Ensemble Data Assimilation without Perturbed Observations

By J. Whitaker, Thomas M. Hamill. 2002

1,627 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/87d76a485c00b7145236ddc0f2533d2107dea0e2

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Citations in the corpus

Continental heat anomalies and the extreme melting of the Greenland ice surface in 2012 and 1889 (2014, methodology): “Given the limited predictability of the GFS, and potentially other global models in the polar regions, such results should provide limits on any future advance planning of intensive field programs studying such extreme events.”
Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model (2006, methodology): “A square root EnKF is used as proposed by Whitaker and Hamill (2002) so that observations are not perturbed.”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, background): “With this simplification, K̃ may be written as K̃= ( 1+ √ R HPbHT +R )−1 K, (5) for an individual observation, where R and HPbHT are scalars, while K and K̃ are vectors of the same dimension as the model state vector (Whitaker and Hamill 2002).”
Implications of Stochastic and Deterministic Filters as Ensemble-Based Data Assimilation Methods in Varying Regimes of Error Growth (2004, methodology): “This model has been used by Lorenz and Emanuel (1998) and Hansen and Smith (2000) to explore strategies in targeted observations and more recently by Anderson (2001) and Whitaker and Hamill (2002) as test beds for their respective deterministic filter schemes.”
Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants (2007, methodology): “To this end, recent focus in NWP has been on representing the knowledge of the atmospheric state using a probability density function (pdf) (Evensen, 1994; Houtekamer et al., 1996; Burgers et al., 1998).”
Spatially and temporally varying adaptive covariance inflation for ensemble filters (2009, methodology): “If the observational error distributions associated with each observation are independent, implementations of many ensemble filters (Burgers et al., 1998; Pham, 2001; Whitaker and Hamill, 2002; Tippett et al., 2003; Ott et al., 2004) can be described as follows.”
Ensemble kalman filter assimilation of doppler radar data with a compressible nonhydrostatic model : OSS experiments (2005, methodology): “…observations or to address the adaptive observational network design problem; these methods include the ensemble square-root filter (EnSRF; Whitaker and Hamill 2002; Tippett et al. 2003), the ensemble adjustment filter (Anderson 2001), and the ensemble transform Kalman filter (Bishop et…”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, methodology): “Examples include the ensemble transform kalman filter (ETKF) by Bishop, Etherton, and Majumdar (2001); Whitaker and Hamill (2002), the ensemble adjustment kalman filter (EAKF) by Anderson (2001), and the maximum likelihood ensemble filter (MLEF) by Zupanski (2005).”
Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF) (2007, methodology): “in P 1 2 f due to insufficient ensemble size [e.g., Houtekamer and Mitchell, 1998; Hamill et al., 2001; Whitaker and Hamill, 2002].”
Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP (2006, methodology): “A noise reduction in ensemble-based data assimilation has been shown to have a positive effect on the quality of the ensemble by, e.g., Whitaker and Hamill (2002).”
A Non-Gaussian Ensemble Filter Update for Data Assimilation (2010, background): “Several variants of deterministic square root ensemble Kalman ﬁlters appear in the literature (Anderson 2001; Bishop et al. 2001; Whitaker and Hamill 2002; Tippett et al. 2003).”
The Ensemble Kalman Filter: theoretical formulation and practical implementation (2003, methodology): “Whitaker and Hamill (2002) proposed another version of the EnKF where the perturbation of observations are avoided.”
Data Assimilation: A Mathematical Introduction (2015, methodology): “Index σ-algebra, 4 Borel, 4 3DVAR, 50, 81, 82, 93, 96, 100, 101, 103–106, 108, 109, 132, 134 4DVAR, 54, 65, 70, 71, 73, 82, 116 strong constraint, 67 weak constraint, 54, 67, 71, 116, 127 absorbing set, 14, 15, 31, 50 allocation, 116 almost surely, 5 analysis, 37, 38, 77 atmospheric sciences, 32, 49”
Model Error Estimation Employing an Ensemble Data Assimilation Approach (2006, background): “…variants of EnsDA (e.g., Houtekamer and Mitchell 1998; Hamill and Snyder 2000; Keppenne 2000; Mitchell and Houtekamer 2000; Anderson 2001; Bishop et al. 2001; van Leeuwen 2001; Reichle et al. 2002a,b; Whitaker and Hamill 2002; Tippett et al. 2003; Zhang et al. 2004; Ott et al. 2004; Zupanski 2005).”
Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter (2003, methodology): “Such dependence on Ne and r is broadly consistent with other examples of the EnKF (e.g., Houtekamer and Mitchell 1998; Anderson 2001; Whitaker and Hamill 2002).”