Articles | Volume 14, issue 10
Model description paper
04 Oct 2021
Model description paper |  | 04 Oct 2021

SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics

Olivier Pannekoucke and Philippe Arbogast


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-89', Anonymous Referee #1, 19 May 2021
  • RC2: 'Comment on gmd-2021-89', Anonymous Referee #2, 08 Jul 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Olivier Pannekoucke on behalf of the Authors (29 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (30 Jul 2021) by Sylwester Arabas
RR by Anonymous Referee #2 (14 Aug 2021)
ED: Publish subject to technical corrections (16 Aug 2021) by Sylwester Arabas
AR by Olivier Pannekoucke on behalf of the Authors (23 Aug 2021)  Author's response    Manuscript

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Olivier Pannekoucke on behalf of the Authors (27 Sep 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (27 Sep 2021) by Sylwester Arabas
Short summary
This contributes to research on uncertainty prediction, which is important either for determining the weather today or estimating the risk in prediction. The problem is that uncertainty prediction is numerically very expensive. An alternative has been proposed wherein uncertainty is presented in a simplified form with only the dynamics of certain parameters required. This tool allows for the determination of the symbolic equations of these parameter dynamics and their numerical computation.