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The Impact of Nonlinearity in Lagrangian Data Assimilation : Volume 20, Issue 3 (23/05/2013)

By Apte, A.

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Book Id: WPLBN0003989030
Format Type: PDF Article :
File Size: Pages 13
Reproduction Date: 2015

Title: The Impact of Nonlinearity in Lagrangian Data Assimilation : Volume 20, Issue 3 (23/05/2013)  
Author: Apte, A.
Volume: Vol. 20, Issue 3
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Apte, A., & R. T. Jone, C. K. (2013). The Impact of Nonlinearity in Lagrangian Data Assimilation : Volume 20, Issue 3 (23/05/2013). Retrieved from

Description: TIFR Centre for Applicable Mathematics, P. O. Bag 6503, Chikkabommasandra, Bangalore 560064, India. The focus of this paper is on how two main manifestations of nonlinearity in low-dimensional systems – shear around a center fixed point (nonlinear center) and the differential divergence of trajectories passing by a saddle (nonlinear saddle) – strongly affect data assimilation. The impact is felt through their leading to non-Gaussian distribution functions. The major factors that control the strength of these effects is time between observations, and covariance of the prior relative to covariance of the observational noise. Both these factors – less frequent observations and larger prior covariance – allow the nonlinearity to take hold. To expose these nonlinear effects, we use the comparison between exact posterior distributions conditioned on observations and the ensemble Kalman filter (EnKF) approximation of these posteriors. We discuss the serious limitations of the EnKF in handling these effects.

The impact of nonlinearity in Lagrangian data assimilation

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