A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation

By P. Houtekamer, H. Mitchell. 2001

1,554 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/338dbcd02bfcf51202c21bdfd1c0f2c778cad95c

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation (2015, background): “Localization is required in the rank-deficient regime, in which the limited size of the ensemble leads to an empirical error covariance matrix of overly small rank, as is often the case in realistic high-dimensional systems (Houtekamer and Mitchell, 2001; Hamill et al., 2001; Ott et al., 2004).”
Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation (2016, background): “See, for instance, Gaspari and Cohn (1999), Houtekamer and Mitchell (2001) and Hamill et al. (2001).”
Additive Noise for Storm-Scale Ensemble Data Assimilation (2009, background): “…experiments, as in Dowell et al. (2004a)]; b is the reduced-gain factor (Whitaker and Hamill 2002); y o is the observation; and W is a localization factor (Houtekamer and Mitchell 2001), which decreases from 1 at the observation location to 0 at and beyond a speciﬁed distance from the observation.”
A non-linear least squares enhanced POD-4DVar algorithm for data assimilation (2015, background): “…1 N 1ð Þ XS k¼1 Py;k T Py;k " # 1 Py;k T ; (28b) and ~Py;k ¼ XS k¼1 Py;k T Py;k þ N 1ð ÞI " # 1 Py;k T R 1k : (29b) In order to filter out the spurious long-range correlations (Houtekamer and Mitchell, 2001) between the initial perturbations and the corresponding prediction increments on model…”
Using the ensemble Kalman filter to estimate multiplicative model parameters (2008, methodology): “In order to avoid filter divergence of the EnKF due to the finite ensemble size and model non-linearities, we apply covariance inflation (Anderson and Anderson, 1999) and localization (Hamill et al., 2001; Houtekamer and Mitchell, 2001).”
The ensemble Kalman filter for combined state and parameter estimation (2009, methodology): “In [48] the ensemble covariance matrix is multiplied with a specified correlation matrix through a Schur product (entry-wise multiplication).”
Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications (2011, methodology): “The use of the Schur product with a correlation function for covariance localization was first introduced by Houtekamer and Mitchell [15].”
Inverse methods in hydrogeology: Evolution and recent trends (2014, methodology): “…assimilation tool in diverse disciplines such as oceanography, meteorology and hydrology (e.g., Bertino et al.,488 2003; Chen and Zhang, 2006; Houtekamer and Mitchell, 2001; Moradkhani et al., 2005; Nowak, 2009; Wen489 and Chen, 2006).490 Notice that the EnKF is neither an optimization…”
A variance-minimizing filter for large-scale applications (2003, positive): “Apart from decreasing the noise in the estimates, as Houtekamer and Mitchell (1998, 2001) argue, it also updates the large-scale structure of each ensemble member.”
Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization (2015, methodology): “This procedure can be seen as a special case of linear filtering and will hereafter be called Schur filtering (Houtekamer and Mitchell 2001; Buehner and Charron 2007).”