Objective filtering of ensemble‐based background‐error variances

Raynaud, Laure; Berre, Loïk; Desroziers, Gérald

doi:10.1002/qj.438

Cited by 56 publications

(121 citation statements)

References 25 publications

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“…As illustrated by Raynaud et al (2008) for instance, the spatial structure of this sampling noise is closely related to the spatial structure of the background error. This is formally explained by the analytical expression of the spatial covariance of the sampling noise (Raynaud et al, 2009), under the assumption of Gaussian errors:…”

Section: Spatial Structure Of Sampling Noisementioning

confidence: 99%

“…The objective spectral filtering proposed by Raynaud et al (2009) is based on the knowledge of the statistical properties of the signal and the noise. Spectral fields are obtained through the application of a spectral transform S to the gridpoint fields: s = S( v), s = S( v ) and s e = S(v e ).…”

Section: Objective Spectral Filteringmentioning

confidence: 99%

“…In recent years, flow-dependent background-error covariance estimates have been derived from ensemble data assimilation systems, either in the Kalman filter context (Evensen, 2003) or in the variational framework (Kucukkaraca and Fisher, 2006;Raynaud et al, 2009;Bonavita et al, 2011). However, the finite size of operational ensembles (namely O(100) members) introduces sampling errors in the covariance estimation.…”

Section: Introductionmentioning

confidence: 99%

“…While sampling noise is expected to be particularly strong for estimated correlations (off-diagonal coefficients of the estimated B matrix; Hamill et al, 2001), it also affects the estimated variances (diagonal coefficients of the estimated B matrix). The particular issue of sampling noise on variances has been widely studied by Raynaud et al (2008Raynaud et al ( , 2009) and Berre and Desroziers (2010) for instance. These studies demonstrated the close link between the spatial structures of the background error and the associated sampling noise.…”

Section: Introductionmentioning

confidence: 99%

“…This issue is examined in the present article, with the introduction of a spatial filtering based on a heterogeneous diffusion equation. Following results from Raynaud et al (2008Raynaud et al ( , 2009, we propose to tune the spatial variations of the diffusion coefficient according to the local length-scale of the sampling noise (Daley, 1991, p 110;. This allows us to smooth the large-scale noise, while the regions affected by small-scale noise are better preserved.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous filtering of ensemble‐based background‐error variances

Raynaud

Pannekoucke

2012

Quart J Royal Meteoro Soc

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Background-error variances estimated from a finite size ensemble of data assimilations are affected by sampling noise, which degrades the accuracy of the variance estimates. Previous work highlighted the close link between the spatial structures of background error and the associated sampling noise, and demonstrated the ability of local spatial averaging to remove this sampling noise.Existing filtering techniques commonly assume a homogeneous smoothing of the estimated variances. However, this assumption can be inadequate to represent error structures of varying scales, e.g. small-scale errors associated with localized severe weather events. To answer this problem, this article introduces and examines a heterogeneous filter based on the knowledge of the local spatial properties of the sampling noise. The filtering is realized with a diffusion process, and the diffusion coefficient is parametrized according to the local correlation length-scale of the sampling noise. This enables the diffusion coefficient to vary spatially in such a way as to encourage smoothing in regions where the background error is large scale in preference to regions where the error is small scale.A simulated 1D framework is considered to test the proposed approach. It is shown that the filtering using a spatially varying diffusion coefficient is able to preserve high-frequency variance structures, while this information tends to be smoothed with homogeneous filtering. The benefits of applying heterogeneous filtering are particularly pronounced with small ensemble sizes and in the vicinity of localized variance maxima.

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Section: Spatial Structure Of Sampling Noisementioning

confidence: 99%

Section: Objective Spectral Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heterogeneous filtering of ensemble‐based background‐error variances

Raynaud

Pannekoucke

2012

Quart J Royal Meteoro Soc

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Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system

Yang

Chen

Kondo

et al. 2017

J Adv Model Earth Syst

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High‐resolution numerical simulations are regularly used for severe weather forecasts. To improve model initial conditions, a single short localization is commonly applied in the ensemble Kalman filter when assimilating observations. This approach prevents large‐scale corrections from appearing in a high‐resolution analysis. To improve heavy rainfall forecasts associated with a multiscale weather system, analyses must be accurate across a range of spatial scales, a task that is difficult to accomplish using a single localization. This study is the first to apply a dual‐localization (DL) method to improve high‐resolution analyses used to forecast a real‐case heavy rainfall event associated with a Meiyu front on 16 June 2008 in Taiwan. A Meiyu front is a multiscale weather system characterized by storm‐scale convection, a mesoscale front, and large‐scale southwesterly monsoonal flow. The use of the DL method to produce the analyses was able to correct both the synoptic‐scale moisture flux transported by southwesterly monsoonal flow and the mesoscale low‐level convergence offshore of southwestern Taiwan. As a result, the forecasted amount, pattern, and temporal evolution of the heavy rainfall event were improved.

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Investigations and Experiments of Variances Filtering Technology in the Ensemble Data Assimilation

Liu

Zhang

Cao

et al. 2016

Chinese J of Geophysics

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An experimental Ensemble Data Assimilation (YH‐EDA) system has been built with 10 ensemble members based on the operational YH4DVAR system. The system can provide flow‐dependent background‐error variances, which are superior to the operational ones both in structure and magnitude. However, the finite ensemble size implies a detrimental sampling noise for the variances estimation. To solve this problem, a spectral filtering technique is implemented to formulate a low‐passing filter. Taking into account the typical horizontal length scales of noise and signal, the filter can eliminate the sampling noise while extracting the signal of interest. In the ensemble variance filtering experimentations of the 9th typhoon “Jebi” in 2013, our results show that the 10‐members' filtered variances exhibit better performance than 30‐members's estimation. The successful implementation of the spectral filtering reduces the requirement of large ensemble size of Ensemble Data Assimilation system, which indicates that spectral filtering has become an important and necessary technology in EDA operational implementation.

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Objective filtering of ensemble‐based background‐error variances

Cited by 56 publications

References 25 publications

Heterogeneous filtering of ensemble‐based background‐error variances

Heterogeneous filtering of ensemble‐based background‐error variances

Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system

Investigations and Experiments of Variances Filtering Technology in the Ensemble Data Assimilation

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