𝔖 Bobbio Scriptorium
✦   LIBER   ✦

On ensemble representation of the observation-error covariance in the Ensemble Kalman Filter

✍ Scribed by J. D. Kepert


Publisher
Springer
Year
2004
Tongue
English
Weight
503 KB
Volume
54
Category
Article
ISSN
1616-7228

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Simultaneous estimation of covariance in
✍ Hong Li; Eugenia Kalnay; Takemasa Miyoshi πŸ“‚ Article πŸ“… 2009 πŸ› John Wiley and Sons 🌐 English βš– 175 KB

## Abstract Covariance inflation plays an important role within the ensemble Kalman filter (EnKF) in preventing filter divergence and handling model errors. However the inflation factor needs to be tuned and tuning a parameter in the EnKF is expensive. Previous studies have adaptively estimated the

A robust formulation of the ensemble Kal
✍ S. J. Thomas; J. P. Hacker; J. L. Anderson πŸ“‚ Article πŸ“… 2009 πŸ› John Wiley and Sons 🌐 English βš– 338 KB

## Abstract The ensemble Kalman filter (EnKF) can be interpreted in the more general context of linear regression theory. The recursive filter equations are equivalent to the normal equations for a weighted least‐squares estimate that minimizes a quadratic functional. Solving the normal equations i

Growing-error correction of ensemble Kal
✍ Yoo-Geun Ham; In-Sik Kang πŸ“‚ Article πŸ“… 2010 πŸ› John Wiley and Sons 🌐 English βš– 474 KB

## Abstract In this study, a new Ensemble Kalman Filter (EnKF) algorithm called EnKF with growing‐error correction (EnKF‐GEC) is developed for minimizing the growing component of the forecast error; for this purpose, prospective observations are assimilated using empirical singular vectors (ESVs).

Accelerating the spin-up of Ensemble Kal
✍ Eugenia Kalnay; Shu-Chih Yang πŸ“‚ Article πŸ“… 2010 πŸ› John Wiley and Sons 🌐 English βš– 185 KB

## Abstract Ensemble Kalman Filter (EnKF) may have a longer spin‐up time to reach its asymptotic level of accuracy than the corresponding spin‐up time in variational methods (3D‐Var or 4D‐Var). During the spin‐up EnKF has to fulfill two independent requirements, namely that the ensemble mean be clo

On numerical properties of the ensemble
✍ Jia Li; Dongbin Xiu πŸ“‚ Article πŸ“… 2008 πŸ› Elsevier Science 🌐 English βš– 401 KB

Ensemble Kalman filter (EnKF) has been widely used as a sequential data assimilation method, primarily due to its ease of implementation resulting from replacing the covariance evolution in the traditional Kalman filter (KF) by an approximate Monte Carlo ensemble sampling. In this paper rigorous ana