23: change `SMB(of)' to `SMB (of)' (missing a space)
• line 27: change `suggest that in Queen Maud land few' to `suggest that in Queen Maud land a few' (missing article)
• line 52: when you say `The relatively high number of studies that were enabled by the availability of this dataset', it seems you are not referring to a specific dataset (such as the one presented in this study), but rather to the availability of the data from the instruments at the research base. If you are referring to a specific dataset, consider providing a link to it or citing it to avoid confusion.
• lines 90-92: Why was this chirp table chosen? The text could benefit from elaborating on how the three resolutions benefit the measurements and what they are targeted for.
• table 1: What is $v_{ny}$, and does `Vel. res.' stand for velocity resolution? Please specify more information about the variable names in the table caption. 
• lines 125-133: The outline of the scanning modes could benefit from easier comparison between the list of the scans used and the lines on figure 1 -- I would suggest either adding a labeled grid to the plot indicating the direction of 0$^\circ$, 90$^\circ$, 270$^\circ$ azimuth, and/or indicating the color/linestyle of the line used in fig. 1 within the list in the text. Additionally, the red and brown coloring is hard to tell apart.
• lines 137-138: I would suggest reminding the reader of the azimuth angles of the RHI scans directed towards the MRR-PRO sites (i.e. 165.6$^\circ$ and $190.1^\circ$). This measurement cycle is complex and the paper would benefit from attempting to further disambiguate its presentation.
• lines 165-173: Specify longitude and latitude with $^\circ{\mathrm{S}}$ and $^\circ{\mathrm{E}}$ rather than using negative numbers.
• lines 175-176: While temporal coverage for each instrument is mentioned in the instrument's respective section, I think the paper would benefit from a plot indicating the period of coverage for each instrument so that the reader can get a better sense of the temporal overlap between instruments.
• line 178: You specify two AWS instrument models but do not specify the difference -- are different MRR-PROs accompanied by different instrument models? If so it may be worthwhile to state the difference between the two models, or if they produce the same results with the same resolution etc.
• section 3.4: Please clarify whether figure 3 is a joint histogram, or a joint PDF. If it's a joint PDF, then referring to an area of frequent occurrence as `counts' may not be accurate. Review of this section is hindered by the formatting issues with figure 3.
• line 249: Typo: `radiosondes', not `radiosoungins'
• line 265: Does figure 3 plot the joint PDF over the entire measurement campaign? This would be worth noting.
• line 276-7: `By comparing the three datasets, a significant difference between the MRR-PRO 22 curve and the ones from the other two MRR-PRO can be noticed.' What is the curve in question? The reader cannot `notice' a difference since it refers to a figure not included in this paper. Perhaps the authors could instead state that another paper found that the MRR-PRO 22 has a significant bias compared to the other two MRR-PRO instruments.
• line 280-281: This discussion is unclear -- please state why comparing the lowest 1% of the data is useful for estimating the bias of the instrument. The implication seems to be that this quantity ought to be the same across all three instruments, but the authors should state why.
• line 281: `For both sets of differences, the interquartile range is 2 dB.' Previously you simply compare the threshold of the 1st percentile, so what is this the interquartile range of?
• line 290: This statement about valid data seems to suggest outages between the start and end date of observations, but these are not mentioned in the paper or the dataset landing page.
• line 296: Missing period at the end of the sentence `.'
• fig. 5: This figure does not have any of the same errors as the previous two. However, in panel (e), the longwave downwelling flux is so small so as to be unreadable and to appear to be negative for much of the time. This could be fixed with a secondary y-axis or separate panels for LW and SW downwelling irradiance.