The manuscript presents a multi-depth snowpack status classification scheme for Antarctica, utilizing multi-frequency spaceborne microwave radiometry. The paper is clearly written, and the subject matter aligns well with the scope and interests of the journal.

The use of the full spectrum of passive microwave radiometry for ice sheet melt detection is a relatively understudied area, which has fortunately begun to receive more attention in recent years. This study contributes meaningfully to that growing body of work.

While the approach may be seen as a preliminary or "low-hanging fruit" analysis, the authors' qualitative investigation of microwave signal behavior in relation to melt evolution represents an essential foundational step. This work is critical for advancing future research aimed at extracting more detailed and quantitative insights from remote sensing data.

I consider this paper a valuable contribution to the field and recommend it for publication following minor revisions:

Line 43: Please change “among” to “amount.”

Line 44: Add citations to relevant recent work, such as:

    Naderpour et al. (2020)

    Mousavi et al. (2022)

    Hossan et al. (2024)

    Moon et al. (2024)

Line 125: The choice to define the new melt season starting in mid-autumn is not intuitive. As Figure 5 suggests, late-season melt events may be incorrectly attributed to the following melt season. Please clarify the rationale or consider adjusting the definition.

Lines 139–140: Please cite additional supporting literature, such as:

    Macelloni et al. (2011)

    Montomoli et al. (2022)

Lines 179–180: This statement may oversimplify the L-band response. The response depends on the amount of active melting and liquid water accumulation. Please clarify this dependency here.

Line 197: Consider replacing “coherent” with “consistent” for improved clarity.

Line 235: Correct the grammar: “this possibilities” should be “these possibilities.”

Lines 297–298: Consider emphasizing the adequate accumulation of liquid water, rather than just the depth, as the key factor influencing the observed response.

References:

Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., & Miller, J. Z. (2024). Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry. https://doi.org/10.5194/egusphere-2024-2563

Macelloni, G., et al. (2011). Technical Support for the Deployment of an L-band Radiometer at Concordia Station During DOMEX-2 and Data Analysis. Final Report. Version 2.0. October 2011. European Space Agency Stify Contract Reports.  https://earth.esa.int/eogateway/documents/20142/37627/DOMEX-2-Final-Report.pdf

Moon, T., Harper, J., Colliander, A., Hossan, A., & Humphrey, N. (2024). L-Band Radiometric Measurement of Liquid Water in Greenland’s Firn: Comparative Analysis with In Situ Measurements and Modeling. California Digital Library (CDL). https://doi.org/10.31223/x56712

Montomoli, F., Brogioni, M., Macelloni, G., Leduc-Leballeur, M., Baldi, M., Martin-Neira, M., & Casal, T. G. D. (2022). Long Term L-Band Brightness Temperature of the DOMEX-3 Experiment: Improvement of Absolute Calibration and Data Analysis. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 7367–7370). https://doi.org/10.1109/igarss46834.2022.9883561

Mousavi, M., Colliander, A., Miller, J., & Kimball, J. S. (2022). A Novel Approach to Map the Intensity of Surface Melting on the Antarctica Ice Sheet Using SMAP L-Band Microwave Radiometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1724–1743. https://doi.org/10.1109/jstars.2022.3147430

Naderpour, R., Houtz, D., & Schwank, M. (2020). Snow wetness retrieved from close-range L-band radiometry in the western Greenland ablation zone. Journal of Glaciology (Vol. 67, Issue 261, pp. 27–38). https://doi.org/10.1017/jog.2020.79

This work proposes a novel synthesis of binary melt determinations from several different passive microwave sources, as well as accounting for varying diurnal observation timings, in order to assert features about the melt state of Antarctic firn. The authors propose a classification system to relate observations from 3 different satellites, and 6 different melt detection algorithms, into a set of categories for the spatial and diurnal variation of liquid water in a firn column. These classes go beyond what has previously been derived from passive microwave observation synthesis, and this work provides a valuable advance in passive microwave observation analysis of melt. This work provides a comparison with ERA5 reanalysis skin temperatures to give confidence that the durnality and spatial distribution of surface melt in this work at least generally reflects real world patterns, which is a reasonable approach for passive microwave melt analysis. Aside from several minor questions below, I believe that this work is suitable for publication in this journal.

My two biggest questions have to do with the new melt detection techniques presented in this work.

1) A new method for melt detection at 37 GHz is proposed here, and given its novelty I would like to see a little additional discussion about this method. In particular, I would like to see an explanation for the rationale of using a running mean of recent dry brightness temperature values and a 1-sigma increase to detect melt. Additionally, I am curious to see a short discussion of how well the method performs, or at least how well it agrees with other melt detection results.

2) A method for identifying if 80% of a 19 GHz pixel is melting is introduced. This method uses the difference between dry and wet snow brightness temperatures to produce a threshold value. I am curious, why did you use a single value for 19 GHz Tdry for all surfaces? I would expect the dry snow 19 GHz value to vary spatially as it is a function of temperature profile, grain size, and ice lenses in a snowpack.

A few specific comments by line:

Lines 141-142: You apply a bound of 20 to 35 K for the min and maximum threshold when applying the Torinesi method on 19V GHz data. How often are these bounds used? If they are required often, then how sensitive is the melt detection to those bounds?

Line 150: as with my previous comment, how often are these bounds used?

Lines 150-153: Just to be clear, does “filtered out” mean that these pixels are given no result for the year, or listed as no melt all year.

Lines 158-159: Is the M37 value calculated from the 5 most recent days that the snow was dry at 19 GHz? The text was unclear what the algorithm does if there are more than 5 days of 19 GHz melting.

Table 1: I am interested to see the occurrence rate of each of these classes or signatures listed somewhere, which could go in this table or Figure 1A in the appendix. Alternatively, in Figure 6 a second row could be added plotting the relative prevalence of each melt class by day of year.