Sentence examples for model errors covariance from inspiring English sources

Next, from this model we obtain within six iterations (typically a few iterations are enough for convergence) the COV-OBS.x1 field model together with its a posteriori model errors' covariance matrix using a 3σ e rejection criterion.

Model error covariance matrix.

Fig. 3 Model error covariance matrices for atmospheric profile errors.

These are derived from the COV-OBS.x1 field model and its posterior model error covariance matrix.

A novel adjoint-based procedure for adaptive tuning of the specified model error covariance matrix is introduced.

The quantities entering the a priori data error and model error covariance matrices are a priori unknown.

The largest errors are likely biases caused by unmodelled sources (Sabaka et al. 2015) which cannot be assessed using a formal model error covariance matrix, or by constructing models using the same technique from independent datasets.

The model error covariance matrix can be computed as, P_{text{e}} = frac{{(U - bar{U})(U - bar{U})^{text{T}} }}{N - 1} (12)ThEnKFKF algorithm consists of two steps, a forecast step (U f) and an update step (U a).

Variances are obtained from the dispersion within 50 realizations of the COV-OBS.x1 model (an ensemble large enough to give a converged estimate of the diagonal elements), derived from the COV-OBS.x1 posterior model error covariance matrix (see Gillet et al. 2013).

The model error covariance R is assumed diagonal, with the diagonal elements defined as R_{lm} = leftvertepsilon_{R}(l) {{b_{l}^{m}}}rightvert^{2}, epsilon_{R}(l) = epsilon_{0}(t) + left[epsilon_{1}(t -epsilon_{0}(t -epsilon_{{l-1}{L_{o}-1}, (40}.

The model presented in this study aims to produce, as far as possible, an unbiased estimate of the core state, considering SV model error covariances via a stochastic equation.