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- W2565909529 abstract "Increasing the size of the ensemble used in hybrid-variational assimilation methods has been shown to be beneficial, but is computationally expensive. This work sets out to see whether similar improvements can be obtained from a smaller ensemble by better estimation of ensemble covariances. Methods for improving these are described and illustrated using a toy model. The optimal settings depend on the ensemble size, the criterion used to measure error and the errors in ensemble generation. A hybrid covariance, filtered by spectral localization using wavebands and scale-dependent spatial localization, is shown to perform well and robustly.In the cycled ensemble data-assimilation scheme used for numerical weather prediction (NWP), another method of increasing the effective ensemble size in covariance estimates is the use of time-lagged and time-shifted perturbations. This is demonstrated to be effective in the Met Office's hybrid-4DEnVar system, both on its own and with waveband and scale-dependent localization. The best such combination performs nearly as well as an increase in ensemble size from 44 to 200." @default.
- W2565909529 created "2017-01-06" @default.
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- W2565909529 date "2017-01-01" @default.
- W2565909529 modified "2023-09-27" @default.
- W2565909529 title "Improving ensemble covariances in hybrid variational data assimilation without increasing ensemble size" @default.
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- W2565909529 doi "https://doi.org/10.1002/qj.2990" @default.
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