Abstract
In the ensemble data assimilation, the background error covariance is estimated from perturbations of the ensemble forecast, while characteristics of the ensemble forecast strongly depend on how the initial ensemble is generated. The ensemble transform is a popular perturbation method that widely used as an ensemble perturbation generator, however, linear combinations of different perturbations in the ensemble transform (off-diagonal components of the transform matrix) may harm the global balance of the meteorological field. In this paper, we discuss this issue and show the structure of initial perturbations. Results of forecast experiments using the local ensemble transform Kalman filter (LETKF) for a simplified global model and a regional NWP model are shown. The spin-up issue in a cloud resolving model is shown with the comparison to an alternative method (diagonal LETKF).
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Notes
- 1.
Local ensemble transform Kalman filter.
- 2.
Ensemble Kalman filter.
- 3.
Ensemble-based variational method.
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Acknowledgements
This study was partly supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) as “Program for Promoting Researches on the Supercomputer Fugaku (hp200128, hp210166)” and Grant-in-Aid for Scientific Research (B) (16H04054) from Japan Society for the Promotion of Science (JSPS). The authors thank Takemasa Miyoshi of RIKEN, Takuya Kawabata of Meteorological Research Institute, Sho Yokota and Masaru Kunii of the Japan Meteorological Agency for their help and comments on data assimilation experiments. We appreciate careful check by an anonymous reviewer which improved the maturity of the manuscript.
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Saito, K., Duc, L., Matsunobu, T., Kurihana, T. (2022). Perturbations by the Ensemble Transform. In: Park, S.K., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV). Springer, Cham. https://doi.org/10.1007/978-3-030-77722-7_5
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DOI: https://doi.org/10.1007/978-3-030-77722-7_5
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