Fingerprints of a riming event on cloud radar Doppler spectra: observations  and modeling

Kalesse, Heike; Szyrmer, Wanda; Kneifel, Stefan; Kollias, Pavlos; Luke, Edward

doi:https://doi.org/10.5194/acp-16-2997-2016

Articles | Volume 16, issue 5

https://doi.org/10.5194/acp-16-2997-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/acp-16-2997-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 5

Research article

|

09 Mar 2016

Research article |

| 09 Mar 2016

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

Heike Kalesse, Wanda Szyrmer, Stefan Kneifel, Pavlos Kollias, and Edward Luke

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Final revised paper (published on 09 Mar 2016)
Preprint (discussion started on 22 Oct 2015)

Interactive discussion

Status: closed

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

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- Supplement

RC C9757: 'Review - "Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling"', Anonymous Referee #1, 25 Nov 2015
- AC C11335: 'Reply to reviewer #1', Heike Kalesse, 11 Jan 2016
RC C12319: 'Referee report', Anonymous Referee #2, 11 Feb 2016

Peer-review completion

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

AR by Heike Kalesse-Los on behalf of the Authors (11 Feb 2016) Author's response Manuscript

ED: Publish subject to technical corrections (16 Feb 2016) by Tuukka Petäjä

Short summary

Mixed-phase clouds are ubiquitous. Process-level understanding is needed to address the complexity of mixed-phase clouds and to improve their representation in models. This study illustrates steps to identify the impact of a microphysical process (riming) on cloud Doppler radar observations. It suggests that in situ observations of key ice properties are needed to complement radar observations before process-oriented studies can effectively evaluate ice microphysical parameterizations in models.