A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography
By Ibrahim Hoteit, Dinh Tuan Pham, G. Triantafyllou, G. Korres. 2008
139 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change, Oceanography
View Paper: https://www.semanticscholar.org/paper/f5fc971732e72723db891e9f039abc79360e43fa
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
- Marc Bocquet
- Pavel Sakov
- Dean S. Oliver
- Laurent Bertino
- Andrew M. Stuart
- Julia Tödter
- Bodo Ahrens
- Konstantinos C. Zygalakis
In Global and Planetary Change
- Marc Bocquet
- Dean S. Oliver
- Pavel Sakov
- Laurent Bertino
- Julia Tödter
- Bodo Ahrens
- Andrew M. Stuart
- P. Jan
In Oceanography
- Dean S. Oliver
- Marc Bocquet
- Julia Tödter
- Bodo Ahrens
- Pavel Sakov
- Andrew M. Stuart
- P. Jan
- Konstantinos C. Zygalakis
Citations in the corpus
- An Iterative EnKF for Strongly Nonlinear Systems (2012, methodology): “2010), merging the EnKF and particle filters (Hoteit et al. 2008; Stordal et al. 2011; Lei and Bickel 2011), try-”
- Particle Filtering in Geophysical Systems (2009, methodology): “Since this is undesirable from the viewpoint of filter divergence, Hoteit et al. (2008) decide to put more weight on avoidance of filter divergence, at the cost of allowing for a biased posterior pdf.”
- Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, background): “Or it can be carried out thanks to a low rank representation of the error covariance matrix (Hoteit et al. 2008) inherited from the reduced rank Kalman filters.”
- Data Assimilation: A Mathematical Introduction (2015, methodology): “Other ensemble-based filters have been developed in recent years bridging the ensemble Kalman filter with the particle filter, for example [57, 56, 98, 102].”
- A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation (2015, background): “…by Anderson and Anderson (1999) and Pham (2001), is the representation of the forecast ensemble as aGaussianmixture, allowing a better consideration of nonlinear features contained in the ensemble (e.g., Hoteit et al. 2008; Stordal et al. 2011; Hoteit et al. 2012; Frei and Künsch 2013a,b).”
- Dynamically orthogonal field equations for continuous stochastic dynamical systems (2009, background): “In recent years, such particle filters and their variants have been applied to stochastic estimations in various fields including ocean and atmosphere dynamics [24] and structural dynamics [25].”