Articles | Volume 16, issue 12
Research article
21 Jun 2023
Research article |  | 21 Jun 2023

A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes

Najmeh Kaffashzadeh


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-310', Anonymous Referee #1, 10 Feb 2023
    • AC1: 'Reply on RC1', Najmeh Kaffashzadeh, 02 Apr 2023
  • RC2: 'Comment on amt-2022-310', Anonymous Referee #2, 16 Mar 2023
    • AC2: 'Reply on RC2', Najmeh Kaffashzadeh, 02 Apr 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Najmeh Kaffashzadeh on behalf of the Authors (25 Apr 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (16 May 2023) by Steffen Beirle
AR by Najmeh Kaffashzadeh on behalf of the Authors (21 May 2023)  Manuscript 
Short summary
Although quality control is a well-known issue in data application, research initiatives and organizations apply given methods based on traditional techniques (ad hoc thresholds and manual). These approaches are not only error prone but also unsuitable for a large volume of data. The method proposed in this paper is based on a new concept (probability) as an intuitive indicator and data’s characteristics, which leads it to be applicable to a wide variety of data and eases its fit for purpose.