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Ensemble-based background error covariance implementations using spatial recursive filters in NCEP's grid-point statistical interpolation system
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    A methodology for specifying ensemble-based background error covariances within the recursive-filter (RF) formalism of the NCEP Grid-point Statistical Interpolation (GSI) is proposed and tested using a low-resolution version of the North-American Regional Data Assimilation System. Perturbation fields from six-hour forecasts from the 80-member NCEP Global Ensemble Forecast System are used to represent the background errors. The RF-generated covariances are found to agree fairly well with the exact covariances computed directly from the ensemble perturbation fields in the vicinity of the selected test points. In addition, they are necessarily free of the spurious correlations at long ranges that characterize the covariance-matrix of the low-dimension space of the sample ensemble perturbations. The anisotropies are found to be rather weak when the 80-member ensemble is used without any further treatment to prescribe the local aspect tensor required by the recursive filter. However, a remarkable enhancement of the anisotropy is obtained when the ensemble size is artificially increased through a special type of local averaging. Results from a case study reveal an improvement of the 500 hPa geopotential height skill scores when the covariances with enhanced anisotropy are applied.
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