This study explores the use of polarimetric radio occultation (PRO) measurements for validation and verification of different microphysics schemes implemented on a limited area atmospheric model (WRF). Model simulations of typhoon cases are used to simulate the actually observed PRO measurements using a forward observation operator that is similar to the one developed for ECMWF's IFS model previously reported in the AMT journal. The simulated PRO observations are compared to the actual PRO measurements to gain useful insight into which microphysics scheme performs well in simulating PRO observations. Such comparisons will be potentially very useful given the scarcity of measurements direct related to 3-dimensional distribution of hydrometeor particles, and this study is a nice demonstration of this potential.

One of the major difficulties in such a validation approach would be how to account for erroneous representation of the typhoon position in the model. The authors meticulously accounted for this error source by manually relocating the WRF model field so that the model's typhoon position matches with the position from best track data.

The manuscript is very well organized and written in clear language. Flow of logic is also clear  and I see no problem in publishing the manuscript as is except for some minor editorial issues.

I just point out below some minor edits that the authors may find useful, but I do not think these are essential for acceptance of the manuscript.

Minor comments:

As I understand, when WRF model is initialized, hydrometeor variables are given zero values at the very beginning of the model integration. In such a "cold start" setting for hydrometeor variables, these variables need to be spun-up before any examination is made. It would be informative to readers who may be interested in replicating your experiments or similar experiments if this point (whether the hydrometeor variables were "cold-started") is explicitly explained in section 2.1.

If you did apply cold-start, then I assume the models are integrated for relatively long 18 hours to ensure the model's microphysics is spun-up. If this is the case, this point should also be explained in the manuscript.

Figure 8a: Looking from top to below on the right panel, the observed PAZ data is nearly zero at around 2km height and then rapidly increases as the height gets lower, and this behaviour looks unnatural. I suggest the authors check the quality flag for the PAZ data. If the data is flagged unreliable at these heights, I suggest not to show the PAZ data for such lower levels in the graph. Similarly for Figure 8b and Figure 12.

Typographic issues:

Equation (1) and elsewhere: delta phi should be typed with $\Delta \Phi" in LaTeX, not with "\Delta \0" or "\Delta \varnothing" as in the manuscript.

Line 175 and elsewhere: "vortexes" should be "vortices".

Line 177 "Even though": should be replaced with "Despite", or the sentence structure should be revised.

Lines 197, 249 etc.  "presented": should be "present"

Line 198 "five schemes however, ...": Start a new sentence with "However", like "...five schemes. However, ..."

Line 269 "-70 degrees": Make it clear that this is Celsius.

Line 296 "contributed by": Probably should be "contributed to by".

The manuscript explores the impact of several microphysics schemes on the polarimetric signature, during radio occultation with polarimetric-capable receivers. The paper shows that the different schemes lead to different expected observables. This difference is clearly above noise for the observable, thus these observations can in principle support the superiority of some microphysical schemes above others.

The theoretical basis for this is in general appropriate, and the authors demonstrate what I understand is the main goal, which is to show that the polarization signature is measurable with better accuracy than the difference between microphysical schemes. Indeed, some schemes lead to significantly better fits than alternative microphysics. This is interesting. Despite this, the authors do not explore sufficiently the caveats of the approach. The relationships between water precipitates and polarization signatures depend on the amount of water/ice, and the average axis ratio. Although the amount of water/ice is quite explicit in any microphysics scheme, the effective axis ratio is hardly an output of any standard scheme. It is here somewhat arbitrarily fixed to a very crude guess of 0.5, and it is unclear how other choices of this quantity may impact the results. It may be a different constant, a profile dependent on the type of precipitate, and depends likely also on turbulence. Besides, the microphysics interact, as is mentioned in the paper, with PBL schemes. Given this wide parameter space, above the mere amount of several precipitate fractions, it is not obvious that we could at this point conclude that some microphysics scheme is superior based on PRO data. We can conclude, however, that through its accuracy and resolution, PRO data has the ability to discern different schemes. It is my understanding that we still ignore too much of the microphysics and of other related parameterizations, such as PBL schemes, to actually benefit from that ability, even if PRO data is available.

I thus encourage the authors to underscore the difficulties that would limit the task of supporting a scheme as unconditionally superior to others, based on PRO, and further develop the caveats of the approach. I believe that this can be done with an appropriately extended comments and conclusion section (beyond the few comments in lines 393-etc).

Review of “Comparisons between Polarimetric Radio Occultation Measurements with WRF Model Simulation for Tropical Cyclones”

This manuscript presents a comparative study of three typhoon cases using five different microphysics schemes and two initial conditions (ICs), validated against polarimetric radio occultation (PRO), conventional radio occultation (RO) retrievals, and radiosonde data. The results highlight the unique capabilities of PRO in capturing hydrometeor structures, offering valuable insights for evaluating microphysics schemes.

Overall, the paper is well written and clearly structured. The language is easy to follow, and there are no major grammatical issues. The topic is timely and relevant, and the use of PRO for model evaluation is an important and innovative direction.

However, I have several concerns that need to be addressed before the paper can be considered for publication:

1. Use and Interpretation of Initial Conditions (ICs):

The manuscript does not clearly articulate the purpose and implications of the two different initial conditions. While it is acknowledged that ICs can influence microphysics scheme performance, the two ICs used in this study (ERA5 and FNL) are significantly different even before considering the influence of microphysics as stated in the paper. The author used ERA5 to verify the results but didn't provide independent verification of typhoon intensity and track (e.g., best track data or satellite-derived observations), therefore it is difficult to assess which combination of IC and microphysics performs better against observations. Besides, ERA5 is a reanalysis product that is not truly independent of the model, which weakens its role as a verification dataset. In its current form, the IC study part lacks a clear scientific objective or demonstrated relevance to the overall goals of the study, and its inclusion is difficult to justify without more though validation. 

2. Use of RO Retrievals as Verification:

The study treats RO temperature and moisture retrievals similarly to radiosonde data. However, this assumption is problematic for several reasons:

• RO retrievals are often derived using a priori information from climatology or model fields, which compromises their use as independent observational data.
• RO measurements are inherently integrated along ray paths, similar to PRO, and thus are not directly equivalent to point measurements like radiosondes.
• The comparison would be more meaningful if the model outputs were validated against rawer RO observables, such as bending angle . Although these raw RO quantities may not provide hydrometeor information like PRO, they are more suitable for comparing the thermodynamic structure along the ray path and could complement the PRO analysis. This important aspect seems to be overlooked, limiting the insightfulness of the comparisons shown in Section 3.

3. PRO Verification and Typhoon Structure Alignment:

The use of PRO for typhoon evaluation is promising, but the manuscript does not sufficiently address potential misalignment between observed and simulated typhoon structures. While center relocation is applied to correct for gross displacement, finer structural differences—such as asymmetries in precipitation bands or peripheral wind fields—are crucial for accurate PRO comparisons. Unlike atmospheric rivers (ARs), typhoons are highly sensitive to orientation and mesoscale features. If the PRO ray trajectory does not intersect the simulated hydrometeor regions properly, it can lead to misleading differences, regardless of microphysics scheme performance.

For example, in Figure 6, Purdue Lin and WSM6 show narrower hydrometeor regions compared to the other schemes. Is this due to genuine model differences, or a result of misalignment between PRO ray paths and the simulated storm structures? The paper would greatly benefit from incorporating additional observational data (e.g., precipitation from satellite sensors) to independently verify which simulations better match reality. This would help determine whether PRO is truly capturing physical differences among the MP schemes, or whether spatial mismatches are driving the observed discrepancies.

Conclusion:

This study has strong potential to make a significant contribution to the field. The use of PRO for evaluating typhoon simulations is novel and valuable. However, the current manuscript does not fully address the limitations of its verification strategy, particularly with respect to the IC interpretation, RO data usage, and structural alignment in PRO comparisons. Addressing these issues would strengthen the scientific rigor and impact of the work.

I encourage the authors to further develop the analysis, especially around item (3), and to consider incorporating additional independent observations to support the conclusions.