Articles | Volume 30, issue 4
Research article
20 Nov 2023
Research article |  | 20 Nov 2023

Robust weather-adaptive post-processing using model output statistics random forests

Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1021', Anonymous Referee #1, 20 Jun 2023
    • AC1: 'Reply on RC1', Thomas Muschinski, 06 Sep 2023
  • RC2: 'Comment on egusphere-2023-1021', Anonymous Referee #2, 26 Jun 2023
    • AC2: 'Reply on RC2', Thomas Muschinski, 06 Sep 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Thomas Muschinski on behalf of the Authors (25 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (26 Sep 2023) by Takemasa Miyoshi
AR by Thomas Muschinski on behalf of the Authors (04 Oct 2023)  Author's response   Manuscript 
Short summary
Statistical post-processing is necessary to generate probabilistic forecasts from physical numerical weather prediction models. To allow for more flexibility, there has been a shift in post-processing away from traditional parametric regression models towards modern machine learning methods. By fusing these two approaches, we developed model output statistics random forests, a new post-processing method that is highly flexible but at the same time also very robust and easy to interpret.