### Abstract

- Top of page
- Abstract
- 1. Introduction
- 2. Modeling tools
- 3. The ‘island’ localization method
- 4. Forecast experiments
- 5. Conclusions
- Acknowledgements
- References

A method to select local statistics for background-error covariance matrix (B) determination is presented and applied to the 1D-Var+nudging assimilation of SEVIRI radiances in the COSMO model. The system is designed as a post-processing algorithm of an ensemble forecast system based on multi-model initial and boundary conditions and perturbations of physical parametrization parameters. Ensemble spread maps are combined to identify regions (‘islands’) inside the model domain of uniform and large error. Thereafter B matrices are calculated using *local* statistics from the identified islands assuming the ensemble spread to be representative of the background-error dispersion. This calculation is repeated at the beginning of each assimilation cycle to ensure the time evolution of the selected regions and thus the flow-dependency of the background-error covariance matrix statistics.

The benefit of calculating B using local statistics is then quantified by comparison to background covariance errors evaluated from global domain statistics. This is done using the same ensemble members over the whole domain, i.e. without applying the localization procedure. The standard NMC approach, which uses long time series of model departures calculated at different forecast times, is also compared for reference. Model departure statistics and comparison of the final analysis to independent observations from surface stations and satellite products highlight the relevance of the localization procedure even though the noisy structure of the variance profiles produces a substantial reduction in the number of valid retrieved profiles. The final findings show that the selective identification of areas of homogeneous spread/error is a suitable approach to characterize local error growth which can bring about sensible improvements in the forecast scores. Copyright © 2011 Royal Meteorological Society