Journal ArticleDOI
Local ensemble Kalman filtering in the presence of model bias
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TLDR
It is found that forecasts can be greatly improved provided that a good model parameterizing the model bias is used to augment the state in the Kalman filter.Abstract:
We modify the local ensemble Kalman filter (LEKF) to incorporate the effect of forecast model bias. The method is based on augmentation of the atmospheric state by estimates of the model bias, and we consider different ways of modeling (i.e. parameterizing) the model bias. We evaluate the effectiveness of the proposed augmented state ensemble Kalman filter through numerical experiments incorporating various model biases into the model of Lorenz and Emanuel. Our results highlight the critical role played by the selection of a good parameterization model for representing the form of the possible bias in the forecast model. In particular, we find that forecasts can be greatly improved provided that a good model parameterizing the model bias is used to augment the state in the Kalman filter.read more
Citations
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Journal ArticleDOI
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter
TL;DR: A practical method for data assimilation in large, spatiotemporally chaotic systems, a type of “ensemble Kalman filter”, in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states.
Journal ArticleDOI
Ensemble Data Assimilation with the NCEP Global Forecast System
TL;DR: In this article, real-data experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the N CEP Global Data Assimilation System (GDAS).
Journal ArticleDOI
Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter
TL;DR: In this article, the authors propose to estimate the inflation factor and observational errors simultaneously within the EnKF, which works very well in the perfect model scenario and in the presence of random model errors or a small systematic model bias.
Journal ArticleDOI
4-D-Var or ensemble Kalman filter?
TL;DR: In this paper, the authors compared the performance of 4-D-Var and EnKF with the SPEEDY model and provided guidance on model error and observation localization for data assimilation.
Book
Filtering Complex Turbulent Systems
Andrew J. Majda,John Harlim +1 more
TL;DR: In this paper, the authors present mathematical strategies for filtering turbulent signal with model error, including the Kalman filter for vector systems, reduced filters and a three-dimensional toy model.
References
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Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal ArticleDOI
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
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
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.
Journal ArticleDOI
Ensemble Data Assimilation without Perturbed Observations
TL;DR: In this paper, the EnSRF algorithm is proposed, which uses the traditional Kalman gain for updating the ensemble mean but uses a reduced loss to update deviations from the EnKF mean.