Articles | Volume 28, issue 1
Nonlin. Processes Geophys., 28, 1–22, 2021
https://doi.org/10.5194/npg-28-1-2021
Nonlin. Processes Geophys., 28, 1–22, 2021
https://doi.org/10.5194/npg-28-1-2021

Research article 14 Jan 2021

Research article | 14 Jan 2021

A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective

Olivier Pannekoucke et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Olivier Pannekoucke on behalf of the Authors (14 Sep 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (26 Sep 2020) by Wansuo Duan
RR by Anonymous Referee #2 (27 Sep 2020)
RR by Anonymous Referee #1 (09 Oct 2020)
RR by Anonymous Referee #3 (31 Oct 2020)
ED: Publish subject to minor revisions (review by editor) (11 Nov 2020) by Wansuo Duan
AR by Olivier Pannekoucke on behalf of the Authors (11 Nov 2020)  Author's response    Manuscript
ED: Publish as is (19 Nov 2020) by Wansuo Duan

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Olivier Pannekoucke on behalf of the Authors (12 Jan 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (13 Jan 2021) by Wansuo Duan
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Short summary
Numerical weather prediction involves numerically solving the mathematical equations, which describe the geophysical flow, by transforming them so that they can be computed. Through this transformation, it appears that the equations actually solved by the machine are then a modified version of the original equations, introducing an error that contributes to the model error. This work helps to characterize the covariance of the model error that is due to this modification of the equations.