Reply on RC1

[General Comments] In the study, the authors explore the idea of improving numerical simulation by improving the representation of the autoconversion from cloud to rain (ACT) with a "weighted ensemble (EN)" ATC parameterization. To construct the EN scheme, four widely used ATC parameterizations are employed, and then the EN scheme is coupled into the Thompson microphysics scheme in WRF. With the EN scheme, the authors run nested (to ~1 km) simulations of an extreme precipitation event over southern China and then examine the results by comparison of accumulated precipitation and radar reflectivity to observations. Besides, a detailed analysis is given in vertical motion and hydrometeor mass mixing ratios. The results show that the WRF model with EN run matches the observations better, compared to the BR scheme which is used originally in the Thompson microphysics scheme.


Response to Reviewer 1's comments [General Comments]
In the study, the authors explore the idea of improving numerical simulation by improving the representation of the autoconversion from cloud to rain (ACT) with a "weighted ensemble (EN)" ATC parameterization. To construct the EN scheme, four widely used ATC parameterizations are employed, and then the EN scheme is coupled into the Thompson microphysics scheme in WRF. With the EN scheme, the authors run nested (to ~1 km) simulations of an extreme precipitation event over southern China and then examine the results by comparison of accumulated precipitation and radar reflectivity to observations. Besides, a detailed analysis is given in vertical motion and hydrometeor mass mixing ratios. The results show that the WRF model with EN run matches the observations better, compared to the BR scheme which is used originally in the Thompson microphysics scheme.
The premise of trying to improve cloud microphysical parameterization through such a kind of ensemble approach is interesting and potentially useful. One unique feature of the ensemble approach is that the weighted mean is calculated within a microphysics scheme with a negligible increase in computation cost. In my opinion, the ensemble approach could easily be extended to other cloud microphysical processes. Besides, the ensemble scheme appears to be a useful tool that can be used to effectively switch between a single scheme alone as desired or to take the average result of chosen ensemble members. This paper is generally in a good shape, well organized, and conclusions well supported. However, there are a few items of concern that the authors should address before being accepted for publication.
Response: Thank you very much for your thorough review and constructive comments that have helped improve the quality of our manuscript.
(1) Several grammar errors and typos throughout the text, please check carefully.

Response:
We apologize for the language problems. We have revised the English writing of the manuscript carefully. The errors of word choice, verb tense, sentence structure as well as grammatical and bibliographical errors have been systematically dealt with and the relevant mistakes have been corrected in the revised manuscript.

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(2) In Section 2, four widely used autoconversion schemes are employed in the present study. Please elaborate on the advantages and disadvantages of these schemes, which might tell readers more information.
Response: Thanks for your kind suggestion. Detailed descriptions about the selected schemes have been added in the revised manuscript. For your convenience, the revised portions are also given as follows.
For the Kessler (KE) scheme: Kessler (1969) initially proposed a simple parameterization scheme that related the autoconversion rate to cloud water content. Owing to the simple and linear expression, the KE scheme is computationally straightforward to implement in numerical models. However, the major limitation of the KE scheme results in its inability to identify different conditions such as maritime and continental clouds (Ghosh and Jonas, 1999). More specifically, the KE scheme only took cloud water content (CWC) into account, while cloud number concentration was not incorporated. This may partially explain the KE scheme yielded the large errors at low CWC proposed by Cotton (1972). Besides, it is impossible to obtain the thresholds directly used in the scheme from observations at present. However, cloud microphysical processes are sensitive to the threshold (Plsselt et al., 2019). In order to get reasonable results, different values of q 0 were chosen by various studies. For instance, a value of 0.5 g m -3 is given in Kessler's (1969), Reisner (1998), andSchultz (1995). Thompson (2004) reduced to a small value of 0.35 g m -3 . Kong and Yau (1997) and Tao and Simpson (1993) gave a value of 2 g kg -1 , while a small value of 0.7 g kg -1 was assigned in Chen and Sun (2002).
For the Berry-Reinhardt (BR) scheme The BR scheme was developed theoretically in which not only CWC but also cloud number concentration was incorporated. An important characteristic is that maritime and continental clouds can be differentiated by the BR scheme using different parameters (Simpson and Wiggert, 1969;Pawlowska and Brenguier, 1996). Cotton (1972) argued that the BR scheme seems to underestimate rain formation in their simulations.

For the Khairoutdinov-Kogan (KK) scheme
The KK scheme was established based on a series of large-eddy simulations. The KK scheme uses a simple power-law expression based on bin microphysical calculations. Generally, speaking, the autoconversion rate increases with increasing CWC and/or decreasing cloud number concentration. The simple expression is a key advantage of the KK scheme, which makes it possible to analytically integrate the microphysical process rates over a probability density function (Griffin and Larson, 2013). In view of Fig. 1c, the KK scheme has a strong dependency on N c . Increasing N c from 100 to 500, ATC rates decrease dramatically, especially at the CWCs over 1.0 g m -3 . Unlike other schemes, ATC is allowable in the KK scheme even with very low CWCs, which might lead to overestimations under such conditions.

For the Liu-Daum-McGraw-Wood (LD) scheme
The LD scheme assumes that autoconversion rate is determined by CWC, cloud number concentration, and relative dispersion of cloud droplets. Xie and Liu (2015) suggested that the LD scheme considering spectral dispersion was more reliable for improving the understanding of the aerosol indirect effects, compared to the KE and BR schemes.