A Non-Gaussian Ensemble Filter Update for Data Assimilation
By Jeffrey L. Anderson. 2010
128 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/0dc9f1cea6d412e1f1f5975af59d664a2caa7bd4
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
- Dean S. Oliver
- Julia Tödter
- Bodo Ahrens
- Laurent Bertino
- Pavel Sakov
- Yan Chen
- J. Poterjoy
- Marc Bocquet
In Global and Planetary Change
- Dean S. Oliver
- Julia Tödter
- Bodo Ahrens
- Laurent Bertino
- Pavel Sakov
- J. Poterjoy
- Marc Bocquet
- Yan Chen
Citations in the corpus
- An Iterative EnKF for Strongly Nonlinear Systems (2012, methodology): “) The rotations prevent the buildup of non-Gaussianity in the ensemble that is often associated with larger ensemble sizes in nonlinear ESRF-based systems (Lawson and Hansen 2004; Sakov and Oke 2008; Anderson 2010).”
- A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation (2015, methodology): “The KF analysis can be interpreted as a linear regression of the model state on the observation vector (Anderson 2010).”
- A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, methodology): “Localization strategies have already been adopted in several non-Gaussian filters, including the local–local Gaussian mixture filter (Bengtsson et al. 2003), the rank histogram filter (Anderson 2010), and the moment-matching filter (Lei and Bickel 2011).”