With respect to the AMT discussion paper, an important improvement is the adoption of different for the propagation differential phase and the total differential phase, which includes the differential phase upon backscattering. The other important addition is the estimate of the computing time, that an important factor for the acceptance of the method by the community. Of course making sw implementation available is good mean to achieve that goal. I welcome and support any attempts to improve Kdp estimation, which is still an important and totally solved aspect of radar meteorology. In my previous revision I noticed an insufficient discussion of results, while granting authors attenuating circumstances based on the impossibility of providing an ultimate proof and also. Authors replied that validation will be presented in a series of new papers. Based on this reply, I cannot change my recommendation to the editor based on authors’ justifications, because I would like more convincing validation results here.
Specific issues (numbers corespond to replies to reviewer; pages and number are referred to the manuscript explicitly showing changes):
Reply 2.2: When figures about computation time are given, the computing environment must be specified in the text.
Reply 2.3: What I asked was to separate Kdp estimation methods based on dp profile derivative from methods based on self-consistency with Zh and , which is valid only in rain. In addition in the introduction, “A linear regression model has been developed to derive Kdp from the slope of the range profile of dp for S-band radars. In Hubbert et al. (1993), ….”. Methods described in Hubbert’s papers from 1990s were evaluated using the POLDIRAD, a C-band radar of DLR (Germany). Following my (and maybe other reviewers’ comments), a couple of sentences were added. According to those sentences, Vulpiani et al’. s method is just applicable to C-band, in Italy, and in complex terrain (actually it is a general method and can be found in published studies at X-band, non necessarily in complex orography regions). About Gorgucci et al (1999) authors say “that the nonuniform rainfall path produces large errors in the Kdp estimates, while the errors increase as the radar reflectivity varies in dimensions.” I do not understand this sentence.
Reply 2.4. I suggested the need to justify the importance of providing the standard deviation of Kdp estimate. At the moment, I am not aware of applications that require this quantity as input. Authors insert a sentence for this: “it can be used to calculate the variances of ZH and ZDR and rain rate, and to study the streamflow trends in the hydrological model”. Exploiting authors wording in replies and in conclusion, a more convincing justification can be found. I recommend to use those words here. I understand that standard deviation of Kdp is useful in modelling error in Kdp-based QPE and attenuation/specific attenuation estimation. I am not sure about the application to hydrological modelling.
Reply 2.5. I think there is a fundamental misunderstanding about the supposed constancy of variance of dp. The formulas summarized in Bringi and Chandrasekar (2001) that were developed in previous studies conducted in 1990s, were actually a-priori models to highlight the dependency of the standard deviation of dp estimates on different characteristics of observed precipitation phenomenon and on the processing parameters, such as the number of samples. They answered to questions like: what is the expected accuracy of the estimates ? how many samples should we use to achieve it? In developing these models, a number of assumptions were made, such as, SNR high, TX phase stability and uniform beam filling. It is a common experience to verify that error is of some degrees higher and that, say at far distances, where SNR (and hv) is likely low, error on dp is higher than predicted by those models. You may discuss the adequacy of the old style a-priori models, and conclude that your model give a better characterization of the error based on measurements, but a sentence like “2(dp) is stable in LR” is wrong.
Reply 3.9. Figure 4 is still without x-label. I requested a justification of the interpretation ofco based on Zdr, but authors prefer to eliminate Zdr (It sounds like hiding the dust under the carpet).
Reply 3.10. Ho do authors know that non-uniform beam filling is not relevant here ? Actually it is an important source of error that is neglected by “old” models but affect your variance estimates.
Reply 3.12. I do think that presenting performance on unfolding is important. You may say that that 100% of unfording are detected with 0% false alarm.
Reply 3.18. Radome attenuation should not determine underestimation in Kdp, unless SNR drops significantly due to attenuation. I disagree with authors’interpretation
Appendix A: What the goal of this section ? The final formula is already available in literature. |

The Referee, despite recognizing the improvements introduced in the revised paper, remarks again the need to improve the validation of the proposed methodology.

You are kindly invited to take care of his/her comments before submitting the revised paper.

Kind regards

Gianfranco Vulpiani