Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean

Deng, Ziwang; Tang, Youmin; Freeland, Howard J.

doi:10.1029/2011jc006942

Cited by 10 publications

(14 citation statements)

References 53 publications

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“…Two runs, one with the spectral nudging scheme and the other without this scheme, are performed for the period 1980 to 2004, as mentioned in Section 2b. The run without the spectral nudging scheme has large biases; for example, there is a large warm bias in the subtropical southern Pacific Ocean and a cold bias in the subtropical northern Pacific Ocean at 180 m (Deng, Tang, & Freeland, 2011). However, the mean bias averaged over the entire Pacific Ocean is very small in both runs because of the cancellation between positive and negative biases in different regions.…”

Section: Results a Evaluation Of The Spectral Nudging Version Of The mentioning

confidence: 95%

A Time-Averaged Covariance Method in the EnKF for Argo Data Assimilation

et al. 2012

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In the ensemble Kalman filter (EnKF), ensemble size is one of the key factors that significantly affects the performance of a data assimilation system. A relatively small ensemble size often must be chosen because of the limitations of computational resources, which often biases the estimation of the background error covariance matrix. This is an issue of particular concern in Argo data assimilation, where the most complex state-of-theart models are often used. In this study, we propose a time-averaged covariance method to estimate the background error covariance matrix. This method assumes that the statistical properties of the background errors do not change significantly at neighbouring analysis steps during a short time window, allowing the ensembles generated at previous steps to be used in present steps. As such, a joint ensemble matrix combining ensembles of previous and present steps can be constructed to form a larger ensemble for estimating the background error covariance. This method can enlarge the ensemble size without increasing the number of model integrations, and this method is equivalent to estimating the background error covariance matrix using the mean ensemble covariance averaged over several assimilation steps. We apply this method to the assimilation of Argo and altimetry datasets with an oceanic general circulation model. Experiments show that the use of this time-averaged covariance can improve the performance of the EnKF by reducing the root mean square error (RMSE) and improving the estimation of error covariance structure as well as the relationship between ensemble spread and RMSE. RÉSUMÉ [Traduit par la rédaction] Dans le filtre de Kalman d'ensemble (EnKF), la taille de l'ensemble est l'un des facteurs clés qui ont une influence importante sur la performance d'un système d'assimilation de données. Il faut souvent choisir une taille d'ensemble assez petite à cause des limites des ressources informatiques, ce qui biaise souvent l'estimation de la matrice de covariance de l'erreur de fond. Cette question revêt une importance particulière pour l'assimilation des données Argo, qui fait souvent appel à des modèles de pointe très complexes. Dans cette étude, nous proposons une méthode de covariance moyennée dans le temps pour estimer la matrice de covariance de l'erreur de fond. Cette méthode suppose que les propriétés statistiques des erreurs de fond ne changent pas de façon importante d'une étape d'analyse à la suivante durant un court laps de temps, ce qui permet d'utiliser dans les étapes courantes les ensembles générés aux étapes précédentes. Ainsi, on peut construire une matrice d'ensembles conjoints combinant les ensembles des étapes précédentes et courantes pour former un plus grand ensemble dans le but d'estimer la covariance de l'erreur de fond. Cette méthode peut accroître la taille de l'ensemble sans augmenter le nombre d'intégrations du modèle; elle équivaut à estimer la matrice de covariance de l'erreur de fond en utilisant la covariance moyenne de l'ensemble calculée sur plus...

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Section: Results a Evaluation Of The Spectral Nudging Version Of The mentioning

confidence: 95%

A Time-Averaged Covariance Method in the EnKF for Argo Data Assimilation

et al. 2012

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“…Several comparisons of these techniques exist in the literature (e.g. Deng et al, 2011;Hamill and Whitaker, 2005;Whitaker et al, 2008).…”

Section: Model Noise Treatment In the Enkfmentioning

confidence: 99%

Extending the Square Root Method to Account for Additive Forecast Noise in Ensemble Methods

Raanes

Carrassi²,

Bertino³

2015

Monthly Weather Review

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A square root approach is considered for the problem of accounting for model noise in the forecast step of the ensemble Kalman filter (EnKF) and related algorithms. The primary aim is to replace the method of simulated, pseudo-random, additive noise so as to eliminate the associated sampling errors. The core method is based on the analysis step of ensemble square root filters, and consists in the deterministic computation of a transform matrix. The theoretical advantages regarding dynamical consistency are surveyed, applying equally well to the square root method in the analysis step. A fundamental problem due to the limited size of the ensemble subspace is discussed, and novel solutions that complement the core method are suggested and studied. Benchmarks from twin experiments with simple, low-order dynamics indicate improved performance over standard approaches such as additive, simulated noise and multiplicative inflation.

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“…Below is a brief introduction to these parameter settings. A detailed discussion about these parameters can be found in Deng et al [2011Deng et al [ , 2012.…”

Section: Et-spkf Assimilation Systemmentioning

confidence: 99%

“…EnKF (ensemble Kalman filter)-based filters have gained great success in assimilating atmospheric and oceanic data [e.g., Evensen, 2003;Zupanski, 2005;Tippett et al, 2003;Deng et al, 2011]. However, concerns still exist, such as generating ensemble members and determining the ensemble size.…”

Section: Introductionmentioning

confidence: 99%

A practical scheme of the sigma‐point Kalman filter for high‐dimensional systems

Tang

Deng

Manoj

et al. 2014

J Adv Model Earth Syst

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While applying a sigma-point Kalman filter (SPKF) to a high-dimensional system such as the oceanic general circulation model (OGCM), a major challenge is to reduce its heavy burden of storage memory and costly computation. In this study, we propose a new scheme for SPKF to address these issues. First, a reduced rank SPKF was introduced on the high-dimensional model state space using the truncated single value decomposition (TSVD) method (T-SPKF). Second, the relationship of SVDs between the model state space and a low-dimensional ensemble space is used to construct sigma points on the ensemble space (ET-SPKF). As such, this new scheme greatly reduces the demand of memory storage and computational cost and makes the SPKF method applicable to high-dimensional systems. Two numerical models are used to test and validate the ET-SPKF algorithm. The first model is the 40-variable Lorenz model, which has been a test bed of new assimilation algorithms. The second model is a realistic OGCM for the assimilation of actual observations, including Argo and in situ observations over the Pacific Ocean. The experiments show that ET-SPKF is computationally feasible for high-dimensional systems and capable of precise analyses. In particular, for realistic oceanic assimilations, the ET-SPKF algorithm can significantly improve oceanic analysis and improve ENSO prediction. A comparison between the ET-SPKF algorithm and EnKF (ensemble Kalman filter) is also tribally conducted using the OGCM and actual observations.

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Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean

Cited by 10 publications

References 53 publications

A Time-Averaged Covariance Method in the EnKF for Argo Data Assimilation

A Time-Averaged Covariance Method in the EnKF for Argo Data Assimilation

Extending the Square Root Method to Account for Additive Forecast Noise in Ensemble Methods

A practical scheme of the sigma‐point Kalman filter for high‐dimensional systems

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