A variance-minimizing filter for large-scale applications

By J. P., van Leeuwen. 2003

162 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/3cb7fe5e4b6b806db4e91d6139969b11cd23a8aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

In Oceanography

Citations in the corpus

Sequential Data Assimilation Techniques in Oceanography (2003, methodology): “in van Leeuwen (2002) should draw more interest on SIR methods.”
Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo (2007, methodology): “This is likely due to the smearing of the ensemble through use of the Kalman gain equations (van Leeuwen, 2003).”
A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography (2008, background): “Interesting discussions on the use of the optimal nonlinear filter for high-dimensional oceanic and atmospheric data assimilation problems can be found in Anderson and Anderson (1999), Kivman (2003), and Van Leeuwen (2003).”
A two-stage ensemble Kalman filter for smooth data assimilation (2008, background): “For related filters relaxing or removing the Gaussian assumption, see Anderson and Anderson (1999), Bengtsson (2003), Doucet et al. (2001), van Leeuwen (2003).”
A local particle filter for high-dimensional geophysical systems (2015, methodology): “Applying such an approach, van Leeuwen (2003) considers a model for the Agulhas Current with dimension of roughly 2× 105.”
A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, background): “Their proof holds for all forms of observation likelihoods, including cases when observation error distributions are specified to have heavy tails to delay the weight collapse (van Leeuwen 2003).”
Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems (2008, background): “Consider a set of n sample points X = {X1, . . . , Xn}, where Xi ∈ ℜd and both the sample size n and system dimension d are “large”.”
Particle Filtering in Geophysical Systems (2009, methodology): “Van Leeuwen (2003a) also used this method with 512 particles to estimate the evolution of a five-layer quasigeostrophic model of the ocean area around South Africa, governed by highly nonlinear dynamics.”
A Non-Gaussian Ensemble Filter Update for Data Assimilation (2010, methodology): “The most general methods are particle ﬁlters that can represent arbitrary analysis probability distributions (Van Leeuwen 2003).”
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”
An oceanographic three-dimensional variational data assimilation scheme (2008, unknown): “If it is not possible to linearize the model, the computationally much more demanding Monte Carlo approach may be used to estimate background error covariances (e.g. van Leeuwen, 2003).”
Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction (2005, methodology): “With the no- table exception of particle methods (e.g. Anderson and An- derson, 1999; Van Leeuwen, 2003) in which the calculation of the Kalman gain is not necessary, all sequential data assimilation methods use an analysis equation like Eq.”