Articles | Volume 17, issue 13
https://doi.org/10.5194/amt-17-4015-2024
https://doi.org/10.5194/amt-17-4015-2024
Research article
 | 
08 Jul 2024
Research article |  | 08 Jul 2024

Bayesian cloud-top phase determination for Meteosat Second Generation

Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt

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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 egusphere-2023-2345', Anonymous Referee #1, 07 Mar 2024
  • RC2: 'Comment on egusphere-2023-2345', Anonymous Referee #2, 05 Apr 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Johanna Mayer on behalf of the Authors (06 May 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (09 May 2024) by Alyn Lambert
RR by Anonymous Referee #2 (13 May 2024)
RR by Anonymous Referee #1 (18 May 2024)
ED: Publish subject to technical corrections (20 May 2024) by Alyn Lambert
AR by Johanna Mayer on behalf of the Authors (21 May 2024)  Author's response   Manuscript 
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Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.