Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data |
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| Authors: | Seung-Woo LEE and Dong-Kyou LEE |
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| Affiliation: | School of Earth and Environmental Sciences, Seoul National University, Seoul 151--747, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul 151--747, Korea |
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| Abstract: | Satellite data obtained over synoptic data-sparse regions such as an oceancontribute toward improving the quality of the initial state of limited-areamodels. Background error covariances are crucial to the proper distributionof satellite-observed information in variational data assimilation. In theNMC (National Meteorological Center) method, background error covariancesare underestimated over data-sparse regions such as an ocean because ofsmall differences between different forecast times. Thus, it is necessary toreconstruct and tune the background error covariances so as to maximize theusefulness of the satellite data for the initial state of limited-areamodels, especially over an ocean where there is a lack of conventional data.In this study, we attempted to estimate background error covariances so asto provide adequate error statistics for data-sparse regions by usingensemble forecasts of optimal perturbations using bred vectors. Thebackground error covariances estimated by the ensemble method reduced theoverestimation of error amplitude obtained by the NMC method. By employingan appropriate horizontal length scale to exclude spurious correlations, theensemble method produced better results than the NMC method in theassimilation of retrieved satellite data. Because the ensemble methoddistributes observed information over a limited local area, it would be moreuseful in the analysis of high-resolution satellite data. Accordingly, theperformance of forecast models can be improved over the area where thesatellite data are assimilated. |
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| Keywords: | 3DVAR background error covariances retrieved satellite data assimilation ensemble forecasts |
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