Data assimilation using a climatologically augmented local ensemble
                        transform Kalman filter

Kretschmer, Matthew; Hunt, Brian R.; Ott, Edward

doi:10.3402/tellusa.v67.26617

Cited by 17 publications

(17 citation statements)

References 20 publications

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“…Choices for

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that we consider are head , orthogonal and projection resampling. Head resampling is the simplest choice for

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, that picks the first n ens members of the augmented ensemble (Kretschmer et al , ). In orthogonal resampling , a random orthogonal matrix is applied to the augmented ensemble (Jazwinski, ; Pham, ) before truncating it.…”

Section: The Flexible Additive Inflation (Fai) Schemementioning

confidence: 99%

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A flexible additive inflation scheme for treating model error in ensemble Kalman filters

Sommer

Janjić

2018

Quart J Royal Meteoro Soc

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Data assimilation algorithms require an accurate estimate of the uncertainty of the prior (background) field that cannot be adequately represented by the ensemble of numerical model simulations. Partially, this is due to the sampling error that arises from the use of a small number of ensemble members to represent the background‐error covariance. It is also partially a consequence of the fact that the geophysical model does not represent its own error. Several mechanisms have been introduced so far to alleviate the detrimental effects of misrepresented ensemble covariances, allowing for the successful implementation of ensemble data assimilation techniques for atmospheric dynamics. One of the established approaches is additive inflation, which consists of perturbing each ensemble member with a sample from a given distribution. This results in a fixed rank of the effective model‐error covariance matrix. In this article, a more flexible approach is introduced, where the model error samples are treated as additional synthetic ensemble members, which are used in the update step of data assimilation but are not forecast. This way, the rank of the model‐error covariance matrix can be chosen independently of the ensemble. The effect of this altered additive inflation method on the performance of the filter is analyzed here in an idealized experiment. It is shown that the additional synthetic ensemble members can make it feasible to achieve convergence in an otherwise divergent parameter setting of data assimilation. The use of this method also allows for a less stringent localization radius.

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“…Choices for

sans-serif S

that we consider are head , orthogonal and projection resampling. Head resampling is the simplest choice for

sans-serif S

Section: The Flexible Additive Inflation (Fai) Schemementioning

confidence: 99%

“…Here we suggest a different approach that combines the ensemble background with the model error perturbations but uses a full-rank observation-error covariance matrix. The formula has been as well used in Kretschmer et al, 2015 for specification of hybrid covariance.…”

Section: Proposed Generalizationmentioning

confidence: 99%

A flexible additive inflation scheme for treating model error in ensemble Kalman filters

Sommer

Janjić

2018

Quart J Royal Meteoro Soc

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“…Penny (2014) introduced an EnVar method by augmenting LETKF with information from 3D Var by combining gain matrices of ensemble and variational method rather than linearly combining their respective background error covariance as done in traditional approaches. Kretschmer et al (2015) introduced a method to improve the performance of EnKF by increasing size of ensemble members at the analysis time, created by adding climatological perturbations to forecast ensemble mean. As climatological perturbations are calculated once, there is negligible computational expense involved in obtaining additional ensemble members.…”

Section: Introductionmentioning

confidence: 99%

Impact of hybrid GSI analysis using ETR ensembles

Prasad

Johny

2016

J Earth Syst Sci

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Performance of a hybrid assimilation system combining 3D Var based NGFS (NCMRWF Global Forecast System) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) of resolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013 and forecast skills over different spatial domains are compared with respect to mean analysis state. Rainfall forecast is verified over Indian region against combined observations of IMD and NCMRWF. Hybrid assimilation produced marginal improvements in overall forecast skill in comparison with 3D Var. Hybrid experiment made significant improvement in wind forecasts in all the regions on verification against mean analysis. The verification of forecasts with radiosonde observations also show improvement in wind forecasts with the hybrid assimilation. On verification against observations, hybrid experiment shows more improvement in temperature and wind forecasts at upper levels. Both hybrid and operational 3D Var failed in prediction of extreme rainfall event over Uttarakhand on 17 June, 2013.

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“…This method has then been leveraged upon by Bishop and Hodyss [14] and used in the literature to perform covariance localisation [15][16][17][18][19]. With an alternative point of view, Kretschmer et al [20] have included localisation in the ensemble transform Kalman filter (ETKF, [21]) by the means of a climatologically augmented ensemble. Finally, Lorenc [22] has shown that the background error covariance matrix can be improved in hybrid ensemble variational data assimilation systems by using time-lagged and time-shifted perturbations.…”

Section: Introductionmentioning

confidence: 99%

On the Efficiency of Covariance Localisation of the Ensemble Kalman Filter Using Augmented Ensembles

Farchi

Bocquet

2019

Front. Appl. Math. Stat.

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Localisation is one of the main reasons for the success of the ensemble Kalman filter (EnKF) in high-dimensional geophysical data assimilation problems. It is based on the assumption that correlations between variables of a dynamical system decrease at a fast rate with the physical distance. In the EnKF, two types of localisation methods have emerged: domain localisation and covariance localisation. In this article, we explore possible implementations for covariance localisation in the deterministic EnKF using augmented ensembles in the analysis. We first discuss and compare two different methods to construct the augmented ensemble. The first method, known as modulation, relies on a factorisation property of the background covariance matrix. The second method is based on randomised singular value decomposition (svd) techniques, and has not been previously applied to covariance localisation. The qualitative properties of both methods are illustrated using a simple one-dimensional covariance model. We then show how localisation can be introduced in the perturbation update step using this augmented ensemble framework and we derive a generic ensemble square root Kalman filter with covariance localisation (LEnSRF). Using twin simulations of the Lorenz-1996 model we show that the LEnSRF is numerically more efficient when combined with the randomised svd method than with the modulation method. Finally we introduce a realistic extension of the LEnSRF that uses domain localisation in the horizontal direction and covariance localisation in the vertical direction. Using twin simulations of a multilayer extension of the Lorenz-1996 model, we show that this approach is adequate to assimilate satellite radiances, for which domain localisation alone is insufficient.

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Data assimilation using a climatologically augmented local ensemble transform Kalman filter

Cited by 17 publications

References 20 publications

A flexible additive inflation scheme for treating model error in ensemble Kalman filters

A flexible additive inflation scheme for treating model error in ensemble Kalman filters

Impact of hybrid GSI analysis using ETR ensembles

On the Efficiency of Covariance Localisation of the Ensemble Kalman Filter Using Augmented Ensembles

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