Preprints
https://doi.org/10.5194/egusphere-2022-928
https://doi.org/10.5194/egusphere-2022-928
22 Sep 2022
 | 22 Sep 2022

Toward a multivariate formulation of the PKF assimilation: application to a simplified chemical transport model

Antoine Perrot, Olivier Pannekoucke, and Vincent Guidard

Abstract. This contribution explores a new approach to forecast multivariate covariances for atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF formalism, the error covariance matrix is modelized by a covariance model relying on parameters, for which the dynamics is then computed. The PKF has been formulated in univariate cases, and a multivariate extension for chemical transport models is explored here. To do so, a simplified two-species chemical transport model over a 1D domain is introduced, based on the nonlinear Lotka-Volterra equations, which allows to propose a multivariate pseudo covariance model. Then, the multivariate PKF dynamics is formulated and its results are compared with a large ensemble Kalman filter (EnKF) in several numerical experiments. In these experiments, the PKF accurately reproduces the EnKF. Eventually, the PKF is formulated for a more complex chemical model composed of six chemical species (Generic Reaction Set). Again, the PKF succeeds at reproducing the multivariate covariances diagnosed on the large ensemble.

Journal article(s) based on this preprint

14 Jun 2023
Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model
Antoine Perrot, Olivier Pannekoucke, and Vincent Guidard
Nonlin. Processes Geophys., 30, 139–166, https://doi.org/10.5194/npg-30-139-2023,https://doi.org/10.5194/npg-30-139-2023, 2023
Short summary

Antoine Perrot et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-928', Annika Vogel, 20 Oct 2022
  • RC2: 'Comment on egusphere-2022-928', Anonymous Referee #2, 27 Oct 2022
  • RC3: 'Comment on egusphere-2022-928', Anonymous Referee #3, 31 Oct 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-928', Annika Vogel, 20 Oct 2022
  • RC2: 'Comment on egusphere-2022-928', Anonymous Referee #2, 27 Oct 2022
  • RC3: 'Comment on egusphere-2022-928', Anonymous Referee #3, 31 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Olivier Pannekoucke on behalf of the Authors (12 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (25 Jan 2023) by Zoltan Toth
RR by Anonymous Referee #3 (05 Feb 2023)
RR by Annika Vogel (16 Feb 2023)
ED: Publish subject to minor revisions (review by editor) (03 Mar 2023) by Zoltan Toth
AR by Olivier Pannekoucke on behalf of the Authors (16 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Apr 2023) by Zoltan Toth
RR by Anonymous Referee #3 (11 Apr 2023)
RR by Annika Vogel (23 Apr 2023)
ED: Publish subject to technical corrections (27 Apr 2023) by Zoltan Toth
AR by Olivier Pannekoucke on behalf of the Authors (09 May 2023)  Author's response   Manuscript 

Journal article(s) based on this preprint

14 Jun 2023
Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model
Antoine Perrot, Olivier Pannekoucke, and Vincent Guidard
Nonlin. Processes Geophys., 30, 139–166, https://doi.org/10.5194/npg-30-139-2023,https://doi.org/10.5194/npg-30-139-2023, 2023
Short summary

Antoine Perrot et al.

Antoine Perrot et al.

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
This work is a theoretical contribution that provides equations for understanding uncertainty prediction applied in air quality where multiple chemical species can interact. A simplified minimal test-bed is introduced that shows the ability our equations to reproduce the statistics estimated from an ensemble of forecast. While the latter estimation is the state of the art, solving equations is numerically less costly, depending on the number of chemical species, and motivates this research.