Articles | Volume 3, issue 1
Weather Clim. Dynam., 3, 113–137, 2022
https://doi.org/10.5194/wcd-3-113-2022
Weather Clim. Dynam., 3, 113–137, 2022
https://doi.org/10.5194/wcd-3-113-2022

Research article 01 Feb 2022

Research article | 01 Feb 2022

Automated detection and classification of synoptic-scale fronts from atmospheric data grids

Stefan Niebler et al.

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2021-27', Peter Düben, 24 Jun 2021
  • RC2: 'Comment on wcd-2021-27', Anonymous Referee #2, 29 Jun 2021
  • AC1: 'FAC on wcd-2021-27', Stefan Niebler, 28 Sep 2021
  • EC1: 'Editor's comment on wcd-2021-27', Lukas Papritz, 30 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Stefan Niebler on behalf of the Authors (15 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (25 Oct 2021) by Lukas Papritz
RR by Anonymous Referee #1 (06 Nov 2021)
RR by Anonymous Referee #2 (09 Nov 2021)
ED: Publish subject to minor revisions (review by editor) (15 Nov 2021) by Lukas Papritz
AR by Stefan Niebler on behalf of the Authors (23 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (29 Nov 2021) by Lukas Papritz
AR by Stefan Niebler on behalf of the Authors (08 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (09 Dec 2021) by Lukas Papritz
Download
Short summary
We use machine learning to create a network that detects and classifies four types of synoptic-scale weather fronts from ERA5 atmospheric reanalysis data. We present an application of our method, showing its use case in a scientific context. Additionally, our results show that multiple sources of training data are necessary to perform well on different regions, implying differences within those regions. Qualitative evaluation shows that the results are physically plausible.