The Resource Nonlinear Data Assimilation, by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich, (electronic resource)

Nonlinear Data Assimilation, by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich, (electronic resource)

Label
Nonlinear Data Assimilation
Title
Nonlinear Data Assimilation
Statement of responsibility
by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich
Creator
Contributor
Author
Author
Subject
Language
  • eng
  • eng
Summary
This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now
Member of
http://library.link/vocab/creatorName
Van Leeuwen, Peter Jan
Dewey number
510
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
  • KMYAk-vXC5I
  • BDT3lzMGF9Y
  • GXR3MHYuNZs
Language note
English
LC call number
QA313
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Cheng, Yuan.
  • Reich, Sebastian.
Series statement
Frontiers in Applied Dynamical Systems: Reviews and Tutorials,
Series volume
2
http://library.link/vocab/subjectName
  • Differentiable dynamical systems
  • Computer science
  • Dynamical Systems and Ergodic Theory
  • Computational Mathematics and Numerical Analysis
  • Mathematical Applications in the Physical Sciences
Label
Nonlinear Data Assimilation, by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich, (electronic resource)
Instantiates
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Content category
text
Content type code
  • txt
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 Sampling2.5 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 `Optimal' proposal density; 6.2 The Implicit Particle Filter
  • 6.3 Variational methods as proposal densities6.3.1 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) methods4 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
Dimensions
unknown
Edition
1st ed. 2015.
Extent
1 online resource (130 p.)
Form of item
online
Isbn
9783319183473
Media category
computer
Media type code
  • c
Other control number
10.1007/978-3-319-18347-3
Specific material designation
remote
System control number
  • (CKB)3710000000452113
  • (EBL)3567844
  • (SSID)ssj0001534797
  • (PQKBManifestationID)11875473
  • (PQKBTitleCode)TC0001534797
  • (PQKBWorkID)11496655
  • (PQKB)11577155
  • (DE-He213)978-3-319-18347-3
  • (MiAaPQ)EBC3567844
  • (EXLCZ)993710000000452113
Label
Nonlinear Data Assimilation, by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich, (electronic resource)
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Content category
text
Content type code
  • txt
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 Sampling2.5 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 `Optimal' proposal density; 6.2 The Implicit Particle Filter
  • 6.3 Variational methods as proposal densities6.3.1 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) methods4 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
Dimensions
unknown
Edition
1st ed. 2015.
Extent
1 online resource (130 p.)
Form of item
online
Isbn
9783319183473
Media category
computer
Media type code
  • c
Other control number
10.1007/978-3-319-18347-3
Specific material designation
remote
System control number
  • (CKB)3710000000452113
  • (EBL)3567844
  • (SSID)ssj0001534797
  • (PQKBManifestationID)11875473
  • (PQKBTitleCode)TC0001534797
  • (PQKBWorkID)11496655
  • (PQKB)11577155
  • (DE-He213)978-3-319-18347-3
  • (MiAaPQ)EBC3567844
  • (EXLCZ)993710000000452113

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