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The Ensemble Kalman Filter: Theoretical formulation and practical implementation
Geir Evensen
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TLDR
The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it as mentioned in this paper, and also presents new ideas and alternative interpretations which further explain the success of the EnkF.Abstract:
The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.read more
Citations
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Journal ArticleDOI
The Twentieth Century Reanalysis Project
Gilbert P. Compo,Gilbert P. Compo,Jeffrey S. Whitaker,Prashant D. Sardeshmukh,Prashant D. Sardeshmukh,N. Matsui,N. Matsui,Rob Allan,Xiaojun Yin,Byron E. Gleason,Russell S. Vose,Glenn Rutledge,P. Bessemoulin,Stefan Brönnimann,Stefan Brönnimann,Manola Brunet,Manola Brunet,R. Crouthamel,Andrea Grant,Pavel Ya. Groisman,Pavel Ya. Groisman,Philip Jones,Michael C. Kruk,Andries Kruger,Gareth J. Marshall,Maurizio Maugeri,H. Mok,Øyvind Nordli,Tom Ross,Ricardo M. Trigo,Xiaolan L. Wang,Scott D. Woodruff,Steven J. Worley +32 more
TL;DR: The Twentieth Century Reanalysis (20CR) dataset as discussed by the authors provides the first estimates of global tropospheric variability, and of the dataset's time-varying quality, from 1871 to the present at 6-hourly temporal and 2° spatial resolutions.
Journal ArticleDOI
The use of the multi-model ensemble in probabilistic climate projections
Claudia Tebaldi,Reto Knutti +1 more
TL;DR: The motivation for using multi-model ensembles, the methodologies published so far and their results for regional temperature projections are outlined, and the challenges in interpreting multi- model results are discussed.
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References
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Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
TL;DR: In this article, a new sequential data assimilation method is proposed based on Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter.
Journal ArticleDOI
The Twentieth Century Reanalysis Project
Gilbert P. Compo,Gilbert P. Compo,Jeffrey S. Whitaker,Prashant D. Sardeshmukh,Prashant D. Sardeshmukh,N. Matsui,N. Matsui,Rob Allan,Xiaojun Yin,Byron E. Gleason,Russell S. Vose,Glenn Rutledge,P. Bessemoulin,Stefan Brönnimann,Stefan Brönnimann,Manola Brunet,Manola Brunet,R. Crouthamel,Andrea Grant,Pavel Ya. Groisman,Pavel Ya. Groisman,Philip Jones,Michael C. Kruk,Andries Kruger,Gareth J. Marshall,Maurizio Maugeri,H. Mok,Øyvind Nordli,Tom Ross,Ricardo M. Trigo,Xiaolan L. Wang,Scott D. Woodruff,Steven J. Worley +32 more
TL;DR: The Twentieth Century Reanalysis (20CR) dataset as discussed by the authors provides the first estimates of global tropospheric variability, and of the dataset's time-varying quality, from 1871 to the present at 6-hourly temporal and 2° spatial resolutions.
Journal ArticleDOI
Data Assimilation Using an Ensemble Kalman Filter Technique
TL;DR: In this article, the authors proposed an ensemble Kalman filter for data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as Ensemble Kalman filtering) in an idealized environment.
Journal ArticleDOI
Analysis Scheme in the Ensemble Kalman Filter
TL;DR: In this article, it is shown that the observations must be treated as random variables at the analysis steps, which results in a completely consistent approach if the covariance of the ensemble of model states is interpreted as the prediction error covariance, and there are no further requirements on the ensemble Kalman filter method.
Journal ArticleDOI
An Ensemble Adjustment Kalman Filter for Data Assimilation
TL;DR: In this paper, an ensemble adjustment Kalman filter is proposed to estimate the probability distribution of the state of a model given a set of observations using Monte Carlo approximations to the nonlinear filter.