Ensemble-Based Four-Dimensional Variational AssimilationUsing Hybrid Samples

SHAO Ai-mei;QIU Xiao-bin;QIU Chong-jian

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Plateau Meteorology
Plateau Meteorology ›› 2011, Vol. 30 ›› Issue (3) : 583-593.

Ensemble-Based Four-Dimensional Variational AssimilationUsing Hybrid Samples

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Abstract

The ensemble-based 4DVar approach with SVD technique (SVD-En4DVar) may be subject to significant uncertainties due to the size of forecast ensemble. Especially, the less ensemblesize will cause more truncation errors when the analysis variables are expressed as a linear expansion with the leading singular vectors extracted from an ensemble of the perturbed forecasts. In order to improve assimilation skills by increasing sample members but without addition computational costs, a hybrid sample analysis scheme is developed. In this scheme some time-invariant samples are added to the flow-dependent samples obtainedby the short-range forecasts over the analysis time window. The static samples can be produced by two approaches. In the first approach, the initial pseudo random fields with a specified statistic structure are added directly to the background field in 4D space without operating by the model integration. In the second approach, the static samples are obtained by integrating the model over the first analysis time window with the initial fields superposed the pseudo random fields on the initial background field. This implies that the partial flow-dependent samples yielded in the first assimilation circle will be used as the static samples in the subsequent circles. The numerical experiments on the different sample structures are tested with the shallow-water-equation model and Lorenz-96 model with 80 variables. When only large size of static sample ensembleis used, the better assimilation skills can be found in the shallow-water-equation model but not for Lorenz-96 model. However, two models perform well when hybrid samples are used. For the same computation costs, the hybrid sample analysis scheme can obviously improve assimilation accuracy and the second approach for statically sampling is better than the first one.

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Data assimilation / 4DVar / EnKF / SVD / Hybrid sample

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SHAO Ai-mei , QIU Xiao-bin , QIU Chong-jian. Ensemble-Based Four-Dimensional Variational AssimilationUsing Hybrid Samples. Plateau Meteorology. 2011, 30(3): 583-593

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