Results 1  10
of
18
Asynchronous data assimilation with the EnKF
 Tellus
"... This study revisits the problem of assimilation of asynchronous observations, or fourdimensional data assimilation, with the ensemble Kalman filter (EnKF). We show that for a system with perfect model and linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution f ..."
Abstract

Cited by 16 (7 self)
 Add to MetaCart
This study revisits the problem of assimilation of asynchronous observations, or fourdimensional data assimilation, with the ensemble Kalman filter (EnKF). We show that for a system with perfect model and linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution for the problem: one just needs to use the ensemble observations (that is, the forecast observations for each ensemble member) from the time of observation during the update, for each assimilated observation. This recipe can be used for assimilating both past and future data; in the context of assimilating generic asynchronous observations we refer to it as the asynchronous EnKF. The asynchronous EnKF is essentially equivalent to the fourdimensional variational data assimilation (4DVar). It requires only one forward integration of the system to obtain and store the data necessary for the analysis, and therefore is feasible for largescale applications. Unlike 4DVar, the asynchronous EnKF requires no tangent linear or adjoint model. 1.
A NonGaussian Ensemble Filter Update for Data Assimilation
, 2009
"... A deterministic square root ensemble Kalman filter and a stochastic perturbed observation ensemble Kalman filter are used for data assimilation in both linear and nonlinear single variable dynamical systems. For the linear system, the deterministic filter is simply a method for computing the Kalman ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
A deterministic square root ensemble Kalman filter and a stochastic perturbed observation ensemble Kalman filter are used for data assimilation in both linear and nonlinear single variable dynamical systems. For the linear system, the deterministic filter is simply a method for computing the Kalman filter and is optimal while the stochastic filter has suboptimal performance due to sampling error. For the nonlinear system, the deterministic filter has increasing error as ensemble size increases because all ensemble members but one become tightly clustered. In this case, the stochastic filter performs better for sufficiently large ensembles. A new method for computing ensemble increments in observation space is proposed that does not suffer from the pathological behavior of the deterministic filter while avoiding much of the sampling error of the stochastic filter. This filter uses the order statistics of the prior observation space ensemble to create an approximate continuous prior probability distribution in a fashion analogous to the use of rank histograms for ensemble forecast evaluation. This rank histogram filter can represent nonGaussian observation space priors and posteriors and is shown to be competitive with existing filters for problems as large as global numerical weather prediction. The ability to represent nonGaussian distributions is useful for a variety of applications such as convectivescale assimilation and assimilation of bounded quantities such as relative humidity. 1.
1 Economic Impacts of Wind Covariance Estimation on Power Grid Operations
"... Abstract—We study the impact of capturing spatiotemporal correlations between multiple wind supply points on economic dispatch procedures. Using a simple dispatch model, we first show analytically that over/underestimation of correlation leads to positive and negative biases of dispatch cost, respec ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
(Show Context)
Abstract—We study the impact of capturing spatiotemporal correlations between multiple wind supply points on economic dispatch procedures. Using a simple dispatch model, we first show analytically that over/underestimation of correlation leads to positive and negative biases of dispatch cost, respectively. A rigorous, largescale computational study for the State of Illinois transmission grid with real topology and physical constraints reveals similar conclusions. For this study, we use the RaoBlackwellLedoitWolf estimator to approximate the wind covariance matrix from a small number of wind samples generated with the numerical weather prediction model WRF and we use the covariance information to generate a large number of wind scenarios. The resulting stochastic dispatch problems are solved by using the interiorpoint solver PIPSIPM on the BlueGene/Q (Mira) supercomputer at Argonne National Laboratory. We find that strong and persistent biases result from neglecting correlation information and indicate to the need to design a market that coordinates weather forecasts and uncertainty characterizations. Index Terms—covariance, correlation, spatiotemporal, estimation, uncertainty, wind power, dispatch
Localization techniques for ensemble transform Kalman filters ∗
, 2009
"... Ensemble Kalman filter techniques are widely used to assimilate observations into dynamical models. The dimension of phase is typically much larger than the number of ensemble members which leads to inaccurate results in the computed covariance matrices. These inaccuracies lead, among others, to spu ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Ensemble Kalman filter techniques are widely used to assimilate observations into dynamical models. The dimension of phase is typically much larger than the number of ensemble members which leads to inaccurate results in the computed covariance matrices. These inaccuracies lead, among others, to spurious long range correlations which can be eliminated by Schurproductbased localization techniques. In this paper, we propose computationally robust and efficient techniques for implementing such localization techniques within the class of ensemble transform/square root Kalman filters. Our approach relies on a continuous embedding of the Kalman analysis update of the ensemble deviation matrix.
© Author(s) 2013. CC Attribution 3.0 License. Nonlinear Processes in Geophysics
"... pen A ccess A mechanism for catastrophic filter divergence in data assimilation for sparse observation networks ..."
Abstract
 Add to MetaCart
pen A ccess A mechanism for catastrophic filter divergence in data assimilation for sparse observation networks
unknown title
"... iRemerciements Cette thèse doit énormément à Thierry Bergot, qui en est l'instigateur. Thierry est le père de cobelisba et connaît à fond le modèle, le brouillard et tous les problèmes liés à sa modélisation. Son expertise, sa disponibilité et sa bonne humeur ont beaucoup apporté à mon travai ..."
Abstract
 Add to MetaCart
iRemerciements Cette thèse doit énormément à Thierry Bergot, qui en est l'instigateur. Thierry est le père de cobelisba et connaît à fond le modèle, le brouillard et tous les problèmes liés à sa modélisation. Son expertise, sa disponibilité et sa bonne humeur ont beaucoup apporté à mon travail; ce manuscrit et les articles doivent énormément à ses relectures critiques et diligentes! C'est lui qui a initié cette aventure de trois ans; qu'il reçoive ici l'expression de toute ma gratitude. Patrick Josse a été mon supérieur hiérarchique dans l'équipe DPrévi/aéro de MétéoFrance pendant ces trois années. Il est le chef que l'on espère tous avoir un jour; disponible, compréhensif et compétent. Toute l'équipe Dprévi/aéro m'a chaleureusement accueilli, ces trois années m'ont permis d'apprécier tous les collègues
Generated using version 3.0 of the official AMS LATEX template A Moment Matching Particle Filter for Nonlinear NonGaussian Data Assimilation
"... The ensemble Kalman filter is now an important component of ensemble forecasting. While using the linear relationship between the observation and state variable makes it applicable for large systems, relying on linearity introduces nonnegligible bias since the true distribution will never be Gauss ..."
Abstract
 Add to MetaCart
The ensemble Kalman filter is now an important component of ensemble forecasting. While using the linear relationship between the observation and state variable makes it applicable for large systems, relying on linearity introduces nonnegligible bias since the true distribution will never be Gaussian. We review the ensemble Kalman filter from a statistical perspective and analyze the sources of its bias. We then propose a debiasing method called the nonlinear ensemble adjustment filter. This new filter transforms the forecast ensemble in a statistically principled manner so that the updated ensemble has the desired mean and variance which is calculated by importance sampling. We also show that the new filter is easily localizable and hence potentially useful for large systems. The new filter is tested through various experiments on Lorenz 63 system and Lorenz 96 system, showing promising performance when compared with other Kalman filter and particle filter variants. The results show that the new filter is stable and accurate for very challenging situations such as nonlinear, high dimensional system with sparse observations.
(www.interscience.wiley.com) DOI: 10.1002/qj.448
"... Adaptation of a particle filtering method for data assimilation in a 1D numerical model used for fog forecasting S. Rémy a, O.Pannekoucke b, T. Bergot c and C.Baehr d ..."
Abstract
 Add to MetaCart
Adaptation of a particle filtering method for data assimilation in a 1D numerical model used for fog forecasting S. Rémy a, O.Pannekoucke b, T. Bergot c and C.Baehr d