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"Perturbation Methods for Ensemble Data Assimilation"

[ 2017年03月02日 ]

RIKEN International Symposium on Data Assimilation 2017
"Perturbation Methods for Ensemble Data Assimilation"

In ensemble data assimilation, the forecast error is estimated by perturbations of the ensemble forecast, while characteristics of the ensemble forecast strongly depend on how the initial ensemble is generated. The ensemble transform (ET), eigenvalue decomposition of the analysis error covariance matrix, is widely used as the initial ensemble perturbation generator. ET has an advantage in that the magnitude of perturbations (initial ensemble spread) reflects the magnitude of the analysis error, but on the other hand, it is known that the growth of the errors is slower than other initial perturbation methods. In the previous studies for the mesoscale ensemble system (e.g., Saito et al.; 2011; 2012), perturbations from LETKF were not necessarily best as the initial perturbations, which may also affect the accuracy of the analysis field. Non-diagonal components in the transform matrix likely contaminate the synoptic scale structure of the bred vectors in the ensemble forecast in the assimilation window when the localization is applied. In the presentation, some preliminary results using the speedy model and the localization scale dependency of the power spectrum of diagonal and non-diagonal perturbations will be shown.
http://www.data-assimilation.riken.jp/risda2017/

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講師プロフィール
名前:Kazuo Saito 所属:Meteorological Research Institute
略歴:外部ページ

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