Articles | Volume 22, issue 15
https://doi.org/10.5194/acp-22-10221-2022
https://doi.org/10.5194/acp-22-10221-2022
Research article
 | 
10 Aug 2022
Research article |  | 10 Aug 2022

Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia

Michael John Weston, Stuart John Piketh, Frédéric Burnet, Stephen Broccardo, Cyrielle Denjean, Thierry Bourrianne, and Paola Formenti

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-152', Anonymous Referee #1, 08 Apr 2022
    • AC1: 'Reply on RC1', Michael Weston, 02 Jun 2022
  • RC2: 'Comment on acp-2022-152', Anonymous Referee #2, 19 Apr 2022
    • AC2: 'Reply on RC2', Michael Weston, 02 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Michael Weston on behalf of the Authors (02 Jun 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (09 Jun 2022) by J.M. Haywood
RR by Anonymous Referee #1 (23 Jun 2022)
RR by Anonymous Referee #2 (23 Jun 2022)
ED: Publish subject to minor revisions (review by editor) (23 Jun 2022) by J.M. Haywood
AR by Michael Weston on behalf of the Authors (10 Jul 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (19 Jul 2022) by J.M. Haywood
AR by Michael Weston on behalf of the Authors (20 Jul 2022)  Manuscript 
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Short summary
An aerosol-aware microphysics scheme is evaluated for fog cases in Namibia. AEROCLO-sA campaign observations are used to access and parameterise the model. The model cloud condensation nuclei activation is lower than the observations. The scheme is designed for clouds with updrafts, while fog typically forms in stable conditions. A pseudo updraft speed assigned to the lowest model levels helps achieve more realistic cloud droplet number concentration and size distribution in the model.
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