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International Conference on Dynamic Data-Driven Environmental Systems Science

DyDESS 2014: Dynamic Data-Driven Environmental Systems Science pp 251–262Cite as

Aspects of Particle Filtering in High-Dimensional Spaces

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8964))

Abstract

Nonlinear data assimilation is high on the agenda in all fields of the geosciences as with ever increasing model resolution and inclusion of more physical (biological etc.) processes, and more complex observation operators the data-assimilation problem becomes more and more nonlinear. The suitability of particle filters to solve the nonlinear data assimilation problem in high-dimensional geophysical problems will be discussed. Several existing and new schemes will be presented and it is shown that at least one of them, the Equivalent-Weights Particle Filter, does indeed beat the curse of dimensionality and provides a way forward to solve the problem of nonlinear data assimilation in high-dimensional systems.

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References

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Author information

Authors and Affiliations

  1. Data Assimilation Research Centre (DARC), University of Reading, Reading, UK

    Peter Jan van Leeuwen

Authors
  1. Peter Jan van Leeuwen
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Correspondence to Peter Jan van Leeuwen .

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Editors and Affiliations

  1. Massachusetts Inst. of Technology, Cambridge, Massachusetts, USA

    Sai Ravela

  2. VA Polytechnic Institute and State Univ., Blacksburg, Virginia, USA

    Adrian Sandu

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van Leeuwen, P.J. (2015). Aspects of Particle Filtering in High-Dimensional Spaces. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-25138-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25137-0

  • Online ISBN: 978-3-319-25138-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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