Articles | Volume 16, issue 5
Research article
07 Mar 2023
Research article |  | 07 Mar 2023

Data quality enhancement for field experiments in atmospheric chemistry via sequential Monte Carlo filters

Lenard L. Röder, Patrick Dewald, Clara M. Nussbaumer, Jan Schuladen, John N. Crowley, Jos Lelieveld, and Horst Fischer


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1080', Anonymous Referee #2, 05 Dec 2022
    • AC1: 'Reply on RC1', Lenard Röder, 19 Jan 2023
  • RC2: 'Comment on egusphere-2022-1080', Anonymous Referee #1, 27 Dec 2022
    • AC2: 'Reply on RC2', Lenard Röder, 19 Jan 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Lenard Röder on behalf of the Authors (20 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (02 Feb 2023) by Keding Lu
AR by Lenard Röder on behalf of the Authors (09 Feb 2023)
Short summary
Field experiments in atmospheric chemistry provide insights into chemical interactions of our atmosphere. However, high data coverage and accuracy are needed to enable further analysis. In this study, we explore a statistical method that combines knowledge about the chemical reactions with information from measurements to increase the quality of field experiment datasets. We test the algorithm for several applications and discuss limitations that depend on the specific variable and the dynamics.