Ensemble singular vectors and their use as additive inflation in EnKF

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/100397

Title:	Ensemble singular vectors and their use as additive inflation in EnKF
Authors:	楊舒芝;Yang, Shu-Chih;Kalnay, Eugenia;Enomoto, Takeshi
Contributors:	地球科學學院大氣科學學系
Keywords:	Colleges & universities;Data assimilation;Data collection;dynamic sensitivity;ensemble Kalman filter;Meteorology;Norms;singular vector
Date:	2015-01-01
Issue Date:	2026-04-21 14:00:31 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Stockholm: Stockholm University Press
Abstract:	摘要： Given an ensemble of forecasts, it is possible to determine the leading ensemble singular vector (ESV), that is, the linear combination of the forecasts that, given the choice of the perturbation norm and forecast interval, will maximise the growth of the perturbations. Because the ESV indicates the directions of the fastest growing forecast errors, we explore the potential of applying the leading ESVs in ensemble Kalman filter (EnKF) for correcting fast-growing errors. The ESVs are derived based on a quasi-geostrophic multi-level channel model, and data assimilation experiments are carried out under framework of the local ensemble transform Kalman filter. We confirm that even during the early spin-up starting with random initial conditions, the final ESVs of the first analysis with a 12-h window are strongly related to the background errors. Since initial ensemble singular vectors (IESVs) grow much faster than Lyapunov Vectors (LVs), and the final ensemble singular vectors (FESVs) are close to convergence to leading LVs, perturbations based on leading IESVs grow faster than those based on FESVs, and are therefore preferable as additive inflation. The IESVs are applied in the EnKF framework for constructing flow-dependent additive perturbations to inflate the analysis ensemble. Compared with using random perturbations as additive inflation, a positive impact from using ESVs is found especially in areas with large growing errors. When an EnKF is ‘cold-started’ from random perturbations and poor initial condition, results indicate that using the ESVs as additive inflation has the advantage of correcting large errors so that the spin-up of the EnKF can be accelerated. 出版者： Stockholm: Stockholm University Press 出版日期： 2015-12 出處： Tellus A: Dynamic Meteorology and Oceanography, 2015-12, Vol.67 (1), p.26536--20 資源來源： Taylor & Francis Journals Auto-Holdings Collection 版權： 2015 S.-C. Yang et al. 2015 版權： Copyright Co-Action Publishing 2015 識別號： ISSN: 1600-0870 識別號： ISSN: 0280-6495 識別號： EISSN: 1600-0870 識別號： DOI: 10.3402/tellusa.v67.26536
Appears in Collections:	[Department of Atmospheric Sciences] journal & Dissertation

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