State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems

State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems

This paper compares several commonly used state-of-the-art ensemble-based data assimilation methods in a coherent mathematical notation. The study encompasses different methods that are applicable to high-dimensional geophysical systems, like ocean and atmosphere and provide an uncertainty estimate....

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Bibliographic Details
Main Authors: Sanita Vetra-Carvalho, Peter Jan van Leeuwen, Lars Nerger, Alexander Barth, M. Umer Altaf, Pierre Brasseur, Paul Kirchgessner, Jean-Marie Beckers
Format: Article
Language:English
Published: Stockholm University Press 2018-01-01
Series:Tellus: Series A, Dynamic Meteorology and Oceanography
Subjects:
ensemble Kalman filter
particle filter
data assimilation
high dimension
non Gaussian
Online Access:http://dx.doi.org/10.1080/16000870.2018.1445364

Internet

http://dx.doi.org/10.1080/16000870.2018.1445364

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