Articles | Volume 2, issue 4
https://doi.org/10.5194/wcd-2-1209-2021
https://doi.org/10.5194/wcd-2-1209-2021
Research article
 | 
15 Dec 2021
Research article |  | 15 Dec 2021

Bimodality in ensemble forecasts of 2 m temperature: identification

Cameron Bertossa, Peter Hitchcock, Arthur DeGaetano, and Riwal Plougonven

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2021-33', Anonymous Referee #1, 20 Jul 2021
  • RC2: 'Comment on wcd-2021-33', Anonymous Referee #2, 25 Jul 2021
  • AC1: 'Comment on wcd-2021-33', Cameron Bertossa, 27 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Cameron Bertossa on behalf of the Authors (24 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (27 Sep 2021) by Heini Wernli
RR by Anonymous Referee #2 (13 Oct 2021)
ED: Publish subject to minor revisions (review by editor) (13 Oct 2021) by Heini Wernli
AR by Cameron Bertossa on behalf of the Authors (24 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (09 Nov 2021) by Heini Wernli
Download
Short summary
While the assumption of Gaussianity leads to many simplifications, ensemble forecasts often exhibit non-Gaussian distributions. This work has systematically identified the presence of a specific case of non-Gaussianity, bimodality. It has been found that bimodality occurs in a large portion of global 2 m temperature forecasts. This has drastic implications on forecast skill as the minimum probability in a bimodal distribution often lies at the maximum probability of a Gaussian distribution.