Collection/aggregation algorithms in Lagrangian cloud microphysical models: rigorous evaluation in box model simulations

Unterstrasser, Simon; Hoffmann, Fabian; Lerch, Marion

doi:https://doi.org/10.5194/gmd-10-1521-2017

Articles | Volume 10, issue 4

https://doi.org/10.5194/gmd-10-1521-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Particle-based methods for simulating atmospheric aerosol...

https://doi.org/10.5194/gmd-10-1521-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 4

Model evaluation paper

|

13 Apr 2017

Model evaluation paper |

| 13 Apr 2017

Collection/aggregation algorithms in Lagrangian cloud microphysical models: rigorous evaluation in box model simulations

Simon Unterstrasser, Fabian Hoffmann, and Marion Lerch

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Final revised paper (published on 13 Apr 2017)
Supplement to the final revised paper
Preprint (discussion started on 28 Nov 2016)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'Referee Comment', Anonymous Referee #1, 11 Jan 2017
- AC1: 'Reply to Reviewer 1', Simon Unterstrasser, 03 Mar 2017
RC2: 'Referee comment', Anna Jaruga, 13 Jan 2017
- AC2: 'Reply to Anna Jaruga', Simon Unterstrasser, 03 Mar 2017

Peer-review completion

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

AR by Simon Unterstrasser on behalf of the Authors (03 Mar 2017) Author's response Manuscript

ED: Publish as is (16 Mar 2017) by Samuel Remy

AR by Simon Unterstrasser on behalf of the Authors (17 Mar 2017)

Short summary

In the last decade, several Lagrangian microphysical models (LCMs) have been developed which use a large number of (computational) particles to represent a cloud. In particular, the collision process leading to coalescence of cloud droplets or aggregation of ice crystals is implemented differently in various models. Three existing implementations are reviewed and extended, and their performance is evaluated by a comparison with well established analytical and bin model solutions.