Particle Filtering in Geophysical Systems

By van Leeuwen, P. Jan. 2009

641 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/6d205f3e779a78c84f8ca026fba7dec251f9d548

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Citations in the corpus

Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, background): “This issue has been largely discussed by van Leeuwen (2009). d.”
Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation: Application to a 1D ocean ecosystem model (2012, methodology): “We refer to the review of van Leeuwen (2009) for more details about particle filters.”
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review (2013, methodology): “PFs (Van Leeuwen 2009, Doucet et al. 2000) have also been applied to estimate model parameters (Vossepoel and Van Leeuwen 2007, Kivman 2003, Ambadan and Tang 2005).”
Impact of Stochastic Physics in a Convection-Permitting Ensemble (2012, methodology): “In data assimilation, the impact of model error has been studied using ensemble-based assimilation algorithms (e.g. Houtekamer 2009, Raynaud 2011, Whitaker 2002), four-dimensional variational data assimilation (e.g. Tremolet, 2007), and particle filters (van Leeuwen, 2009).”
Assimilation of remote sensing into crop growth models: Current status and perspectives (2019, methodology): “A number of implementations of these filters are straightforward extensions of the basic MCMC algorithms (Dowd, 2006, 2007), while others are more directly based on importance sampling (van Leeuwen, 2009; Ristic et al., 2004; Arulampalam et al., 2002).”
A consistent interpretation of the stochastic version of the Ensemble Kalman Filter (2020, background): “van Leeuwen, P. J. (2020) A consistent interpretation of the”
Data Assimilation: A Mathematical Introduction (2015, background): “The development of new filters is a very active area of research and the reader is directed to the book [82], together with the articles [25],[83] and [114] for insight into some of the recent developments with an applied mathematics perspective.”
Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, methodology): “However, the approximation of the posterior pdf with the particle filter requires a large number of model evaluations and is not as efficient as the EnKF (van Leeuwen, 2009).”
The role of forcing and internal dynamics in explaining the “Medieval Climate Anomaly” (2011, methodology): “The likelihood is based on a Gaussian probability density (see van Leeuwen 2009; Dubinkina et al. 2011 for the exact formulation).”
Inverse problems: A Bayesian perspective (2010, background): “Recent contributions to the study of filtering in the context of the high-dimensional systems of interest in geophysical applications include Bergemann and Reich (2010), Cui et al. (2010), Chorin and Krause (2004), Chorin and Tu (2009, 2010), Majda and Grote (2007), Majda and Gershgorin (2008), Majd”