Articles | Volume 28, issue 1
Nonlin. Processes Geophys., 28, 1–22, 2021
Nonlin. Processes Geophys., 28, 1–22, 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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Cited articles

Berre, L., Pannekoucke, O., Desroziers, G., Stefanescu, S., Chapnik, B., and Raynaud, L.: A variational assimilation ensemble and the spatial filtering of its error covariances: increase of sample size by local spatial averaging, available at: (last access: 13 January 2021), ECMWF Workshop on Flow-dependent aspecyts of data assimilation, Reading, UK, 11–13 June 2007, 151–168, 2007. a
Boisserie, M., Arbogast, P., Descamps, L., Pannekoucke, O., and Raynaud, L.: Estimating and diagnosing model error variances in the Meteo-France global NWP model, Q. J. Roy. Meteor. Soc., 140, 846–854,, 2013. a
Boyd, J.: Chebyshev and Fourier Spectral Methods, Dover Publications, Mineola, New York, USA, 2001. a
Carrassi, A. and Vannitsem, S.: Accounting for Model Error in Variational Data Assimilation: A Deterministic Formulation, Mon. Weather Rev., 138, 3369–3386,, 2010. a
Cohn, S.: Dynamics of Short-Term Univariate Forecast Error Covariances, Mon. Weather Rev., 121, 3123–3149, 1993. a, b, c, d, e, f
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.