Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-483-2025
https://doi.org/10.5194/gmd-18-483-2025
Development and technical paper
 | 
28 Jan 2025
Development and technical paper |  | 28 Jan 2025

Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation

Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-632', Anonymous Referee #1, 26 Apr 2024
  • RC2: 'Comment on egusphere-2024-632', Anonymous Referee #2, 31 May 2024
  • EC1: 'Comment on egusphere-2024-632', Shu-Chih Yang, 12 Jun 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Pieter Rijsdijk on behalf of the Authors (11 Sep 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Oct 2024) by Shu-Chih Yang
RR by Anonymous Referee #1 (04 Oct 2024)
RR by Anonymous Referee #2 (04 Oct 2024)
ED: Publish subject to minor revisions (review by editor) (24 Oct 2024) by Shu-Chih Yang
AR by Pieter Rijsdijk on behalf of the Authors (03 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (17 Nov 2024) by Shu-Chih Yang
AR by Pieter Rijsdijk on behalf of the Authors (26 Nov 2024)  Manuscript 
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Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.