Journal ArticleDOI
An exploration of the equivalent weights particle filter
Melanie Ades,P. J. van Leeuwen +1 more
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TLDR
In this article, a particle filter is presented which uses the proposal density to ensure that all particles end up in the high probability region of the posterior probability density function, which gives rise to the possibility of nonlinear data assimilation in large-dimensional systems.Abstract:
Particle filters are fully nonlinear data assimilation techniques that aim to represent the probability distribution of the model state given the observations (the posterior) by a number of particles. In high-dimensional geophysical applications, the number of particles required by the sequential importance resampling (SIR) particle filter (in order to capture the high-probability region of the posterior) is too large to make them usable. However particle filters can be formulated using proposal densities, which give greater freedom in how particles are sampled and allow for a much smaller number of particles. Here a particle filter is presented which uses the proposal density to ensure that all particles end up in the high-probability region of the posterior probability density function. This gives rise to the possibility of nonlinear data assimilation in large-dimensional systems. The particle filter formulation is compared to the optimal proposal density particle filter and the implicit particle filter, both of which also utilise a proposal density. We show that, when observations are available every time step, both schemes will be degenerate when the number of independent observations is large, unlike the new scheme. The sensitivity of the new scheme to its parameter values is explored theoretically and demonstrated using the Lorenz (1963) model. Copyright © 2012 Royal Meteorological Societyread more
Citations
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Journal ArticleDOI
Assimilation of remote sensing into crop growth models: Current status and perspectives
Jianxi Huang,Jose Gomez-Dans,Hai Huang,Hongyuan Ma,Qingling Wu,Philip Lewis,Shunlin Liang,Shunlin Liang,Zhongxin Chen,Jing-Hao Xue,Yantong Wu,Feng Zhao,Jing Wang,Xianhong Xie +13 more
TL;DR: A critique of both the advantages and disadvantages of both EO data and crop growth models is provided, and a solid and robust framework for DA is introduced, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function using Bayes’ rule.
Journal ArticleDOI
Particle filters for high-dimensional geoscience applications: A review
Peter Jan van Leeuwen,Peter Jan van Leeuwen,Hans R. Künsch,Lars Nerger,Roland Potthast,Sebastian Reich +5 more
TL;DR: Initial experiments show that particle filters can be competitive with present‐day methods for numerical weather prediction, suggesting that they will become mainstream soon.
Journal ArticleDOI
Data assimilation: making sense of Earth Observation
William Lahoz,Philipp Schneider +1 more
TL;DR: This review paper motivates data assimilation as a methodology to fill in the gaps in observational information; illustrates the dataAssimilation approach with examples that span a broad range of features of the Earth System (atmosphere, including chemistry; ocean; land surface); and discusses the outlook for data Assimilation, including the novel application of data ass assimilation ideas to observational information obtained using Citizen Science.
Journal ArticleDOI
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
TL;DR: This review examines the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty.
Journal ArticleDOI
Regional Ocean Data Assimilation
TL;DR: The past 15 years of developments in regional ocean data assimilation are reviewed, with exciting recent advances in ensemble and four-dimensional variational approaches.
References
More filters
Journal ArticleDOI
Deterministic nonperiodic flow
TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
BookDOI
Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Journal ArticleDOI
Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
TL;DR: In this article, a new sequential data assimilation method is proposed based on Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter.
Journal ArticleDOI
On sequential Monte Carlo sampling methods for Bayesian filtering
TL;DR: An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.