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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Institution: | 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 ocean
contribute toward improving the quality of the initial state of limited-area
models. Background error covariances are crucial to the proper distribution
of satellite-observed information in variational data assimilation. In the
NMC (National Meteorological Center) method, background error covariances
are underestimated over data-sparse regions such as an ocean because of
small differences between different forecast times. Thus, it is necessary to
reconstruct and tune the background error covariances so as to maximize the
usefulness of the satellite data for the initial state of limited-area
models, especially over an ocean where there is a lack of conventional data.
In this study, we attempted to estimate background error covariances so as
to provide adequate error statistics for data-sparse regions by using
ensemble forecasts of optimal perturbations using bred vectors. The
background error covariances estimated by the ensemble method reduced the
overestimation of error amplitude obtained by the NMC method. By employing
an appropriate horizontal length scale to exclude spurious correlations, the
ensemble method produced better results than the NMC method in the
assimilation of retrieved satellite data. Because the ensemble method
distributes observed information over a limited local area, it would be more
useful in the analysis of high-resolution satellite data. Accordingly, the
performance of forecast models can be improved over the area where the
satellite data are assimilated. |
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Keywords: | 3DVAR background error covariances retrieved satellite data assimilation ensemble forecasts |
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