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Implications of the form of the ensemble transformation in the ensemble square root filters
journal contribution
posted on 2023-05-17, 06:54 authored by Sakov, P, Oke, P, Stuart CorneyStuart CorneyThis paper considers implications of different forms of the ensemble transformation in the ensemble square root filters (ESRFs) for the performance of ESRF-based data assimilation systems. It highlights the importance of using mean-preserving solutions for the ensemble transform matrix (ETM). The paper shows that an arbitrary mean-preserving ETM can be represented as a product of the symmetric solution and an orthonormal mean-preserving matrix. The paper also introduces a new flavor of ESRF, referred to as ESRF with mean-preserving random rotations. To investigate the performance of different solutions for the ETM in ESRFs, experiments with two small models are conducted. In these experiments, the performances of two mean-preserving solutions, two non-mean-preserving solutions, and a traditional ensemble Kalman filter with perturbed observations are compared. The experiments show a significantly better performance of the mean-preserving solutions for the ETM in ESRFs compared to non-mean-preserving solutions. They also show that applying the mean-preserving random rotations prevents the buildup of ensemble outliers in ESRF-based data assimilation systems
History
Publication title
Monthly Weather ReviewVolume
136Pagination
1042-1053ISSN
0027-0644Department/School
Institute for Marine and Antarctic StudiesPublisher
Amer Meteorological SocPlace of publication
45 Beacon St, Boston, USA, Ma, 02108-3693Repository Status
- Restricted