Articles | Volume 16, issue 6
Research article
09 Jun 2022
Research article |  | 09 Jun 2022

Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods

Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-48', Ross Brown, 24 Mar 2022
    • AC1: 'Reply on RC1', Moritz Buchmann, 20 Apr 2022
  • RC2: 'Comment on tc-2022-48', Anonymous Referee #2, 26 Apr 2022
    • AC2: 'Reply on RC2', Moritz Buchmann, 04 May 2022
  • RC3: 'Comment on tc-2022-48', Anonymous Referee #3, 04 May 2022
    • AC3: 'Reply on RC3', Moritz Buchmann, 11 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (13 May 2022) by Chris Derksen
AR by Moritz Buchmann on behalf of the Authors (18 May 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (27 May 2022) by Chris Derksen
AR by Moritz Buchmann on behalf of the Authors (27 May 2022)
Short summary
Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.