Abstract
Abstract. A hybrid variational-ensemble data assimilation scheme to estimate the vertical and horizontal parts of the background error covariance matrix for an ocean variational data assimilation system is presented and tested in a limited-area ocean model implemented in the western Mediterranean Sea. An extensive data set collected during the Recognized Environmental Picture Experiments conducted in June 2014 by the Centre for Maritime Research and Experimentation has been used for assimilation and validation. The hybrid scheme is used to both correct the systematic error introduced in the system from the external forcing (initialisation, lateral and surface open boundary conditions) and model parameterisation, and improve the representation of small-scale errors in the background error covariance matrix. An ensemble system is run offline for further use in the hybrid scheme, generated through perturbation of assimilated observations. Results of four different experiments have been compared. The reference experiment uses the classical stationary formulation of the background error covariance matrix and has no systematic error correction. The other three experiments account for, or not, systematic error correction and hybrid background error covariance matrix combining the static and the ensemble-derived errors of the day. Results show that the hybrid scheme when used in conjunction with the systematic error correction reduces the mean absolute error of temperature and salinity misfit by 55 and 42 % respectively, versus statistics arising from standard climatological covariances without systematic error correction.
Highlights
The study and the characterisation of the ocean is a complex discipline involving different aspects of modern science
The model has been initialised and forced at the lateral open boundaries using Mercator-Ocean (Drévillon et al, 2008) daily analyses, while atmospheric forcing was computed by means of interactive bulk formulae (Oddo et al, 2009) using the hourly operational products from the COnsortium for Small-scale MOdelling (COSMO)-ME limited-area atmospheric model
Following Dobricic and Pinardi (2008) the present variational scheme decomposes the background error covariance matrix (B) in a sequence of linear operators, each of them representing a specific component of the error structure
Summary
The study and the characterisation of the ocean is a complex discipline involving different aspects of modern science. The recourse to hybrid covariances and the choice of the relative weights given to them remains empirical in practice, it has been shown in particular that hybrid models tend to be more robust than conventional ensemble-based data assimilation schemes, especially when the model errors are larger than observational ones (Wang et al, 2007, 2008, 2009) This feature is attractive for the regional assimilation problems in oceanography, where information on the background state is often scant and incomplete. Recent work has started addressing the issue of multiscale data assimilation, where the analyses are a combination of corrections with different spatial-scale signals, assuming somehow that spatial scales are separable and that observations may naturally bear information across several spatial scales Examples of these schemes range from multiscale 3DVAR systems (MS-VAR), sequential applications of horizontal operators with different correlation length scales (Mirouze et al, 2016), or inclusion of a large-scale analysis in the analysis formulation as additional constraint (Guidard and Fischer, 2008).
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