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The Resource Algorithms for Nonlinear Data Assimilation

Algorithms for Nonlinear Data Assimilation

Label
Algorithms for Nonlinear Data Assimilation
Title
Algorithms for Nonlinear Data Assimilation
Creator
Contributor
Subject
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Frontiers in Applied Dynamical Systems: Reviews and Tutorials Ser.
Series volume
v.2
Algorithms for Nonlinear Data Assimilation
Label
Algorithms for Nonlinear Data Assimilation
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=3567844
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface to the Series -- Preface -- Contents -- 1 Nonlinear Data Assimilation for high-dimensional systems -- 1 Introduction -- 1.1 What is data assimilation? -- 1.2 How do inverse methods fit in? -- 1.3 Issues in geophysical systems and popular present-day data-assimilation methods -- 1.4 Potential nonlinear data-assimilation methods for geophysical systems -- 1.5 Organisation of this paper -- 2 Nonlinear data-assimilation methods -- 2.1 The Gibbs sampler -- 2.2 Metropolis-Hastings sampling -- 2.2.1 Crank-Nicolson Metropolis Hastings -- 2.3 Hybrid Monte-Carlo Sampling -- 2.3.1 Dynamical systems -- 2.3.2 Hybrid Monte-Carlo -- 2.4 Langevin Monte-Carlo Sampling -- 2.5 Discussion and preview -- 3 A simple Particle filter based on Importance Sampling -- 3.1 Importance Sampling -- 3.2 Basic Importance Sampling -- 4 Reducing the variance in the weights -- 4.1 Resampling -- 4.2 The Auxiliary Particle Filter -- 4.3 Localisation in particle filters -- 5 Proposal densities -- 5.1 Proposal densities: theory -- 5.2 Moving particles at observation time -- 5.2.1 The Ensemble Kalman Filter -- 5.2.2 The Ensemble Kalman Filter as proposal density -- 6 Changing the model equations -- 6.1 The √íptimal' proposal density -- 6.2 The Implicit Particle Filter -- 6.3 Variational methods as proposal densities -- 6.3.1 4DVar as stand-alone method -- 6.3.2 What does 4Dvar actually calculate? -- 6.3.3 4DVar in a proposal density -- 6.4 The Equivalent-Weights Particle Filter -- 6.4.1 Convergence of the EWPF -- 6.4.2 Simple implementations for high-dimensional systems -- 6.4.3 Comparison of nonlinear data assimilation methods -- 7 Conclusions -- References -- 2 Assimilating data into scientific models: An optimal coupling perspective -- 1 Introduction -- 2 Data assimilation and Feynman-Kac formula -- 3 Monte Carlo methods in path space
  • 3.1 Ensemble prediction and importance sampling -- 3.2 Markov chain Monte Carlo (MCMC) methods -- 4 McKean optimal transportation approach -- 5 Linear ensemble transform methods -- 5.1 Sequential Monte Carlo methods (SMCMs) -- 5.2 Ensemble Kalman filter (EnKF) -- 5.3 Ensemble transform particle filter (ETPF) -- 5.4 Quasi-Monte Carlo (QMC) convergence -- 6 Spatially extended dynamical systems and localization -- 7 Applications -- 7.1 Lorenz-63 model -- 7.2 Lorenz-96 model -- 8 Historical comments -- 9 Summary and Outlook -- References
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9783319183473&userID=ebsco-test&password=ebsco-test
Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b4383841'}
Extent
1 online resource (130 pages)
Form of item
online
Isbn
9783319183473
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

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