๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Ensemble-based Kalman filters in strongly nonlinear dynamics

โœ Scribed by Zhaoxia Pu; Joshua Hacker

Publisher
Springer-Verlag
Year
2009
Tongue
English
Weight
336 KB
Volume
26
Category
Article
ISSN
0256-1530

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Analysis sensitivity calculation in an e
Analysis sensitivity calculation in an ensemble Kalman filter
โœ Junjie Liu; Eugenia Kalnay; Takemasa Miyoshi; Carla Cardinali ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 522 KB

## Abstract Analysis sensitivity indicates the sensitivity of an analysis to the observations, which is complementary to the sensitivity of the analysis to the background. In this paper, we discuss a method to calculate this quantity in an Ensemble Kalman Filter (EnKF). The calculation procedure an

A generalized polynomial chaos based ens
A generalized polynomial chaos based ensemble Kalman filter with high accuracy
โœ Jia Li; Dongbin Xiu ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 849 KB

As one of the most adopted sequential data assimilation methods in many areas, especially those involving complex nonlinear dynamics, the ensemble Kalman filter (EnKF) has been under extensive investigation regarding its properties and efficiency. Compared to other variants of the Kalman filter (KF)

Covariance localisation and balance in a
Covariance localisation and balance in an Ensemble Kalman Filter
โœ Jeffrey D. Kepert ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 519 KB

## Abstract A major limitation of the Ensemble Kalman Filter (EnKF) is that the finite ensemble size introduces sampling error into the background covariances, with severe consequences for atmospheric and oceanographic applications. The negative effects of sampling error are customarily limited by