Articles | Volume 24, issue 6
https://doi.org/10.5194/nhess-24-1929-2024
https://doi.org/10.5194/nhess-24-1929-2024
Brief communication
 | 
11 Jun 2024
Brief communication |  | 11 Jun 2024

Brief communication: SWM – stochastic weather model for precipitation-related hazard assessments using ERA5-Land data

Melody Gwyneth Whitehead and Mark Stephen Bebbington

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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 nhess-2023-160', Anonymous Referee #1, 06 Oct 2023
    • AC1: 'Reply on RC1', Melody Whitehead, 06 Dec 2023
  • RC2: 'Comment on nhess-2023-160', Anonymous Referee #2, 02 Nov 2023
    • AC2: 'Reply on RC2', Melody Whitehead, 06 Dec 2023

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 Dec 2023) by Dan Li
AR by Melody Whitehead on behalf of the Authors (18 Feb 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (29 Feb 2024) by Dan Li
RR by Anonymous Referee #1 (12 Mar 2024)
RR by Anonymous Referee #2 (25 Mar 2024)
ED: Publish subject to minor revisions (review by editor) (07 Apr 2024) by Dan Li
AR by Melody Whitehead on behalf of the Authors (08 Apr 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (21 Apr 2024) by Dan Li
AR by Melody Whitehead on behalf of the Authors (29 Apr 2024)  Author's response   Manuscript 
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Short summary
Precipitation-driven hazards including floods, landslides, and lahars can be catastrophic and difficult to forecast due to high uncertainty around future weather patterns. This work presents a stochastic weather model that produces statistically similar (realistic) rainfall over long time periods at minimal computational cost. These data provide much-needed inputs for hazard simulations to support long-term, time and spatially varying risk assessments.
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