This article discusses an attempt to objectively assess the performance of KDP retrievals in rain. The authors make use of the self-consistency in rain of Zh, Zdr and KDP to retrieve KDP from Zh and Zdr observations from a heavily quality-controlled dataset. They then proceed to compare the self-consistency KDP with optimized KDP retrieval algorithms available from open-source libraries Py-ART and wradlib and the KDP retrieval available in the IRIS software.

It is undoubtedly an interesting and worthy exercise. The article is well written, and the topic is of interest. I have some general comments that I would like that they are addressed before it being accepted for publication.

General comments:

1. Many Met Services (including WSR-88D) use a simpler KDP retrieval consisting of one or two moving median windows to smooth PhiDP and a least square retrieval of the KDP. The MeteoSwiss version of Py-ART has functions kdp_leastsquare_single_window, kdp_leastsquare_double_window, smooth_phidp_single_window and smooth_phidp_double_window available. I suggest the authors add those into the evaluated methods. It might be of interest for many Met Services to see where they retrievals stand.
2. I do not understand why the authors did not try to tune KDP Schneebeli and KDP iris. KDP Schneebeli has some parameters that can be tuned (Measurement Error Covariance Matrix, Scaled State Transition Error Covariance Matrix and all the arguments for the pre-filtering of the PsiDP (which is not performed in the article). I am not familiar with the IRIS implementation, but I would assume there are also parameters that can be tuned. Not tuning those algorithms while tuning the others puts them in an unfair position when performing the evaluation.
3. The conclusions should emphasize more the limits of this study. With all the data filtering performed the authors are evaluating KDP in effectively idealised conditions. In operational conditions, robustness to outliers, performance in low SNR, performance in the presence of residual clutter and/or non-liquid precipitation are also important factors. It would be interesting also to have some data on computational time of each of the retrievals.

Specific comments:

1. According to the github repository the right way to write the name of the software package is Py-ART.
2. For the sake of completeness, and since they have such a long list of citations, the authors maybe should cite Figueras i Ventura and Tabary (2013) since it was one of the first papers demonstrating the superior performance of KDP-based algorithms at all 3 regularly used weather radar bands (S, C and X) in an operational context in Europe.
3. A recapitulative table with the parameters chosen for each tunned algorithm would be appreciated. It will also be helpful to highlight the effective range resolution achieved after tunning the retrievals.

References

2013: The new French operational polarimetric radar rainfall rate product, J Figueras i Ventura, P Tabary, Journal of Applied Meteorology and Climatology 52 (8), 1817-1835

https://github.com/meteoswiss/pyart

Review report of

Benchmarking K_DP in Rainfall: a Quantitative Assessment of Estimation Algorithms Using C-Band Weather Radar Observations

By Aldana et al.

Summary of paper:

The manuscript examines the performance of several commonly-used methods for KDP retrievals in rainfall observations with a C-band weather radar, which is located near Helsinki, Finland. The authors firstly determined carefully quality-controlling radar data by applying several filters to the measured ZH and Zdr and, then, deployed a polarimetric self-consistency approach to compute a reference KDP from radar quantities in rainfall observations. The self-consistency KDP was compared with the derived KDP applying different methods available in Py-ART and wradlib which were optimized accordingly under different conditions. With the performance assessment of different methods, the authors finally directly compared the optimized KDP and calculated their correlation coefficients, which, however indicates generally a high-level difference between different methods.  

As general comments, I think the work is very important and comprehensive for the relevant studies in the radar community. It is well written and certainly worthy for publication.

I would like to recommend to add a short introduction to the self-consistency method that is used to probably improve the accuracy of different variables against each other for given hydrometeor types. For the direct comparison between the estimated KDP with different methods, a significance test should be given.  

Here are the lists of typos and suggestions (but not limited to):

1. Lines 17-18, to my knowledge, Höller et al., (1994) developed one of the first algorithms for hydrometeor classification by using ZDR, LDR, KDP and phiHV measurements during the evolution of a thunderstorm and the authors should probably cite it.   

Reference: Höller, H., Hagen, M., Meischner, P. F., Bringi, V. N., and Hubbert, J.: Life Cycle and Precipitation Formation in a Hybrid-Type Hailstorm Revealed by Polarimetric and Doppler Radar Measurements, J. Atmos. Sci., 51, 2500– 2522, https://doi.org/10.1175/1520-0469(1994)051<_x0032_500:LCAPFI>2.0.CO;2, 1994.

1. Lines 102-103, is Thurai et al. (2007) one of the settings? Please check it.
2. Lines 109-110, please rephrase the sentence “Observations were … only.”
3. Figure 4 (probably also other figures), Please add numbers (or letters) to the labels of figure.
4. Lines 233-234, please rephrase the sentence “whereas …”
5. Line 256, k_DP -> K_DP
6. Figure 6, which panel is (a)-(p)? Please add.
7. Lines 343-348, is this paragraph necessary? Please make a check.
8. Line 400, remove one of the “using”.
9. Line 426, it’s -> its.
10. Line 427, it’s -> its.
11. Line 433, remove “from methods”
12. Line 456, a significance test should be given when the comparisons between two KDP are conducted.
13. Figure 15, remove “plot”.
14. Lines 478-479, please rephrase the sentence “both use …… independent.”
15. Line 496, in 8 -> in Figure 8 (I think).
16. Lines 669-670, it seems the reference is not complete. Please revise it.