Turbulent enhancement of radar reflectivity factor  for polydisperse cloud droplets

Matsuda, Keigo; Onishi, Ryo

doi:https://doi.org/10.5194/acp-19-1785-2019

Articles | Volume 19, issue 3

https://doi.org/10.5194/acp-19-1785-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-19-1785-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 3

Research article

|

08 Feb 2019

Research article |

| 08 Feb 2019

Turbulent enhancement of radar reflectivity factor for polydisperse cloud droplets

Keigo Matsuda and Ryo Onishi

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Final revised paper (published on 08 Feb 2019)
Preprint (discussion started on 13 Jun 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of of “Turbulent enhancement of radar reflectivity factor for polydisperse cloud droplets” by Matsuda and Onishi', Anonymous Referee #1, 06 Jul 2018
- AC1: 'Reply to the Referee Comment #1', Keigo Matsuda, 05 Nov 2018
RC2: 'Review of "Turbulent enhancement of radar reflectivity factor for polydisperse cloud droplets" by Matsuda and Onishi', Anonymous Referee #2, 15 Jul 2018
- AC2: 'Reply to the Referee Comment #2', Keigo Matsuda, 05 Nov 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Keigo Matsuda on behalf of the Authors (05 Nov 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (18 Nov 2018) by Graham Feingold

RR by Anonymous Referee #2 (19 Nov 2018)

RR by Anonymous Referee #1 (13 Dec 2018)

ED: Publish as is (07 Jan 2019) by Graham Feingold

AR by Keigo Matsuda on behalf of the Authors (13 Jan 2019)

Short summary

This paper presents a parameterization to predict the influence of microscale turbulent clustering of cloud droplets on the radar reflectivity factor, based on a direct numerical simulation (DNS) of turbulence. The proposed parameterization takes account of the turbulent clustering structure of droplets with arbitrary size distributions. This paper also discusses quantitative influences on realistic radar observations, applying the parameterization to high-resolution cloud-simulation data.