Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4539-2024
https://doi.org/10.5194/hess-28-4539-2024
Research article
 | 
23 Oct 2024
Research article |  | 23 Oct 2024

Assessing rainfall radar errors with an inverse stochastic modelling framework

Amy C. Green, Chris Kilsby, and András Bárdossy

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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-2024-26', Anonymous Referee #1, 25 Mar 2024
    • AC1: 'Reply on RC1', Amy Green, 23 Apr 2024
  • RC2: 'Comment on egusphere-2024-26', Anonymous Referee #2, 25 Mar 2024
    • AC2: 'Reply on RC2', Amy Green, 23 Apr 2024
    • AC3: 'Figure 16', Amy Green, 23 Apr 2024
    • AC4: 'Figure 17', Amy Green, 23 Apr 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (06 May 2024) by Efrat Morin
AR by Amy Green on behalf of the Authors (26 Jun 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (04 Jul 2024) by Efrat Morin
RR by Anonymous Referee #2 (21 Aug 2024)
ED: Publish subject to technical corrections (29 Aug 2024) by Efrat Morin
AR by Amy Green on behalf of the Authors (06 Sep 2024)  Author's response   Manuscript 
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Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.