Data assimilation with correlated observation errors: experiments with a 1-D shallow water model

By L. Stewart, Sarah L. Dance, Nancy Nichols. 2013

108 citations recorded by the source corpus.

Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. In this article, we consider several approaches to approximating observation error correlation matrices: diagonal approximations, eigendecomposition approximations and Markov matrices. These approximations are applied in incremental variational assimilation experiments with a 1-D shallow water model using synthetic observations. Our experiments quantify analysis accuracy in comparison with a reference or ‘truth’ trajectory, as well as with analyses using the ‘true’ observation error covariance matrix. We show that it is often better to include an approximate correlation structure in the observation error covariance matrix than to incorrectly assume error independence. Furthermore, by choosing a suitable matrix approximation, it is feasible and computationally cheap to include error correlation structure in a variational data assimilation algorithm.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/343fa46edefebf08bf7930ac4902f3e0a08ed82e

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Citations in the corpus

On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “The analysis RMSE was used as a metric of the impact of OECs in many previous studies, e.g. Bergman and Bonner (1976), Seaman (1977), Liu and Rabier (2002), Miyoshi et al. (2013), Stewart et al. (2013) and Rainwater et al. (2015).”
The benefits of correlated observation errors for small scales (2015, background): “Thus, inflating the identity gives a reasonable estimate for the large-scale errors (Stewart et al., 2008, 2013) while reducing the accuracy of the estimate for small scales.”
On the representation error in data assimilation (2018, unknown): “…of approximated forms of spatial correlation matrices (or their inverses) have been proposed in the literature to increase numerical efﬁciency while preserving observation information content and analysis accuracy (Healy and White, 2005; Fisher, 2005; Stewart et al. , 2008, 2013; Stewart, 2010).”